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42e05efa-5507-430c-9a9d-27e1772499d7	weakly-supervised-action-transition-learning	2205.15608	null	https://arxiv.org/abs/2205.15608v1	https://arxiv.org/pdf/2205.15608v1.pdf	Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction	We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history. This differs from existing works, which predict motions that either do not respect any specific action category, or follow a single action label. In particular, addressing this task requires tackling two challenges: The transitions between the different actions must be smooth; the length of the predicted motion depends on the action sequence and varies significantly across samples. As we cannot realistically expect training data to cover sufficiently diverse action transitions and motion lengths, we propose an effective training strategy consisting of combining multiple motions from different actions and introducing a weak form of supervision to encourage smooth transitions. We then design a VAE-based model conditioned on both the observed motion and the action label sequence, allowing us to generate multiple plausible future motions of varying length. We illustrate the generality of our approach by exploring its use with two different temporal encoding models, namely RNNs and Transformers. Our approach outperforms baseline models constructed by adapting state-of-the-art single action-conditioned motion generation methods and stochastic human motion prediction approaches to our new task of action-driven stochastic motion prediction. Our code is available at https://github.com/wei-mao-2019/WAT.	['Mathieu Salzmann', 'Miaomiao Liu', 'Wei Mao']	2022-05-31	null	http://openaccess.thecvf.com//content/CVPR2022/html/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.pdf	cvpr-2022-1	['stochastic-human-motion-prediction']	['computer-vision']	[ 6.01007998e-01 1.27639860e-01 -5.00276983e-01 -1.63647115e-01 -8.27272952e-01 -4.41129565e-01 9.55064237e-01 -6.08986616e-01 -1.89087838e-01 7.81586289e-01 8.83216500e-01 -2.02900529e-01 2.78463721e-01 -6.01413369e-01 -8.40385258e-01 -6.21571898e-01 -2.31452864e-02 4.98177767e-01 6.28020883e-01 -6.30777776e-02 2.32321560e-01 2.71888763e-01 -1.49533761e+00 5.43691218e-01 4.80791032e-01 4.87879217e-01 4.62723494e-01 1.15666854e+00 8.00022036e-02 1.30915630e+00 -2.26715490e-01 -4.24927510e-02 2.23131925e-01 -1.13203216e+00 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6e3150b7-d251-4455-a09c-2428270f4cab	wesinger-data-augmented-singing-voice	2203.1075	null	https://arxiv.org/abs/2203.10750v5	https://arxiv.org/pdf/2203.10750v5.pdf	WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses	In this paper, we develop a new multi-singer Chinese neural singing voice synthesis (SVS) system named WeSinger. To improve the accuracy and naturalness of synthesized singing voice, we design several specifical modules and techniques: 1) A deep bi-directional LSTM-based duration model with multi-scale rhythm loss and post-processing step; 2) A Transformer-alike acoustic model with progressive pitch-weighted decoder loss; 3) a 24 kHz pitch-aware LPCNet neural vocoder to produce high-quality singing waveforms; 4) A novel data augmentation method with multi-singer pre-training for stronger robustness and naturalness. To our knowledge, WeSinger is the first SVS system to adopt 24 kHz LPCNet and multi-singer pre-training simultaneously. Both quantitative and qualitative evaluation results demonstrate the effectiveness of WeSinger in terms of accuracy and naturalness, and WeSinger achieves state-of-the-art performance on the recent public Chinese singing corpus Opencpop\footnote{https://wenet.org.cn/opencpop/}. Some synthesized singing samples are available online\footnote{https://zzw922cn.github.io/wesinger/}.	['Li Lu', 'Xinhui Li', 'Yibin Zheng', 'Zewang Zhang']	2022-03-21	null	null	null	null	['singing-voice-synthesis']	['speech']	[-2.50974447e-01 -3.99410933e-01 7.49765188e-02 1.09846242e-01 -1.44803429e+00 -5.26150465e-01 6.09258451e-02 -5.74564159e-01 -1.26837611e-01 5.57939410e-01 4.60448533e-01 -1.74200386e-01 2.65077323e-01 -2.81302989e-01 -6.08215392e-01 -8.02214265e-01 7.40651786e-02 5.60298711e-02 -1.43225780e-02 -3.47856790e-01 -2.58245260e-01 8.30273032e-02 -1.56001282e+00 3.44245493e-01 9.71415699e-01 9.46936071e-01 5.16470492e-01 1.12138629e+00 3.61836791e-01 3.93196225e-01 -8.02773714e-01 -8.05949122e-02 2.41360188e-01 -8.67329955e-01 -4.09192175e-01 -5.77324212e-01 4.12013710e-01 -2.84629166e-01 -4.91167426e-01 7.78066695e-01 1.30386710e+00 4.14000899e-01 1.73731267e-01 -8.11122119e-01 -7.18240380e-01 1.07154024e+00 -1.41025875e-02 2.64080852e-01 1.36715755e-01 5.93296289e-01 1.33019519e+00 -1.13681352e+00 1.80456787e-01 9.56563413e-01 7.52831876e-01 8.52242649e-01 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eca398bf-d277-46a6-b45d-28db4dd9ca88	hierarchical-clustering-guided-re-id-with	1910.12278	null	https://arxiv.org/abs/1910.12278v2	https://arxiv.org/pdf/1910.12278v2.pdf	Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification	For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate on the target dataset, which often focuses on learning differences between the source dataset and the target dataset to improve the generalization of the model. Base on these, we explore how to make use of the similarity of samples to conduct a fully unsupervised method which just trains on the unlabeled target dataset. Concretely, we propose a hierarchical clustering-guided re-ID (HCR) method. We use hierarchical clustering to generate pseudo labels and use these pseudo labels as monitors to conduct the training. In order to exclude hard examples and promote the convergence of the model, We use PK sampling in each iteration, which randomly selects a fixed number of samples from each cluster for training. We evaluate our model on Market-1501, DukeMTMC-reID and MSMT17. Results show that HCR gets the state-of-the-arts and achieves 55.3% mAP on Market-1501 and 46.8% mAP on DukeMTMC-reID. Our code will be released soon.	['Kaiwei Zeng']	2019-10-27	hierarchical-clustering-with-hard-batch	http://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.pdf	cvpr-2020-6	['unsupervised-person-re-identification']	['computer-vision']	[-1.06853597e-01 -4.33894731e-02 -1.35442942e-01 -6.71885490e-01 -4.94852304e-01 -2.35401377e-01 7.58657575e-01 -1.14446811e-01 -7.48151898e-01 7.44367421e-01 2.75490582e-01 8.88652503e-02 1.80927455e-01 -7.14585841e-01 -4.61454809e-01 -4.70947474e-01 1.18736289e-01 9.98196244e-01 2.64328979e-02 1.07168958e-01 -9.61328447e-02 -6.55786647e-03 -1.39819431e+00 2.01464631e-02 1.15382290e+00 4.38500315e-01 1.12707630e-01 4.30991143e-01 1.09544091e-01 4.52910095e-01 -5.07048845e-01 -5.81894636e-01 3.93180192e-01 -4.89416093e-01 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8f096f49-fb2c-41fb-85a5-e35a7ce99e61	differentiable-inductive-logic-programming-in	2208.06652	null	https://arxiv.org/abs/2208.06652v2	https://arxiv.org/pdf/2208.06652v2.pdf	Differentiable Inductive Logic Programming in High-Dimensional Space	Synthesizing large logic programs through symbolic Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates typically degrades performance. In contrast, gradient descent provides an efficient way to find solutions within such high- dimensional spaces. Neuro-symbolic ILP approaches have not fully exploited this so far. We propose extending the {\delta}ILP approach to inductive synthesis with large-scale predicate invention, thus allowing us to exploit the efficacy of high-dimensional gradient descent. We show that large-scale predicate invention benefits differentiable inductive synthesis through gradient descent and allows one to learn solutions for tasks beyond the capabilities of existing neuro-symbolic ILP systems. Furthermore, we achieve these results without specifying the precise structure of the solution within the language bias.	['Cezary Kaliszyk', 'David M. Cerna', 'Stanisław J. Purgał']	2022-08-13	null	null	null	null	['inductive-logic-programming']	['methodology']	[ 1.27390325e-01 4.92513627e-01 -5.71188390e-01 -2.95164675e-01 -5.08435786e-01 -7.21816063e-01 4.89863724e-01 -7.22458065e-02 -2.08479077e-01 1.09790552e+00 -6.35865331e-02 -7.16999412e-01 -2.14858353e-01 -1.13690460e+00 -1.08295119e+00 -2.23491430e-01 -4.07449901e-01 6.70401871e-01 -9.64082628e-02 -4.45475042e-01 1.60180733e-01 6.87998235e-01 -1.61406529e+00 4.27392513e-01 9.65413153e-01 7.15777278e-01 -9.70894024e-02 4.54633296e-01 -5.78609407e-01 8.17219794e-01 -5.01268208e-01 -1.75748095e-01 3.13249469e-01 -2.84023851e-01 -6.68503463e-01 -6.55935168e-01 4.02595371e-01 -2.07270399e-01 -1.59362897e-01 1.05489206e+00 7.13916197e-02 1.01884156e-01 3.86366546e-01 -1.36776054e+00 -7.63869286e-01 1.39251351e+00 6.39434010e-02 -1.08375803e-01 3.75269771e-01 1.91612184e-01 1.36408317e+00 -7.92081416e-01 8.60763371e-01 1.49209321e+00 8.64963591e-01 5.81448734e-01 -1.65324092e+00 -8.08922708e-01 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65fcee45-4de7-4f69-b367-3577e5cc709b	large-capacity-image-steganography-based-on	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.pdf	Large-Capacity Image Steganography Based on Invertible Neural Networks	Many attempts have been made to hide information in images, where the main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.	['Paul L. Rosin', 'Tao Zhong', 'Rong Wang', 'Shao-Ping Lu']	2021-06-19	null	null	null	cvpr-2021-1	['image-steganography']	['computer-vision']	[ 1.17098415e+00 6.85163438e-01 3.88346352e-02 4.17784333e-01 -3.24971616e-01 -6.42965496e-01 5.44186473e-01 -7.68770695e-01 -2.57382005e-01 2.85611272e-01 -5.62142767e-02 -7.96071589e-01 3.93726856e-01 -9.52511072e-01 -7.74273992e-01 -9.13134933e-01 -3.21933895e-01 -3.27317476e-01 3.09958845e-01 -3.38455439e-01 1.89655900e-01 1.32208064e-01 -9.82125700e-01 2.46477827e-01 5.22331655e-01 9.58144903e-01 1.18320234e-01 9.12779629e-01 3.56736720e-01 9.14649665e-01 -4.13377821e-01 -3.82781863e-01 7.66143143e-01 -9.52823400e-01 -7.30220497e-01 4.94425327e-01 -1.75390989e-01 -6.99293554e-01 -8.03385615e-01 1.10270751e+00 3.67589325e-01 -6.22780204e-01 2.89764881e-01 -1.45295739e+00 -7.44267285e-01 7.85752118e-01 -6.15693629e-01 -2.31440172e-01 -2.75855213e-02 4.27560985e-01 4.98133928e-01 -5.44579625e-01 8.81040573e-01 9.16605413e-01 4.20244575e-01 4.20845538e-01 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-2.79096365e-01 -6.37453139e-01 2.32601345e-01 5.86421013e-01 8.72855186e-02 -5.71365535e-01 4.32149529e-01]	[4.343638896942139, 8.041733741760254]
fb66b885-d3a1-47fd-81b5-1da68ac4ad4d	generating-adversarial-examples-with-an	2007.00146	null	https://arxiv.org/abs/2007.00146v1	https://arxiv.org/pdf/2007.00146v1.pdf	Generating Adversarial Examples with an Optimized Quality	Deep learning models are widely used in a range of application areas, such as computer vision, computer security, etc. However, deep learning models are vulnerable to Adversarial Examples (AEs),carefully crafted samples to deceive those models. Recent studies have introduced new adversarial attack methods, but, to the best of our knowledge, none provided guaranteed quality for the crafted examples as part of their creation, beyond simple quality measures such as Misclassification Rate (MR). In this paper, we incorporateImage Quality Assessment (IQA) metrics into the design and generation process of AEs. We propose an evolutionary-based single- and multi-objective optimization approaches that generate AEs with high misclassification rate and explicitly improve the quality, thus indistinguishability, of the samples, while perturbing only a limited number of pixels. In particular, several IQA metrics, including edge analysis, Fourier analysis, and feature descriptors, are leveraged into the process of generating AEs. Unique characteristics of the evolutionary-based algorithm enable us to simultaneously optimize the misclassification rate and the IQA metrics of the AEs. In order to evaluate the performance of the proposed method, we conduct intensive experiments on different well-known benchmark datasets(MNIST, CIFAR, GTSRB, and Open Image Dataset V5), while considering various objective optimization configurations. The results obtained from our experiments, when compared with the exist-ing attack methods, validate our initial hypothesis that the use ofIQA metrics within generation process of AEs can substantially improve their quality, while maintaining high misclassification rate.Finally, transferability and human perception studies are provided, demonstrating acceptable performance.	['David Mohaisen', 'Aminollah Khormali', 'DaeHun Nyang']	2020-06-30	null	null	null	null	['computer-security']	['miscellaneous']	[ 3.37159723e-01 -2.41594106e-01 2.12016985e-01 -2.05540895e-01 -5.84154129e-01 -8.21465909e-01 5.93824208e-01 9.43781063e-02 -6.82941675e-01 6.99818134e-01 -2.94610620e-01 -2.77828664e-01 -3.05760354e-01 -9.15111482e-01 -7.64784694e-01 -7.95844197e-01 -1.29404619e-01 -2.15916753e-01 -1.46218777e-01 -2.10756525e-01 4.26540554e-01 6.05456412e-01 -1.43410003e+00 4.61969152e-02 1.14220524e+00 1.09530401e+00 -3.89663696e-01 6.45876050e-01 2.88194716e-01 4.54724818e-01 -1.04445148e+00 -8.55767310e-01 5.08004725e-01 -3.22925657e-01 -4.24334884e-01 -3.03899109e-01 2.38236189e-01 -2.77052790e-01 -3.74619991e-01 1.28378332e+00 7.72824585e-01 7.09369928e-02 5.83413661e-01 -1.39620078e+00 -6.72765613e-01 5.01331031e-01 -3.44709039e-01 3.72422069e-01 1.09329455e-01 5.38430572e-01 8.67939591e-01 -5.06592631e-01 2.18561545e-01 9.98218954e-01 6.76345944e-01 5.61801374e-01 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65e02393-84a3-4f99-a32d-8ef6416e13f2	diffpack-a-torsional-diffusion-model-for	2306.01794	null	https://arxiv.org/abs/2306.01794v1	https://arxiv.org/pdf/2306.01794v1.pdf	DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing	Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from \c{hi}1 to \c{hi}4 and training diffusion models for each torsional angle. We evaluate the method on several benchmarks for protein side-chain packing and show that our method achieves improvements of 11.9% and 13.5% in angle accuracy on CASP13 and CASP14, respectively, with a significantly smaller model size (60x fewer parameters). Additionally, we show the effectiveness of our method in enhancing side-chain predictions in the AlphaFold2 model. Code will be available upon the accept.	['Jian Tang', 'Sanchit Misra', 'Bozitao Zhong', 'Zuobai Zhang', 'Yangtian Zhan']	2023-06-01	null	null	null	null	['protein-structure-prediction']	['miscellaneous']	[ 1.70745760e-01 -6.73645213e-02 -3.07781935e-01 -2.32555434e-01 -3.80206972e-01 -5.55060983e-01 1.31092936e-01 3.81023407e-01 -4.20483440e-01 1.21529734e+00 2.27704227e-01 -6.77603543e-01 3.09309453e-01 -3.45370620e-01 -8.09961200e-01 -1.39918089e+00 -3.67833406e-01 5.86794794e-01 3.09335321e-01 -1.66375026e-01 5.24070084e-01 7.67112553e-01 -8.39691043e-01 2.72607654e-01 9.77462053e-01 6.17564440e-01 1.75701350e-01 5.29841721e-01 8.41381624e-02 4.87591147e-01 -4.81843501e-01 -2.03310236e-01 -1.04959331e-01 -5.30900240e-01 -7.91159928e-01 -2.67679900e-01 1.40552029e-01 -6.29803762e-02 -2.91309394e-02 8.89311194e-01 6.84938550e-01 1.64903164e-01 9.85141873e-01 -4.53991205e-01 -4.99422967e-01 -1.58477724e-01 -6.70122564e-01 1.12868592e-01 3.45142901e-01 4.47970152e-01 9.93949711e-01 -7.39573479e-01 1.03100300e+00 9.96676624e-01 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02741630-db45-445b-93bc-5d855ae51deb	pack-together-entity-and-relation-extraction	2109.06067	null	https://arxiv.org/abs/2109.06067v5	https://arxiv.org/pdf/2109.06067v5.pdf	Packed Levitated Marker for Entity and Relation Extraction	Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder. In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information. Furthermore, for those more complicated span pair classification tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects to model the interrelation between the same-subject span pairs. The experimental results show that, with the enhanced marker feature, our model advances baselines on six NER benchmarks, and obtains a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models on ACE04 and ACE05.	['Maosong Sun', 'Peng Li', 'Yankai Lin', 'Deming Ye']	2021-09-13	null	https://aclanthology.org/2022.acl-long.337	https://aclanthology.org/2022.acl-long.337.pdf	acl-2022-5	['joint-entity-and-relation-extraction']	['natural-language-processing']	[-5.16652279e-02 3.44938785e-01 -6.05993927e-01 -2.93870419e-01 -5.89360356e-01 -3.68090719e-01 1.69266969e-01 3.79674464e-01 -3.18274677e-01 8.46328020e-01 5.08153677e-01 -9.76159871e-02 -1.52131766e-01 -8.83866727e-01 -8.24272275e-01 -3.40595424e-01 -3.19269925e-01 3.02201867e-01 4.63746667e-01 -1.67902380e-01 4.15107645e-02 3.24572921e-01 -1.08621919e+00 1.58297122e-01 1.14766312e+00 1.07011867e+00 1.60700068e-01 4.00846377e-02 -1.40955895e-01 6.82695031e-01 -6.88874006e-01 -6.27098978e-01 7.41805509e-02 -2.10006475e-01 -1.11302745e+00 -1.45372480e-01 3.07907224e-01 -3.56706053e-01 -6.62055075e-01 8.28234315e-01 3.13976824e-01 8.39577243e-02 3.53731692e-01 -1.01733243e+00 -8.94178569e-01 1.21995974e+00 -8.14426124e-01 3.52491200e-01 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c199e93f-f70b-4463-9b6d-72ba4eaabb31	sscu-net-spatial-spectral-collaborative	2203.06375	null	https://arxiv.org/abs/2203.06375v2	https://arxiv.org/pdf/2203.06375v2.pdf	SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images	Linear spectral unmixing is an essential technique in hyperspectral image processing and interpretation. In recent years, deep learning-based approaches have shown great promise in hyperspectral unmixing, in particular, unsupervised unmixing methods based on autoencoder networks are a recent trend. The autoencoder model, which automatically learns low-dimensional representations (abundances) and reconstructs data with their corresponding bases (endmembers), has achieved superior performance in hyperspectral unmixing. In this article, we explore the effective utilization of spatial and spectral information in autoencoder-based unmixing networks. Important findings on the use of spatial and spectral information in the autoencoder framework are discussed. Inspired by these findings, we propose a spatial-spectral collaborative unmixing network, called SSCU-Net, which learns a spatial autoencoder network and a spectral autoencoder network in an end-to-end manner to more effectively improve the unmixing performance. SSCU-Net is a two-stream deep network and shares an alternating architecture, where the two autoencoder networks are efficiently trained in a collaborative way for estimation of endmembers and abundances. Meanwhile, we propose a new spatial autoencoder network by introducing a superpixel segmentation method based on abundance information, which greatly facilitates the employment of spatial information and improves the accuracy of unmixing network. Moreover, extensive ablation studies are carried out to investigate the performance gain of SSCU-Net. Experimental results on both synthetic and real hyperspectral data sets illustrate the effectiveness and competitiveness of the proposed SSCU-Net compared with several state-of-the-art hyperspectral unmixing methods.	['Lin Qi', 'Qian Du', 'Xinbo Gao', 'Junyu Dong', 'Feng Gao']	2022-03-12	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 3.41877371e-01 -6.45642102e-01 8.84432867e-02 6.35320023e-02 -2.11254358e-01 -3.74848545e-01 4.72017080e-01 -3.21780354e-01 -1.99392378e-01 5.91928244e-01 2.65270263e-01 -2.33424455e-02 -3.47981155e-01 -9.58589613e-01 -5.79652071e-01 -1.37516344e+00 1.39875993e-01 1.94301143e-01 -5.66574037e-01 -2.09238231e-01 -3.34298342e-01 5.61491668e-01 -1.75456846e+00 1.37735978e-01 1.30667281e+00 9.83055174e-01 4.51903671e-01 3.25383514e-01 -8.36663917e-02 6.09546125e-01 -3.78728807e-01 2.06756189e-01 5.49420238e-01 -5.51892042e-01 -2.48324633e-01 5.67988932e-01 4.46649909e-01 -5.98047733e-01 -6.24517202e-01 1.37577963e+00 5.76281786e-01 4.53899741e-01 7.77567923e-01 -1.02180362e+00 -7.36466885e-01 8.15988541e-01 -7.39232361e-01 -1.11586660e-01 -5.53593278e-01 1.07437536e-01 7.59184778e-01 -5.52735090e-01 -1.15341231e-01 8.14849019e-01 7.48622656e-01 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-3.90506566e-01 -1.39400768e+00 -1.27558970e+00 2.56965071e-01 4.10994262e-01 -3.63375902e-01 -4.67344224e-01 2.48134732e-01]	[10.085527420043945, -1.9552838802337646]
40a9c9f3-73ee-4676-837c-89aec430340b	surgical-video-motion-magnification-with	2009.07432	null	https://arxiv.org/abs/2009.07432v1	https://arxiv.org/pdf/2009.07432v1.pdf	Surgical Video Motion Magnification with Suppression of Instrument Artefacts	Video motion magnification could directly highlight subsurface blood vessels in endoscopic video in order to prevent inadvertent damage and bleeding. Applying motion filters to the full surgical image is however sensitive to residual motion from the surgical instruments and can impede practical application due to aberration motion artefacts. By storing the temporal filter response from local spatial frequency information for a single cardiovascular cycle prior to tool introduction to the scene, a filter can be used to determine if motion magnification should be active for a spatial region of the surgical image. In this paper, we propose a strategy to reduce aberration due to non-physiological motion for surgical video motion magnification. We present promising results on endoscopic transnasal transsphenoidal pituitary surgery with a quantitative comparison to recent methods using Structural Similarity (SSIM), as well as qualitative analysis by comparing spatio-temporal cross sections of the videos and individual frames.	['Neil L. Dorward', 'Danail Stoyanov', 'Mirek Janatka', 'Hani J. Marcus']	2020-09-16	null	null	null	null	['motion-magnification']	['computer-vision']	[ 1.94980815e-01 8.06605890e-02 8.93633366e-02 1.89511567e-01 -1.13478631e-01 -8.26488674e-01 3.04734319e-01 1.02716111e-01 -7.74322152e-01 3.18024099e-01 5.64879775e-01 -2.43878603e-01 -2.28625506e-01 -2.47829497e-01 -4.62140322e-01 -8.29995453e-01 -3.23583931e-01 -3.59828174e-01 4.64121014e-01 -5.48985414e-02 4.73020792e-01 6.97019696e-01 -1.33931136e+00 4.31476355e-01 3.76848131e-01 4.87464219e-01 3.09106469e-01 9.47612822e-01 3.29783410e-01 4.75557685e-01 -6.57817841e-01 1.51863202e-01 3.95087540e-01 -8.45345259e-01 -5.21738291e-01 9.72281992e-02 4.25644606e-01 -5.07494688e-01 -3.28993320e-01 1.28239214e+00 4.93458241e-01 1.47988766e-01 3.47293556e-01 -5.20463586e-01 1.50089547e-01 3.51702720e-01 -5.86929560e-01 8.28575194e-01 5.16472220e-01 2.45827377e-01 7.87618011e-03 -7.51445293e-01 1.32159734e+00 7.58272171e-01 5.02975523e-01 4.52460945e-01 -1.29131591e+00 -1.20102689e-01 -1.91096753e-01 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fc71477c-5f1e-4d39-82b2-72a21c391520	to-find-waldo-you-need-contextual-cues-1	null	null	https://aclanthology.org/2022.acl-short.39	https://aclanthology.org/2022.acl-short.39.pdf	To Find Waldo You Need Contextual Cues: Debiasing Who’s Waldo	We present a debiased dataset for the Person-centric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who’s Waldo dataset. Given an image and a caption, PCVG requires pairing up a person’s name mentioned in a caption with a bounding box that points to the person in the image. We find that the original Who’s Waldo dataset compiled for this task contains a large number of biased samples that are solvable simply by heuristic methods; for instance, in many cases the first name in the sentence corresponds to the largest bounding box, or the sequence of names in the sentence corresponds to an exact left-to-right order in the image. Naturally, models trained on these biased data lead to over-estimation of performance on the benchmark. To enforce models being correct for the correct reasons, we design automated tools to filter and debias the original dataset by ruling out all examples of insufficient context, such as those with no verb or with a long chain of conjunct names in their captions. Our experiments show that our new sub-sampled dataset contains less bias with much lowered heuristic performances and widened gaps between heuristic and supervised methods. We also demonstrate the same benchmark model trained on our debiased training set outperforms that trained on the original biased (and larger) training set on our debiased test set. We argue our debiased dataset offers the PCVG task a more practical baseline for reliable benchmarking and future improvements.	['Chitta Baral', 'Yezhou Yang', 'Tejas Gokhale', 'Pratyay Banerjee', 'Yiran Luo']	null	null	null	null	acl-2022-5	['person-centric-visual-grounding']	['computer-vision']	[ 2.13498518e-01 3.12110543e-01 -2.87019640e-01 -3.62592131e-01 -9.47869062e-01 -9.09996331e-01 7.44058549e-01 -1.00263841e-01 -4.51952338e-01 9.28064048e-01 4.58996207e-01 -4.24365997e-01 -4.84471060e-02 -3.96755368e-01 -9.28586900e-01 -3.97651821e-01 3.30206573e-01 9.13489819e-01 1.37868956e-01 -1.26293629e-01 2.60792077e-01 2.35427737e-01 -1.54500067e+00 5.90677023e-01 6.16277039e-01 8.88487458e-01 9.82441604e-02 5.06546199e-01 7.89933652e-02 5.89434266e-01 -7.69939303e-01 -8.15584362e-01 4.62138295e-01 -6.37172043e-01 -1.03169107e+00 2.94983983e-01 1.47240317e+00 -2.24828243e-01 -1.87510207e-01 1.02612638e+00 2.93410361e-01 -1.76160172e-01 6.92908645e-01 -1.48863196e+00 -7.50593781e-01 4.86549973e-01 -5.95784485e-01 9.84990150e-02 4.73156720e-01 4.14680272e-01 1.29928625e+00 -9.14040089e-01 1.28543806e+00 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32ed1531-5c82-4c8a-9947-a175c291b030	semi-supervised-learning-for-few-shot-audio	2102.08074	null	https://arxiv.org/abs/2102.08074v1	https://arxiv.org/pdf/2102.08074v1.pdf	Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining	Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the form of a mean vector of the embedded support points within a class. The performance of prototypical networks in extreme few-shot scenarios (like one-shot) degrades drastically, mainly due to the desuetude of variations within the clusters while constructing prototypes. In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique. The conventional triplet selection leads to overfitting, because of all possible combinations being used during training. We incorporate episodic training for mining the semi hard positive and the semi hard negative triplets to overcome the overfitting. We also propose an adaptation to make use of unlabeled training samples for better modeling. Experimenting on two different audio processing tasks, namely speaker recognition and audio event detection; show improved performances and hence the efficacy of ETM over the prototypical loss function and other meta-learning frameworks. Further, we show improved performances when unlabeled training samples are used.	['Sunil Kumar Kopparapu', 'Rupayan Chakraborty', 'Swapnil Bhosale']	2021-02-16	null	null	null	null	['few-shot-audio-classification']	['audio']	[ 2.49748409e-01 7.08429217e-02 -2.17240080e-02 -5.71583927e-01 -8.80833387e-01 7.87225924e-03 4.79167998e-01 1.11854345e-01 -4.81587648e-01 8.70864511e-01 -1.17365621e-01 1.75867021e-01 -4.37881589e-01 -6.82774723e-01 -6.20671630e-01 -8.07307899e-01 -2.00417787e-01 4.63816017e-01 2.70445943e-01 -1.15736574e-01 3.27824466e-02 2.31543794e-01 -2.31190825e+00 4.62358117e-01 8.62519085e-01 1.10772491e+00 5.77941984e-02 4.44172859e-01 -3.24169606e-01 5.23848236e-01 -8.23111415e-01 -1.69797614e-01 1.67452216e-01 -5.13216138e-01 -3.61654371e-01 3.89108032e-01 2.76181400e-01 2.25164071e-01 2.13106468e-01 9.35770094e-01 7.26293743e-01 6.53952479e-01 6.85579121e-01 -1.49984324e+00 -1.28743529e-01 6.56282187e-01 -5.71163058e-01 2.31534019e-01 1.59429207e-01 -9.55695584e-02 8.73854995e-01 -1.31115830e+00 4.67627019e-01 1.02856219e+00 8.12381625e-01 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-1.06582022e+00 -1.15665793e+00 -9.71878111e-01 3.27056080e-01 2.96510428e-01 -8.41715336e-01 -5.67302346e-01 -2.60740668e-01]	[9.954097747802734, 3.199692964553833]
