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b7db5826-76aa-474b-bf2e-f8c47c947c7f	context-aware-neural-based-dialog-act	1902.11060	null	http://arxiv.org/abs/1902.11060v1	http://arxiv.org/pdf/1902.11060v1.pdf	Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions	This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional neural networks (CNNs) and conditional random fields (CRFs) for context modeling in DA classification. We explore the impact of transcriptions generated from different automatic speech recognition systems such as hybrid TDNN/HMM and End-to-End systems on the final performance. Experimental results on two benchmark datasets (MRDA and SwDA) show that the combination CNN and CRF improves consistently the accuracy. Furthermore, they show that although the word error rates are comparable, End-to-End ASR system seems to be more suitable for DA classification.	['Gisela Vallejo', 'Chia-Yu Li', 'Daniel Ortega', 'Pavel Denisov', 'Ngoc Thang Vu']	2019-02-28	null	null	null	null	['dialog-act-classification']	['natural-language-processing']	[-1.54568523e-01 1.52874380e-01 1.20534867e-01 -8.49294007e-01 -6.45488143e-01 -5.65861821e-01 1.05421984e+00 -1.17523121e-02 -7.14739084e-01 9.61169720e-01 6.90538168e-01 -4.10305589e-01 5.41728973e-01 -4.04939145e-01 1.97164699e-01 -4.94619548e-01 3.00720543e-01 8.39094341e-01 1.07439056e-01 -4.32756007e-01 8.96978453e-02 5.19992113e-01 -9.57002044e-01 4.96889740e-01 5.45358539e-01 8.47526193e-01 1.54977992e-01 1.01212585e+00 -3.97536069e-01 1.24944496e+00 -1.14179003e+00 -3.89366865e-01 -1.46462128e-01 -2.09619626e-01 -1.29533231e+00 6.59115687e-02 2.48243660e-01 -3.19762558e-01 -3.87656838e-01 5.10548055e-01 5.26266992e-01 4.77322340e-01 6.16541088e-01 -6.46396935e-01 -6.63528800e-01 7.77501583e-01 4.21334594e-01 2.48798862e-01 5.82149148e-01 3.37782413e-01 7.35274851e-01 -8.73510361e-01 6.64714754e-01 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4508e971-2302-48db-89e3-ae7ec17eae62	revisit-dictionary-learning-for-video	2110.04966	null	https://arxiv.org/abs/2110.04966v2	https://arxiv.org/pdf/2110.04966v2.pdf	Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework	Aiming at high-dimensional (HD) data acquisition and analysis, snapshot compressive imaging (SCI) obtains the 2D compressed measurement of HD data with optical imaging systems and reconstructs HD data using compressive sensing algorithms. While the Plug-and-Play (PnP) framework offers an emerging solution to SCI reconstruction, its intrinsic denoising process is still a challenging problem. Unfortunately, existing denoisers in the PnP framework either suffer limited performance or require extensive training data. In this paper, we propose an efficient and effective shallow-learning-based algorithm for video SCI reconstruction. Revisiting dictionary learning methods, we empower the PnP framework with a new denoiser, the kernel singular value decomposition (KSVD). Benefited from the advent of KSVD, our algorithm retains a good trade-off among quality, speed, and training difficulty. On a variety of datasets, both quantitative and qualitative evaluations of our simulation results demonstrate the effectiveness of our proposed method. In comparison to a typical baseline using total variation, our method achieves around $2$ dB improvement in PSNR and 0.2 in SSIM. We expect that our proposed PnP-KSVD algorithm can serve as a new baseline for video SCI reconstruction.	['Yaping Zhao', 'Qing Yang']	2021-10-11	null	null	null	null	['video-compressive-sensing']	['computer-vision']	[ 3.56769860e-01 -5.33682227e-01 -1.14771418e-01 1.32823944e-01 -9.28178370e-01 -3.49416643e-01 9.27901939e-02 -4.47199225e-01 1.74321979e-02 4.71330285e-01 4.06700701e-01 -2.43274972e-01 -3.71953815e-01 -3.63790929e-01 -6.95773780e-01 -8.63675714e-01 -7.06914440e-02 -2.97964569e-02 1.69822928e-02 4.15924452e-02 3.31377126e-02 3.28525484e-01 -1.19424796e+00 3.06714792e-02 8.69398296e-01 1.32405663e+00 4.20851171e-01 4.12356287e-01 3.05295974e-01 1.03889740e+00 -4.11287919e-02 -2.47507721e-01 8.08333099e-01 -5.03045619e-01 -3.47903579e-01 4.13233399e-01 3.31285387e-01 -9.16209638e-01 -1.07749617e+00 1.07850599e+00 7.92981803e-01 -1.77872449e-01 2.73016214e-01 -9.03179765e-01 -6.90282881e-01 2.65643895e-01 -6.32081330e-01 1.90093458e-01 4.48911577e-01 2.58942217e-01 7.17666507e-01 -1.17677784e+00 6.05718434e-01 7.77050853e-01 8.41405272e-01 2.31840894e-01 -1.24508166e+00 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3.07820916e-01 6.00537300e-01 -3.73950094e-01 -5.85282505e-01 3.02629501e-01]	[11.141098022460938, -2.132469892501831]