84ddb4a0-7821-4216-b7f6-7cfeed58a09d	voicefilter-targeted-voice-separation-by	1810.04826	null	https://arxiv.org/abs/1810.04826v6	https://arxiv.org/pdf/1810.04826v6.pdf	VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking	In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals.	['Zelin Wu', 'Hannah Muckenhirn', 'Ye Jia', 'Ron J. Weiss', 'Rif A. Saurous', 'John Hershey', 'Prashant Sridhar', 'Kevin Wilson', 'Ignacio Lopez Moreno', 'Quan Wang']	2018-10-11	null	null	null	null	['speaker-separation']	['speech']	[ 5.28933585e-01 2.28644073e-01 1.92843482e-01 -6.78439200e-01 -1.19338131e+00 -3.99032742e-01 3.73298019e-01 -3.44712824e-01 -3.93677980e-01 2.42907479e-01 3.65298450e-01 -4.98139739e-01 5.96165717e-01 -2.24444076e-01 -4.15910333e-01 -6.00390077e-01 7.51290545e-02 -8.04486200e-02 2.60095447e-01 -1.51680321e-01 -1.22452356e-01 3.99565458e-01 -1.63678837e+00 3.22085500e-01 5.36251962e-01 8.25232983e-01 3.27382594e-01 1.20153797e+00 -1.26694530e-01 5.02128541e-01 -1.18740344e+00 2.00929120e-01 5.88872470e-02 -9.95870411e-01 -6.73724353e-01 2.06932366e-01 4.46188807e-01 -1.29665196e-01 -2.63660163e-01 1.07653618e+00 6.63589597e-01 2.41952553e-01 4.34855431e-01 -8.86100888e-01 -5.85491776e-01 9.77311492e-01 -1.91261142e-01 6.62423551e-01 3.59931231e-01 -4.85259071e-02 8.97057533e-01 -1.01070833e+00 -3.32692266e-02 1.46968389e+00 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1.07229757e+00 -8.62595379e-01 -9.09210622e-01 -1.36581433e+00 -9.30071354e-01 -7.38969892e-02 9.70499575e-01 -9.85861495e-02 -1.05690432e+00 2.68701702e-01]	[14.568918228149414, 6.269887447357178]
9dfe7dbf-f15c-42a5-8eba-4093fe867ed4	deep-cross-modality-adaptation-via-semantics	1807.01806	null	http://arxiv.org/abs/1807.01806v1	http://arxiv.org/pdf/1807.01806v1.pdf	Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval	Due to the large cross-modality discrepancy between 2D sketches and 3D shapes, retrieving 3D shapes by sketches is a significantly challenging task. To address this problem, we propose a novel framework to learn a discriminative deep cross-modality adaptation model in this paper. Specifically, we first separately adopt two metric networks, following two deep convolutional neural networks (CNNs), to learn modality-specific discriminative features based on an importance-aware metric learning method. Subsequently, we explicitly introduce a cross-modality transformation network to compensate for the divergence between two modalities, which can transfer features of 2D sketches to the feature space of 3D shapes. We develop an adversarial learning based method to train the transformation model, by simultaneously enhancing the holistic correlations between data distributions of two modalities, and mitigating the local semantic divergences through minimizing a cross-modality mean discrepancy term. Experimental results on the SHREC 2013 and SHREC 2014 datasets clearly show the superior retrieval performance of our proposed model, compared to the state-of-the-art approaches.	['Yi Fang', 'Jiaxin Chen']	2018-07-04	deep-cross-modality-adaptation-via-semantics-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.pdf	eccv-2018-9	['3d-shape-retrieval']	['computer-vision']	[ 1.73380390e-01 -4.18759584e-01 4.35933992e-02 -4.46962416e-01 -9.69931006e-01 -7.58117616e-01 7.62735486e-01 -2.55730093e-01 -1.37515947e-01 3.02580774e-01 2.56820738e-01 1.08587705e-01 -3.20943773e-01 -8.32912982e-01 -7.26072907e-01 -6.06185973e-01 3.56129557e-01 3.00198108e-01 -2.11209834e-01 -9.12555084e-02 3.21055949e-01 7.09549248e-01 -1.26537645e+00 1.07561760e-01 8.22452962e-01 1.22565210e+00 -1.23406023e-01 1.97279930e-01 -3.80162776e-01 1.89422891e-01 -3.12221795e-01 -5.98445773e-01 5.19217074e-01 -2.19126910e-01 -4.34535176e-01 1.08222179e-01 7.93297946e-01 -3.85982186e-01 -6.97523952e-01 1.04978538e+00 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dbbd90af-a179-4bdf-8b81-7ae0de896f41	promptunet-toward-interactive-medical-image	2305.103	null	https://arxiv.org/abs/2305.10300v1	https://arxiv.org/pdf/2305.10300v1.pdf	PromptUNet: Toward Interactive Medical Image Segmentation	Prompt-based segmentation, also known as interactive segmentation, has recently become a popular approach in image segmentation. A well-designed prompt-based model called Segment Anything Model (SAM) has demonstrated its ability to segment a wide range of natural images, which has sparked a lot of discussion in the community. However, recent studies have shown that SAM performs poorly on medical images. This has motivated us to design a new prompt-based segmentation model specifically for medical image segmentation. In this paper, we combine the prompted-based segmentation paradigm with UNet, which is a widly-recognized successful architecture for medical image segmentation. We have named the resulting model PromptUNet. In order to adapt the real-world clinical use, we expand the existing prompt types in SAM to include novel Supportive Prompts and En-face Prompts. We have evaluated the capabilities of PromptUNet on 19 medical image segmentation tasks using a variety of image modalities, including CT, MRI, ultrasound, fundus, and dermoscopic images. Our results show that PromptUNet outperforms a wide range of state-of-the-art (SOTA) medical image segmentation methods, including nnUNet, TransUNet, UNetr, MedSegDiff, and MSA. Code will be released at: https://github.com/WuJunde/PromptUNet.	['Junde Wu']	2023-05-17	null	null	null	null	['interactive-segmentation']	['computer-vision']	[ 5.03047705e-01 3.08353305e-01 -3.89386922e-01 -5.18489242e-01 -8.69202077e-01 -7.29880512e-01 2.65319854e-01 1.43520281e-01 -4.32924151e-01 4.99232739e-01 -4.85331044e-02 -6.20555758e-01 -1.05047479e-01 -3.42602581e-01 -3.14670682e-01 -5.47264159e-01 1.48560151e-01 6.93974853e-01 6.23655975e-01 2.42118128e-02 3.43797714e-01 2.93777138e-01 -9.96355534e-01 4.07667249e-01 1.49377549e+00 6.41839385e-01 2.31465280e-01 8.55964243e-01 -4.58774030e-01 4.80591148e-01 -4.42566603e-01 -4.30963516e-01 1.48961172e-01 -7.93720901e-01 -1.26078367e+00 1.22783855e-01 4.35773253e-01 -4.62514073e-01 1.71703100e-01 7.27249384e-01 7.00097680e-01 -7.39965439e-02 5.54845810e-01 -1.03276253e+00 -3.58762026e-01 6.05225384e-01 -7.77769983e-01 3.28425914e-01 5.44917762e-01 3.68520826e-01 3.43925059e-01 -2.90122479e-01 1.01745856e+00 9.82719302e-01 8.78598571e-01 9.69051838e-01 -9.45953250e-01 -4.55482334e-01 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-4.21576977e-01 3.38015169e-01 2.02712536e-01 -3.07738572e-01 8.26393813e-02 2.18226358e-01]	[14.682249069213867, -2.263587236404419]
1b0a96ce-d11e-4d1f-8744-14d946868cbc	graph-augmentation-clustering-network	2211.10627	null	https://arxiv.org/abs/2211.10627v1	https://arxiv.org/pdf/2211.10627v1.pdf	Graph Augmentation Clustering Network	Existing graph clustering networks heavily rely on a predefined graph and may fail if the initial graph is of low quality. To tackle this issue, we propose a novel graph augmentation clustering network capable of adaptively enhancing the initial graph to achieve better clustering performance. Specifically, we first integrate the node attribute and topology structure information to learn the latent feature representation. Then, we explore the local geometric structure information on the embedding space to construct an adjacency graph and subsequently develop an adaptive graph augmentation architecture to fuse that graph with the initial one dynamically. Finally, we minimize the Jeffreys divergence between multiple derived distributions to conduct network training in an unsupervised fashion. Extensive experiments on six commonly used benchmark datasets demonstrate that the proposed method consistently outperforms several state-of-the-art approaches. In particular, our method improves the ARI by more than 9.39\% over the best baseline on DBLP. The source codes and data have been submitted to the appendix.	['Junhui Hou', 'Yuheng Jia', 'Hui Liu', 'Zhihao Peng']	2022-11-19	null	null	null	null	['graph-clustering']	['graphs']	[ 3.18584777e-02 2.06015989e-01 -2.88834333e-01 -4.04686064e-01 -5.88082731e-01 -6.01581812e-01 4.37262893e-01 4.00990635e-01 -2.05307469e-01 4.54496622e-01 8.72731283e-02 -1.49125814e-01 -3.28681260e-01 -8.28782916e-01 -6.32853925e-01 -8.60765338e-01 -3.42050433e-01 5.56348741e-01 1.60083055e-01 6.50258735e-02 9.10670310e-02 4.68218654e-01 -1.09680665e+00 -2.77082086e-01 1.15330791e+00 7.31279910e-01 1.18403494e-01 2.42511079e-01 -1.26460925e-01 3.21109116e-01 -5.01725435e-01 -1.98558345e-01 3.25969279e-01 -1.81785792e-01 -5.39154768e-01 4.71441180e-01 2.29694650e-01 1.56927004e-01 -4.44772601e-01 1.30937994e+00 4.01658595e-01 3.16967547e-01 6.11182749e-01 -1.32842076e+00 -6.02477193e-01 7.87843406e-01 -8.37815046e-01 1.03120245e-01 6.76673651e-02 -5.89412414e-02 1.16441762e+00 -8.68136466e-01 4.53497350e-01 1.01920509e+00 4.90679204e-01 1.24071993e-01 -1.55035269e+00 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4.63261902e-01 -7.57167414e-02 -6.87636018e-01 -6.09951198e-01 -1.93675920e-01]	[7.253442764282227, 5.995439052581787]
9d2d9b4c-7f74-43c2-95f8-65c4c8fb6bc8	revisiting-unsupervised-meta-learning	2011.14663	null	https://arxiv.org/abs/2011.14663v3	https://arxiv.org/pdf/2011.14663v3.pdf	Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks	Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are constructed with data augmentations from unlabeled base classes during meta-training, and we apply embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test. We observe two elements play important roles in UML, i.e., the way to sample tasks and measure similarities between instances. Thus we obtain a strong baseline with two simple modifications -- a sufficient sampling strategy constructing multiple tasks per episode efficiently together with a semi-normalized similarity. We then take advantage of the characteristics of tasks from two directions to get further improvements. First, synthesized confusing instances are incorporated to help extract more discriminative embeddings. Second, we utilize an additional task-specific embedding transformation as an auxiliary component during meta-training to promote the generalization ability of the pre-adapted embeddings. Experiments on few-shot learning benchmarks verify that our approaches outperform previous UML methods and achieve comparable or even better performance than its supervised variants.	['De-Chuan Zhan', 'Lu Han', 'Han-Jia Ye']	2020-11-30	null	null	null	null	['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification']	['computer-vision', 'computer-vision']	[ 3.40567052e-01 -1.25721306e-01 -5.94976604e-01 -6.13284886e-01 -9.06583905e-01 -2.16964841e-01 8.14564645e-01 1.30973026e-01 -5.08452356e-01 5.56068242e-01 2.63561487e-01 2.02106044e-01 2.44046431e-02 -7.93283582e-01 -6.34011567e-01 -8.53770018e-01 2.26816103e-01 1.77739114e-01 4.07529563e-01 -1.79663017e-01 2.78176785e-01 -3.63037810e-02 -1.85617292e+00 5.69262028e-01 9.90400016e-01 8.97296727e-01 3.91579896e-01 2.92126060e-01 -3.68725210e-01 7.02260852e-01 -5.09884834e-01 -3.10045689e-01 1.88416228e-01 -6.27148032e-01 -7.55290866e-01 6.13361359e-01 1.34294122e-01 -2.86568999e-01 -4.32994366e-01 9.86946344e-01 5.26517749e-01 6.72415972e-01 8.65925670e-01 -1.31360304e+00 -8.84533405e-01 4.61335629e-01 -5.65741241e-01 2.52536029e-01 9.35949981e-02 4.19972092e-01 9.12680984e-01 -1.21076858e+00 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-9.34784770e-01 -1.01663232e+00 -1.20641589e+00 -6.89541519e-01 7.55363166e-01 8.66160691e-02 -9.92525935e-01 -5.68965375e-01 6.28118142e-02]	[10.045866012573242, 3.0908405780792236]
4ba91db2-e20d-457d-93ed-7aa8c413514c	reinforcement-federated-learning-method-based	2306.12859	null	https://arxiv.org/abs/2306.12859v2	https://arxiv.org/pdf/2306.12859v2.pdf	Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering	Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the non-independent and identical distribu-tion of data across different user terminals. In order to alleviate this problem, this paper pro-poses a strengthened federation aggregation method based on adaptive OPTICS clustering. Specifically, this method perceives the clustering environment as a Markov decision process, and models the adjustment process of parameter search direction, so as to find the best clus-tering parameters to achieve the best federated aggregation method. The core contribution of this paper is to propose an adaptive OPTICS clustering algorithm for federated learning. The algorithm combines OPTICS clustering and adaptive learning technology, and can effective-ly deal with the problem of non-independent and identically distributed data across different user terminals. By perceiving the clustering environment as a Markov decision process, the goal is to find the best parameters of the OPTICS cluster without artificial assistance, so as to obtain the best federated aggregation method and achieve better performance. The reliability and practicability of this method have been verified on the experimental data, and its effec-tiveness and superiority have been proved.	['Zeli Guan', 'Yingxia Shao', 'Junping Du', 'Tianyu Zhao']	2023-06-22	null	null	null	null	['clustering']	['methodology']	[-0.61336094 -0.18388158 0.20785786 -0.5316895 -0.29938743 -0.61234534 -0.07541193 -0.3088176 -0.34080487 0.3630086 -0.16352154 -0.08554724 -0.36906573 -0.7382259 -0.50330067 -1.2635117 0.08180067 0.32204694 -0.13122715 0.37441903 0.01546424 0.5482175 -1.6572423 0.44010377 0.9552528 1.1417804 0.02203827 0.33160335 -0.35294497 0.85215735 -0.7307489 -0.37168744 0.5094229 -0.3580306 -0.55073804 0.0771999 0.03220905 -0.06100512 -0.06011646 1.4889455 0.51975757 0.04189107 0.4168077 -1.4949688 -0.62667525 0.5801364 -0.19154435 -0.31787622 -0.3516166 -0.06409325 0.6172924 -0.27616477 0.16077399 1.068343 0.51284015 0.73720044 -0.92560905 -1.1237391 0.2608236 0.35682493 -1.717519 -0.4013246 0.6432554 -0.09602766 0.12359794 0.61786777 0.4189437 0.34134477 0.52290434 0.6191783 1.0539839 -0.5140149 0.68718415 0.50285894 -0.06544492 0.83710295 0.41147208 -0.06877472 -0.52259535 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161debf9-195c-482e-b804-ce57f5b29a27	residual-gated-graph-convnets	1711.07553	null	http://arxiv.org/abs/1711.07553v2	http://arxiv.org/pdf/1711.07553v2.pdf	Residual Gated Graph ConvNets	Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks. Most existing works have focused on recurrent neural networks (RNNs) to learn meaningful representations of graphs, and more recently new convolutional neural networks (ConvNets) have been introduced. In this work, we want to compare rigorously these two fundamental families of architectures to solve graph learning tasks. We review existing graph RNN and ConvNet architectures, and propose natural extension of LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of analytically controlled experiments on two basic graph problems, i.e. subgraph matching and graph clustering, to test the different architectures. Numerical results show that the proposed graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than variational (non-learning) techniques. Finally, the most effective graph ConvNet architecture uses gated edges and residuality. Residuality plays an essential role to learn multi-layer architectures as they provide a 10% gain of performance.	['Thomas Laurent', 'Xavier Bresson']	2017-11-20	residual-gated-graph-convnets-1	https://openreview.net/forum?id=HyXBcYg0b	https://openreview.net/pdf?id=HyXBcYg0b	iclr-2018-1	['graph-regression']	['graphs']	[-1.00619551e-02 5.08450508e-01 -4.56452221e-02 -2.52493083e-01 9.53359604e-02 -2.28303716e-01 3.47443044e-01 1.03089556e-01 -1.95072100e-01 7.66234577e-01 -1.54600456e-01 -5.64322531e-01 -2.14341730e-01 -1.22279620e+00 -7.85670340e-01 -6.14153504e-01 -6.18453503e-01 4.92037266e-01 -2.27650888e-02 -3.85807663e-01 -2.53255907e-02 6.11096561e-01 -1.08913136e+00 -1.67347327e-01 6.64435446e-01 6.55736506e-01 1.19116537e-01 8.71572077e-01 -2.24344924e-01 9.44067001e-01 -4.94888037e-01 -3.23187590e-01 5.15178181e-02 -4.29702878e-01 -9.70015764e-01 -6.00552335e-02 1.62042826e-01 8.99472013e-02 -7.64647007e-01 1.27010727e+00 4.35544819e-01 4.40782338e-01 5.94243228e-01 -1.31202567e+00 -1.14887130e+00 1.07911325e+00 -3.40102971e-01 3.19559455e-01 7.31381178e-02 -3.15638110e-02 1.14475119e+00 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8b52265a-2bb4-4504-ad49-12606833d163	codet-a-benchmark-for-contrastive-dialectal	2305.17267	null	https://arxiv.org/abs/2305.17267v1	https://arxiv.org/pdf/2305.17267v1.pdf	CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation	Neural machine translation (NMT) systems exhibit limited robustness in handling source-side linguistic variations. Their performance tends to degrade when faced with even slight deviations in language usage, such as different domains or variations introduced by second-language speakers. It is intuitive to extend this observation to encompass dialectal variations as well, but the work allowing the community to evaluate MT systems on this dimension is limited. To alleviate this issue, we compile and release \dataset, a contrastive dialectal benchmark encompassing 882 different variations from nine different languages. We also quantitatively demonstrate the challenges large MT models face in effectively translating dialectal variants. We are releasing all code and data.	['Antonios Anastasopoulos', 'Sina Ahmadi', 'Md Mahfuz ibn Alam']	2023-05-26	null	null	null	null	['nmt']	['computer-code']	[ 1.13220707e-01 -2.94147432e-01 -4.91400450e-01 -4.17186350e-01 -1.15176034e+00 -1.05633628e+00 8.67142737e-01 -4.23271537e-01 -4.61631924e-01 9.51088190e-01 2.83207625e-01 -7.41050124e-01 3.92517954e-01 -3.19807500e-01 -7.76033580e-01 -2.54054189e-01 2.23385558e-01 7.40075409e-01 -7.83123374e-02 -6.62395000e-01 1.62857816e-01 1.21958941e-01 -9.31880832e-01 3.91218871e-01 1.22353256e+00 9.26379785e-02 8.70789662e-02 3.60614151e-01 -3.01343352e-01 3.60429704e-01 -7.29828417e-01 -9.26221490e-01 5.17874599e-01 -4.25494105e-01 -8.46096992e-01 -9.24232528e-02 7.69490123e-01 -1.78573020e-02 -7.65685961e-02 1.06277359e+00 6.88528955e-01 -2.84061670e-01 7.24053800e-01 -9.12376046e-01 -1.22069216e+00 1.06038046e+00 -5.21150470e-01 4.55049843e-01 5.26609719e-01 4.00582969e-01 9.02191579e-01 -1.13061178e+00 8.67146790e-01 1.42766392e+00 8.37476075e-01 5.95912814e-01 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f6cc8dcf-16ee-42db-b38a-64310cb39c8d	ai-generated-characters-for-supporting	null	null	https://www.nature.com/articles/s42256-021-00417-9	https://www.nature.com/articles/s42256-021-00417-9.pdf	AI-generated characters for supporting personalized learning and well-being	Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspire and intrigue us. AI-generated portrayals of characters can feature synthesized faces, bodies and voices of anyone, from a fictional character to a historical figure, or even a deceased family member. Although negative use cases of this technology have dominated the conversation so far, in this Perspective we highlight emerging positive use cases of AI-generated characters, specifically in supporting learning and well-being. We demonstrate an easy-to-use AI character generation pipeline to enable such outcomes and discuss ethical implications as well as the need for including traceability to help maintain trust in the generated media. As we look towards the future, we foresee generative media as a crucial part of the ever growing landscape of human–AI interaction.	['Pattie Maes & Misha Sra', 'Dan Novy', 'Parinya Punpongsanon', 'Joanne Leong', 'Valdemar Danry', 'Pat Pataranutaporn']	2021-12-15	null	null	null	nature-machine-intelligence-2021-12	['talking-head-generation', 'text-to-face-generation', 'face-reenactment']	['computer-vision', 'computer-vision', 'computer-vision']	[ 6.51493490e-01 9.30597007e-01 3.73913169e-01 -2.25127250e-01 -5.07049382e-01 -5.25811493e-01 1.08526671e+00 1.96011752e-01 -5.83494529e-02 7.73364127e-01 7.93789268e-01 1.05681727e-02 4.48834360e-01 -7.71097183e-01 -5.89184642e-01 -2.78828919e-01 4.12276052e-02 2.56997764e-01 -4.00854409e-01 -3.91744047e-01 8.99374858e-02 4.00602341e-01 -1.76405251e+00 5.64948559e-01 6.91616237e-01 4.72035676e-01 -4.05473769e-01 8.59132409e-01 -4.67251837e-02 9.71035659e-01 -1.10959554e+00 -9.97854769e-01 -1.62863612e-01 -6.72753870e-01 -5.65573573e-01 -4.37928438e-02 4.96693194e-01 -5.92968047e-01 8.94675776e-02 5.89355171e-01 7.32572913e-01 -1.74717754e-01 3.65130335e-01 -1.21391952e+00 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000fb91a-5c4a-46d5-9b94-69335dc706c2	eaml-ensemble-self-attention-based-mutual	2305.06923	null	https://arxiv.org/abs/2305.06923v1	https://arxiv.org/pdf/2305.06923v1.pdf	EAML: Ensemble Self-Attention-based Mutual Learning Network for Document Image Classification	In the recent past, complex deep neural networks have received huge interest in various document understanding tasks such as document image classification and document retrieval. As many document types have a distinct visual style, learning only visual features with deep CNNs to classify document images have encountered the problem of low inter-class discrimination, and high intra-class structural variations between its categories. In parallel, text-level understanding jointly learned with the corresponding visual properties within a given document image has considerably improved the classification performance in terms of accuracy. In this paper, we design a self-attention-based fusion module that serves as a block in our ensemble trainable network. It allows to simultaneously learn the discriminant features of image and text modalities throughout the training stage. Besides, we encourage mutual learning by transferring the positive knowledge between image and text modalities during the training stage. This constraint is realized by adding a truncated-Kullback-Leibler divergence loss Tr-KLD-Reg as a new regularization term, to the conventional supervised setting. To the best of our knowledge, this is the first time to leverage a mutual learning approach along with a self-attention-based fusion module to perform document image classification. The experimental results illustrate the effectiveness of our approach in terms of accuracy for the single-modal and multi-modal modalities. Thus, the proposed ensemble self-attention-based mutual learning model outperforms the state-of-the-art classification results based on the benchmark RVL-CDIP and Tobacco-3482 datasets.	['Marçal Rusiñol', 'Mickael Coustaty', 'Ziheng Ming', 'Souhail Bakkali']	2023-05-11	null	null	null	null	['document-image-classification']	['computer-vision']	[ 2.59136558e-01 -3.40106398e-01 -3.67035508e-01 -4.57413226e-01 -7.25901008e-01 -4.57330972e-01 1.01071084e+00 2.20359832e-01 -4.24814582e-01 4.73311573e-01 -7.19294995e-02 -1.35838062e-01 -2.56161660e-01 -5.91663301e-01 -5.48310280e-01 -9.75953162e-01 5.25377929e-01 1.92523196e-01 -1.56953067e-01 1.61424994e-01 3.56312156e-01 3.32036316e-01 -1.68655741e+00 5.98075211e-01 1.11677885e+00 1.44524539e+00 1.98698953e-01 5.79719722e-01 -4.26629156e-01 1.09122801e+00 -3.61408949e-01 -5.17348230e-01 -1.99772641e-01 -4.17589217e-01 -7.30983198e-01 3.71305585e-01 4.98889416e-01 -3.59056771e-01 -3.79618376e-01 9.53626633e-01 4.84212965e-01 2.03328893e-01 1.07819688e+00 -1.24379003e+00 -1.31579947e+00 3.50707978e-01 -9.10694778e-01 -5.52947558e-02 1.23830542e-01 -5.31018339e-02 1.20028138e+00 -1.04062617e+00 3.12749773e-01 8.76486182e-01 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d2886cc5-9535-46d0-a952-441db0058480	satimnet-structured-and-harmonised-training	2006.10623	null	https://arxiv.org/abs/2006.10623v2	https://arxiv.org/pdf/2006.10623v2.pdf	SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification	Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets. While there exists a plethora of data sets that can be used for this purpose, they are usually very heterogeneous and not interoperable. In this context, the present work has a twofold objective: i) to describe procedures of open-source training data management, integration, and data retrieval, and ii) to demonstrate the practical use of varying source training data for remote sensing image classification. For the former, we propose SatImNet, a collection of open training data, structured and harmonized according to specific rules. For the latter, two modelling approaches based on convolutional neural networks have been designed and configured to deal with satellite image classification and segmentation.	['Vasileios Syrris', 'Pierre Soille', 'Ondrej Pesek']	2020-06-18	null	null	null	null	['satellite-image-classification', 'remote-sensing-image-classification']	['computer-vision', 'miscellaneous']	[ 6.12004064e-02 -1.71666831e-01 1.79888114e-01 -5.94987392e-01 -3.40095431e-01 -3.94616872e-01 5.74781597e-01 2.66634285e-01 -5.96280158e-01 7.19147384e-01 -3.42113316e-01 -4.61878031e-01 -6.48986399e-01 -1.24412262e+00 -2.92446017e-01 -7.73156762e-01 -1.76978454e-01 7.74317384e-01 8.31093732e-03 -4.06387448e-01 -2.31285654e-02 9.90364611e-01 -2.12410235e+00 1.51843950e-01 1.09841812e+00 1.25436902e+00 4.77191240e-01 4.48381633e-01 -4.22246933e-01 6.73425734e-01 -4.65815961e-01 1.60659507e-01 2.82167703e-01 1.18443519e-01 -1.04640436e+00 1.19587176e-01 3.89260352e-01 -1.86899424e-01 1.62945256e-01 8.14776957e-01 7.49839962e-01 1.06204458e-01 5.72263122e-01 -9.21627045e-01 -3.30669671e-01 6.56048059e-01 2.53988057e-01 -1.16786100e-02 -4.45719391e-01 -2.35776212e-02 6.85313523e-01 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cf94574a-3130-4841-afa4-5fde28738470	a-multiresolution-3d-morphable-face-model-and	null	null	https://www.scitepress.org/Link.aspx?doi=10.5220%2f0005669500790086	https://www.scitepress.org/Link.aspx?doi=10.5220%2f0005669500790086	A Multiresolution 3D Morphable Face Model and Fitting Framework	3D Morphable Face Models are a powerful tool in computer vision. They consists of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmark detection and tracking. However, they are not as widely used as 2D methods - the process of building and using a 3D model is much more involved. In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes. The model contains different mesh resolution levels and landmark point annotations as well as metadata for texture remapping. Accompanying the model is a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals. In addition to basic functionality, it contains pose estimation and face frontalisation algorithms. With the tools presented in this paper, we aim to close two gaps. First, by offering different model resolution levels and fast fitting functionality, we enable the use of a 3D Morphable Model in time-critical applications like tracking. Second, the software library makes it easy for the community to adopt the 3D Morphable Face Model in their research, and it offers a public place for collaboration.	['Josef Kittler', 'Matthias Rätsch', 'William Christmas', 'Willem P. Koppen', 'Pouria Mortazavian', 'Rafael Tena', 'Guosheng Hu', 'Patrik Huber']	2016-02-01	null	null	null	null	['head-pose-estimation', 'face-model']	['computer-vision', 'computer-vision']	[-1.35111421e-01 2.27249116e-01 9.01286379e-02 -3.35419148e-01 -6.43766046e-01 -3.34366560e-01 3.10366601e-01 -2.41231933e-01 -2.08179131e-01 2.22054645e-01 -1.57740023e-02 -7.82331731e-03 8.66640806e-02 -5.89179993e-01 -3.81855637e-01 -5.83347142e-01 -1.25372306e-01 8.85917306e-01 2.54062235e-01 -8.25392306e-02 1.37357898e-02 1.20323515e+00 -2.11341596e+00 2.21997872e-02 2.34788224e-01 9.52980399e-01 7.24813640e-02 4.44791883e-01 -7.21464381e-02 -1.25355184e-01 1.96888074e-02 -5.48279881e-01 3.02032918e-01 -2.30049878e-01 -6.29721224e-01 2.03666627e-01 8.46785963e-01 -3.51657361e-01 2.14739248e-01 5.73017716e-01 8.19505155e-01 -2.26591110e-01 5.08633077e-01 -1.00801837e+00 1.52341733e-02 -2.77451217e-01 -4.92172062e-01 -3.28245729e-01 6.27210498e-01 -1.92341134e-01 2.11800247e-01 -1.06304085e+00 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3.51231456e-01 -1.60789955e+00 1.31009057e-01 -7.18540430e-01 -9.66878891e-01 2.92587169e-02 9.65813324e-02 -1.00528276e+00 -3.16566914e-01 8.70766863e-02]	[13.36933708190918, 0.08758172392845154]
abebccb5-54d0-4966-b145-908c7876bdb7	cross-modal-local-shortest-path-and-global	2206.04401	null	https://arxiv.org/abs/2206.04401v1	https://arxiv.org/pdf/2206.04401v1.pdf	Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification	In addition to considering the recognition difficulty caused by human posture and occlusion, it is also necessary to solve the modal differences caused by different imaging systems in the Visible-Thermal cross-modal person re-identification (VT-ReID) task. In this paper,we propose the Cross-modal Local Shortest Path and Global Enhancement (CM-LSP-GE) modules,a two-stream network based on joint learning of local and global features. The core idea of our paper is to use local feature alignment to solve occlusion problem, and to solve modal difference by strengthening global feature. Firstly, Attention-based two-stream ResNet network is designed to extract dual-modality features and map to a unified feature space. Then, to solve the cross-modal person pose and occlusion problems, the image are cut horizontally into several equal parts to obtain local features and the shortest path in local features between two graphs is used to achieve the fine-grained local feature alignment. Thirdly, a batch normalization enhancement module applies global features to enhance strategy, resulting in difference enhancement between different classes. The multi granularity loss fusion strategy further improves the performance of the algorithm. Finally, joint learning mechanism of local and global features is used to improve cross-modal person re-identification accuracy. The experimental results on two typical datasets show that our model is obviously superior to the most state-of-the-art methods. Especially, on SYSU-MM01 datasets, our model can achieve a gain of 2.89%and 7.96% in all search term of Rank-1 and mAP. The source code will be released soon.	['Xiangcai Ma', 'Chaoqi Li', 'XiaoHong Wang']	2022-06-09	null	null	null	null	['cross-view-person-re-identification']	['computer-vision']	[-1.37281641e-01 -5.84916353e-01 1.35448322e-01 -4.13014919e-01 -7.32403994e-01 -2.91735865e-02 3.68157893e-01 -2.74719298e-01 -6.49210453e-01 4.33712810e-01 4.38696682e-01 4.88084853e-01 -3.21250021e-01 -6.62890315e-01 -3.08800071e-01 -8.09797049e-01 1.78452522e-01 2.20546961e-01 1.30757149e-02 -3.94942909e-01 -8.33739489e-02 3.93909067e-01 -1.66922247e+00 6.53823167e-02 8.90972376e-01 9.43460345e-01 -1.37368636e-02 2.07953513e-01 -2.04595383e-02 2.10156217e-01 -6.33922696e-01 -5.87769389e-01 4.05253559e-01 -1.94067329e-01 -6.39153004e-01 1.11133315e-01 6.37903631e-01 -3.97208422e-01 -6.05692685e-01 1.21170831e+00 1.13988078e+00 4.11675453e-01 3.87337685e-01 -1.30556893e+00 -6.71866000e-01 2.01263830e-01 -9.55715716e-01 3.11620563e-01 5.20166814e-01 2.14091778e-01 4.22697753e-01 -7.93245852e-01 2.96460658e-01 1.59587944e+00 1.01571047e+00 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af04e08f-6d29-4fa6-88b7-d3e4717f68ed	meta-learning-triplet-network-with-adaptive	2302.07739	null	https://arxiv.org/abs/2302.07739v1	https://arxiv.org/pdf/2302.07739v1.pdf	Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition	Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet.	['Wei Wu', 'Xuezhi Cao', 'Ming Gao', 'Xiang Li', 'FengJiao Chen', 'Jun Kuang', 'Renyu Zhu', 'Chengcheng Han']	2023-02-14	null	null	null	null	['miscellaneous', 'few-shot-ner']	['miscellaneous', 'natural-language-processing']	[-3.26855779e-01 -1.88335672e-01 -5.26128471e-01 -4.77559894e-01 -6.82177424e-01 -3.06705654e-01 3.23622674e-01 1.90960929e-01 -6.57568038e-01 6.08558118e-01 9.95044857e-02 1.97562039e-01 -7.96720386e-02 -1.01961219e+00 -4.90028918e-01 -5.20912290e-01 2.29888827e-01 5.53675890e-01 3.32156032e-01 -2.16889128e-01 7.84049258e-02 1.05054505e-01 -1.37479758e+00 1.36665076e-01 9.63918865e-01 8.79320145e-01 2.04235345e-01 1.08319081e-01 -5.31888425e-01 4.35975641e-01 -5.40816844e-01 -5.92829049e-01 1.38899267e-01 -3.02745968e-01 -7.23459482e-01 -1.71266824e-01 6.69619814e-02 5.97600751e-02 -3.54643553e-01 1.20355248e+00 6.58616424e-01 6.28241956e-01 6.78700149e-01 -1.31167531e+00 -1.00376976e+00 8.86264801e-01 -3.67617279e-01 2.05177128e-01 7.70019516e-02 -1.92540511e-01 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84031d2f-c664-4eb8-b3b3-560a6c4044f0	perceiving-and-modeling-density-is-all-you	2111.09733	null	https://arxiv.org/abs/2111.09733v1	https://arxiv.org/pdf/2111.09733v1.pdf	Perceiving and Modeling Density is All You Need for Image Dehazing	In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze is varied from image to image. Recent methods adopt deep neural networks to recover clean scenes from hazy images directly. However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal.To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution. We propose a novel Separable Hybrid Attention (SHA) module to encode haze density by capturing features in the orthogonal directions to achieve this goal. Moreover, a density map is proposed to model the uneven distribution of the haze explicitly. The density map generates positional encoding in a semi-supervised way. Such a haze density perceiving and modeling capture the unevenly distributed degeneration at the feature level effectively. Through a suitable combination of SHA and density map, we design a novel dehazing network architecture, which achieves a good complexity-performance trade-off. The extensive experiments on two large-scale datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 28.53 dB to 33.49 dB on the Haze4k test dataset and from 37.17 dB to 38.41 dB on the SOTS indoor test dataset.	['Zhiyong Lu', 'Pen Chen', 'ErKang Chen', 'Liang Chen', 'Yunchen Zhang', 'Mingchao Jiang', 'Tian Ye']	2021-11-18	null	null	null	null	['image-dehazing']	['computer-vision']	[ 3.79176177e-02 -5.80192924e-01 4.53958213e-01 -3.45939487e-01 -4.42704797e-01 -1.16360977e-01 2.44853824e-01 -4.55595940e-01 -1.42573193e-01 6.95465386e-01 2.99292743e-01 -3.21121030e-02 -2.84698635e-01 -9.16852057e-01 -7.73408234e-01 -1.44072449e+00 -3.09422221e-02 2.84625590e-02 3.29044253e-01 -4.99945045e-01 -9.22921347e-04 3.37046325e-01 -1.67455840e+00 2.04235733e-01 1.39892292e+00 1.11884725e+00 5.88932037e-01 5.40811956e-01 1.95287451e-01 8.51315737e-01 -9.53037918e-01 -1.05484910e-01 4.63649392e-01 -4.32174057e-01 5.12470566e-02 2.82495439e-01 6.82395399e-01 -6.91817164e-01 -8.29247415e-01 1.40958416e+00 6.25064254e-01 1.17884897e-01 5.99062502e-01 -9.63367164e-01 -1.22168744e+00 1.19267017e-01 -6.29974961e-01 5.72666585e-01 -3.24877590e-01 3.03581834e-01 4.46858197e-01 -8.03263962e-01 -7.36152753e-02 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8f6b1473-ed3b-48bb-baad-96904a577470	global-and-local-interpretation-of-black-box	2109.05087	null	https://arxiv.org/abs/2109.05087v1	https://arxiv.org/pdf/2109.05087v1.pdf	Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data	The COVID-19 corona virus has claimed 4.1 million lives, as of July 24, 2021. A variety of machine learning models have been applied to related data to predict important factors such as the severity of the disease, infection rate and discover important prognostic factors. Often the usefulness of the findings from the use of these techniques is reduced due to lack of method interpretability. Some recent progress made on the interpretability of machine learning models has the potential to unravel more insights while using conventional machine learning models. In this work, we analyze COVID-19 blood work data with some of the popular machine learning models; then we employ state-of-the-art post-hoc local interpretability techniques(e.g.- SHAP, LIME), and global interpretability techniques(e.g. - symbolic metamodeling) to the trained black-box models to draw interpretable conclusions. In the gamut of machine learning algorithms, regressions remain one of the simplest and most explainable models with clear mathematical formulation. We explore one of the most recent techniques called symbolic metamodeling to find the mathematical expression of the machine learning models for COVID-19. We identify Acute Kidney Injury (AKI), initial Albumin level (ALBI), Aspartate aminotransferase (ASTI), Total Bilirubin initial(TBILI) and D-Dimer initial (DIMER) as major prognostic factors of the disease severity. Our contributions are- (i) uncover the underlying mathematical expression for the black-box models on COVID-19 severity prediction task (ii) we are the first to apply symbolic metamodeling to this task, and (iii) discover important features and feature interactions.	['Dimitris Metaxas', 'Vinod Rustgi', 'Carlos D. Minacapelli', 'Ananya Jana']	2021-09-10	null	null	null	null	['explainable-models', 'severity-prediction']	['computer-vision', 'computer-vision']	[ 1.66682169e-01 1.57326102e-01 -9.27771404e-02 -5.21549881e-01 5.13687283e-02 -3.54372859e-01 3.10086995e-01 5.28659165e-01 1.93166956e-01 8.88565004e-01 5.90749260e-04 -8.45309973e-01 -7.70604849e-01 -5.35321116e-01 -5.21701574e-01 -3.78909260e-01 -5.63230693e-01 9.50437129e-01 -4.22680259e-01 -2.58929193e-01 9.71408412e-02 7.31318891e-01 -1.19235766e+00 5.24273694e-01 1.10168850e+00 7.58636296e-01 -4.32777524e-01 8.91187370e-01 4.71996814e-02 1.23992300e+00 -3.33972007e-01 -2.89534688e-01 -1.69736370e-02 -5.72317481e-01 -7.29824126e-01 -4.79602933e-01 -1.19909965e-01 -1.59916893e-01 1.25392899e-01 4.72344071e-01 -6.72878996e-02 -4.20568377e-01 1.12579489e+00 -1.73796225e+00 -3.32824826e-01 3.62566948e-01 -2.39176109e-01 1.17781825e-01 1.46542698e-01 6.75099194e-02 7.03231633e-01 -6.32631481e-01 4.37452763e-01 1.36293936e+00 9.71405745e-01 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9c20e4c9-34d0-4673-8463-facdfd0845a9	playgol-learning-programs-through-play	1904.08993	null	https://arxiv.org/abs/1904.08993v2	https://arxiv.org/pdf/1904.08993v2.pdf	Playgol: learning programs through play	Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge. Before solving the tasks, the learner enters an unsupervised playing stage where it creates its own tasks to solve, tries to solve them, and saves any solutions (programs) to the background knowledge. After the playing stage is finished, the learner enters the supervised building stage where it tries to solve the user-supplied tasks and can reuse solutions learnt whilst playing. The idea is that playing allows the learner to discover reusable general programs on its own which can then help solve the user-supplied tasks. We claim that playing can improve learning performance. We show that playing can reduce the textual complexity of target concepts which in turn reduces the sample complexity of a learner. We implement our idea in Playgol, a new inductive logic programming system. We experimentally test our claim on two domains: robot planning and real-world string transformations. Our experimental results suggest that playing can substantially improve learning performance. We think that the idea of playing (or, more verbosely, unsupervised bootstrapping for supervised program induction) is an important contribution to the problem of developing program induction approaches that self-discover BK.	['Andrew Cropper']	2019-04-18	null	null	null	null	['program-induction']	['computer-code']	[ 4.75652158e-01 7.35696852e-01 -2.62866437e-01 -2.71517336e-01 -5.62726378e-01 -8.25726986e-01 3.04140449e-01 2.70025045e-01 -2.08239064e-01 7.20401406e-01 -1.79766312e-01 -6.11162424e-01 -1.60642549e-01 -1.44997597e+00 -1.07824469e+00 -5.03883898e-01 -3.94661307e-01 8.80118430e-01 7.69870937e-01 -3.00037593e-01 2.65881926e-01 1.22170404e-01 -2.08203912e+00 4.73340988e-01 1.05056572e+00 2.77030706e-01 4.62495387e-01 8.74059379e-01 -5.84471226e-01 1.49464238e+00 -1.13150202e-01 -3.34609568e-01 4.13152482e-03 -4.03605402e-01 -1.54603326e+00 -1.97388127e-01 -5.16591221e-03 -1.16035178e-01 1.06519088e-01 1.12734270e+00 -1.59074530e-01 1.48119703e-01 4.16772723e-01 -1.51939213e+00 -1.54216498e-01 1.25934744e+00 1.93309113e-02 -2.09213391e-01 7.37593651e-01 -4.72107669e-03 1.04155719e+00 -4.48682517e-01 6.53771639e-01 1.16221404e+00 7.01876998e-01 8.25737059e-01 -1.43395174e+00 -4.63342488e-01 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-1.03847578e-01 3.64186913e-02 -5.54737806e-01 -1.87084705e-01 5.91488719e-01]	[8.75359058380127, 7.138139247894287]
1695bff1-0bff-4c78-925a-2dd44fa475b2	cost-splitting-for-multi-objective-conflict	2211.12885	null	https://arxiv.org/abs/2211.12885v1	https://arxiv.org/pdf/2211.12885v1.pdf	Cost Splitting for Multi-Objective Conflict-Based Search	The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such cost metrics include arrival times, travel distances, and energy consumption.In this paper, we focus on the Multi-Objective Conflict-Based Search (MO-CBS) algorithm, a state-of-the-art MO-MAPF algorithm. We show that the standard splitting strategy used by MO-CBS can lead to duplicate search nodes and hence can duplicate the search effort that MO-CBS needs to make. To address this issue, we propose two new splitting strategies for MO-CBS, namely cost splitting and disjoint cost splitting. Our theoretical results show that, when combined with either of these two new splitting strategies, MO-CBS maintains its completeness and optimality guarantees. Our experimental results show that disjoint cost splitting, our best splitting strategy, speeds up MO-CBS by up to two orders of magnitude and substantially improves its success rates in various settings.	['Sven Koenig', 'Jiaoyang Li', 'Han Zhang', 'Cheng Ge']	2022-11-23	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[-1.31436259e-01 -1.05320282e-01 -3.85056674e-01 2.65403628e-01 -6.37950122e-01 -7.93381631e-01 4.33676168e-02 4.55531806e-01 -4.13879812e-01 1.03706491e+00 -3.87525350e-01 -3.68631124e-01 -8.45092475e-01 -8.62906992e-01 -3.13622802e-01 -6.37552619e-01 -7.06997335e-01 8.80289614e-01 8.24800432e-01 -3.91065031e-01 5.70206881e-01 5.37690282e-01 -1.03611863e+00 -2.58389264e-01 8.01319659e-01 6.50790691e-01 2.73805767e-01 7.47751713e-01 -5.87473363e-02 4.17079508e-01 -6.72361314e-01 -1.21211939e-01 4.98687744e-01 -3.39000911e-01 -1.31017232e+00 -1.54481769e-01 -5.39911747e-01 -2.56456524e-01 -2.00050607e-01 7.88581192e-01 1.48573026e-01 4.00296986e-01 3.50737512e-01 -2.35144639e+00 -1.12255812e-02 6.31056070e-01 -7.77069211e-01 3.73050064e-01 4.13203627e-01 -1.39440551e-01 8.80327463e-01 -2.39528373e-01 8.41556311e-01 1.25463784e+00 4.34881657e-01 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-2.52222985e-01 -3.41342121e-01 -4.20371771e-01 3.24070692e-01 -9.91816640e-01 -2.93514580e-01 -7.48099148e-01]	[4.980977535247803, 1.8696259260177612]
f4cc5d0e-5388-4160-961a-e94574acbe53	deep-boosting-for-image-denoising	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.pdf	Deep Boosting for Image Denoising	Boosting is a classic algorithm which has been successfully applied to diverse computer vision tasks. In the scenario of image denoising, however, the existing boosting algorithms are surpassed by the emerging learning-based models. In this paper, we propose a novel deep boosting framework (DBF) for denoising, which integrates several convolutional networks in a feed-forward fashion. Along with the integrated networks, however, the depth of the boosting framework is substantially increased, which brings difficulty to training. To solve this problem, we introduce the concept of dense connection that overcomes the vanishing of gradients during training. Furthermore, we propose a path-widening fusion scheme cooperated with the dilated convolution to derive a lightweight yet efficient convolutional network as the boosting unit, named Dilated Dense Fusion Network (DDFN). Comprehensive experiments demonstrate that our DBF outperforms existing methods on widely used benchmarks, in terms of different denoising tasks.	['Chang Chen', 'Xinmei Tian', 'Feng Wu', 'Zhiwei Xiong']	2018-09-01	null	null	null	eccv-2018-9	['salt-and-pepper-noise-removal']	['computer-vision']	[-6.65538991e-03 -5.22789657e-01 2.54960507e-01 -5.93805790e-01 -3.89021397e-01 3.20176631e-02 6.74385548e-01 -1.68473888e-02 -5.17270863e-01 5.97217858e-01 2.24730462e-01 -2.71247387e-01 9.99496654e-02 -8.91713500e-01 -7.42863715e-01 -1.03076255e+00 3.24828506e-01 -5.10278583e-01 1.39143080e-01 -6.58561230e-01 7.57315755e-02 2.93551654e-01 -1.28807247e+00 2.15998828e-01 1.15622389e+00 1.21577430e+00 1.35803223e-01 1.77497044e-01 -2.04211220e-01 8.87394249e-01 -4.02392805e-01 -7.19257951e-01 2.54616916e-01 -2.39617407e-01 -3.26673508e-01 -1.07364193e-01 2.16811314e-01 -6.08783185e-01 -3.21560681e-01 1.08119965e+00 6.14406168e-01 5.85215725e-02 1.92356676e-01 -1.05674422e+00 -4.38802004e-01 3.75474036e-01 -8.67241979e-01 2.39449516e-01 -1.41377926e-01 2.25821193e-02 6.03062749e-01 -1.01382244e+00 1.92509100e-01 1.28414106e+00 1.05672479e+00 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852ebe96-ccb0-4f27-878f-049d8c8450b8	protnn-fast-and-accurate-nearest-neighbor	1511.00736	null	http://arxiv.org/abs/1511.00736v2	http://arxiv.org/pdf/1511.00736v2.pdf	ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space	Studying the function of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the determination of the function of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the determination of protein functions in living cells. In this paper, we propose ProtNN, a novel approach for protein function prediction. Given an unannotated protein structure and a set of annotated proteins, ProtNN finds the nearest neighbor annotated structures based on protein-graph pairwise similarities. Given a query protein, ProtNN finds the nearest neighbor reference proteins based on a graph representation model and a pairwise similarity between vector embedding of both query and reference protein-graphs in structural and topological spaces. ProtNN assigns to the query protein the function with the highest number of votes across the set of k nearest neighbor reference proteins, where k is a user-defined parameter. Experimental evaluation demonstrates that ProtNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that ProtNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude of runtime compared to state-of-the-art approaches.	['Abdoulaye Baniré Diallo', 'Wajdi Dhifli']	2015-11-02	null	null	null	null	['protein-function-prediction']	['medical']	[ 1.77068591e-01 -1.88789606e-01 -3.28672044e-02 -3.07590783e-01 -4.81416285e-01 -7.26057589e-01 1.34193778e-01 7.79004276e-01 -3.51910412e-01 8.36501896e-01 -1.13813832e-01 -3.16641837e-01 -3.23721170e-01 -6.73501670e-01 -7.39105284e-01 -8.73676896e-01 -1.26597166e-01 9.39458549e-01 5.97259879e-01 -4.97500338e-02 3.71299446e-01 7.90974975e-01 -1.22558057e+00 2.64146835e-01 5.71980953e-01 7.09395826e-01 4.83373910e-01 5.61312616e-01 -1.08300008e-01 3.95111442e-01 -3.51377785e-01 -2.17894018e-01 8.89213830e-02 -5.49772859e-01 -1.03244019e+00 -1.50837123e-01 1.54647768e-01 4.65240330e-01 -1.81424171e-01 8.99930060e-01 5.23081899e-01 6.97722584e-02 5.42987645e-01 -8.48713577e-01 -5.33483207e-01 -1.20549507e-01 -2.48365089e-01 4.71872017e-02 4.55436587e-01 4.67721298e-02 1.25270975e+00 -9.33931768e-01 1.16613209e+00 1.07052171e+00 5.13734996e-01 1.85797513e-01 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e293f01f-5e7c-46ef-b89c-dfa837715b20	causal-aware-safe-policy-improvement-for-task	2103.0637	null	https://arxiv.org/abs/2103.06370v1	https://arxiv.org/pdf/2103.06370v1.pdf	Causal-aware Safe Policy Improvement for Task-oriented dialogue	The recent success of reinforcement learning's (RL) in solving complex tasks is most often attributed to its capacity to explore and exploit an environment where it has been trained. Sample efficiency is usually not an issue since cheap simulators are available to sample data on-policy. On the other hand, task oriented dialogues are usually learnt from offline data collected using human demonstrations. Collecting diverse demonstrations and annotating them is expensive. Unfortunately, use of RL methods trained on off-policy data are prone to issues of bias and generalization, which are further exacerbated by stochasticity in human response and non-markovian belief state of a dialogue management system. To this end, we propose a batch RL framework for task oriented dialogue policy learning: causal aware safe policy improvement (CASPI). This method gives guarantees on dialogue policy's performance and also learns to shape rewards according to intentions behind human responses, rather than just mimicking demonstration data; this couple with batch-RL helps overall with sample efficiency of the framework. We demonstrate the effectiveness of this framework on a dialogue-context-to-text Generation and end-to-end dialogue task of the Multiwoz2.0 dataset. The proposed method outperforms the current state of the art on these metrics, in both case. In the end-to-end case, our method trained only on 10\% of the data was able to out perform current state in three out of four evaluation metrics.	['Caiming Xiong', 'Kazuma Hashimoto', 'Govardana Sachithanandam Ramachandran']	2021-03-10	null	null	null	null	['dialogue-management']	['natural-language-processing']	[ 8.36096630e-02 5.91338813e-01 -1.81732357e-01 -3.52868378e-01 -9.03366506e-01 -5.91220081e-01 1.00867248e+00 -6.30722344e-02 -6.93507612e-01 1.33390808e+00 2.96313494e-01 -2.46578142e-01 3.51727419e-02 -3.57876569e-01 -5.69721878e-01 -4.50760424e-01 -1.53238848e-01 1.01701188e+00 9.69798341e-02 -6.31519198e-01 2.84671992e-01 1.61397994e-01 -1.27454472e+00 2.54970104e-01 9.76072311e-01 6.54747546e-01 5.24562120e-01 1.17021704e+00 -8.81247073e-02 1.14891803e+00 -8.62862468e-01 -1.02801703e-01 1.35074884e-01 -5.94959080e-01 -1.09051406e+00 2.53870152e-02 1.42329605e-02 -7.03829944e-01 -2.22727269e-01 6.74498022e-01 6.76530957e-01 4.45672870e-01 5.24795711e-01 -1.11366904e+00 6.72211871e-02 7.59758651e-01 -8.28047246e-02 -6.49306029e-02 6.04052424e-01 8.89951110e-01 9.57193553e-01 -5.02793431e-01 8.05229068e-01 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6aba92cf-15ad-4fb7-84a1-4b2ef4bb6269	a-universally-deployable-asr-frontend-for	2209.0641	null	https://arxiv.org/abs/2209.06410v1	https://arxiv.org/pdf/2209.06410v1.pdf	A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation	Recent work has shown that it is possible to train a single model to perform joint acoustic echo cancellation (AEC), speech enhancement, and voice separation, thereby serving as a unified frontend for robust automatic speech recognition (ASR). The joint model uses contextual information, such as a reference of the playback audio, noise context, and speaker embedding. In this work, we propose a number of novel improvements to such a model. First, we improve the architecture of the Cross-Attention Conformer that is used to ingest noise context into the model. Second, we generalize the model to be able to handle varying lengths of noise context. Third, we propose Signal Dropout, a novel strategy that models missing contextual information. In the absence of one or more signals, the proposed model performs nearly as well as task-specific models trained without these signals; and when such signals are present, our system compares well against systems that require all context signals. Over the baseline, the final model retains a relative word error rate reduction of 25.0% on background speech when speaker embedding is absent, and 61.2% on AEC when device playback is absent.	['Quan Wang', 'Arun Narayanan', "Tom O'Malley"]	2022-09-14	null	null	null	null	['acoustic-echo-cancellation', 'acoustic-echo-cancellation']	['medical', 'speech']	[ 4.99762088e-01 -6.50776103e-02 3.95253330e-01 -3.24568152e-01 -1.31894052e+00 -3.97762179e-01 4.20264691e-01 -6.71710074e-02 -6.61982000e-01 3.11897725e-01 5.09656370e-01 -5.25997698e-01 2.88706899e-01 -2.51012370e-02 -8.35303962e-01 -6.40056431e-01 2.21125588e-01 -2.17981204e-01 3.91156077e-01 -3.61356527e-01 -1.40475512e-01 5.46589673e-01 -1.56647241e+00 3.48304749e-01 6.60955667e-01 8.59301925e-01 4.44434196e-01 1.12665427e+00 1.10888980e-01 5.23641288e-01 -9.99884486e-01 -6.84640631e-02 1.22896349e-02 -2.78777778e-01 -3.88937801e-01 -8.50535110e-02 5.68136394e-01 -3.95964444e-01 -5.71064830e-01 8.01574171e-01 9.30795193e-01 2.08513632e-01 2.59481281e-01 -6.86350942e-01 -3.52003634e-01 5.72575331e-01 -1.25502214e-01 2.02995270e-01 2.06203714e-01 2.02885777e-01 7.73413420e-01 -1.08675528e+00 1.66553304e-01 1.21317506e+00 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73f811b4-206c-42e9-8fcf-1655f911ca28	augmenting-robot-knowledge-consultants-with	1811.10229	null	http://arxiv.org/abs/1811.10229v1	http://arxiv.org/pdf/1811.10229v1.pdf	Augmenting Robot Knowledge Consultants with Distributed Short Term Memory	Human-robot communication in situated environments involves a complex interplay between knowledge representations across a wide variety of modalities. Crucially, linguistic information must be associated with representations of objects, locations, people, and goals, which may be represented in very different ways. In previous work, we developed a Consultant Framework that facilitates modality-agnostic access to information distributed across a set of heterogeneously represented knowledge sources. In this work, we draw inspiration from cognitive science to augment these distributed knowledge sources with Short Term Memory Buffers to create an STM-augmented algorithm for referring expression generation. We then discuss the potential performance benefits of this approach and insights from cognitive science that may inform future refinements in the design of our approach.	['Matthias Scheutz', 'Bradley Oosterveld', 'Evan Krause', 'Ravenna Thielstrom', 'Tom Williams']	2018-11-26	null	null	null	null	['referring-expression-generation']	['computer-vision']	[ 2.99185038e-01 2.90099651e-01 1.99955016e-01 -3.86674285e-01 -6.71177924e-01 -6.84720755e-01 8.70033860e-01 4.58763331e-01 -3.21175307e-01 7.59469569e-01 1.04489517e+00 -1.59969941e-01 -2.94339687e-01 -1.02614522e+00 -3.48834068e-01 -1.01169147e-01 2.34397538e-02 3.63052249e-01 2.52945453e-01 -5.71270585e-01 5.59795499e-01 5.01685441e-01 -1.78998291e+00 5.28034627e-01 2.87358403e-01 6.31467521e-01 4.07923043e-01 4.34029430e-01 -4.23155606e-01 1.34770489e+00 -6.83373809e-01 -2.07037836e-01 -3.72745931e-01 -4.20177490e-01 -1.29644430e+00 -1.24522328e-01 -1.53340504e-01 5.24811866e-03 -4.20870781e-02 7.01301813e-01 3.88766021e-01 6.61539197e-01 6.18783832e-01 -8.94339561e-01 -1.02154326e+00 7.17041552e-01 5.34669608e-02 2.51565635e-01 1.01043046e+00 3.14056613e-02 6.55894637e-01 -8.80744100e-01 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83c6bf87-8254-4992-89fe-accb30b89e8b	autoexp-a-multidisciplinary-multi-sensor	2306.03115	null	https://arxiv.org/abs/2306.03115v1	https://arxiv.org/pdf/2306.03115v1.pdf	AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars	The adoption of self-driving cars will certainly revolutionize our lives, even though they may take more time to become fully autonomous than initially predicted. The first vehicles are already present in certain cities of the world, as part of experimental robot-taxi services. However, most existing studies focus on the navigation part of such vehicles. We currently miss methods, datasets, and studies to assess the in-cabin human component of the adoption of such technology in real-world conditions. This paper proposes an experimental framework to study the activities of occupants of self-driving cars using a multidisciplinary approach (computer vision associated with human and social sciences), particularly non-driving related activities. The framework is composed of an experimentation scenario, and a data acquisition module. We seek firstly to capture real-world data about the usage of the vehicle in the nearest possible, real-world conditions, and secondly to create a dataset containing in-cabin human activities to foster the development and evaluation of computer vision algorithms. The acquisition module records multiple views of the front seats of the vehicle (Intel RGB-D and GoPro cameras); in addition to survey data about the internal states and attitudes of participants towards this type of vehicle before, during, and after the experimentation. We evaluated the proposed framework with the realization of real-world experimentation with 30 participants (1 hour each) to study the acceptance of SDCs of SAE level 4.	['Laure Tougne Rodet', 'Stephanie Souche-Le Corvec', 'Florent Laroche', 'Christophe Jallais', 'Romain Guesdon', 'Carlos Crispim-Junior']	2023-06-05	null	null	null	null	['self-driving-cars']	['computer-vision']	[-3.41136813e-01 1.07849903e-01 -2.92851534e-02 -4.74283636e-01 6.90972954e-02 -2.21074969e-01 7.42952585e-01 -2.14878842e-01 -6.49125695e-01 3.72205198e-01 -3.27007979e-01 -6.22005761e-01 6.08170107e-02 -9.12030339e-01 -7.23121345e-01 -4.70258296e-01 1.65792197e-01 6.05226696e-01 4.11885858e-01 -5.76715767e-01 1.45348325e-01 7.88076639e-01 -2.32390976e+00 -1.23102337e-01 6.46189153e-01 6.82720244e-01 4.05412763e-01 4.70389247e-01 2.33202294e-01 4.34829354e-01 -1.30105197e-01 -9.71325487e-02 3.86086941e-01 3.27140480e-01 -2.84536421e-01 3.83572161e-01 2.01908767e-01 -3.58987987e-01 -2.91649252e-01 9.19309199e-01 1.17692433e-01 1.38191283e-01 4.19514894e-01 -1.99637818e+00 1.24716789e-01 -2.47032881e-01 -1.18421741e-01 8.14079642e-02 4.13782716e-01 7.44497597e-01 1.46227062e-01 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b7929047-6f79-41e3-9635-874161211923	dialog2api-task-oriented-dialogue-with-api	2212.09946	null	https://arxiv.org/abs/2212.09946v1	https://arxiv.org/pdf/2212.09946v1.pdf	Dialog2API: Task-Oriented Dialogue with API Description and Example Programs	Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.	['Dan Roth', 'Yi Zhang', 'Saab Mansour', 'Arshit Gupta', 'Salvatore Romeo', 'Nikolaos Pappas', 'Tamer Alkhouli', 'Elman Mansimov', 'Raphael Shu']	2022-12-20	null	null	null	null	['semantic-parsing', 'task-oriented-dialogue-systems']	['natural-language-processing', 'natural-language-processing']	[ 1.41151920e-01 8.44686329e-01 3.40914540e-02 -7.67022848e-01 -6.03913665e-01 -1.05866146e+00 9.81028378e-01 -6.74973428e-02 -1.67413607e-01 5.51163137e-01 5.43037415e-01 -4.18667734e-01 8.80036652e-02 -8.58211279e-01 -9.31824967e-02 -6.62063658e-02 3.38805318e-01 9.06983793e-01 4.89337295e-01 -9.53334093e-01 1.28874525e-01 -5.74394949e-02 -1.51944995e+00 9.95740533e-01 8.67141664e-01 7.33870029e-01 4.49259847e-01 9.93973553e-01 -1.03309035e+00 1.16975689e+00 -9.02234674e-01 -3.80586594e-01 -5.05814441e-02 -4.24497098e-01 -1.48896182e+00 6.41452745e-02 -1.78648308e-01 -4.04140621e-01 3.87967736e-01 7.18567908e-01 3.79945993e-01 2.11615086e-01 7.63828829e-02 -1.54881585e+00 -5.10140788e-03 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41f9c8cb-7bf4-4172-9064-4a9592490f45	hate-a-little-less-love-a-little-more	null	null	https://openreview.net/forum?id=KSvkXL6bRU7	https://openreview.net/pdf?id=KSvkXL6bRU7	Hate a Little Less, Love a Little More! Proactively Curbing Online Hatred via Hate Speech Normalization	Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible due to several political, geographical, and cultural reasons. To reduce the severity of the problem, in this paper, we introduce a novel task, hate speech normalization – weakening the intensity of hatred exhibited by an online post by paraphrasing the original content. The intention of hate speech normalization is to not support hate but instead, provide the users with a stepping stone towards non-hate while giving online platforms more time to monitor any improvement in the user’s behaviour. To this end, we manually curated a parallel corpus of hate texts and their normalized counterparts (a normalized text is less hateful and more benign). We then introduce NACL, a Neural hAte speeCh normaLizer that operates in three stages – first, it measures the hate intensity of the original sample; second, it identifies the harmful span(s) within it; and finally, it reduces hate intensity by paraphrasing the hate spans. We perform extensive experiments to measure the efficacy of individual components and the overall performance of NACL via three-way evaluation (intrinsic, extrinsic, and human-study). We observe that NACL outperforms its respective baselines – NACL yields a score of 0.683 Pearson correlation for the intensity prediction, 0.6911 F1-score in the span identification, and 67.71 BLEU and 75.83 perplexity for the normalized text generation. We further show the generalizability of NACL across other platforms (Reddit, Facebook, Gab). A scalable prototype of NACL was also deployed for the user study.	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9102d190-bcb3-4099-a321-d350955911f4	sent2span-span-detection-for-pico-extraction	2109.02254	null	https://arxiv.org/abs/2109.02254v1	https://arxiv.org/pdf/2109.02254v1.pdf	Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations	The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening - the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in human-machine pipeline for turning low-quality crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews.	['Adam G. Dunn', 'Florence T. Bourgeois', 'Wei Wang', 'Bing Li', 'Yifang Sun', 'Shifeng Liu']	2021-09-06	null	https://aclanthology.org/2021.findings-emnlp.147	https://aclanthology.org/2021.findings-emnlp.147.pdf	findings-emnlp-2021-11	['pico']	['natural-language-processing']	[ 2.89363682e-01 2.86813408e-01 -6.63810909e-01 -3.05391252e-01 -1.39292228e+00 -6.29827440e-01 3.01579654e-01 1.07080829e+00 -7.56057620e-01 8.52522969e-01 4.65822428e-01 -7.43516505e-01 -4.54680622e-02 -4.42172587e-01 -5.61838925e-01 -1.95655301e-01 3.11283946e-01 4.20069665e-01 2.72218287e-01 2.56434321e-01 4.45359379e-01 3.23294342e-01 -1.13004160e+00 6.12599492e-01 9.38594937e-01 4.73546118e-01 3.46646070e-01 5.73408782e-01 -2.22338453e-01 6.52303517e-01 -8.73630404e-01 -3.95930231e-01 -2.75162049e-02 -5.53619564e-01 -6.90569878e-01 -2.21982479e-01 4.90355901e-02 2.12878617e-03 4.35961694e-01 9.59752679e-01 8.77010584e-01 -4.71060067e-01 4.77123797e-01 -7.19754159e-01 -6.21753454e-01 8.52343321e-01 -6.08430028e-01 1.84401497e-01 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0504b42d-3c66-41f2-baf1-1ac078965827	190807888	1908.07888	null	https://arxiv.org/abs/1908.07888v1	https://arxiv.org/pdf/1908.07888v1.pdf	Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition	In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework. Intent recognition is a powerful technique for dialog flow management in turn-oriented, human-machine dialogs. This technique can also be very useful in the context of human-human dialogs, though it serves a different purpose of key insight extraction from conversations. We argue that currently available intent recognition techniques are not applicable to human-human dialogs due to the complex structure of turn-taking and various disfluencies encountered in spontaneous conversations, exacerbated by speech recognition errors and scarcity of domain-specific labeled data. Without efficient key insight extraction techniques, raw human-human dialog transcripts remain significantly unexploited. Our contribution consists of a novel FST for intent indexing and an algorithm for fuzzy intent search over the lattice - a compact graph encoding of ASR's hypotheses. We also develop a pruning strategy to constrain the fuzziness of the FST index search. Extracted intents represent linguistic domain knowledge and help us improve (rescore) the original transcript. We compare our method with a baseline, which uses only the most likely transcript hypothesis (best path), and find an increase in the total number of recognized intents by 25%.	['Łukasz Augustyniak', 'Piotr Szymański', 'Mikołaj Morzy', 'Piotr Żelasko', 'Yishay Carmiel', 'Jan Mizgajski', 'Adrian Szymczak']	2019-08-21	null	null	null	null	['intent-recognition']	['natural-language-processing']	[ 5.55569351e-01 6.23585045e-01 -1.26714140e-01 -5.14249802e-01 -7.82321274e-01 -8.40885460e-01 6.04904890e-01 8.63890126e-02 -1.93827271e-01 7.55180180e-01 7.78032422e-01 -8.23114634e-01 7.56835788e-02 -2.80828983e-01 1.72203988e-01 -4.44431342e-02 1.22253977e-01 9.12349105e-01 3.21317941e-01 -9.46169138e-01 5.33360660e-01 4.80482757e-01 -1.19724905e+00 4.49274987e-01 7.64709413e-01 5.42002857e-01 2.08742291e-01 1.01393783e+00 -6.69914961e-01 1.33069253e+00 -9.60210264e-01 -4.33900476e-01 -9.08889398e-02 -6.32509351e-01 -1.55808270e+00 3.07598084e-01 7.75851607e-02 -1.12585187e-01 -4.78075773e-01 9.58942413e-01 7.83397406e-02 5.18570244e-01 5.30064881e-01 -1.12699640e+00 4.05236036e-02 8.50606620e-01 2.00225547e-01 4.56610322e-01 1.10104108e+00 1.19421475e-01 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0c10e6b3-1cda-474b-b1fd-b79892445759	tdeer-an-efficient-translating-decoding	null	null	https://aclanthology.org/2021.emnlp-main.635	https://aclanthology.org/2021.emnlp-main.635.pdf	TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations	Joint extraction of entities and relations from unstructured texts to form factual triples is a fundamental task of constructing a Knowledge Base (KB). A common method is to decode triples by predicting entity pairs to obtain the corresponding relation. However, it is still challenging to handle this task efficiently, especially for the overlapping triple problem. To address such a problem, this paper proposes a novel efficient entities and relations extraction model called TDEER, which stands for Translating Decoding Schema for Joint Extraction of Entities and Relations. Unlike the common approaches, the proposed translating decoding schema regards the relation as a translating operation from subject to objects, i.e., TDEER decodes triples as subject + relation \rightarrow objects. TDEER can naturally handle the overlapping triple problem, because the translating decoding schema can recognize all possible triples, including overlapping and non-overlapping triples. To enhance model robustness, we introduce negative samples to alleviate error accumulation at different stages. Extensive experiments on public datasets demonstrate that TDEER produces competitive results compared with the state-of-the-art (SOTA) baselines. Furthermore, the computation complexity analysis indicates that TDEER is more efficient than powerful baselines. Especially, the proposed TDEER is 2 times faster than the recent SOTA models. The code is available at https://github.com/4AI/TDEER.	['Zhen He', 'Beidi Luan', 'Daichuan Yang', 'Chenghao Dong', 'Xiaotian Luo', 'Xianming Li']	null	null	null	null	emnlp-2021-11	['joint-entity-and-relation-extraction']	['natural-language-processing']	[-1.12133317e-01 4.87190396e-01 -5.00657141e-01 -2.44381130e-01 -9.82349455e-01 -5.22975802e-01 4.23506677e-01 8.90851170e-02 -1.04077205e-01 9.11608279e-01 3.01177531e-01 -4.49393600e-01 2.14179918e-01 -1.06727278e+00 -9.88747895e-01 -3.30771267e-01 3.91256034e-01 8.03276598e-01 2.37765789e-01 -3.92357558e-01 -2.68452078e-01 -1.75754964e-01 -1.18793190e+00 5.58326066e-01 1.38636506e+00 8.33609641e-01 -2.75186542e-02 1.11151896e-01 -4.61177588e-01 7.40755856e-01 -2.93248892e-01 -1.21275187e+00 9.89110619e-02 -2.30362207e-01 -1.00570893e+00 -3.29922646e-01 1.35786816e-01 -1.82265282e-01 -2.60933787e-01 1.04166889e+00 2.05392823e-01 -3.07633162e-01 5.66090226e-01 -1.38211656e+00 -9.54732478e-01 1.29103291e+00 -6.06220722e-01 -9.94438529e-02 6.13845587e-01 -5.95158577e-01 1.45957184e+00 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57a6271c-8c34-4d09-b64a-0b2438b53c7e	parallel-data-augmentation-for-formality	2005.07522	null	https://arxiv.org/abs/2005.07522v1	https://arxiv.org/pdf/2005.07522v1.pdf	Parallel Data Augmentation for Formality Style Transfer	The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.	['Xu sun', 'Tao Ge', 'Yi Zhang']	2020-05-14	parallel-data-augmentation-for-formality-1	https://aclanthology.org/2020.acl-main.294	https://aclanthology.org/2020.acl-main.294.pdf	acl-2020-6	['formality-style-transfer']	['natural-language-processing']	[ 4.16039646e-01 3.11857730e-01 -9.00361910e-02 -5.38941562e-01 -7.68842638e-01 -5.19085050e-01 7.58272350e-01 -2.38280203e-02 -7.76978910e-01 1.10887408e+00 2.97233403e-01 -5.22067130e-01 3.32193404e-01 -5.10394394e-01 -6.75654292e-01 -1.41568735e-01 2.27432892e-01 7.48220682e-01 -3.35897096e-02 -1.02304125e+00 2.87228435e-01 1.38413757e-01 -1.20367408e+00 6.22973740e-01 1.26261926e+00 4.61702406e-01 2.29078367e-01 7.00331509e-01 -2.52852738e-01 5.30393064e-01 -8.42481196e-01 -8.16959143e-01 2.27124825e-01 -7.09277987e-01 -1.39151669e+00 -4.67785358e-01 6.09639108e-01 -3.12004089e-01 -1.18383504e-01 7.30778515e-01 3.58980536e-01 3.72145362e-02 6.05914474e-01 -1.01707900e+00 -9.85500097e-01 8.58141661e-01 -2.45435014e-01 3.08496952e-01 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b305b605-78d2-4a60-a362-500e0b1762c3	a-faithful-deep-sensitivity-estimation-for	2210.12723	null	https://arxiv.org/abs/2210.12723v1	https://arxiv.org/pdf/2210.12723v1.pdf	A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging	Recent deep learning is superior in providing high-quality images and ultra-fast reconstructions in accelerated magnetic resonance imaging (MRI). Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps, leading to greatly improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the accelerated factor is high. Additionally, JDSI owns nice robustness to abnormal subjects and different number of autocalibration signals.	['Xiaobo Qu', 'Di Guo', 'Jianzhong Lin', 'Wenping Wei', 'Jianjun Zhou', 'Liuhong Zhu', 'Lijun Bao', 'Boxuan Shi', 'Chen Qian', 'Haoming Fang', 'Zi Wang']	2022-10-23	null	null	null	null	['mri-reconstruction']	['computer-vision']	[ 8.72251857e-03 -1.09003089e-01 1.51381284e-01 -3.80511761e-01 -5.89146972e-01 -1.19336203e-01 9.35586244e-02 -1.34550080e-01 -3.67265463e-01 6.97736740e-01 2.10867018e-01 -9.53967571e-02 -4.61729616e-01 -2.93164611e-01 -7.42179930e-01 -9.53405023e-01 -4.59026754e-01 1.21277705e-01 3.51497591e-01 -1.74335361e-01 -1.54070795e-01 4.71035570e-01 -7.73434103e-01 -1.87414065e-01 9.08850670e-01 8.24818432e-01 4.00123864e-01 1.73611313e-01 4.45841998e-01 9.66829896e-01 -3.75149637e-01 -1.44130528e-01 3.79802436e-02 -2.16141015e-01 -7.62213707e-01 -3.72853190e-01 -7.05420375e-02 -7.36296654e-01 -6.29664779e-01 1.26359701e+00 9.88202572e-01 -1.70066327e-01 5.00698209e-01 -7.43844151e-01 -3.48355263e-01 1.02558064e+00 -9.00341213e-01 6.82986021e-01 -4.48694862e-02 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489746af-147b-4642-b361-29d283f3ba51	multiwave-multiresolution-deep-architectures	2306.10164	null	https://arxiv.org/abs/2306.10164v1	https://arxiv.org/pdf/2306.10164v1.pdf	MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction	The analysis of multivariate time series data is challenging due to the various frequencies of signal changes that can occur over both short and long terms. Furthermore, standard deep learning models are often unsuitable for such datasets, as signals are typically sampled at different rates. To address these issues, we introduce MultiWave, a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals. MultiWave uses wavelets to decompose each signal into subsignals of varying frequencies and groups them into frequency bands. Each frequency band is handled by a different component of our model. A gating mechanism combines the output of the components to produce sparse models that use only specific signals at specific frequencies. Our experiments demonstrate that MultiWave accurately identifies informative frequency bands and improves the performance of various deep learning models, including LSTM, Transformer, and CNN-based models, for a wide range of applications. It attains top performance in stress and affect detection from wearables. It also increases the AUC of the best-performing model by 5% for in-hospital COVID-19 mortality prediction from patient blood samples and for human activity recognition from accelerometer and gyroscope data. We show that MultiWave consistently identifies critical features and their frequency components, thus providing valuable insights into the applications studied.	['Madalina Fiterau', 'Iman Deznabi']	2023-06-16	null	null	null	null	['activity-recognition', 'human-activity-recognition', 'mortality-prediction', 'human-activity-recognition', 'time-series-prediction']	['computer-vision', 'computer-vision', 'medical', 'time-series', 'time-series']	[-3.62318754e-02 -4.48405892e-01 -3.28654557e-01 -2.64141828e-01 -7.63438046e-01 -2.84763813e-01 -2.30128076e-02 4.81636792e-01 -1.73355386e-01 5.35260737e-01 5.83769739e-01 -1.74306761e-02 -6.46027923e-02 -7.16729522e-01 -5.95173776e-01 -6.06325746e-01 -6.58392310e-01 -1.16700873e-01 -2.39589438e-01 -8.95514414e-02 -2.64303237e-01 3.03427368e-01 -1.21456766e+00 5.82080007e-01 3.92637938e-01 1.20745409e+00 -4.36968118e-01 5.55697978e-01 3.47668290e-01 5.79492569e-01 -9.46059167e-01 8.31147581e-02 -2.30212733e-01 -3.05637240e-01 -1.95129484e-01 -4.24065948e-01 5.42058237e-02 -2.52476692e-01 -2.30166033e-01 5.63423693e-01 8.41261208e-01 -1.73588505e-03 3.06379378e-01 -1.00807941e+00 -9.34512615e-02 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07400cf0-57bf-4b7a-bc0f-7c0fc28cc04c	ttan-two-stage-temporal-alignment-network-for	2107.04782	null	https://arxiv.org/abs/2107.04782v4	https://arxiv.org/pdf/2107.04782v4.pdf	TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition	Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects -- action duration misalignment and action evolution misalignment. We address them sequentially through a Two-stage Action Alignment Network (TA2N). The first stage locates the action by learning a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature (e.g. background). Next, the second stage coordinates query feature to match the spatial-temporal action evolution of support by performing temporally rearrange and spatially offset prediction. Extensive experiments on benchmark datasets show the potential of the proposed method in achieving state-of-the-art performance for few-shot action recognition.The code of this project can be found at https://github.com/R00Kie-Liu/TA2N	['Weiyao Lin', 'Xiaoyuan Yu', 'Mengjuan Fei', 'John See', 'Yuxi Li', 'Rui Qian', 'Huabin Liu', 'Shuyuan Li']	2021-07-10	null	null	null	null	['few-shot-action-recognition']	['computer-vision']	[ 5.45713484e-01 -4.48545694e-01 -4.21778172e-01 -4.50517446e-01 -8.25818658e-01 -4.44470853e-01 6.33653760e-01 -1.71107620e-01 -3.46674740e-01 4.47006702e-01 3.10878307e-01 2.49699101e-01 -1.07569635e-01 -3.06814939e-01 -5.06146550e-01 -8.48205328e-01 -1.01863153e-01 -3.01795099e-02 6.26958311e-01 1.11760244e-01 6.03228569e-01 4.96379554e-01 -1.58325541e+00 5.21093547e-01 5.37644923e-01 1.08021736e+00 -2.72373110e-02 6.34946883e-01 1.52277321e-01 1.04337406e+00 -5.62977374e-01 -1.20354079e-01 5.42610109e-01 -8.48343134e-01 -5.75172246e-01 9.50918570e-02 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9bed4ad0-a7c6-4095-8d9c-c8f002229957	re2tal-rewiring-pretrained-video-backbones	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.pdf	Re2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization	Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challenge. Most methods can only train on pre-extracted features without optimizing them for the localization problem, consequently limiting localization performance. In this work, to extend the potential in TAL networks, we propose a novel end-to-end method Re2TAL, which rewires pretrained video backbones for reversible TAL. Re2TAL builds a backbone with reversible modules, where the input can be recovered from the output such that the bulky intermediate activations can be cleared from memory during training. Instead of designing one single type of reversible module, we propose a network rewiring mechanism, to transform any module with a residual connection to a reversible module without changing any parameters. This provides two benefits: (1) a large variety of reversible networks are easily obtained from existing and even future model designs, and (2) the reversible models require much less training effort as they reuse the pre-trained parameters of their original non-reversible versions. Re2TAL, only using the RGB modality, reaches 37.01% average mAP on ActivityNet-v1.3, a new state-of-the-art record, and mAP 64.9% at tIoU=0.5 on THUMOS-14, outperforming all other RGB-only methods. Code is available at https://github.com/coolbay/Re2TAL.	['Bernard Ghanem', 'Karttikeya Mangalam', 'Shuming Liu', 'Chen Zhao']	2023-01-01	null	null	null	cvpr-2023-1	['action-localization', 'action-recognition']	['computer-vision', 'computer-vision']	[ 1.69372991e-01 -1.72349159e-02 -4.79947686e-01 -1.58998400e-01 -4.99480695e-01 -6.05791748e-01 3.88494432e-01 -5.45868874e-01 -6.00898504e-01 6.59468234e-01 3.07124883e-01 -3.82255018e-02 2.79311717e-01 -5.90113819e-01 -1.22844326e+00 -5.51624000e-01 -4.50840220e-02 1.32383600e-01 5.68299830e-01 4.80400324e-02 9.67659801e-02 3.48279923e-01 -1.12505853e+00 6.88693762e-01 5.02000988e-01 1.14534724e+00 4.42756385e-01 6.38632417e-01 2.56220251e-01 1.46531308e+00 -2.16405392e-01 -2.13987991e-01 2.72369236e-01 -4.62036192e-01 -7.73669899e-01 -2.65358329e-01 4.64037657e-01 -7.18433619e-01 -9.82766807e-01 5.54275036e-01 4.02276814e-01 1.61595553e-01 2.34799609e-01 -1.07769680e+00 -5.81595838e-01 7.12301552e-01 -3.05300742e-01 2.02617392e-01 7.15365112e-02 5.97825468e-01 9.15017426e-01 -1.01708722e+00 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98d68d95-f49f-4afa-bdd0-e8247c1dd4e3	impact-of-visual-assistance-for-automated	2211.10539	null	https://arxiv.org/abs/2211.10539v2	https://arxiv.org/pdf/2211.10539v2.pdf	Impact of visual assistance for automated audio captioning	We study the impact of visual assistance for automated audio captioning. Utilizing multi-encoder transformer architectures, which have previously been employed to introduce vision-related information in the context of sound event detection, we analyze the usefulness of incorporating a variety of pretrained features. We perform experiments on a YouTube-based audiovisual data set and investigate the effect of applying the considered transfer learning technique in terms of a variety of captioning metrics. We find that only one of the considered kinds of pretrained features provides consistent improvements, while the others do not provide any noteworthy gains at all. Interestingly, the outcomes of prior research efforts indicate that the exact opposite is true in the case of sound event detection, leading us to conclude that the optimal choice of visual embeddings is strongly dependent on the task at hand. More specifically, visual features focusing on semantics appear appropriate in the context of automated audio captioning, while for sound event detection, time information seems to be more important.	['Hugo Van hamme', 'Wim Boes']	2022-11-18	null	null	null	null	['sound-event-detection', 'audio-captioning']	['audio', 'audio']	[ 2.35605955e-01 2.31970288e-02 1.54169887e-01 -2.08515614e-01 -6.89467609e-01 -5.27378023e-01 1.03103042e+00 7.30180085e-01 -8.66333723e-01 4.26238507e-01 6.90521836e-01 -1.02050833e-01 -2.40212932e-01 -5.09001493e-01 -6.26482964e-01 -6.34580433e-01 2.94421017e-02 1.85243994e-01 3.11084360e-01 -2.81765968e-01 3.33822042e-01 3.17371309e-01 -1.83126640e+00 2.03879938e-01 2.62861609e-01 1.05435395e+00 1.87110946e-01 4.42654043e-01 3.63630503e-02 6.06232762e-01 -5.77817440e-01 -4.73139703e-01 -4.79074381e-03 -2.55735874e-01 -7.59487212e-01 -1.40962442e-02 4.41374332e-01 -6.64691329e-02 -3.10697556e-01 6.90399885e-01 6.26341045e-01 7.19823465e-02 6.02017760e-01 -1.19024396e+00 -5.45477986e-01 5.41678190e-01 9.95410047e-03 5.88603675e-01 5.88672042e-01 2.12397695e-01 1.35153568e+00 -7.19233871e-01 5.60621381e-01 1.00202644e+00 5.26588261e-01 7.33536482e-02 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-1.22259989e-01 5.37679017e-01 5.87315917e-01 -8.10129106e-01 -4.51842159e-01 -5.47265112e-02]	[15.180192947387695, 5.005707740783691]
ae15daed-856b-49e3-ab0a-5e1a40c47713	multi-domain-learning-for-accurate-and-few	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Multi-Domain_Learning_for_Accurate_and_Few-Shot_Color_Constancy_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Xiao_Multi-Domain_Learning_for_Accurate_and_Few-Shot_Color_Constancy_CVPR_2020_paper.pdf	Multi-Domain Learning for Accurate and Few-Shot Color Constancy	Color constancy is an important process in camera pipeline to remove the color bias of captured image caused by scene illumination. Recently, significant improvements in color constancy accuracy have been achieved by using deep neural networks (DNNs). However, existing DNNbased color constancy methods learn distinct mappings for different cameras, which require a costly data acquisition process for each camera device. In this paper, we start a pioneer work to introduce multi-domain learning to color constancy area. For different camera devices, we train a branch of networks which share the same feature extractor and illuminant estimator, and only employ a camera-specific channel re-weighting module to adapt to the camera-specific characteristics. Such a multi-domain learning strategy enables us to take benefit from crossdevice training data. The proposed multi-domain learning color constancy method achieved state-of-the-art performance on three commonly used benchmark datasets. Furthermore, we also validate the proposed method in a fewshot color constancy setting. Given a new unseen device with limited number of training samples, our method is capable of delivering accurate color constancy by merely learning the camera-specific parameters from the few-shot dataset. Our project page is publicly available at https://github.com/msxiaojin/MDLCC.	[' Lei Zhang', ' Shuhang Gu', 'Jin Xiao']	2020-06-01	null	null	null	cvpr-2020-6	['color-constancy']	['computer-vision']	[ 9.68774706e-02 -8.04562211e-01 -1.77434444e-01 -4.88870412e-01 -4.77887571e-01 -7.48872995e-01 3.72720391e-01 -3.90766203e-01 -5.01387417e-01 4.83031273e-01 -3.11034411e-01 -8.91576633e-02 3.61755610e-01 -4.50853854e-01 -8.39093089e-01 -7.78411925e-01 5.73245525e-01 -2.14532882e-01 2.50072479e-01 -1.51476651e-01 2.81810999e-01 3.11714649e-01 -1.35815811e+00 6.96887672e-02 9.08631742e-01 1.23494518e+00 3.70449245e-01 9.11891222e-01 -1.62808895e-01 6.93821371e-01 -3.49607646e-01 -3.40354890e-01 6.46010339e-01 -5.78859270e-01 -1.82283238e-01 1.02337182e-01 8.44738722e-01 -6.22121990e-01 -3.46662730e-01 1.28091335e+00 6.76528215e-01 -3.22082005e-02 3.50358069e-01 -1.44560468e+00 -1.13554907e+00 1.10175036e-01 -7.20220566e-01 1.94385514e-01 -2.17112154e-01 4.21396405e-01 7.49123096e-01 -7.30183125e-01 2.22566113e-01 7.05761492e-01 5.79807878e-01 9.96507347e-01 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ba096bb5-2462-4036-9c6c-73e9ebdae712	cbnet-a-plug-and-play-network-for	2212.0234	null	https://arxiv.org/abs/2212.02340v2	https://arxiv.org/pdf/2212.02340v2.pdf	CBNet: A Plug-and-Play Network for Segmentation-based Scene Text Detection	Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion. However, the segmentation process only considers each pixel independently, and the expansion process is difficult to achieve a favorable accuracy-speed trade-off. In this paper, we propose a Context-aware and Boundary-guided Network (CBN) to tackle these problems. In CBN, a basic text detector is firstly used to predict initial segmentation results. Then, we propose a context-aware module to enhance text kernel feature representations, which considers both global and local contexts. Finally, we introduce a boundary-guided module to expand enhanced text kernels adaptively with only the pixels on the contours, which not only obtains accurate text boundaries but also keeps high speed, especially on high-resolution output maps. In particular, with a lightweight backbone, the basic detector equipped with our proposed CBN achieves state-of-the-art results on several popular benchmarks, and our proposed CBN can be plugged into several segmentation-based methods. Code will be available on https://github.com/XiiZhao/cbn.pytorch.	['Jingping Shao', 'Jinghe Hu', 'Zhangang Lin', 'Xin Zhu', 'Jingjing Lv', 'Zheng Zhang', 'Wei Feng', 'Xi Zhao']	2022-12-05	null	null	null	null	['scene-text-detection']	['computer-vision']	[ 1.40469655e-01 -4.60128725e-01 -1.03880875e-01 -2.38176182e-01 -4.33137923e-01 -1.18733704e-01 2.51342058e-01 1.42214894e-01 -5.37240684e-01 1.01535683e-02 -3.97834219e-02 -1.44250467e-01 3.67672443e-01 -9.90426958e-01 -4.07197773e-01 -6.51988864e-01 6.55723870e-01 2.05555409e-01 1.00424063e+00 8.10928717e-02 3.00286889e-01 3.03266376e-01 -1.36017334e+00 1.38662045e-03 1.17080116e+00 1.03375328e+00 4.97061223e-01 4.66409504e-01 -5.35893381e-01 4.85875100e-01 -1.26317799e-01 -4.59917299e-02 1.03139430e-01 -3.15517962e-01 -5.29002547e-01 2.15541229e-01 2.14370072e-01 -5.65340817e-01 -3.79961967e-01 1.28483176e+00 5.43455422e-01 5.78620173e-02 5.06186962e-01 -1.00596404e+00 -4.69954461e-01 7.09032834e-01 -9.27411616e-01 -2.64499802e-02 -9.43633839e-02 2.06642538e-01 8.51605177e-01 -9.59368527e-01 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a1aff1a1-6df5-4979-ad32-7f73a690fe8e	learning-syntactic-and-dynamic-selective	2003.11173	null	https://arxiv.org/abs/2003.11173v1	https://arxiv.org/pdf/2003.11173v1.pdf	Learning Syntactic and Dynamic Selective Encoding for Document Summarization	Text summarization aims to generate a headline or a short summary consisting of the major information of the source text. Recent studies employ the sequence-to-sequence framework to encode the input with a neural network and generate abstractive summary. However, most studies feed the encoder with the semantic word embedding but ignore the syntactic information of the text. Further, although previous studies proposed the selective gate to control the information flow from the encoder to the decoder, it is static during the decoding and cannot differentiate the information based on the decoder states. In this paper, we propose a novel neural architecture for document summarization. Our approach has the following contributions: first, we incorporate syntactic information such as constituency parsing trees into the encoding sequence to learn both the semantic and syntactic information from the document, resulting in more accurate summary; second, we propose a dynamic gate network to select the salient information based on the context of the decoder state, which is essential to document summarization. The proposed model has been evaluated on CNN/Daily Mail summarization datasets and the experimental results show that the proposed approach outperforms baseline approaches.	['Haiyang Xu', 'Xiangang Li', 'Yahao He', 'Kun Han', 'Junwen Chen']	2020-03-25	null	null	null	null	['constituency-parsing']	['natural-language-processing']	[ 5.96389174e-01 2.66205579e-01 -2.48899102e-01 -5.30028880e-01 -7.51780927e-01 -3.47119391e-01 4.47743833e-01 3.14183652e-01 -2.75370628e-01 6.94181979e-01 1.23692822e+00 -4.72748448e-04 4.50190902e-01 -7.65558898e-01 -6.63244724e-01 -3.98975343e-01 5.24613798e-01 1.43820018e-01 2.46561214e-01 -3.73014003e-01 7.75709808e-01 -1.13518469e-01 -1.15470922e+00 7.70058215e-01 1.15194666e+00 7.11912572e-01 7.47809172e-01 8.64050388e-01 -5.87006807e-01 8.64172041e-01 -1.05750191e+00 -1.18899159e-01 -1.84870929e-01 -1.13662255e+00 -8.42711389e-01 -1.30835231e-02 3.13378513e-01 -6.85988486e-01 -5.16544104e-01 1.14571726e+00 8.15097511e-01 1.10458665e-01 5.68449259e-01 -5.40523946e-01 -7.23395526e-01 1.13683653e+00 -3.10743719e-01 4.03107584e-01 3.88798922e-01 -6.63261861e-02 1.18654382e+00 -4.99456197e-01 6.80454373e-01 1.27882850e+00 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448dd757-6537-43f2-b11c-73905f677cb1	prediction-of-prognosis-and-survival-of	null	null	https://doi.org/10.5114/aoms/135594	https://www.archivesofmedicalscience.com/pdf-135594-63895?filename=Prediction%20of%20Prognosis.pdf	Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model	Introduction: It’s very necessary to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random Forest is an excellent machine learning algorithm even without any modification. We propose a new Random Forest weighting method and apply it to the gastric cancer patient data from the Surveillance, Epidemiology, and End Results (SEER) program, and then evaluated the generalization ability of this weighted Random Forest algorithm on 10 public medical datasets. Furthermore, for the same weighting mode, the difference between using out-of-bag (OOB) data and all training sets as the weighting basis is explored. Material and methods: 110697 cases of gastric cancer patients diagnosed between 1975 and 2016 obtained from the SEER database were contained in the experiment. In addition, 10 public medical datasets are used for the generalization ability evaluation of this weighted Random Forest algorithm. Results: Through experimental verification, on the SEER gastric cancer patient data, the weighted Random Forest algorithm improves the accuracy by 0.79% compared with the original Random Forest. In AUC, Macro-averaging increased by 2.32% and Micro-averaging increased by 0.51% on average. Among the 10 public datasets, the Random Forest weighted in accuracy has the best performance on 6 datasets, with an average increase of 1.44% in accuracy and an average increase of 1.2% in AUC. Conclusions: Compared with the original Random Forest, the weighted Random Forest model has a significant improvement in performance, and the effect of using all training data as the weighting basis is better than using OOB data.	['Fan Ye', 'Yue Cao', 'TianLong Zheng', 'Jing Wang', 'Cheng Xu']	2021-04-10	null	null	null	archives-of-medical-science-2021-4	['epidemiology']	['medical']	[ 6.20980971e-02 2.81460192e-02 -9.01125312e-01 -4.76081192e-01 -6.91149354e-01 4.36129458e-02 3.36968243e-01 3.57156277e-01 -6.60255551e-01 1.07304394e+00 3.58366251e-01 -6.81425273e-01 -3.33817780e-01 -1.22587216e+00 -5.36351046e-03 -9.81334567e-01 -3.15251797e-01 5.74645460e-01 1.70613855e-01 7.35649467e-02 -4.63805422e-02 3.02964240e-01 -1.01980829e+00 3.70282710e-01 1.07041180e+00 9.35747445e-01 6.84488267e-02 3.17255557e-01 9.36445817e-02 6.93091154e-01 -3.17684025e-01 -1.16806053e-01 1.21253356e-01 -9.21724141e-02 -8.28818440e-01 -4.80251253e-01 -3.53652626e-01 -2.89291024e-01 -1.13001063e-01 6.08808756e-01 5.42900980e-01 -2.12734252e-01 9.29295838e-01 -1.17955518e+00 -1.02577507e-01 7.52888799e-01 -7.48099208e-01 -9.38252658e-02 3.17526042e-01 -1.09371163e-01 7.27714062e-01 -5.58280110e-01 5.43339550e-01 8.66663814e-01 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06a3ef2e-3c98-4861-af2a-5e32d8525613	overview-and-evaluation-of-sound-event	2009.02792	null	https://arxiv.org/abs/2009.02792v2	https://arxiv.org/pdf/2009.02792v2.pdf	Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019	Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal activity of sounds of interest. This paper presents an overview of the first international evaluation on sound event localization and detection, organized as a task of the DCASE 2019 Challenge. A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset. The overview presents in detail how the systems were evaluated and ranked and the characteristics of the best-performing systems. Common strategies in terms of input features, model architectures, training approaches, exploitation of prior knowledge, and data augmentation are discussed. Since ranking in the challenge was based on individually evaluating localization and event classification performance, part of the overview focuses on presenting metrics for the joint measurement of the two, together with a reevaluation of submissions using these new metrics. The new analysis reveals submissions that performed better on the joint task of detecting the correct type of event close to its original location than some of the submissions that were ranked higher in the challenge. Consequently, ranking of submissions which performed strongly when evaluated separately on detection or localization, but not jointly on both, was affected negatively.	['Tuomas Virtanen', 'Toni Heittola', 'Sharath Adavanne', 'Annamaria Mesaros', 'Archontis Politis']	2020-09-06	null	null	null	null	['sound-event-localization-and-detection']	['audio']	[-2.46420186e-02 -4.69350666e-01 7.10126638e-01 -2.04479843e-01 -1.63162541e+00 -9.48646367e-01 4.88720357e-01 6.82799876e-01 -7.64473498e-01 3.52495372e-01 3.52809608e-01 6.67929649e-03 -3.00861746e-01 -3.02190930e-01 -4.85104322e-01 -6.42963111e-01 -5.01080692e-01 3.05690039e-02 7.82968938e-01 1.78103924e-01 4.03914958e-01 5.02974689e-01 -1.93578827e+00 7.35130429e-01 -4.87629473e-02 9.97692883e-01 3.28770190e-01 1.19143295e+00 1.92621633e-01 7.21827090e-01 -1.17262149e+00 1.32196009e-01 -1.81762755e-01 -4.64928240e-01 -6.86338544e-01 -7.16000438e-01 6.57747447e-01 2.83873826e-01 -8.00345242e-02 6.22083902e-01 1.09915173e+00 4.75917190e-01 3.65876079e-01 -1.12561882e+00 1.49324670e-01 5.87825358e-01 2.71077007e-01 1.16983771e+00 7.27743387e-01 -2.44605809e-01 1.09237731e+00 -1.24859786e+00 3.15307915e-01 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3fbd9332-d9a3-4787-a16e-79c2e7afc9b6	l3das21-challenge-machine-learning-for-3d	2104.05499	null	https://arxiv.org/abs/2104.05499v3	https://arxiv.org/pdf/2104.05499v3.pdf	L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing	The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dual-mic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and SELDNet for SELD. This report is aimed at providing all needed information to participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21 dataset, the challenge tasks and the baseline models.	['Danilo Comminiello', 'Aurelio Uncini', 'Enrico Rocchi', 'Sveva Pepe', 'Marco Pennese', 'Ludovica Paglialunga', 'Leonardo Nucciarelli', 'Giuseppe Nachira', 'Claudia Medaglia', 'Edoardo Massaro', 'Christian Marinoni', 'Saeid Jamili', 'Riccardo F. Gramaccioni', 'Eric Guizzo']	2021-04-12	null	null	null	null	['audio-signal-processing']	['audio']	[-1.74326077e-01 -3.23482394e-01 7.02403486e-01 -7.79503840e-04 -1.37887120e+00 -5.83244920e-01 4.63024974e-01 -9.73306298e-02 -2.89687127e-01 -5.34335561e-02 5.99709392e-01 -9.74242613e-02 -5.23483716e-02 -1.62041172e-01 -7.14275420e-01 -7.07307339e-01 -4.54544544e-01 1.40313581e-01 3.83443050e-02 -5.94732426e-02 -1.08612813e-01 5.19132555e-01 -1.93613231e+00 6.12923265e-01 -1.50492415e-01 1.36267090e+00 1.23858303e-01 1.32517529e+00 2.78143138e-01 2.32950911e-01 -8.41827154e-01 7.72137847e-03 3.41300368e-01 -7.33057931e-02 -3.56726378e-01 -2.60687500e-01 6.86742902e-01 -9.67587680e-02 -2.12597504e-01 5.97041547e-01 1.42854917e+00 1.77445248e-01 4.38148439e-01 -1.15563726e+00 -2.36385912e-01 6.30115688e-01 -2.13895902e-01 2.97106445e-01 7.19119489e-01 7.25550577e-02 1.10680163e+00 -1.40131199e+00 4.29412760e-02 1.28366804e+00 1.07400501e+00 2.14797914e-01 -9.04004037e-01 -5.95027506e-01 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-2.06226632e-01 8.40751469e-01 -6.53113365e-01 -9.12182987e-01 -2.62789637e-01]	[15.03058910369873, 5.6143012046813965]
c9536c10-3bd8-4c1c-9a44-26195bebcbc2	complex-a-new-corpus-for-lexical-complexity-1	null	null	https://aclanthology.org/2020.readi-1.9	https://aclanthology.org/2020.readi-1.9.pdf	CompLex --- A New Corpus for Lexical Complexity Prediction from Likert Scale Data	Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studieshave approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) fora set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using abinary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexityprediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl,and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.	['Michael Cooper', 'Matthew Shardlow', 'Marcos Zampieri']	2020-05-01	null	null	null	lrec-2020-5	['lexical-complexity-prediction', 'complex-word-identification']	['natural-language-processing', 'natural-language-processing']	[ 7.04783946e-02 3.85791928e-01 -4.52540964e-01 -5.56390166e-01 -8.55955362e-01 -7.88102806e-01 7.06873715e-01 9.12710428e-01 -1.12722170e+00 1.04950428e+00 6.56039774e-01 -2.86231399e-01 -1.35804281e-01 -5.34380734e-01 -7.27065578e-02 -1.53810307e-01 4.85799849e-01 7.95490980e-01 -1.68474585e-01 -2.33206391e-01 5.14301181e-01 3.42012048e-01 -1.36788988e+00 2.40018755e-01 1.16380692e+00 5.83622158e-01 2.14351386e-01 6.78785264e-01 -5.46307385e-01 4.57258016e-01 -8.37034881e-01 -7.51665831e-01 -2.44139552e-01 -4.03004378e-01 -1.02937675e+00 -4.22776192e-01 1.89913496e-01 4.54674393e-01 2.15747565e-01 8.88913393e-01 6.70318067e-01 -6.53408021e-02 7.70268083e-01 -7.91241884e-01 -1.70291409e-01 9.36898232e-01 5.29978937e-03 2.76234031e-01 7.25144804e-01 -2.98410624e-01 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dc6d93f4-7fb1-496b-be1a-e60a861af777	feature-compression-for-rate-constrained	2204.07314	null	https://arxiv.org/abs/2204.07314v1	https://arxiv.org/pdf/2204.07314v1.pdf	Feature Compression for Rate Constrained Object Detection on the Edge	Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural networks. An emerging approach to solve this problem is to offload the computation of these neural networks to computing resources at an edge server. Efficient computation offloading requires optimizing the trade-off between multiple objectives including compressed data rate, analytics performance, and computation speed. In this work, we consider a "split computation" system to offload a part of the computation of the YOLO object detection model. We propose a learnable feature compression approach to compress the intermediate YOLO features with light-weight computation. We train the feature compression and decompression module together with the YOLO model to optimize the object detection accuracy under a rate constraint. Compared to baseline methods that apply either standard image compression or learned image compression at the mobile and perform image decompression and YOLO at the edge, the proposed system achieves higher detection accuracy at the low to medium rate range. Furthermore, the proposed system requires substantially lower computation time on the mobile device with CPU only.	['Yao Wang', 'Elza Erkip', 'Siddharth Garg', 'Samyak Rawlekar', 'Zhongzheng Yuan']	2022-04-15	null	null	null	null	['feature-compression']	['computer-vision']	[ 2.64376700e-01 -2.59540915e-01 -3.08389455e-01 -2.14400128e-01 -1.55681387e-01 -9.23775882e-02 -2.57132966e-02 -4.67080288e-02 -6.40272975e-01 -1.15856223e-01 -3.06516975e-01 -1.76297188e-01 1.55482784e-01 -8.20169091e-01 -7.70691037e-01 -5.96812963e-01 2.34076768e-01 3.48620385e-01 3.41638893e-01 2.76163995e-01 -1.12385745e-03 4.01160747e-01 -1.92548740e+00 1.99879587e-01 7.53739655e-01 1.59797871e+00 5.72014213e-01 1.17982590e+00 2.98168570e-01 9.33080554e-01 -3.79344910e-01 -2.20024064e-01 4.82347906e-01 -1.90834459e-02 -1.73013918e-02 1.24203473e-01 7.33553946e-01 -7.51855016e-01 -5.35135508e-01 1.17525744e+00 6.10694110e-01 -8.04395042e-03 3.69003028e-01 -1.72245336e+00 -5.64158976e-01 2.77278930e-01 -6.76693499e-01 5.73110163e-01 -1.73261538e-01 1.71647564e-01 8.29821467e-01 -9.49884772e-01 4.48795378e-01 7.60173738e-01 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ac265321-1442-4f84-bfde-e2f7d43a29d3	a-dataset-of-multi-illumination-images-in-the	1910.08131	null	https://arxiv.org/abs/1910.08131v1	https://arxiv.org/pdf/1910.08131v1.pdf	A Dataset of Multi-Illumination Images in the Wild	Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. To fill this gap, we introduce a new multi-illumination dataset of more than 1000 real scenes, each captured under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance.	['Miika Aittala', 'Lukas Murmann', 'Michael Gharbi', 'Fredo Durand']	2019-10-17	a-dataset-of-multi-illumination-images-in-the-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Murmann_A_Dataset_of_Multi-Illumination_Images_in_the_Wild_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Murmann_A_Dataset_of_Multi-Illumination_Images_in_the_Wild_ICCV_2019_paper.pdf	iccv-2019-10	['image-relighting']	['computer-vision']	[ 7.63624072e-01 -3.98651689e-01 -1.26218628e-02 -3.72455388e-01 -7.49947965e-01 -6.50859475e-01 6.12978637e-01 -4.45217907e-01 -2.58069158e-01 7.43920267e-01 -2.79175639e-01 -2.73753792e-01 2.62881905e-01 -2.34084368e-01 -7.43427038e-01 -7.07070291e-01 4.19659317e-01 2.63403058e-01 -8.22516754e-02 -4.63634208e-02 1.92647398e-01 3.81850362e-01 -1.85971987e+00 1.23627722e-01 8.81025493e-01 7.98538804e-01 8.51991028e-02 8.75630200e-01 -6.54917285e-02 5.93524516e-01 -3.57012600e-01 -3.36521566e-01 4.66058820e-01 -4.54314172e-01 -4.21374470e-01 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9754cfbc-1dd2-4809-a57a-8cc31fcfe541	planar-object-tracking-in-the-wild-a	1703.07938	null	http://arxiv.org/abs/1703.07938v2	http://arxiv.org/pdf/1703.07938v2.pdf	Planar Object Tracking in the Wild: A Benchmark	Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.	['Haibin Ling', 'Chunyuan Liao', 'Liming Wang', 'Hu Lu', 'Yifan Wu', 'Pengpeng Liang']	2017-03-23	null	null	null	null	['homography-estimation']	['computer-vision']	[-3.29725333e-02 -5.26251674e-01 -1.20044351e-01 -1.32426977e-01 -4.00762081e-01 -9.50564921e-01 5.94992578e-01 -2.90651888e-01 -3.22916746e-01 4.91481006e-01 -2.17689380e-01 2.90709198e-01 6.58618100e-03 -1.08734556e-01 -8.65767539e-01 -6.75685167e-01 -4.57028985e-01 3.54170620e-01 1.10017765e+00 2.46653765e-01 1.99453428e-01 7.08672166e-01 -1.39960146e+00 -1.84186921e-01 3.98269564e-01 9.51575994e-01 4.30615932e-01 6.66707098e-01 3.78342241e-01 7.12088943e-01 -7.26797998e-01 -5.69387078e-01 6.31231964e-01 1.26613155e-01 -3.31153989e-01 3.85268718e-01 1.00524986e+00 -4.99234796e-01 -6.91672385e-01 1.54973376e+00 3.32738250e-01 1.43043339e-01 4.45124090e-01 -1.44303453e+00 -4.63943660e-01 6.72971159e-02 -7.74689615e-01 1.69741496e-01 4.15855527e-01 5.18299758e-01 5.28004169e-01 -7.62495100e-01 1.06436610e+00 1.38891506e+00 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3538462f-7c21-446b-bf9d-d67b4f1e46b9	diachronic-embedding-for-temporal-knowledge	1907.03143	null	https://arxiv.org/abs/1907.03143v1	https://arxiv.org/pdf/1907.03143v1.pdf	Diachronic Embedding for Temporal Knowledge Graph Completion	Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.	['Rishab Goel', 'Marcus Brubaker', 'Pascal Poupart', 'Seyed Mehran Kazemi']	2019-07-06	null	null	null	null	['temporal-knowledge-graph-completion']	['knowledge-base']	[-4.35743690e-01 6.18001461e-01 -4.21711147e-01 -7.30530992e-02 -3.70832413e-01 -6.45301044e-01 8.38425756e-01 7.24836528e-01 -3.32628548e-01 6.97031200e-01 4.06959832e-01 -1.12983644e-01 -4.12629575e-01 -1.16400540e+00 -7.18928993e-01 -4.23368871e-01 -6.98861361e-01 5.13504922e-01 6.39179409e-01 -2.07334712e-01 -2.69761384e-01 2.44252384e-01 -1.36826539e+00 5.88177405e-02 6.15277886e-01 6.25072956e-01 -1.05795644e-01 5.26932716e-01 -2.73372084e-01 9.34303641e-01 -3.13304424e-01 -1.01243567e+00 7.80531615e-02 1.11467406e-01 -9.84462261e-01 -4.67106253e-01 2.34325320e-01 -2.87309080e-01 -6.95766091e-01 6.84496939e-01 3.62850547e-01 8.90407339e-02 2.79187113e-01 -1.79068005e+00 -6.49649680e-01 1.18552339e+00 -1.24821514e-01 8.00274219e-03 5.47497809e-01 -3.35518271e-01 1.46823490e+00 -7.99667835e-01 9.92778003e-01 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6bfda603-949f-44d0-908f-67dd64193135	iranis-a-large-scale-dataset-of-farsi-license	2101.00295	null	https://arxiv.org/abs/2101.00295v1	https://arxiv.org/pdf/2101.00295v1.pdf	Iranis: A Large-scale Dataset of Farsi License Plate Characters	Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.	['Alireza Akoushideh', 'Asadollah Shahbahrami', 'Sajjad Soroori', 'Ali Tourani']	2021-01-01	null	null	null	null	['license-plate-detection']	['computer-vision']	[-4.28480245e-02 -7.56163061e-01 3.59808207e-02 -1.84497282e-01 -2.41454914e-01 -7.07771838e-01 5.67020774e-01 -4.37840462e-01 -5.40291488e-01 6.39033616e-01 -3.11053395e-01 -1.35852143e-01 1.53019696e-01 -7.90333211e-01 -7.63140202e-01 -8.61020505e-01 3.36581916e-01 3.25787485e-01 3.78507078e-01 -1.81560665e-01 6.56845033e-01 1.19999421e+00 -1.49297059e+00 1.96247101e-01 4.96616662e-01 9.52020705e-01 4.06549543e-01 7.79655099e-01 -6.66225851e-02 1.07247734e+00 -7.50223219e-01 -6.18390620e-01 5.38746655e-01 -4.41505611e-02 -1.68068275e-01 5.29073954e-01 7.27371812e-01 -7.36358404e-01 -6.87422991e-01 1.20184231e+00 3.08433235e-01 1.14418983e-01 9.10153210e-01 -1.29808009e+00 -1.10184658e+00 -1.45503478e-02 -3.77102941e-01 5.50093532e-01 -2.17377201e-01 2.47631371e-01 3.31441283e-01 -9.31679070e-01 4.82533067e-01 8.56370986e-01 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1d2b870e-e182-46f1-9fae-1c7edb25f1a5	3d-dual-fusion-dual-domain-dual-query-camera-1	2211.13529	null	https://arxiv.org/abs/2211.13529v2	https://arxiv.org/pdf/2211.13529v2.pdf	3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection	Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which is designed to mitigate the gap between the feature representations of camera and LiDAR data. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through deformable attention. We redesign the transformer fusion encoder to aggregate the information from the two domains. Two major changes include 1) dual query-based deformable attention to fuse the dual-domain features interactively and 2) 3D local self-attention to encode the voxel-domain queries prior to dual-query decoding. The results of an experimental evaluation show that the proposed camera-LiDAR fusion architecture achieved competitive performance on the KITTI and nuScenes datasets, with state-of-the-art performances in some 3D object detection benchmarks categories.	['Jun Won Choi', 'Dongsuk Kum', 'Minwook Kim', 'Konyul Park', 'Yecheol Kim']	2022-11-24	3d-dual-fusion-dual-domain-dual-query-camera	https://arxiv.org/abs/2211.13529	https://arxiv.org/abs/2211.13529	null	['robust-3d-object-detection']	['computer-vision']	[ 8.06602985e-02 -4.25500602e-01 1.84174255e-02 -5.54664671e-01 -1.42608535e+00 -7.17516541e-01 5.86658537e-01 -3.73521373e-02 -2.41200656e-01 1.95994973e-02 4.61336784e-02 -1.21133529e-01 1.65572062e-01 -6.19651973e-01 -9.99303043e-01 -5.23087800e-01 4.53954875e-01 4.28543150e-01 6.31400347e-01 -1.16093829e-02 2.28714824e-01 8.04533958e-01 -2.00773168e+00 4.48580503e-01 8.46056879e-01 1.43978941e+00 6.03608906e-01 7.17144310e-01 -3.34420800e-01 2.51753151e-01 -3.17011267e-01 -3.10122192e-01 6.25204504e-01 2.58337796e-01 -6.60785735e-02 2.70015866e-01 1.17494583e+00 -5.82804263e-01 -3.56932700e-01 1.12231410e+00 6.10626221e-01 -1.41689116e-02 6.76413536e-01 -1.30125129e+00 -9.71580446e-01 1.01766542e-01 -9.49690521e-01 3.49486321e-01 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da05a02e-48b7-49fd-b03a-c063f39d19ee	implementation-and-comparative-quantitative	1511.04659	null	http://arxiv.org/abs/1511.04659v1	http://arxiv.org/pdf/1511.04659v1.pdf	Implementation and comparative quantitative assessment of different multispectral image pansharpening approches	In remote sensing, images acquired by various earth observation satellites tend to have either a high spatial and low spectral resolution or vice versa. Pansharpening is a technique which aims to improve spatial resolution of multispectral image. The challenges involve in the pansharpening are not only to improve the spatial resolution but also to preserve spectral quality of the multispectral image. In this paper, various pansharpening algorithms are discussed and classified based on approaches they have adopted. Using MATLAB image processing toolbox, several state-of-art pan-sharpening algorithms are implemented. Quality of pansharpened images are assessed visually and quantitatively. Correlation coefficient (CC), Root mean square error (RMSE), Relative average spectral error (RASE) and Universal quality index (Q) indices are used to easure spectral quality while to spatial-CC (SCC) quantitative parameter is used for spatial quality measurement. Finally, the paper is concluded with useful remarks.	['Shailesh Panchal', 'Rajesh Thakker']	2015-11-15	null	null	null	null	['pansharpening']	['computer-vision']	[ 7.95682669e-01 -5.76338112e-01 8.38127732e-02 -6.12240145e-03 -6.32802129e-01 -6.56443059e-01 3.79221141e-01 4.61819842e-02 -2.97618866e-01 8.73535097e-01 -8.20330009e-02 -1.36358276e-01 -6.95546389e-01 -1.05725288e+00 9.66000929e-03 -1.02560318e+00 -4.56834920e-02 -4.29580569e-01 2.68637002e-01 -4.57328349e-01 2.52263784e-01 8.72093856e-01 -1.48441386e+00 -2.37786621e-01 1.36063302e+00 8.95066321e-01 6.62893355e-01 7.91424990e-01 3.62647086e-01 2.67898172e-01 -2.03147903e-01 2.65827507e-01 4.98229384e-01 -4.73077595e-01 -6.86618924e-01 3.69735718e-01 2.17946693e-01 -1.92355856e-01 8.65409523e-02 1.57130182e+00 2.73668408e-01 2.67870903e-01 6.53513253e-01 -7.50877261e-01 -5.89714348e-01 2.18471348e-01 -1.12679636e+00 3.48169237e-01 -1.75730549e-02 6.30182307e-03 7.01187611e-01 -7.59359658e-01 2.34084800e-01 6.82984352e-01 6.67982519e-01 -3.16758186e-01 -1.34943783e+00 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-2.51471885e-02 4.82852429e-01 -3.75529051e-01 -3.45882744e-01 1.45385191e-01]	[10.146171569824219, -2.130579948425293]
5594e2a2-9850-463f-bf56-e7d401089ccd	improving-contextualized-topic-models-with	2303.14951	null	https://arxiv.org/abs/2303.14951v1	https://arxiv.org/pdf/2303.14951v1.pdf	Improving Contextualized Topic Models with Negative Sampling	Topic modeling has emerged as a dominant method for exploring large document collections. Recent approaches to topic modeling use large contextualized language models and variational autoencoders. In this paper, we propose a negative sampling mechanism for a contextualized topic model to improve the quality of the generated topics. In particular, during model training, we perturb the generated document-topic vector and use a triplet loss to encourage the document reconstructed from the correct document-topic vector to be similar to the input document and dissimilar to the document reconstructed from the perturbed vector. Experiments for different topic counts on three publicly available benchmark datasets show that in most cases, our approach leads to an increase in topic coherence over that of the baselines. Our model also achieves very high topic diversity.	['Partha Pratim Das', 'Debarshi Kumar Sanyal', 'Avishek Lahiri', 'Suman Adhya']	2023-03-27	null	null	null	null	['topic-models']	['natural-language-processing']	[-1.21659353e-01 3.44806492e-01 -4.01051790e-01 -2.31317371e-01 -1.14857638e+00 -4.27607536e-01 1.09328794e+00 2.15502217e-01 -9.68897156e-03 8.10566604e-01 7.34853923e-01 -6.53776079e-02 1.55782342e-01 -7.28716791e-01 -8.18613112e-01 -9.30832803e-01 1.56019941e-01 8.97979319e-01 1.66275054e-01 -5.18496186e-02 3.92451674e-01 -2.37433732e-01 -1.43176246e+00 2.37564906e-01 8.74039590e-01 5.19914508e-01 5.06003320e-01 4.81582642e-01 -6.23334706e-01 1.42528564e-01 -9.61199760e-01 -1.08807169e-01 2.61492208e-02 -3.28149170e-01 -6.08352542e-01 2.20270291e-01 2.32745767e-01 -1.40535280e-01 -6.97758645e-02 8.53573382e-01 2.15212271e-01 4.07388449e-01 9.60430503e-01 -1.07270801e+00 -4.73172396e-01 8.86193216e-01 -8.29434276e-01 -5.28629497e-02 2.12510396e-02 -3.66163313e-01 1.14023089e+00 -1.01069605e+00 8.93876135e-01 1.57875443e+00 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20ba4b9b-5a06-4fe9-8774-202b2ef54b8d	std-net-search-of-image-steganalytic-deep	2206.05651	null	https://arxiv.org/abs/2206.05651v1	https://arxiv.org/pdf/2206.05651v1.pdf	STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition	Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The existing model compression method cannot flexibly compress the convolutional layer in residual shortcut block so that a satisfactory shrinking rate cannot be obtained. In this paper, we propose STD-NET, an unsupervised deep-learning architecture search approach via hierarchical tensor decomposition for image steganalysis. Our proposed strategy will not be restricted by various residual connections, since this strategy does not change the number of input and output channels of the convolution block. We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one. Extensive experiments have confirmed that, on one hand, our model can achieve comparable or even better detection performance in various steganalytic scenarios due to the great adaptivity of the obtained network architecture. On the other hand, the experimental results also demonstrate that our proposed strategy is more efficient and can remove more redundancy compared with previous steganalytic network compression methods.	['Jiwu Huang', 'Bin Li', 'Laiyuan Li', 'Qiushi Li', 'Shunquan Tan']	2022-06-12	null	null	null	null	['steganalysis']	['computer-vision']	[ 5.47017455e-01 -2.28222311e-02 4.41628508e-02 9.71627906e-02 3.17693949e-01 -3.25870246e-01 4.19543475e-01 -4.73927319e-01 -4.50106561e-01 3.19211572e-01 -1.44156724e-01 -5.31449676e-01 -3.67539860e-02 -9.82028544e-01 -5.36804080e-01 -1.03430390e+00 6.55706003e-02 -1.59851357e-01 5.95778286e-01 -3.80998224e-01 4.19895768e-01 4.19966340e-01 -1.39579642e+00 2.75839359e-01 6.20766997e-01 9.11755979e-01 3.04634213e-01 4.33623761e-01 2.48912171e-01 6.98344111e-01 -3.69818091e-01 -5.57419181e-01 5.47496736e-01 -7.21217930e-01 -5.80676496e-01 3.82536985e-02 -1.99809849e-01 -4.82070327e-01 -8.49771261e-01 1.48977435e+00 3.11373979e-01 -4.08441007e-01 4.56617415e-01 -8.81669402e-01 -2.12526619e-01 1.12891757e+00 -5.58070958e-01 2.56043434e-01 -4.84897435e-01 2.02237025e-01 7.39486992e-01 -4.26450580e-01 5.53868234e-01 1.11579061e+00 5.25469124e-01 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5091e908-01fc-4193-9ac1-097513378708	a-cooperation-aware-lane-change-method-for-1	null	null	https://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=A%20Cooperation-Aware%20Lane%20Change%20Method%20for%20Automated%20Vehicles	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9971784	A Cooperation-Aware Lane Change Method for Automated Vehicles	Lane change for automated vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guaranteeing safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation on safety, efficiency and comfort is designed to make a decision on lane change. Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV’s decision and surrounding vehicles’ interactive behaviors into constraints so as to avoid collisions during lane change. Quantitative testing results show that compared with the methods without an interactive prediction, our method enhances driving efficiencies of the AV and other vehicles by 14.8% and 2.6%, respectively, which indicates that a proper utilization of vehicle interactions can effectively reduce the conservatism of the AV and promote the cooperation between the AV and others.	['Shibei Xue', 'Lin Liu', 'Zihao Sheng']	2023-03-01	null	null	null	ieee-transactions-on-intelligent-16	['trajectory-prediction', 'motion-planning']	['computer-vision', 'robots']	[-3.64135891e-01 3.35781455e-01 -2.94283241e-01 -2.99439669e-01 2.66781193e-03 -3.49659592e-01 4.32604611e-01 -1.49083257e-01 -4.71865773e-01 8.05360198e-01 -1.36221156e-01 -7.98787892e-01 -1.57965139e-01 -9.67638969e-01 -5.01674056e-01 -8.72411072e-01 -4.37849686e-02 -2.11415254e-03 7.97559023e-01 -4.99558002e-01 2.91920662e-01 7.43944645e-01 -1.49186993e+00 -5.22421420e-01 1.28849018e+00 7.36997545e-01 5.71482897e-01 2.16626942e-01 2.54501589e-02 4.88490433e-01 5.88066839e-02 -2.05265373e-01 3.10021847e-01 -1.35053813e-01 -2.30362371e-01 4.59306911e-02 -5.72811186e-01 -4.86752123e-01 -3.20648342e-01 7.85388112e-01 9.64371487e-02 6.96722627e-01 6.39256120e-01 -2.05597281e+00 9.84574854e-02 1.92582339e-01 -4.90072191e-01 -8.57952237e-02 -2.73544043e-01 5.86011052e-01 5.39020777e-01 -4.71754581e-01 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32c5f3d0-8191-463d-b3b7-026c7f49fb44	disfluencyfixer-a-tool-to-enhance-language	2305.16957	null	https://arxiv.org/abs/2305.16957v1	https://arxiv.org/pdf/2305.16957v1.pdf	DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction	Conversational speech often consists of deviations from the speech plan, producing disfluent utterances that affect downstream NLP tasks. Removing these disfluencies is necessary to create fluent and coherent speech. This paper presents DisfluencyFixer, a tool that performs speech-to-speech disfluency correction in English and Hindi using a pipeline of Automatic Speech Recognition (ASR), Disfluency Correction (DC) and Text-To-Speech (TTS) models. Our proposed system removes disfluencies from input speech and returns fluent speech as output along with its transcript, disfluency type and total disfluency count in source utterance, providing a one-stop destination for language learners to improve the fluency of their speech. We evaluate the performance of our tool subjectively and receive scores of 4.26, 4.29 and 4.42 out of 5 in ASR performance, DC performance and ease-of-use of the system. Our tool can be accessed openly at the following link.	['Pushpak Bhattacharyya', 'Preethi Jyothi', 'Vineet Bhat']	2023-05-26	null	null	null	null	['automatic-speech-recognition']	['speech']	[-1.50625721e-01 2.70319968e-01 2.74946958e-01 -5.03004670e-01 -8.48980248e-01 -9.64599192e-01 3.50917369e-01 -1.37570411e-01 -2.47287378e-01 9.46216345e-01 9.79909360e-01 -6.96116388e-01 3.06476653e-01 -1.33003145e-01 -4.13965493e-01 -2.55082428e-01 4.43946213e-01 4.43179190e-01 1.00362211e-01 -5.15489280e-01 1.28138095e-01 2.75245011e-01 -1.29954278e+00 7.24798739e-01 1.08173835e+00 9.45188180e-02 7.51618445e-01 1.18261290e+00 -1.93343312e-01 1.07444346e+00 -1.16671312e+00 -7.65817687e-02 -2.60560989e-01 -4.61436570e-01 -1.25904071e+00 -2.02429622e-01 -3.52681838e-02 -1.34845302e-01 -2.19531864e-01 8.37397814e-01 5.18234134e-01 4.06410664e-01 2.15949222e-01 -5.64169645e-01 -4.76048172e-01 1.03199196e+00 6.27877176e-01 7.43024409e-01 8.91397715e-01 3.46833825e-01 5.43000281e-01 -8.50939691e-01 6.24512613e-01 1.37094426e+00 2.06881851e-01 1.07778287e+00 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4135c74a-a9ed-4b2d-8ab4-8859c422ed45	faithful-learning-with-sure-data-for-lung	2202.12515	null	https://arxiv.org/abs/2202.12515v1	https://arxiv.org/pdf/2202.12515v1.pdf	Faithful learning with sure data for lung nodule diagnosis	Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic diagnoses at the nodule level. We termed these data as "unsure data". Second, a classifier does not necessarily acquire reliable nodule features for stable learning and robust prediction with patch-level labels during learning. In this study, we construct a sure dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure data knowledge through nodule segmentation and malignancy score regression. A loss function is designed to learn reliable features by introducing interpretability constraints regulated with nodule segmentation maps. Furthermore, based on model inference results that reflect the understanding from both machine and experts, we explore a new nodule analysis method for similar historical nodule retrieval and interpretable diagnosis. Detailed experimental results demonstrate that our approach is beneficial for achieving improved performance coupled with faithful model reasoning for lung cancer prediction. Extensive cross-evaluation results further illustrate the effect of unsure data for deep-learning-based methods in lung nodule classification.	['Guang-Zhong Yang', 'Yun Gu', 'Zhexin Wang', 'Feng Yao', 'Yulei Qin', 'Minghui Zhang', 'Xiao Gu', 'Liang Chen', 'Hanxiao Zhang']	2022-02-25	null	null	null	null	['lung-nodule-classification']	['medical']	[ 1.25561342e-01 7.29072511e-01 -6.45296514e-01 -6.69189692e-01 -1.16462052e+00 -4.28543419e-01 9.83700827e-02 -1.91473663e-01 1.24033637e-01 4.94709879e-01 8.31550136e-02 -5.47605574e-01 -6.16148174e-01 -7.65647411e-01 -4.72179562e-01 -6.99229836e-01 4.14036177e-02 9.18847322e-01 4.22789425e-01 2.15391234e-01 -3.29442948e-01 4.58115458e-01 -1.05663705e+00 8.72152746e-01 1.00445890e+00 1.23589802e+00 1.97877839e-01 5.60696661e-01 2.03662798e-01 1.47750330e+00 -3.09795700e-03 -5.76331496e-01 4.48103994e-01 -3.71760905e-01 -1.19085336e+00 3.75093400e-01 1.92182690e-01 -4.86789286e-01 -1.80863619e-01 8.61499548e-01 2.97648579e-01 -5.41498363e-01 1.13436091e+00 -9.86373007e-01 -6.83987677e-01 9.13320363e-01 -2.16156334e-01 -1.64952219e-01 -3.60140622e-01 2.41793677e-01 1.34093118e+00 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44080de4-1e85-48d1-9fff-94574d5a28ae	multilingual-few-shot-learning-via-language	2306.10964	null	https://arxiv.org/abs/2306.10964v1	https://arxiv.org/pdf/2306.10964v1.pdf	Multilingual Few-Shot Learning via Language Model Retrieval	Transformer-based language models have achieved remarkable success in few-shot in-context learning and drawn a lot of research interest. However, these models' performance greatly depends on the choice of the example prompts and also has high variability depending on how samples are chosen. In this paper, we conduct a comprehensive study of retrieving semantically similar few-shot samples and using them as the context, as it helps the model decide the correct label without any gradient update in the multilingual and cross-lingual settings. We evaluate the proposed method on five natural language understanding datasets related to intent detection, question classification, sentiment analysis, and topic classification. The proposed method consistently outperforms random sampling in monolingual and cross-lingual tasks in non-English languages.	['Yash Chandarana', 'Soumya Vadlamannati', 'Liang-Kang Huang', 'Genta Indra Winata']	2023-06-19	null	null	null	null	['few-shot-learning', 'sentiment-analysis', 'intent-detection']	['methodology', 'natural-language-processing', 'natural-language-processing']	[ 1.72616113e-02 -6.61402762e-01 -5.25477231e-01 -6.77490413e-01 -1.25909507e+00 -5.04283905e-01 9.39185143e-01 5.14391482e-01 -8.85743022e-01 4.84884769e-01 4.20979261e-01 -1.50000334e-01 2.17637226e-01 -5.50007761e-01 -3.06717396e-01 -3.71320307e-01 5.18980205e-01 5.35874665e-01 4.05180722e-01 -2.85839856e-01 5.41283607e-01 -2.30607092e-01 -1.42638862e+00 4.87503260e-01 9.33568478e-01 9.06388760e-01 5.00148237e-01 3.59572440e-01 -6.75246358e-01 1.08066201e+00 -4.85478878e-01 -5.48915863e-01 -3.14595789e-01 -4.46131825e-01 -7.80257761e-01 -1.29743710e-01 4.15328652e-01 1.02202103e-01 1.30613118e-01 8.77016187e-01 6.59216046e-01 4.08848375e-01 7.73342013e-01 -8.91012549e-01 -7.12402821e-01 7.53724635e-01 -4.32235152e-01 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5.91763735e-01 1.35448265e+00 2.84068823e-01 2.35825524e-01 8.02110195e-01 -1.74459368e-01 -6.98860049e-01 -1.22417271e+00 -3.03037971e-01 3.96355897e-01 9.74506978e-03 -5.11612594e-01 -4.24910486e-01 -4.94046323e-02]	[10.775749206542969, 7.647121429443359]
8f90c4b6-a7e5-41d6-b8c1-38ac937ede5c	x-maps-direct-depth-lookup-for-event-based	null	null	https://fraunhoferhhi.github.io/X-maps/	https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_X-Maps_Direct_Depth_Lookup_for_Event-based_Structured_Light_Systems.pdf	X-maps: Direct Depth Lookup for Event-based Structured Light Systems	We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras. These dynamic vision sensors are a great fit to be paired with laser projectors for depth estimation in a structured light approach. Our key contributions involve a conversion of the projector time map into a rectified X-map, capturing x-axis correspondences for incoming events and enabling direct disparity lookup without any additional search. Compared to previous implementations, this significantly simplifies depth estimation, making it more efficient, while the accuracy is similar to the time map-based process. Moreover, we compensate non-linear temporal behavior of cheap laser projectors by a simple time map calibration, resulting in improved performance and increased depth estimation accuracy. Since depth estimation is executed by two lookups only, it can be executed almost instantly (less than 3 ms per frame with a Python implementation) for incoming events. This allows for real-time interactivity and responsiveness, which makes our approach especially suitable for SAR experiences where low latency, high frame rates and direct feedback are crucial. We present valuable insights gained into data transformed into X-maps and evaluate our depth from disparity estimation against the state of the art time map-based results.	['Peter Eisert', 'Anna Hilsmann', 'Simon Baumann', 'Niklas Gard', 'Wieland Morgenstern']	2023-06-19	null	null	null	cvpr-workshop-on-event-based-vision-2023-6	['depth-estimation', 'disparity-estimation']	['computer-vision', 'computer-vision']	[ 4.18842643e-01 -2.31668070e-01 3.40615928e-01 -5.09905577e-01 -5.54564416e-01 -5.01756072e-01 5.25922954e-01 3.66130397e-02 -7.63413668e-01 6.16921067e-01 3.32066640e-02 -1.51805043e-01 1.93141058e-01 -1.14366341e+00 -6.51431084e-01 -3.15643638e-01 2.54436940e-01 6.27245843e-01 7.81220496e-01 -2.03529894e-01 4.91693735e-01 8.38387430e-01 -2.05721951e+00 1.31583631e-01 4.85028535e-01 1.10139370e+00 5.08856654e-01 9.63712692e-01 6.98818788e-02 8.09851289e-01 -3.60474706e-01 -1.73428088e-01 4.01953161e-01 3.42173763e-02 -3.03073555e-01 -6.94486424e-02 8.07869315e-01 -1.05250680e+00 -5.03495812e-01 5.33205748e-01 6.77018464e-01 -1.59964734e-03 -2.86089387e-02 -8.38142514e-01 4.31449205e-01 -3.10601681e-01 -6.46854222e-01 4.17336114e-02 9.99701560e-01 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edfec7e0-8f98-46a1-95c6-c2142a4bbfc1	efficient-exploration-with-self-imitation	1907.10247	null	https://arxiv.org/abs/1907.10247v3	https://arxiv.org/pdf/1907.10247v3.pdf	Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards	Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow. Recent work demonstrated that using a memory buffer of previous successful trajectories can result in more effective policies. However, existing methods may overly exploit past successful experiences, which can encourage the agent to adopt sub-optimal and myopic behaviors. In this work, instead of focusing on good experiences with limited diversity, we propose to learn a trajectory-conditioned policy to follow and expand diverse past trajectories from a memory buffer. Our method allows the agent to reach diverse regions in the state space and improve upon the past trajectories to reach new states. We empirically show that our approach significantly outperforms count-based exploration methods (parametric approach) and self-imitation learning (parametric approach with non-parametric memory) on various complex tasks with local optima. In particular, without using expert demonstrations or resetting to arbitrary states, we achieve the state-of-the-art scores under five billion number of frames, on challenging Atari games such as Montezuma's Revenge and Pitfall.	['Honglak Lee', 'Mohammad Norouzi', 'Samy Bengio', 'Shengyu Feng', 'Jongwook Choi', 'Yijie Guo', 'Marcin Moczulski']	2019-07-24	memory-based-trajectory-conditioned-policies	http://proceedings.neurips.cc/paper/2020/hash/2df45244f09369e16ea3f9117ca45157-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/2df45244f09369e16ea3f9117ca45157-Paper.pdf	neurips-2020-12	['montezumas-revenge']	['playing-games']	[-2.96264201e-01 -1.53312802e-01 -4.20450389e-01 1.71321407e-01 -7.46869981e-01 -5.09736419e-01 6.70904875e-01 -2.12136298e-01 -8.92146647e-01 1.49723768e+00 7.06923604e-02 -1.66078985e-01 -1.30160809e-01 -5.15274704e-01 -9.11563456e-01 -7.91067183e-01 -6.59576476e-01 5.09867728e-01 2.77871817e-01 -3.11236620e-01 4.87800479e-01 2.34307796e-01 -1.49126911e+00 -2.48569876e-01 9.98680770e-01 6.03928983e-01 6.05399668e-01 8.28586936e-01 2.94240206e-01 1.09525061e+00 -6.24264836e-01 2.68220663e-01 4.46742445e-01 -4.98267591e-01 -5.95897257e-01 3.30225900e-02 -1.05527237e-01 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506f9b41-1671-44ef-995d-45e7d0ef31f2	a-birds-eye-view-on-knowledge-graph	2205.09088	null	https://arxiv.org/abs/2205.09088v1	https://arxiv.org/pdf/2205.09088v1.pdf	A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges	In recent years, Knowledge Graph (KG) development has attracted significant researches considering the applications in web search, relation prediction, natural language processing, information retrieval, question answering to name a few. However, often KGs are incomplete due to which Knowledge Graph Completion (KGC) has emerged as a sub-domain of research to automatically track down the missing connections in a KG. Numerous strategies have been suggested to work out the KGC dependent on different representation procedures intended to embed triples into a low-dimensional vector space. Given the difficulties related to KGC, researchers around the world are attempting to comprehend the attributes of the problem statement. This study intends to provide an overview of knowledge bases combined with different challenges and their impacts. We discuss existing KGC approaches, including the state-of-the-art Knowledge Graph Embeddings (KGE), not only on static graphs but also for the latest trends such as multimodal, temporal, and uncertain knowledge graphs. In addition, reinforcement learning techniques are reviewed to model complex queries as a link prediction problem. Subsequently, we explored popular software packages for model training and examine open research challenges that can guide future research.	['Dwaipayan Roy', 'Satvik Garg']	2022-05-18	null	null	null	null	['knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'methodology']	[-1.46876842e-01 4.39511269e-01 -5.96289694e-01 -1.33202761e-01 -2.39094749e-01 -4.20707405e-01 4.36863810e-01 9.04151618e-01 -1.80893868e-01 7.12656200e-01 1.69568151e-01 -3.75314802e-01 -6.40618801e-01 -1.07331729e+00 -5.60494483e-01 -2.59353667e-01 -4.47817266e-01 5.43575644e-01 2.78134584e-01 -3.70481491e-01 2.48922810e-01 2.60908991e-01 -1.54408836e+00 -4.68489015e-03 9.32477713e-01 7.92492270e-01 -8.59484002e-02 4.32511896e-01 -7.08109438e-01 8.08534443e-01 -2.90668726e-01 -1.11039925e+00 -3.15354317e-01 -3.07394825e-02 -1.14172673e+00 -3.19957167e-01 3.19737017e-01 3.12347740e-01 -5.99714398e-01 1.07766676e+00 3.46375227e-01 1.71529964e-01 6.36413872e-01 -1.71251512e+00 -1.22229111e+00 8.52241814e-01 -2.23469764e-01 -4.01153084e-04 6.44973695e-01 -5.39954901e-01 1.31462920e+00 -9.23371136e-01 7.12459385e-01 1.18786538e+00 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0520c5dd-fd6c-4db2-abce-fa5b7d550257	end-to-end-music-source-separation-is-it	1810.12187	null	https://arxiv.org/abs/1810.12187v2	https://arxiv.org/pdf/1810.12187v2.pdf	End-to-end music source separation: is it possible in the waveform domain?	Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into account all the information available in the raw audio signal, including the phase. Although during the last decades end-to-end music source separation has been considered almost unattainable, our results confirm that waveform-based models can perform similarly (if not better) than a spectrogram-based deep learning model. Namely: a Wavenet-based model we propose and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model.	['Francesc Lluís', 'Xavier Serra', 'Jordi Pons']	2018-10-29	null	null	null	null	['music-source-separation']	['music']	[ 7.24923089e-02 -3.30447674e-01 2.46040002e-01 1.15319163e-01 -1.00373292e+00 -8.67019951e-01 5.16523123e-01 1.82039753e-01 -3.31323296e-01 5.86011112e-01 5.77105463e-01 -1.33239940e-01 -5.05101025e-01 -4.83367056e-01 -3.64744633e-01 -8.35567236e-01 -4.02989805e-01 4.91577052e-02 -1.99138243e-02 -2.94442713e-01 8.64462852e-02 2.22323239e-01 -1.65885806e+00 2.37725675e-01 7.49764502e-01 9.64927614e-01 -1.36809111e-01 9.78233874e-01 1.67244244e-02 8.76547098e-01 -7.02670515e-01 1.53821278e-02 2.32764915e-01 -8.57809007e-01 -5.04448533e-01 -5.81163287e-01 4.80349928e-01 -2.06989765e-01 -1.90899298e-01 9.10917938e-01 9.00956333e-01 1.66318059e-01 6.39357507e-01 -9.11943734e-01 -3.01577430e-02 9.49038923e-01 -2.55999237e-01 2.95883805e-01 4.03828651e-01 -4.40020002e-02 1.25159156e+00 -7.53027618e-01 3.29269320e-01 8.81682575e-01 1.16460013e+00 -1.10400841e-01 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4d5ed394-596f-4204-b992-e2b2da10f9a3	a-low-rank-tensor-regularization-strategy-for	1803.06355	null	http://arxiv.org/abs/1803.06355v1	http://arxiv.org/pdf/1803.06355v1.pdf	A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing	Tensor-based methods have recently emerged as a more natural and effective formulation to address many problems in hyperspectral imaging. In hyperspectral unmixing (HU), low-rank constraints on the abundance maps have been shown to act as a regularization which adequately accounts for the multidimensional structure of the underlying signal. However, imposing a strict low-rank constraint for the abundance maps does not seem to be adequate, as important information that may be required to represent fine scale abundance behavior may be discarded. This paper introduces a new low-rank tensor regularization that adequately captures the low-rank structure underlying the abundance maps without hindering the flexibility of the solution. Simulation results with synthetic and real data show that the the extra flexibility introduced by the proposed regularization significantly improves the unmixing results.	['José Carlos Moreira Bermudez', 'Tales Imbiriba', 'Ricardo Augusto Borsoi']	2018-03-16	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 5.08491874e-01 -5.42389095e-01 1.03725299e-01 -1.32861644e-01 -1.29633456e-01 -5.15017450e-01 4.39021230e-01 -1.77364379e-01 -8.51376727e-02 6.66769028e-01 2.72701651e-01 7.35765919e-02 -7.19186604e-01 -4.74841058e-01 -1.69290349e-01 -1.23444724e+00 -1.37677789e-01 6.34205565e-02 -2.50244349e-01 -3.00691277e-01 6.77677542e-02 6.87697828e-01 -1.74356461e+00 1.74590081e-01 1.06531560e+00 8.78431797e-01 3.15748304e-01 2.66196728e-01 1.07550465e-01 5.07053256e-01 -1.89565703e-01 1.73337191e-01 5.97864270e-01 -3.32190543e-01 -4.54798192e-01 6.38738811e-01 7.17672348e-01 -2.25021645e-01 -4.15113777e-01 1.43762016e+00 1.81581184e-01 1.19934008e-01 7.00933456e-01 -9.25470591e-01 -2.05245748e-01 2.74053246e-01 -9.93209064e-01 1.47477649e-02 -9.47727859e-02 -8.31542313e-02 1.21962154e+00 -9.74801362e-01 3.62308115e-01 9.59441364e-01 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ca6bea22-8352-4f40-8779-435136cd427a	efficient-relation-aware-neighborhood	2212.05581	null	https://arxiv.org/abs/2212.05581v3	https://arxiv.org/pdf/2212.05581v3.pdf	Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition	Many Graph Neural Networks (GNNs) are proposed for Knowledge Graph Embedding (KGE). However, lots of these methods neglect the importance of the information of relations and combine it with the information of entities inefficiently, leading to low expressiveness. To address this issue, we introduce a general knowledge graph encoder incorporating tensor decomposition in the aggregation function of Relational Graph Convolutional Network (R-GCN). In our model, neighbor entities are transformed using projection matrices of a low-rank tensor which are defined by relation types to benefit from multi-task learning and produce expressive relation-aware representations. Besides, we propose a low-rank estimation of the core tensor using CP decomposition to compress and regularize our model. We use a training method inspired by contrastive learning, which relieves the training limitation of the 1-N method on huge graphs. We achieve favorably competitive results on FB15k-237 and WN18RR with embeddings in comparably lower dimensions.	['Hadi Moradi', 'Reshad Hosseini', 'Peyman Baghershahi']	2022-12-11	null	null	null	null	['knowledge-graph-embedding', 'general-knowledge']	['graphs', 'miscellaneous']	[-3.30801785e-01 4.21809137e-01 -3.60301673e-01 -2.06825018e-01 4.02002595e-02 -5.31289220e-01 3.19600403e-01 9.86411050e-02 -4.12021339e-01 5.17664015e-01 5.06984413e-01 -4.24847901e-01 -4.57118690e-01 -1.25505972e+00 -8.35522115e-01 -4.57402468e-01 -3.86462927e-01 4.39934283e-01 7.49744335e-03 -5.15119851e-01 -4.19680297e-01 4.10631150e-01 -1.10506558e+00 4.15600598e-01 6.61920965e-01 9.34277773e-01 -2.00750902e-01 3.58413219e-01 -2.64266729e-01 1.19369054e+00 -3.16564649e-01 -1.04145992e+00 2.12194309e-01 5.36948219e-02 -1.11571908e+00 -2.55401641e-01 3.39823872e-01 -3.50763559e-01 -1.19775856e+00 9.48875666e-01 3.76563877e-01 2.63472348e-01 5.11737466e-01 -1.31546950e+00 -1.26347053e+00 1.11821842e+00 -4.45501745e-01 1.05807021e-01 1.28162071e-01 -2.75476575e-01 1.64056408e+00 -7.67355800e-01 8.35132062e-01 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4.07705069e-01 -1.83622420e-01 -4.62613255e-01 -8.11438501e-01 -1.03186762e+00 5.29666185e-01 1.04312368e-01 -7.26140618e-01 -3.63597095e-01 -4.02158141e-01]	[8.741929054260254, 7.870265960693359]
92e10089-6ad9-422c-a6e3-522ee65b7293	training-custom-modality-specific-u-net	2102.10607	null	https://arxiv.org/abs/2102.10607v3	https://arxiv.org/pdf/2102.10607v3.pdf	Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations	Deep learning (DL) has drawn tremendous attention in object localization and recognition for both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional handcrafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those that are pretrained on stock photography images. This helps improve model adaptation, generalization, and class-specific region of interest (ROI) localization. In this study, we train chest X-ray (CXR) modality-specific U-Nets and other state-of-the-art U-Net models for semantic segmentation of tuberculosis (TB)-consistent findings. Automated segmentation of such manifestations could help radiologists reduce errors and supplement decision-making while improving patient care and productivity. Our approach uses the publicly available TBX11K CXR dataset with weak TB annotations, typically provided as bounding boxes, to train a set of U-Net models. Next, we improve the results by augmenting the training data with weak localizations, post-processed into an ROI mask, from a DL classifier that is trained to classify CXRs as showing normal lungs or suspected TB manifestations. Test data are individually derived from the TBX11K CXR training distribution and other cross-institutional collections including the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution as well as from cross-institutional collections (p < 0.05).	['Sameer Antani', 'Philip Alderson', 'Jane Dimperio', 'Les Folio', 'Sivaramakrishnan Rajaraman']	2021-02-21	null	null	null	null	['unet-segmentation']	['computer-vision']	[ 6.34393394e-01 -1.97600976e-01 -5.09015679e-01 -4.81090486e-01 -1.27752709e+00 -5.47021329e-01 3.37836623e-01 1.60640150e-01 -5.65751076e-01 8.41136515e-01 1.03384525e-01 -8.41890037e-01 -2.91544586e-01 -8.67195070e-01 -8.24702919e-01 -5.05907953e-01 2.16520071e-01 7.54266620e-01 3.43015254e-01 4.76615518e-01 -1.46018475e-01 6.07399046e-01 -6.42496228e-01 7.99202740e-01 6.56368673e-01 1.31182623e+00 5.31892478e-01 1.11629450e+00 6.81149811e-02 1.06298733e+00 -4.26659644e-01 1.54355139e-01 2.61614889e-01 -5.04915476e-01 -1.15797424e+00 1.56480402e-01 6.10621154e-01 -5.41377902e-01 -4.74936306e-01 3.96944940e-01 6.70854390e-01 -4.58925478e-02 8.21345448e-01 -6.25533760e-01 -9.04561281e-01 2.04020888e-01 -2.71223009e-01 6.80445671e-01 -7.06064031e-02 4.57877070e-01 7.52597988e-01 -4.92150456e-01 8.75487506e-01 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1.11767638e+00 2.32051507e-01 -5.27996838e-01 -4.30064589e-01 -9.63662267e-01 -2.76447624e-01 6.08125985e-01 5.41238308e-01 -3.56652826e-01 4.49865311e-02 8.66601840e-02]	[15.171575546264648, -2.0236711502075195]
305de6ea-730b-4086-b270-7f11855d036f	deep-neural-models-for-medical-concept	1907.07972	null	https://arxiv.org/abs/1907.07972v1	https://arxiv.org/pdf/1907.07972v1.pdf	Deep Neural Models for Medical Concept Normalization in User-Generated Texts	In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions. Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform an existing state of the art models.	['Elena Tutubalina', 'Zulfat Miftahutdinov']	2019-07-18	deep-neural-models-for-medical-concept-1	https://aclanthology.org/P19-2055	https://aclanthology.org/P19-2055.pdf	acl-2019-7	['medical-concept-normalization']	['medical']	[ 7.95039654e-01 5.17133057e-01 -5.28756022e-01 -2.55207717e-01 -6.09798551e-01 -3.77336890e-02 3.19003075e-01 8.65367055e-01 -1.06643856e+00 7.36513674e-01 7.95169115e-01 -3.55960935e-01 -1.44844074e-02 -8.94567728e-01 -3.27145427e-01 -4.16252226e-01 1.36307091e-01 5.83458364e-01 3.78441502e-04 -7.02914953e-01 -1.16298847e-01 1.29328910e-02 -1.10386550e+00 2.37629637e-01 5.47411323e-01 6.28369510e-01 -1.32209674e-01 3.91330481e-01 -7.22238123e-01 1.06663513e+00 -7.06449449e-01 -4.71982777e-01 -2.67098725e-01 -4.76296872e-01 -1.22177804e+00 -4.00684118e-01 -1.12966262e-01 4.46839362e-01 -1.11717572e-02 1.53733623e+00 4.51803833e-01 3.48621696e-01 6.92167401e-01 -6.72449768e-01 -9.15820658e-01 7.94491053e-01 -3.62342745e-01 2.11279213e-01 4.38137889e-01 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8feabe72-526e-404f-bd65-5283bc98e756	improving-replay-based-continual-semantic	2209.09839	null	https://arxiv.org/abs/2209.09839v1	https://arxiv.org/pdf/2209.09839v1.pdf	Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection	Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains. A central challenge in Continual Learning is overcoming the effects of catastrophic forgetting, which refers to the sudden drop in accuracy on previously learned tasks after the model is trained on new classes or domains. In continual classification this challenge is often overcome by replaying a small selection of samples from previous tasks, however replay is rarely considered in CSS. Therefore, we investigate the influences of various replay strategies for semantic segmentation and evaluate them in class- and domain-incremental settings. Our findings suggest that in a class-incremental setting, it is critical to achieve a uniform distribution for the different classes in the buffer to avoid a bias towards newly learned classes. In the domain-incremental setting, it is most effective to select buffer samples by uniformly sampling from the distribution of learned feature representations or by choosing samples with median entropy. Finally, we observe that the effective sampling methods help to decrease the representation shift significantly in early layers, which is a major cause of forgetting in domain-incremental learning.	['Jürgen Beyerer', 'Björn Mauthe', 'Tobias Kalb']	2022-09-20	null	null	null	null	['continual-semantic-segmentation']	['computer-vision']	[ 5.62078297e-01 -8.68523270e-02 -8.44886526e-02 -2.90567160e-01 -6.11353040e-01 -6.25240386e-01 5.44835091e-01 4.26516622e-01 -9.58918273e-01 9.71032977e-01 1.18964417e-02 -1.49750160e-02 -1.42022669e-01 -6.63298011e-01 -8.72411966e-01 -7.45037436e-01 2.40148395e-01 5.84748864e-01 6.90759420e-01 3.15720178e-02 4.27028358e-01 3.98730844e-01 -1.81343555e+00 3.38732690e-01 9.82399404e-01 6.54927373e-01 5.53486824e-01 5.19466519e-01 -4.12087739e-01 6.42315686e-01 -8.39610696e-01 -1.02139875e-01 2.67145842e-01 -5.68070590e-01 -8.03055108e-01 2.38700122e-01 4.96688992e-01 -3.96816224e-01 5.03989160e-02 1.01682687e+00 3.68771613e-01 4.11412537e-01 6.95545971e-01 -1.00365341e+00 -3.32882106e-01 8.08259428e-01 -5.81259012e-01 4.39858586e-01 2.69574523e-02 1.69038162e-01 7.24243701e-01 -7.00832665e-01 7.11143136e-01 1.05774117e+00 6.93332613e-01 6.52178288e-01 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-5.62303007e-01 4.35444534e-01 4.66751099e-01 -9.04304683e-01 -6.59339428e-01 -5.32231927e-02]	[9.757970809936523, 3.2733848094940186]
cb8b4723-ef51-4d9d-8370-63f3041203aa	clinet-joint-detection-of-road-network	2302.02259	null	https://arxiv.org/abs/2302.02259v1	https://arxiv.org/pdf/2302.02259v1.pdf	CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D	This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. In contrast to existing work that focuses on detection of visual cues, we explore feature extraction methods that are directly amenable to the urban driving task. To develop and evaluate our approach, a large urban driving dataset dubbed AV Breadcrumbs is automatically labeled by leveraging vector map representations and projective geometry to annotate over 900,000 images. Our results demonstrate potential for dynamic scene modeling across various urban driving scenarios. Our model achieves an F1 score of 0.684 and an average normalized depth error of 2.083. The code and data annotations are publicly available.	['Henrik I. Christensen', 'Yunchao Yao', 'Srinidhi Kalgundi Srinivas', 'David Paz']	2023-02-04	null	null	null	null	['3d-depth-estimation']	['computer-vision']	[-2.37089366e-01 2.79091001e-02 -1.71113923e-01 -6.97918534e-01 -8.84897947e-01 -7.81978548e-01 9.03953135e-01 1.32297024e-01 -3.79722893e-01 2.74422854e-01 1.17954753e-01 -5.90087771e-01 2.73691088e-01 -8.14085603e-01 -5.31174064e-01 -2.10869804e-01 -8.10981467e-02 1.50615320e-01 5.32280624e-01 -3.02688509e-01 5.80327868e-01 7.76310742e-01 -1.71241498e+00 2.03394219e-02 7.75446713e-01 8.24045897e-01 1.16320178e-01 9.28744495e-01 7.69482031e-02 6.85797393e-01 -2.71052510e-01 -4.99699622e-01 5.76630056e-01 1.75551727e-01 -2.88288385e-01 3.89537454e-01 1.19859040e+00 -5.43369234e-01 -7.55927205e-01 9.91173506e-01 1.94146976e-01 1.20441683e-01 7.65874445e-01 -1.42088675e+00 -4.50839221e-01 -1.06785208e-01 -7.39684403e-01 4.47545499e-01 2.56773651e-01 4.07823026e-01 1.14929247e+00 -1.28474641e+00 7.89800644e-01 1.13548756e+00 5.73556066e-01 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f1e8ca11-1676-42ee-bd27-e6721fa323b3	ns3d-neuro-symbolic-grounding-of-3d-objects	2303.13483	null	https://arxiv.org/abs/2303.13483v1	https://arxiv.org/pdf/2303.13483v1.pdf	NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations	Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D grounded language. Hence, essential desiderata for models are to be data-efficient, generalize to different data distributions and tasks with unseen semantic forms, as well as ground complex language semantics (e.g., view-point anchoring and multi-object reference). To address these challenges, we propose NS3D, a neuro-symbolic framework for 3D grounding. NS3D translates language into programs with hierarchical structures by leveraging large language-to-code models. Different functional modules in the programs are implemented as neural networks. Notably, NS3D extends prior neuro-symbolic visual reasoning methods by introducing functional modules that effectively reason about high-arity relations (i.e., relations among more than two objects), key in disambiguating objects in complex 3D scenes. Modular and compositional architecture enables NS3D to achieve state-of-the-art results on the ReferIt3D view-dependence task, a 3D referring expression comprehension benchmark. Importantly, NS3D shows significantly improved performance on settings of data-efficiency and generalization, and demonstrate zero-shot transfer to an unseen 3D question-answering task.	['Jiajun Wu', 'Jiayuan Mao', 'Joy Hsu']	2023-03-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.pdf	cvpr-2023-1	['referring-expression', 'visual-reasoning', 'visual-reasoning']	['computer-vision', 'computer-vision', 'reasoning']	[-3.17863747e-02 3.50835323e-01 -1.48938164e-01 -4.52192456e-01 -3.96982819e-01 -8.64073753e-01 6.75868273e-01 2.36680180e-01 9.32748020e-02 -3.17821242e-02 3.05984199e-01 -5.78970551e-01 -7.80809000e-02 -1.07812083e+00 -8.81997168e-01 -1.46326557e-01 -3.43742184e-02 6.50213420e-01 1.30171776e-01 -6.04839444e-01 4.71992940e-02 6.52223527e-01 -1.69601381e+00 6.38104737e-01 6.64286971e-01 1.10701144e+00 2.68150181e-01 2.33756840e-01 -6.08574688e-01 9.88160014e-01 -6.06707692e-01 -3.76521975e-01 9.87544805e-02 -2.10108131e-01 -1.12300622e+00 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d0e695aa-2aa1-4475-b834-cf799cb37166	intel-tut-dataset-for-camera-invariant-color	1703.09778	null	http://arxiv.org/abs/1703.09778v2	http://arxiv.org/pdf/1703.09778v2.pdf	INTEL-TUT Dataset for Camera Invariant Color Constancy Research	In this paper, we provide a novel dataset designed for camera invariant color constancy research. Camera invariance corresponds to the robustness of an algorithm's performance when run on images of the same scene taken by different cameras. Accordingly, images in the database correspond to several lab and field scenes each of which are captured by three different cameras with minimal registration errors. The lab scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the lab light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation and testing partitions. Accordingly, we evaluate a recently proposed convolutional neural network based color constancy algorithm as a baseline for future research. As a side contribution, this dataset also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well.	['Moncef Gabbouj', 'Jarno Nikkanen', 'Caglar Aytekin']	2017-03-21	null	null	null	null	['color-constancy']	['computer-vision']	[ 4.38910663e-01 -7.24628389e-01 1.57780394e-01 -5.84164798e-01 -1.81366578e-01 -8.62986684e-01 2.96498865e-01 -1.60476208e-01 -3.23616743e-01 5.38441956e-01 -2.08831310e-01 -2.08231747e-01 3.99469197e-01 -4.06247765e-01 -6.39069915e-01 -7.68171430e-01 2.42677078e-01 -3.50180238e-01 7.68195763e-02 -2.11636890e-02 4.10873502e-01 6.97948217e-01 -1.72272086e+00 1.94068760e-01 6.83333814e-01 8.55611801e-01 3.53152663e-01 9.19362128e-01 3.30252796e-01 6.98059082e-01 -5.75433731e-01 -1.49564296e-01 6.88428164e-01 -5.03434062e-01 -4.02640581e-01 5.85176647e-01 1.29344881e+00 -5.76266110e-01 -5.72195202e-02 9.63784158e-01 5.15208781e-01 2.56034553e-01 1.78546116e-01 -1.19686902e+00 -8.52167428e-01 -1.65648952e-01 -4.85313296e-01 1.98898409e-02 4.54110503e-01 4.61219817e-01 7.63245165e-01 -6.53869569e-01 3.33771527e-01 8.03875864e-01 5.00013173e-01 3.68666798e-01 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-8.95505846e-01 6.43214285e-01 4.51064140e-01 -2.80231982e-01 -4.94411051e-01 6.98050618e-01]	[10.387214660644531, -2.530217170715332]
3bc0d6d3-ac55-460d-bc28-800e2c3c7f6b	instance-conditioned-gan	2109.0507	null	https://arxiv.org/abs/2109.05070v2	https://arxiv.org/pdf/2109.05070v2.pdf	Instance-Conditioned GAN	Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging in unconditional settings. In this paper, we take inspiration from kernel density estimation techniques and introduce a non-parametric approach to modeling distributions of complex datasets. We partition the data manifold into a mixture of overlapping neighborhoods described by a datapoint and its nearest neighbors, and introduce a model, called instance-conditioned GAN (IC-GAN), which learns the distribution around each datapoint. Experimental results on ImageNet and COCO-Stuff show that IC-GAN significantly improves over unconditional models and unsupervised data partitioning baselines. Moreover, we show that IC-GAN can effortlessly transfer to datasets not seen during training by simply changing the conditioning instances, and still generate realistic images. Finally, we extend IC-GAN to the class-conditional case and show semantically controllable generation and competitive quantitative results on ImageNet; while improving over BigGAN on ImageNet-LT. Code and trained models to reproduce the reported results are available at https://github.com/facebookresearch/ic_gan.	['Adriana Romero-Soriano', 'Michal Drozdzal', 'Jakob Verbeek', 'Marlène Careil', 'Arantxa Casanova']	2021-09-10	null	http://proceedings.neurips.cc/paper/2021/hash/e7ac288b0f2d41445904d071ba37aaff-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/e7ac288b0f2d41445904d071ba37aaff-Paper.pdf	neurips-2021-12	['conditional-image-generation']	['computer-vision']	[ 8.40327740e-02 3.97200376e-01 -9.83319804e-02 -3.75064135e-01 -9.59577084e-01 -6.82710588e-01 7.19083011e-01 -7.89331734e-01 2.77963467e-02 9.36774313e-01 2.14867219e-01 -1.22517638e-01 3.56125683e-01 -9.03158247e-01 -1.14240503e+00 -7.49506116e-01 3.83626759e-01 8.84453118e-01 -5.13927698e-01 2.27005988e-01 -4.36802447e-01 3.22964132e-01 -1.13341403e+00 2.12801009e-01 9.19903457e-01 8.06493282e-01 6.75388873e-02 6.97888851e-01 1.75914362e-01 9.21318293e-01 -6.22626483e-01 -5.33016264e-01 3.46158504e-01 -7.92827725e-01 -5.80555856e-01 2.38461420e-01 7.41114259e-01 -6.75176978e-01 -5.64709783e-01 9.90742326e-01 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6beff70c-d996-4d86-bb46-8722297f7969	differentiating-concepts-and-instances-for	1811.04588	null	http://arxiv.org/abs/1811.04588v1	http://arxiv.org/pdf/1811.04588v1.pdf	Differentiating Concepts and Instances for Knowledge Graph Embedding	Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.	['Zhiyuan Liu', 'Xin Lv', 'Lei Hou', 'Juanzi Li']	2018-11-12	differentiating-concepts-and-instances-for-1	https://aclanthology.org/D18-1222	https://aclanthology.org/D18-1222.pdf	emnlp-2018-10	['triple-classification']	['graphs']	[-4.56304818e-01 1.79212511e-01 -6.02238894e-01 -2.99338877e-01 2.54267812e-01 -6.09433234e-01 5.97863972e-01 5.45683622e-01 -1.55316219e-01 6.18357956e-01 3.92302364e-01 -1.59603357e-03 -5.36905885e-01 -1.34863949e+00 -4.83726740e-01 -5.12518108e-01 -1.70103595e-01 5.12240827e-01 2.57461518e-01 -3.75818521e-01 -1.11741647e-01 6.30895868e-02 -1.48791969e+00 2.91212112e-01 7.14927316e-01 1.04707861e+00 -1.12806126e-01 -1.46723241e-01 -6.99653387e-01 7.76358366e-01 -4.86105978e-01 -7.31498837e-01 -1.53006613e-01 -2.77152538e-01 -1.12267292e+00 -3.17898184e-01 1.44679755e-01 2.67303824e-01 -7.12317705e-01 1.13842440e+00 8.14043581e-02 1.00496843e-01 7.07603693e-01 -1.76333904e+00 -1.60051155e+00 7.00118303e-01 -3.60437721e-01 1.63038597e-01 4.44212377e-01 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0d16c622-82fd-42d8-9c51-e4ce6781920f	assessing-neural-referential-form-selectors	2210.04828	null	https://arxiv.org/abs/2210.04828v2	https://arxiv.org/pdf/2210.04828v2.pdf	Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset	Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) use. To tackle this issue, we build a dataset based on the OntoNotes corpus that contains a broader range of RE use in both English and Chinese (a language that uses zero pronouns). We build neural Referential Form Selection (RFS) models accordingly, assess them on the dataset and conduct probing experiments. The experiments suggest that, compared to WebNLG, OntoNotes is better for assessing REG/RFS models. We compare English and Chinese RFS and confirm that, in line with linguistic theories, Chinese RFS depends more on discourse context than English.	['Kees Van Deemter', 'Fahime Same', 'Guanyi Chen']	2022-10-10	null	null	null	null	['referring-expression-generation', 'referring-expression']	['computer-vision', 'computer-vision']	[ 1.74189970e-01 4.35044616e-01 -5.50625920e-01 -4.07578617e-01 -9.18646276e-01 -8.87382984e-01 9.98807609e-01 -1.05978534e-01 -6.53959095e-01 1.10636163e+00 1.27990365e+00 -4.97385204e-01 1.68489423e-02 -9.41282392e-01 -5.08231401e-01 6.05173036e-02 4.62764293e-01 2.63089240e-01 -2.47586712e-01 -6.39049530e-01 4.96927321e-01 1.37165695e-01 -1.09475470e+00 6.49650276e-01 7.16726303e-01 3.86844635e-01 4.71659750e-02 2.29393855e-01 -3.95239711e-01 1.15609741e+00 -1.05351913e+00 -5.44951379e-01 -1.72865391e-01 -5.32637298e-01 -1.31490910e+00 -6.26305342e-01 5.37435353e-01 -4.85046133e-02 -1.90652519e-01 7.48268604e-01 7.25945532e-01 4.80034232e-01 6.91550195e-01 -6.62954628e-01 -1.40836918e+00 1.36391473e+00 5.85974269e-02 5.00098705e-01 6.77327752e-01 -1.93891302e-01 1.33077776e+00 -8.25002015e-01 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c819865b-6571-4548-8730-c9c35bf3f39a	tsrformer-table-structure-recognition-with	2208.04921	null	https://arxiv.org/abs/2208.04921v1	https://arxiv.org/pdf/2208.04921v1.pdf	TSRFormer: Table Structure Recognition with Transformers	We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognizing the structures of complex tables with geometrical distortions from various table images. Unlike previous methods, we formulate table separation line prediction as a line regression problem instead of an image segmentation problem and propose a new two-stage DETR based separator prediction approach, dubbed \textbf{Sep}arator \textbf{RE}gression \textbf{TR}ansformer (SepRETR), to predict separation lines from table images directly. To make the two-stage DETR framework work efficiently and effectively for the separation line prediction task, we propose two improvements: 1) A prior-enhanced matching strategy to solve the slow convergence issue of DETR; 2) A new cross attention module to sample features from a high-resolution convolutional feature map directly so that high localization accuracy is achieved with low computational cost. After separation line prediction, a simple relation network based cell merging module is used to recover spanning cells. With these new techniques, our TSRFormer achieves state-of-the-art performance on several benchmark datasets, including SciTSR, PubTabNet and WTW. Furthermore, we have validated the robustness of our approach to tables with complex structures, borderless cells, large blank spaces, empty or spanning cells as well as distorted or even curved shapes on a more challenging real-world in-house dataset.	['Qiang Huo', 'Lei Sun', 'Jiawei Wang', 'Mingze Li', 'Chixiang Ma', 'Zheng Sun', 'WeiHong Lin']	2022-08-09	null	null	null	null	['table-recognition']	['computer-vision']	[ 2.69103885e-01 -9.57971066e-02 -5.18529527e-02 -3.31165940e-01 -1.06644571e+00 -8.21741402e-01 3.41356546e-01 4.96511728e-01 -9.25173908e-02 7.56480098e-01 -2.25278035e-01 -3.75053495e-01 4.29328904e-02 -9.10238802e-01 -1.18583059e+00 -3.20822954e-01 2.94803381e-01 9.51106966e-01 3.54167223e-01 -2.63402551e-01 5.64545870e-01 9.09466207e-01 -1.21043444e+00 6.38223767e-01 1.18871498e+00 1.41259158e+00 -6.71150237e-02 5.53684592e-01 -4.49198604e-01 8.49153519e-01 -5.21218359e-01 -6.90137565e-01 4.06577855e-01 4.75184321e-02 -8.11228573e-01 3.63869011e-01 6.51746690e-01 -6.02983683e-02 -3.56321037e-01 7.63666928e-01 4.25332427e-01 -9.64599028e-02 8.21452141e-01 -8.29881072e-01 -5.99491179e-01 5.37432313e-01 -1.08698630e+00 3.47941667e-02 4.93372560e-01 -2.16145113e-01 8.27761531e-01 -1.29155302e+00 6.38747573e-01 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4.28891361e-01 -6.10554814e-01 4.57463339e-02 -7.84611940e-01 -6.87889755e-01 3.02061558e-01 4.77138497e-02 -6.25382662e-01 2.36610234e-01 2.82372870e-02]	[11.711536407470703, 3.0497686862945557]
5a9c0049-d665-4eaa-b86e-5497220c05f0	nicer-slam-neural-implicit-scene-encoding-for	2302.03594	null	https://arxiv.org/abs/2302.03594v1	https://arxiv.org/pdf/2302.03594v1.pdf	NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM	Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, previous works in this direction either rely on RGB-D sensors, or require a separate monocular SLAM approach for camera tracking and do not produce high-fidelity dense 3D scene reconstruction. In this paper, we present NICER-SLAM, a dense RGB SLAM system that simultaneously optimizes for camera poses and a hierarchical neural implicit map representation, which also allows for high-quality novel view synthesis. To facilitate the optimization process for mapping, we integrate additional supervision signals including easy-to-obtain monocular geometric cues and optical flow, and also introduce a simple warping loss to further enforce geometry consistency. Moreover, to further boost performance in complicated indoor scenes, we also propose a local adaptive transformation from signed distance functions (SDFs) to density in the volume rendering equation. On both synthetic and real-world datasets we demonstrate strong performance in dense mapping, tracking, and novel view synthesis, even competitive with recent RGB-D SLAM systems.	['Marc Pollefeys', 'Andreas Geiger', 'Martin R. Oswald', 'Zhaopeng Cui', 'Viktor Larsson', 'Songyou Peng', 'Zihan Zhu']	2023-02-07	null	null	null	null	['simultaneous-localization-and-mapping', '3d-scene-reconstruction']	['computer-vision', 'computer-vision']	[ 7.48516470e-02 -2.50138193e-01 -2.77160201e-02 -5.63012958e-01 -5.25020242e-01 -6.10484898e-01 6.70324624e-01 -1.83349580e-01 -3.29981267e-01 7.57044971e-01 7.75873438e-02 -7.77267590e-02 -2.43546683e-02 -7.74905562e-01 -8.98553014e-01 -3.60660642e-01 3.21699947e-01 6.33993506e-01 -2.99039427e-02 -9.48206708e-02 1.08072966e-01 7.91025817e-01 -1.41544557e+00 -3.34588498e-01 9.07531857e-01 8.52923095e-01 5.08853734e-01 5.39990902e-01 6.58945693e-03 7.96644807e-01 -9.50715542e-02 -9.64057297e-02 5.12396991e-01 -2.48761371e-01 -4.79300469e-01 2.21258119e-01 1.00007415e+00 -5.28344274e-01 -7.19610155e-01 9.60481942e-01 5.18953502e-01 2.34279647e-01 3.17623675e-01 -1.27777851e+00 -5.97357094e-01 -1.67919278e-01 -5.36423802e-01 -4.47880059e-01 6.28484190e-01 6.24607280e-02 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d16340a4-3f90-47b9-975e-ce8faf2d1a61	exploiting-class-activation-value-for-partial	null	null	https://openreview.net/forum?id=qqdXHUGec9h	https://openreview.net/pdf?id=qqdXHUGec9h	Exploiting Class Activation Value for Partial-Label Learning	Partial-label learning (PLL) solves the multi-class classification problem, where each training instance is assigned a set of candidate labels that include the true label. Recent advances showed that PLL can be compatible with deep neural networks, which achieved state-of-the-art performance. However, most of the existing deep PLL methods focus on designing proper training objectives under various assumptions on the collected data, which may limit their performance when the collected data cannot satisfy the adopted assumptions. In this paper, we propose to exploit the learned intrinsic representation of the model to identify the true label in the training process, which does not rely on any assumptions on the collected data. We make two key contributions. As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels. Unfortunately, as CAM is confined to image inputs with convolutional neural networks, we are yet unable to directly leverage CAM to address the PLL problem with general inputs and models. Thus, as the second contribution, we propose the class activation value (CAV), which owns similar properties of CAM, while CAV is versatile in various types of inputs and models. Building upon CAV, we propose a novel method named CAV Learning (CAVL) that selects the true label by the class with the maximum CAV for model training. Extensive experiments on various datasets demonstrate that our proposed CAVL method achieves state-of-the-art performance.	['Masashi Sugiyama', 'Tao Qin', 'Gang Niu', 'Tongliang Liu', 'Bo Han', 'Lei Feng', 'Fei Zhang']	2021-09-29	null	null	null	iclr-2022-4	['partial-label-learning']	['methodology']	[ 4.56629753e-01 -1.28132086e-02 -6.08835697e-01 -4.71776187e-01 -6.44563198e-01 -6.10533237e-01 5.15241683e-01 3.31375152e-02 -3.54457587e-01 4.79585826e-01 -4.91248161e-01 -2.92565465e-01 -2.27715313e-01 -6.90896392e-01 -6.64398849e-01 -8.43291581e-01 2.70411253e-01 2.90282339e-01 2.15197101e-01 2.07083538e-01 2.84738809e-01 4.90796238e-01 -1.81338620e+00 5.07408977e-01 5.97380996e-01 1.49285948e+00 9.71201360e-02 2.76988417e-01 -2.16175050e-01 9.33056653e-01 -5.70134521e-01 -1.13372557e-01 2.93411553e-01 -5.52453637e-01 -7.64646173e-01 1.01745874e-01 4.50967669e-01 3.47712240e-03 1.28595784e-01 9.80174482e-01 3.62769425e-01 -1.33118570e-01 9.40125287e-01 -1.46692896e+00 -3.42570335e-01 3.24520022e-01 -4.63561147e-01 1.80927143e-02 -1.59685224e-01 5.49829099e-03 1.17405307e+00 -9.62749243e-01 3.42389971e-01 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ed2e50ed-5bc0-452e-9434-3866f26efabd	representing-videos-as-discriminative-sub-1	2201.04027	null	https://arxiv.org/abs/2201.04027v1	https://arxiv.org/pdf/2201.04027v1.pdf	Representing Videos as Discriminative Sub-graphs for Action Recognition	Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.	['Tao Mei', 'Houqiang Li', 'Ting Yao', 'Yingwei Pan', 'Zhaofan Qiu', 'Dong Li']	2022-01-11	representing-videos-as-discriminative-sub	http://openaccess.thecvf.com//content/CVPR2021/html/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.pdf	cvpr-2021-1	['online-clustering']	['computer-vision']	[-2.34292746e-01 -1.03313506e-01 -3.56103778e-01 -6.42632693e-02 -1.68809414e-01 -5.30231297e-01 6.20779812e-01 1.41854554e-01 6.88276961e-02 1.20313451e-01 3.74722272e-01 2.23942176e-01 -2.75333107e-01 -4.13789898e-01 -6.70710862e-01 -6.66319013e-01 -5.86605728e-01 5.43694854e-01 5.37955344e-01 2.62296647e-01 9.21931416e-02 4.91152197e-01 -1.45082152e+00 5.09585798e-01 4.13560957e-01 9.66208756e-01 6.25340864e-02 6.54040098e-01 -7.93152023e-03 1.16600204e+00 -4.09423083e-01 -2.60116458e-01 1.91068858e-01 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bc52332a-d884-4409-8844-52122253890b	assemblyhands-towards-egocentric-activity	2304.12301	null	https://arxiv.org/abs/2304.12301v1	https://arxiv.org/pdf/2304.12301v1.pdf	AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation	We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object interactions. The dataset includes synchronized egocentric and exocentric images sampled from the recent Assembly101 dataset, in which participants assemble and disassemble take-apart toys. To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset. Our annotation model uses multi-view feature fusion and an iterative refinement scheme, and achieves an average keypoint error of 4.20 mm, which is 85% lower than the error of the original annotations in Assembly101. AssemblyHands provides 3.0M annotated images, including 490K egocentric images, making it the largest existing benchmark dataset for egocentric 3D hand pose estimation. Using this data, we develop a strong single-view baseline of 3D hand pose estimation from egocentric images. Furthermore, we design a novel action classification task to evaluate predicted 3D hand poses. Our study shows that having higher-quality hand poses directly improves the ability to recognize actions.	['Cem Keskin', 'Luan Tran', 'Tomas Hodan', 'Fadime Sener', 'Kun He', 'Takehiko Ohkawa']	2023-04-24	null	http://openaccess.thecvf.com//content/CVPR2023/html/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-hand-pose-estimation', 'action-classification', 'hand-pose-estimation', '3d-hand-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'graphs']	[-1.53434828e-01 -1.85947210e-01 -2.05486804e-01 -9.81194079e-02 -8.27771544e-01 -9.31123674e-01 4.77573901e-01 -5.74007750e-01 -4.44553971e-01 4.32820559e-01 7.19246924e-01 3.44825625e-01 1.72799528e-01 5.96890040e-02 -7.79967606e-01 -5.61613500e-01 8.36112499e-02 1.01284909e+00 1.81907550e-01 -2.75699422e-02 3.94978017e-01 6.66590393e-01 -1.36677444e+00 3.29561889e-01 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8406554a-1ba4-4a57-96dc-a895db6a7929	dual-networks-based-3d-multi-person-pose	2205.00748	null	https://arxiv.org/abs/2205.00748v3	https://arxiv.org/pdf/2205.00748v3.pdf	Dual networks based 3D Multi-Person Pose Estimation from Monocular Video	Monocular 3D human pose estimation has made progress in recent years. Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i.e., the coordinates based on the center of the target person. Hence, these methods are inapplicable for multi-person 3D pose estimation, where the absolute coordinates (e.g., the camera coordinates) are required. Moreover, multi-person pose estimation is more challenging than single pose estimation, due to inter-person occlusion and close human interactions. Existing top-down multi-person methods rely on human detection (i.e., top-down approach), and thus suffer from the detection errors and cannot produce reliable pose estimation in multi-person scenes. Meanwhile, existing bottom-up methods that do not use human detection are not affected by detection errors, but since they process all persons in a scene at once, they are prone to errors, particularly for persons in small scales. To address all these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. To address the common gaps between training and testing data, we do optimization during the test time, by refining the estimated 3D human poses using high-order temporal constraint, re-projection loss, and bone length regularizations. Our evaluations demonstrate the effectiveness of the proposed method. Code and models are available: https://github.com/3dpose/3D-Multi-Person-Pose.	['Robby T. Tan', 'Bo wang', 'Yu Cheng']	2022-05-02	null	null	null	null	['3d-pose-estimation', '3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', 'monocular-3d-human-pose-estimation', '3d-multi-person-pose-estimation', 'multi-person-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[-2.87128329e-01 -2.16045752e-01 1.07514165e-01 -7.87975788e-02 -5.43746471e-01 -3.05522084e-01 1.62593752e-01 -2.03922868e-01 -6.10715687e-01 5.14394760e-01 2.26713538e-01 4.81739670e-01 1.55091956e-01 -7.16225326e-01 -6.60199761e-01 -5.70474684e-01 8.45975429e-02 6.77403152e-01 4.80145723e-01 -1.72088534e-01 -3.12740177e-01 4.63835210e-01 -1.30434370e+00 -1.56321138e-01 6.27768338e-01 5.43045759e-01 -1.27814680e-01 5.40850043e-01 6.05237186e-01 -1.33889779e-01 -6.72648489e-01 -4.10485893e-01 4.70428944e-01 -2.65762866e-01 -1.29072696e-01 2.05412999e-01 7.24972963e-01 -5.60558558e-01 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eed054a9-7c45-494c-bc4f-0a92a6891428	rotate-and-render-unsupervised-photorealistic	2003.08124	null	https://arxiv.org/abs/2003.08124v1	https://arxiv.org/pdf/2003.08124v1.pdf	Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images	Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods. The current generative models heavily rely on datasets with multi-view images of the same person. Thus, their generated results are restricted by the scale and domain of the data source. To overcome these challenges, we propose a novel unsupervised framework that can synthesize photo-realistic rotated faces using only single-view image collections in the wild. Our key insight is that rotating faces in the 3D space back and forth, and re-rendering them to the 2D plane can serve as a strong self-supervision. We leverage the recent advances in 3D face modeling and high-resolution GAN to constitute our building blocks. Since the 3D rotation-and-render on faces can be applied to arbitrary angles without losing details, our approach is extremely suitable for in-the-wild scenarios (i.e. no paired data are available), where existing methods fall short. Extensive experiments demonstrate that our approach has superior synthesis quality as well as identity preservation over the state-of-the-art methods, across a wide range of poses and domains. Furthermore, we validate that our rotate-and-render framework naturally can act as an effective data augmentation engine for boosting modern face recognition systems even on strong baseline models.	['Yu Liu', 'Jihao Liu', 'Hang Zhou', 'Ziwei Liu', 'Xiaogang Wang']	2020-03-18	rotate-and-render-unsupervised-photorealistic-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.pdf	cvpr-2020-6	['3d-face-modeling']	['computer-vision']	[ 1.03840545e-01 -1.98891923e-01 -2.03701228e-01 -5.10373831e-01 -6.35572910e-01 -6.28977895e-01 9.28957462e-01 -1.02052724e+00 9.14067030e-02 5.28875232e-01 2.27278367e-01 4.68387157e-02 3.20805728e-01 -7.44804561e-01 -7.34988391e-01 -7.24240303e-01 4.53724295e-01 6.09999180e-01 -3.64723414e-01 -4.16384935e-01 -3.45142126e-01 7.60582566e-01 -1.71737957e+00 6.23147637e-02 5.87588191e-01 7.16511846e-01 -1.56349510e-01 2.87350982e-01 2.31899664e-01 2.84324944e-01 -4.85713661e-01 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3b615e6d-e1fa-4242-811f-f2d0fd4e01dd	goalienet-a-multi-stage-network-for-joint	2306.15853	null	https://arxiv.org/abs/2306.15853v1	https://arxiv.org/pdf/2306.15853v1.pdf	GoalieNet: A Multi-Stage Network for Joint Goalie, Equipment, and Net Pose Estimation in Ice Hockey	In the field of computer vision-driven ice hockey analytics, one of the most challenging and least studied tasks is goalie pose estimation. Unlike general human pose estimation, goalie pose estimation is much more complex as it involves not only the detection of keypoints corresponding to the joints of the goalie concealed under thick padding and mask, but also a large number of non-human keypoints corresponding to the large leg pads and gloves worn, the stick, as well as the hockey net. To tackle this challenge, we introduce GoalieNet, a multi-stage deep neural network for jointly estimating the pose of the goalie, their equipment, and the net. Experimental results using NHL benchmark data demonstrate that the proposed GoalieNet can achieve an average of 84\% accuracy across all keypoints, where 22 out of 29 keypoints are detected with more than 80\% accuracy. This indicates that such a joint pose estimation approach can be a promising research direction.	['Alexander Wong', 'David Clausi', 'Marjan Shahi']	2023-06-28	null	null	null	null	['pose-estimation']	['computer-vision']	[-2.71773994e-01 4.79764938e-02 2.04973876e-01 3.30391169e-01 -6.15798533e-01 -5.88711023e-01 2.70052105e-02 -1.09862737e-01 -5.06324410e-01 1.92012906e-01 -1.89443439e-01 4.77631062e-01 -2.20168501e-01 -3.21312994e-01 -1.00324297e+00 -3.38167995e-01 -1.93638399e-01 4.95261729e-01 3.03232461e-01 -3.28089833e-01 -3.99715500e-03 3.35401148e-01 -1.26455522e+00 -3.72800916e-01 1.05796844e-01 1.41567683e+00 -6.34776056e-02 5.49297988e-01 9.84987497e-01 5.57929397e-01 -9.43624496e-01 -6.10429704e-01 5.46065986e-01 2.19456866e-01 -1.23436609e-02 -1.34302363e-01 8.32795084e-01 -7.95206547e-01 -4.35618758e-01 8.25727403e-01 5.47193170e-01 1.14344865e-01 4.72123682e-01 -1.58404481e+00 1.75885051e-01 1.76029637e-01 -8.39167833e-01 -2.33911470e-01 4.82452750e-01 2.55022764e-01 7.13989794e-01 -8.41773689e-01 5.56782961e-01 8.01794767e-01 1.20967090e+00 -2.16599200e-02 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77c9c5bc-b6cb-4892-8b9d-3cc2f12e7bbe	grid-tagging-scheme-for-aspect-oriented-fine	2010.0464	null	https://arxiv.org/abs/2010.04640v2	https://arxiv.org/pdf/2010.04640v2.pdf	Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction	Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.	['Rui Xia', 'Xinyu Dai', 'Zhifang Fan', 'Fei Zhao', 'Chengcan Ying', 'Zhen Wu']	2020-10-09	null	https://aclanthology.org/2020.findings-emnlp.234	https://aclanthology.org/2020.findings-emnlp.234.pdf	findings-of-the-association-for-computational	['aspect-sentiment-opinion-triplet-extraction', 'aspect-sentiment-triplet-extraction']	['natural-language-processing', 'natural-language-processing']	[-3.89313325e-02 -2.69041155e-02 -9.16108266e-02 -5.83150208e-01 -1.00315320e+00 -6.61509216e-01 5.14514685e-01 1.92849301e-02 -1.69985637e-01 5.73576570e-01 3.47356111e-01 -4.15338427e-01 2.65742302e-01 -7.69270003e-01 -4.17840421e-01 -6.53765500e-01 2.47528002e-01 2.32824042e-01 -1.82545781e-02 -1.71378881e-01 2.76825160e-01 -1.12020209e-01 -1.14726174e+00 5.05269945e-01 1.04352164e+00 1.32065165e+00 -2.20572710e-01 4.62041885e-01 -4.13471758e-01 8.04649293e-01 -8.42851520e-01 -1.01604593e+00 1.05818912e-01 -2.72234350e-01 -5.14076412e-01 3.30424696e-01 2.75825579e-02 -3.81551236e-02 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3ff18420-b7b6-49fa-84d7-869c472e22bb	deep-learning-for-spatio-temporal-forecasting	2205.03571	null	https://arxiv.org/abs/2205.03571v1	https://arxiv.org/pdf/2205.03571v1.pdf	Deep learning for spatio-temporal forecasting -- application to solar energy	This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge. The first direction concerns the role of the training loss function. We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models. We address both the deterministic context with the proposed DILATE loss function and the probabilistic context with the STRIPE model. Our second direction is to augment incomplete physical models with deep data-driven networks for accurate forecasting. For video prediction, we introduce the PhyDNet model that disentangles physical dynamics from residual information necessary for prediction, such as texture or details. We further propose a learning framework (APHYNITY) that ensures a principled and unique linear decomposition between physical and data-driven components under mild assumptions, leading to better forecasting performances and parameter identification.	['Vincent Le Guen']	2022-05-07	null	null	null	null	['video-prediction', 'spatio-temporal-forecasting']	['computer-vision', 'time-series']	[ 3.07923090e-02 -2.39186183e-01 -4.19050068e-01 -3.18020105e-01 -2.21504167e-01 -3.84160876e-01 1.01957798e+00 -4.76974338e-01 2.33827546e-01 8.92297983e-01 9.18448344e-02 -3.67232472e-01 -5.65933883e-01 -9.32127833e-01 -9.35982168e-01 -1.37688208e+00 -5.25339618e-02 -6.69892132e-03 -1.49975438e-02 -1.70547113e-01 2.09005207e-01 9.26017404e-01 -1.54218423e+00 1.48427725e-01 8.74387205e-01 1.42071331e+00 2.74422020e-01 9.73813355e-01 1.31819025e-03 1.02894461e+00 -5.20012900e-02 1.93708852e-01 4.68273520e-01 -1.97454348e-01 -4.50468302e-01 7.41505204e-03 3.87486845e-01 -5.39789557e-01 -6.07074082e-01 4.82832730e-01 4.09989923e-01 2.84345150e-01 8.66448462e-01 -1.32442331e+00 -5.23842096e-01 9.74258110e-02 -2.94427305e-01 1.05298638e-01 -2.58278996e-01 3.08871806e-01 7.41548121e-01 -5.15664577e-01 3.82024497e-01 9.43692386e-01 1.00179207e+00 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-6.74585879e-01 -5.06809711e-01 -8.83305892e-02 8.13634038e-01 -7.09894419e-01 -5.96457541e-01 1.19495071e-01]	[6.559482097625732, 3.3089616298675537]
e723e2e4-48b1-4648-8ffc-0ca630530fae	personalized-automatic-sleep-staging-with	2004.11349	null	https://arxiv.org/abs/2004.11349v2	https://arxiv.org/pdf/2004.11349v2.pdf	Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization	Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and the output of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Experimental results on the Sleep-EDF Expanded database with 75 subjects show that sleep staging personalization with a single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen's kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to non-personalization and 2.2 percentage points compared to personalization without regularization.	['Preben Kidmose', 'Oliver Y. Chén', 'Alfred Mertins', 'Philipp Koch', 'Kaare Mikkelsen', 'Huy Phan', 'Maarten De Vos']	2020-04-23	null	null	null	null	['sleep-staging']	['medical']	[-9.41505432e-02 1.85471967e-01 -2.40588598e-02 -7.27319300e-01 -5.66557884e-01 -6.95307180e-02 5.34103028e-02 -1.00479955e-02 -1.03867686e+00 1.04214954e+00 6.19917884e-02 7.57182762e-02 -2.23416835e-01 -4.58132386e-01 -4.73949611e-01 -8.63742292e-01 4.02507037e-02 4.59022850e-01 2.54946142e-01 6.91721365e-02 -8.64948146e-03 4.90055010e-02 -1.26170278e+00 -2.56762534e-01 1.23824954e+00 9.29853261e-01 1.18442923e-01 4.15802300e-01 2.74586082e-01 8.10844079e-02 -7.37544775e-01 -4.11512166e-01 1.86635807e-01 -4.35492843e-01 -7.09031880e-01 2.50137262e-02 1.33390874e-01 -1.64464582e-02 3.18280458e-02 9.19722140e-01 5.15495837e-01 5.82218289e-01 4.60109502e-01 -1.03094327e+00 -5.10907531e-01 2.41529495e-01 -4.46579754e-01 6.04117453e-01 -1.15072213e-01 1.12653576e-01 6.85830534e-01 -4.14807588e-01 1.70196220e-01 5.27570724e-01 9.79331374e-01 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a90b59b5-1214-493d-bbf8-ccabbb0e16ad	swim-a-general-purpose-high-performing-and	2303.0264	null	https://arxiv.org/abs/2303.02640v1	https://arxiv.org/pdf/2303.02640v1.pdf	Swim: A General-Purpose, High-Performing, and Efficient Activation Function for Locomotion Control Tasks	Activation functions play a significant role in the performance of deep learning algorithms. In particular, the Swish activation function tends to outperform ReLU on deeper models, including deep reinforcement learning models, across challenging tasks. Despite this progress, ReLU is the preferred function partly because it is more efficient than Swish. Furthermore, in contrast to the fields of computer vision and natural language processing, the deep reinforcement learning and robotics domains have seen less inclination to adopt new activation functions, such as Swish, and instead continue to use more traditional functions, like ReLU. To tackle those issues, we propose Swim, a general-purpose, efficient, and high-performing alternative to Swish, and then provide an analysis of its properties as well as an explanation for its high-performance relative to Swish, in terms of both reward-achievement and efficiency. We focus on testing Swim on MuJoCo's locomotion continuous control tasks since they exhibit more complex dynamics and would therefore benefit most from a high-performing and efficient activation function. We also use the TD3 algorithm in conjunction with Swim and explain this choice in the context of the robot locomotion domain. We then conclude that Swim is a state-of-the-art activation function for continuous control locomotion tasks and recommend using it with TD3 as a working framework.	['Tony Dear', 'Maryam Abdool']	2023-03-05	null	null	null	null	['continuous-control']	['playing-games']	[-3.39265764e-01 -2.62679905e-01 -1.39835507e-01 7.35818818e-02 -2.64555030e-03 -3.12740952e-01 6.89474463e-01 -1.03295773e-01 -9.36151981e-01 1.05346036e+00 9.60328430e-02 -1.81308404e-01 -5.53070068e-01 -8.60857725e-01 -5.65240264e-01 -7.82691061e-01 -2.75516063e-01 5.50689220e-01 2.54580021e-01 -7.83987701e-01 2.63202280e-01 3.08376878e-01 -1.88452625e+00 -2.37906709e-01 8.18162739e-01 4.20904696e-01 3.94966245e-01 5.18912256e-01 1.91668287e-01 8.32516789e-01 -6.41526401e-01 1.46424294e-01 -1.80612933e-02 -7.26892471e-01 -1.10689867e+00 -4.19600725e-01 -3.00089896e-01 -1.17025219e-01 7.21867830e-02 4.00796741e-01 7.17177629e-01 6.71504796e-01 7.04489827e-01 -1.28867447e+00 -2.86524773e-01 6.42368317e-01 -3.91687185e-01 2.55218327e-01 1.51649430e-01 4.08824176e-01 1.02753413e+00 -5.38423538e-01 8.02916348e-01 1.18855417e+00 7.27590322e-01 7.42273152e-01 -1.23361325e+00 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387537bf-3aee-4b50-9757-8d9f6f560c0d	attention-based-clinical-note-summarization	2104.08942	null	https://arxiv.org/abs/2104.08942v3	https://arxiv.org/pdf/2104.08942v3.pdf	Attention-based Clinical Note Summarization	In recent years, the trend of deploying digital systems in numerous industries has hiked. The health sector has observed an extensive adoption of digital systems and services that generate significant medical records. Electronic health records contain valuable information for prospective and retrospective analysis that is often not entirely exploited because of the complicated dense information storage. The crude purpose of condensing health records is to select the information that holds most characteristics of the original documents based on a reported disease. These summaries may boost diagnosis and save a doctor's time during a saturated workload situation like the COVID-19 pandemic. In this paper, we are applying a multi-head attention-based mechanism to perform extractive summarization of meaningful phrases on clinical notes. Our method finds major sentences for a summary by correlating tokens, segments, and positional embeddings of sentences in a clinical note. The model outputs attention scores that are statistically transformed to extract critical phrases for visualization on the heat-mapping tool and for human use.	['Giuseppe Rizzo', 'Neel Kanwal']	2021-04-18	null	null	null	null	['clinical-information-retreival']	['natural-language-processing']	[ 5.19114077e-01 4.23749626e-01 -2.12415248e-01 -2.34187752e-01 -1.11116445e+00 -3.26096326e-01 2.40478635e-01 1.31123590e+00 -3.47501695e-01 6.31152630e-01 1.27010858e+00 -3.55603844e-01 -3.14826101e-01 -5.49315453e-01 -1.72714040e-01 -6.43577993e-01 -2.98116356e-01 6.10524416e-01 -2.93369651e-01 -3.20881084e-02 5.48318207e-01 3.73992652e-01 -1.22501874e+00 7.83858299e-01 8.55016172e-01 4.84456003e-01 4.06021655e-01 9.71525431e-01 -4.24703270e-01 5.58007240e-01 -9.32855904e-01 -2.38218322e-01 -1.53556004e-01 -6.13926411e-01 -7.59573817e-01 -3.24845612e-01 -5.18691167e-02 -1.34215012e-01 -1.65621132e-01 9.57951725e-01 9.98660862e-01 -8.65384489e-02 4.67997164e-01 -6.14411116e-01 -6.46633565e-01 7.74731040e-01 -4.09103274e-01 6.65197790e-01 6.56413615e-01 -2.30750497e-02 8.90053511e-01 -6.56492472e-01 1.05369818e+00 9.90063369e-01 6.64799511e-01 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-4.74575877e-01 -3.45506966e-01 -1.03459060e+00 -2.48193637e-01 4.74661440e-01 6.40273154e-01 1.64202228e-01 8.79842192e-02 2.45684966e-01]	[8.56306266784668, 8.560612678527832]
7f86d0cd-967d-4408-a6c7-1a4ba4da39c1	representation-learning-over-dynamic-graphs	1803.04051	null	http://arxiv.org/abs/1803.04051v2	http://arxiv.org/pdf/1803.04051v2.pdf	Representation Learning over Dynamic Graphs	How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embeddings drive the dynamics of two key processes namely, communication and association between nodes in dynamic graphs. These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings. We employ a time-scale dependent multivariate point process model to capture these dynamics. We devise an efficient unsupervised learning procedure and demonstrate that our approach significantly outperforms representative baselines on two real-world datasets for the problem of dynamic link prediction and event time prediction.	['Hongyuan Zha', 'Rakshit Trivedi', 'Prasenjeet Biswal', 'Mehrdad Farajtabar']	2018-03-11	null	null	null	null	['dynamic-link-prediction']	['graphs']	[-2.99317509e-01 1.46782398e-01 -2.75776714e-01 -1.82435453e-01 -8.35407674e-02 -6.36077046e-01 9.77131128e-01 6.04788065e-01 -5.60151115e-02 2.96098202e-01 4.98756260e-01 -3.65045100e-01 -4.92916703e-01 -1.30101562e+00 -5.59467793e-01 -4.12088364e-01 -9.86741364e-01 9.86726284e-01 1.57404855e-01 -2.91269422e-01 -3.55367869e-01 5.79472244e-01 -8.57016444e-01 -2.29029596e-01 2.61787921e-01 3.84413511e-01 -5.21954477e-01 1.25471020e+00 -8.41585696e-02 7.66242862e-01 -3.39182884e-01 -6.34627104e-01 1.21653043e-01 -2.66683791e-02 -6.95193350e-01 -1.62138864e-01 -2.66798228e-01 -2.61508763e-01 -1.39873099e+00 5.50572634e-01 2.59664178e-01 1.76514715e-01 1.09783947e+00 -1.39460373e+00 -1.24082494e+00 7.11710334e-01 -4.48034495e-01 9.01798606e-01 5.24055287e-02 1.13689475e-01 1.34941876e+00 -4.60021496e-01 1.00839043e+00 1.44687068e+00 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65403034-5fbe-4c87-af4d-492e4f504962	visual-slam-what-are-the-current-trends-and	2210.10491	null	https://arxiv.org/abs/2210.10491v2	https://arxiv.org/pdf/2210.10491v2.pdf	Visual SLAM: What are the Current Trends and What to Expect?	Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. We can see many research works that demonstrated VSLAMs can outperform traditional methods, which rely only on a particular sensor, such as a Lidar, even with lower costs. VSLAM approaches utilize different camera types (e.g., monocular, stereo, and RGB-D), have been tested on various datasets (e.g., KITTI, TUM RGB-D, and EuRoC) and in dissimilar environments (e.g., indoors and outdoors), and employ multiple algorithms and methodologies to have a better understanding of the environment. The mentioned variations have made this topic popular for researchers and resulted in a wide range of VSLAMs methodologies. In this regard, the primary intent of this survey is to present the recent advances in VSLAM systems, along with discussing the existing challenges and trends. We have given an in-depth literature survey of forty-five impactful papers published in the domain of VSLAMs. We have classified these manuscripts by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. We also discuss the current trends and future directions that may help researchers investigate them.	['Holger Voos', 'Jose Luis Sanchez-Lopez', 'Hriday Bavle', 'Ali Tourani']	2022-10-19	null	null	null	null	['simultaneous-localization-and-mapping']	['computer-vision']	[-1.97510682e-02 -6.56644583e-01 -1.33376688e-01 -4.52836096e-01 -1.73559338e-01 -7.75308609e-01 6.73938274e-01 2.57154703e-02 -4.97078627e-01 9.78678763e-01 -1.41955256e-01 -2.53171660e-02 -4.80067655e-02 -7.56214261e-01 -6.20957077e-01 -4.81948167e-01 6.32998347e-02 2.64760554e-01 3.24929625e-01 -2.63886720e-01 6.53283298e-01 8.63997757e-01 -1.91432512e+00 -4.47906911e-01 7.84595907e-01 8.40033054e-01 6.67764068e-01 2.82087654e-01 -3.03650379e-01 2.42939919e-01 -4.63536799e-01 -1.14425328e-02 2.90473104e-01 -1.71068400e-01 -2.51855731e-01 -2.29659408e-01 5.88150918e-01 -3.51894237e-02 -2.67295122e-01 9.57935810e-01 6.74396932e-01 -3.23337689e-02 3.79954398e-01 -1.50044382e+00 -4.96259481e-01 -1.33453026e-01 -7.52805889e-01 -5.57266884e-02 1.02058017e+00 -1.69790432e-01 3.41239452e-01 -1.08704138e+00 6.21047616e-01 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-1.09862030e+00 -3.96669470e-02 -8.22477400e-01 -5.66848099e-01 -9.04453546e-02 3.58140290e-01 -1.14920437e+00 -3.06746989e-01 -5.64071596e-01]	[7.36745023727417, -2.1522836685180664]
343bd28d-d51a-4f23-9cc1-79aa44025eb1	simple-and-effective-unsupervised-speech	2204.02524	null	https://arxiv.org/abs/2204.02524v3	https://arxiv.org/pdf/2204.02524v3.pdf	Simple and Effective Unsupervised Speech Synthesis	We introduce the first unsupervised speech synthesis system based on a simple, yet effective recipe. The framework leverages recent work in unsupervised speech recognition as well as existing neural-based speech synthesis. Using only unlabeled speech audio and unlabeled text as well as a lexicon, our method enables speech synthesis without the need for a human-labeled corpus. Experiments demonstrate the unsupervised system can synthesize speech similar to a supervised counterpart in terms of naturalness and intelligibility measured by human evaluation.	['Alexei Baevski', 'James Glass', 'Michael Auli', 'Wei-Ning Hsu', 'Cheng-I Jeff Lai', 'Alexander H. Liu']	2022-04-06	null	null	null	null	['unsupervised-speech-recognition']	['speech']	[ 3.81245255e-01 6.44170821e-01 -1.45821452e-01 -5.44083178e-01 -9.55848157e-01 -6.17979765e-01 9.10713196e-01 -1.06879517e-01 -1.43977642e-01 6.20167077e-01 7.63004243e-01 -5.21149099e-01 4.46972668e-01 -4.76314336e-01 -4.28416252e-01 -3.74502599e-01 3.77042860e-01 3.76881242e-01 -9.45689082e-02 -3.93864274e-01 -1.72596261e-01 1.96216986e-01 -1.47593319e+00 6.41836822e-02 7.54389405e-01 8.62960756e-01 3.65805984e-01 9.02335703e-01 -1.53990641e-01 1.09511411e+00 -9.77888405e-01 -2.54975826e-01 1.18965628e-02 -6.94386184e-01 -7.97496378e-01 5.93394101e-01 -1.85171999e-02 -5.69383800e-01 -6.02817833e-01 7.63437986e-01 5.70862055e-01 6.73292160e-01 6.14185572e-01 -6.87807560e-01 -7.38144875e-01 9.95071471e-01 6.15447581e-01 -1.53439909e-01 5.29459596e-01 3.30022484e-01 1.14490306e+00 -1.10340881e+00 5.29813290e-01 1.15108383e+00 2.09310919e-01 8.54009628e-01 -1.25906610e+00 -2.92039722e-01 -9.95494276e-02 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5.43542087e-01 -2.20488697e-01 -4.15035129e-01 4.73328322e-01]	[14.692024230957031, 6.82486629486084]
17b4fb8a-bb0f-46ce-a3c1-834bc496d91f	confidence-guided-semi-supervised-learning-in	2305.10344	null	https://arxiv.org/abs/2305.10344v2	https://arxiv.org/pdf/2305.10344v2.pdf	Confidence-Guided Semi-supervised Learning in Land Cover Classification	Semi-supervised learning has been well developed to help reduce the cost of manual labelling by exploiting a large quantity of unlabelled data. Especially in the application of land cover classification, pixel-level manual labelling in large-scale imagery is labour-intensive, time-consuming and expensive. However, existing semi-supervised learning methods pay limited attention to the quality of pseudo-labels during training even though the quality of training data is one of the critical factors determining network performance. In order to fill this gap, we develop a confidence-guided semi-supervised learning (CGSSL) approach to make use of high-confidence pseudo labels and reduce the negative effect of low-confidence ones for land cover classification. Meanwhile, the proposed semi-supervised learning approach uses multiple network architectures to increase the diversity of pseudo labels. The proposed semi-supervised learning approach significantly improves the performance of land cover classification compared to the classic semi-supervised learning methods and even outperforms fully supervised learning with a complete set of labelled imagery of the benchmark Potsdam land cover dataset.	['Paul L. Rosin', 'Oktay Karakus', 'Wanli Ma']	2023-05-17	null	null	null	null	['pseudo-label']	['miscellaneous']	[ 6.06630802e-01 2.49290213e-01 -6.28413618e-01 -7.00971127e-01 -7.32878745e-01 -4.09063101e-01 5.45674086e-01 4.56861585e-01 -6.10001206e-01 1.04947186e+00 -1.12654261e-01 -5.48329175e-01 -2.68281251e-01 -1.05616879e+00 -5.53297400e-01 -7.62851238e-01 -1.06262952e-01 3.60275120e-01 1.74608484e-01 -2.12247044e-01 -1.45235911e-01 2.56197661e-01 -1.74400055e+00 -3.46868634e-02 1.29501474e+00 8.80634844e-01 3.65967423e-01 8.55327025e-02 -1.37706190e-01 7.92918324e-01 8.26212168e-02 1.56347349e-01 2.68740773e-01 -4.02025819e-01 -8.18886578e-01 3.94509792e-01 1.61043614e-01 -8.70185196e-02 3.17376137e-01 1.26688349e+00 1.39635473e-01 -1.24095969e-01 7.95023024e-01 -9.95778084e-01 1.03808179e-01 7.07294762e-01 -6.73877895e-01 -1.56709775e-01 -4.57796723e-01 -2.05871329e-01 1.02242208e+00 -5.90906918e-01 3.67939323e-01 9.22601461e-01 7.49783516e-01 4.57119606e-02 -1.25567484e+00 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6.70414045e-02 4.29638386e-01 -7.29524076e-01 -3.15068156e-01 1.67719945e-01]	[9.670392990112305, -1.4026988744735718]
cc76acc6-68b5-4c3b-984a-2e8d67bdd693	application-of-information-spectrum-method-on	1907.02713	null	http://arxiv.org/abs/1907.02713v3	http://arxiv.org/pdf/1907.02713v3.pdf	Application of Information Spectrum Method on Small Molecules and Target Recognition	Current methods for investigation of receptor - ligand interactions in drug discovery are based on three-dimensional complementarity of receptor and ligand surfaces, and they include pharmacophore modelling, QSAR, molecular docking etc. Those methods only consider short-range molecular interactions (distances <5A), and not include long-range interactions (distances >5A) which are essential for kinetic of biochemical reactions because they influence the number of productive collisions between interacting molecules. Previously was shown that the electron-ion interaction potential (EIIP) represents the physical property which determines the long-range properties of biological molecules. This molecular descriptor served as a base for development of the informational spectrum method (ISM), a virtual spectroscopy method for investigation of protein-protein interactions. In this paper, we proposed a new approach to treat small molecules as linear entities, allowing study of the small molecule - protein interaction by ISM. We analyzed here 21 sets of KEGG drug-protein interactions and showed that this new approach allows an efficient discrimination between biologically active and inactive ligands, and consistence with AA regions of their binding site on the target protein.	[]	2020-04-15	null	null	null	null	['molecular-docking']	['medical']	[ 2.41145864e-01 -7.82723501e-02 -2.79199332e-01 -4.08508033e-01 -1.58711568e-01 -6.15574181e-01 3.90826434e-01 5.74140549e-01 -5.09106815e-01 1.48088193e+00 1.95764020e-01 -5.06602526e-01 -2.35849530e-01 -7.98834920e-01 -6.90481246e-01 -8.67239892e-01 -3.54766548e-01 7.72352338e-01 3.68233711e-01 -3.66915971e-01 4.78105158e-01 1.06251550e+00 -1.38790178e+00 4.68106389e-01 9.66526747e-01 8.69615749e-02 4.33683023e-02 3.08524579e-01 -3.95584166e-01 2.08164364e-01 -3.76093000e-01 3.91775407e-02 -4.43935841e-01 -7.16863811e-01 -7.75194883e-01 -7.07628727e-01 -3.50640476e-01 5.10834098e-01 4.68957156e-01 6.61168456e-01 7.51141250e-01 1.66441202e-02 1.36691797e+00 -4.54943061e-01 -4.75548685e-01 3.15966219e-01 -3.40520233e-01 8.02018419e-02 1.00394428e+00 -1.14774875e-01 8.38526905e-01 -1.07988477e+00 1.03958929e+00 1.32060564e+00 4.54042763e-01 5.46827555e-01 -1.42334020e+00 -6.47921741e-01 -5.06284237e-01 3.71914625e-01 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