7ff58bf6-4693-4d7b-9ccd-8c0f5f425ee1	attribute-surrogates-learning-and-spectral	2203.09064	null	https://arxiv.org/abs/2203.09064v1	https://arxiv.org/pdf/2203.09064v1.pdf	Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning	This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought of as a promising alternative to convolutional neural networks for visual recognition. But when there is no sufficient data, it gets stuck in overfitting and shows inferior performance. To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates. The intrinsic image structure is utilized to reduce the ambiguity between foreground content and background noise by spectral tokens pooling. And the attribute surrogate learning scheme is designed to benefit from the rich visual information in image-label pairs instead of simple visual concepts assigned by their labels. Our Hierarchically Cascaded Transformers, called HCTransformers, is built upon a self-supervised learning framework DINO and is tested on several popular few-shot learning benchmarks. In the inductive setting, HCTransformers surpass the DINO baseline by a large margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on miniImageNet, which demonstrates HCTransformers are efficient to extract discriminative features. Also, HCTransformers show clear advantages over SOTA few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings on four popular benchmark datasets, including miniImageNet, tieredImageNet, FC100, and CIFAR-FS. The trained weights and codes are available at https://github.com/StomachCold/HCTransformers.	['Wenqiang Zhang', 'Yizhou Yu', 'Weifeng Ge', 'Hong-Yu Zhou', 'Dongyang Zhao', 'Weihan Liang', 'Yangji He']	2022-03-17	null	http://openaccess.thecvf.com//content/CVPR2022/html/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.pdf	cvpr-2022-1	['few-shot-image-classification']	['computer-vision']	[ 5.69067299e-02 -1.38228610e-01 -4.13902521e-01 -4.94075805e-01 -7.83828259e-01 -2.72569567e-01 6.10225677e-01 -1.79331213e-01 -5.89141071e-01 3.92631292e-01 2.12293521e-01 8.89577263e-04 4.89143245e-02 -7.15725124e-01 -7.09921658e-01 -8.39946747e-01 9.44999680e-02 1.11772813e-01 3.87787372e-01 -7.16046840e-02 -1.15485333e-01 4.83306721e-02 -1.71265411e+00 6.44121468e-01 6.71542823e-01 1.53377569e+00 3.48521881e-02 5.25122106e-01 -1.49357766e-01 1.26614571e+00 -3.92735809e-01 -4.13569689e-01 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9106637d-e5fa-4faa-a286-afd120aa32eb	2d-human-pose-estimation-a-survey	2204.07370	null	https://arxiv.org/abs/2204.07370v1	https://arxiv.org/pdf/2204.07370v1.pdf	2D Human Pose Estimation: A Survey	Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e.g., images, videos, or signals). It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields. Deep learning techniques allow learning feature representations directly from the data, significantly pushing the performance boundary of human pose estimation. In this paper, we reap the recent achievements of 2D human pose estimation methods and present a comprehensive survey. Briefly, existing approaches put their efforts in three directions, namely network architecture design, network training refinement, and post processing. Network architecture design looks at the architecture of human pose estimation models, extracting more robust features for keypoint recognition and localization. Network training refinement tap into the training of neural networks and aims to improve the representational ability of models. Post processing further incorporates model-agnostic polishing strategies to improve the performance of keypoint detection. More than 200 research contributions are involved in this survey, covering methodological frameworks, common benchmark datasets, evaluation metrics, and performance comparisons. We seek to provide researchers with a more comprehensive and systematic review on human pose estimation, allowing them to acquire a grand panorama and better identify future directions.	['Zhenguang Liu', 'Fengcheng Zhou', 'Hao Xu', 'Sifan Wu', 'Runyang Feng', 'Haoming Chen']	2022-04-15	null	null	null	null	['2d-human-pose-estimation']	['computer-vision']	[ 1.73148289e-01 1.28368931e-02 -5.89343309e-01 -1.74487278e-01 -2.90610820e-01 -3.27929705e-01 2.86819875e-01 9.07391757e-02 -7.12408602e-01 2.85559386e-01 2.82548338e-01 2.78216422e-01 -2.05724493e-01 -4.04206216e-01 -4.86287892e-01 -4.36073869e-01 -4.22353476e-01 4.48551655e-01 1.05638251e-01 -2.09661081e-01 8.60110074e-02 8.12050819e-01 -1.26148689e+00 7.26925163e-03 1.15790851e-01 1.17530525e+00 -4.11068052e-02 4.50913668e-01 3.75627786e-01 4.33403611e-01 -6.53970242e-01 -3.58639926e-01 2.30158880e-01 -6.18859231e-02 -8.54409337e-01 -1.32869974e-01 4.92929757e-01 -3.20181936e-01 -6.41707003e-01 7.10777581e-01 8.20862591e-01 4.64235507e-02 5.94142973e-01 -1.27394772e+00 -1.26910657e-01 3.30920488e-01 -4.43309933e-01 3.68517339e-01 4.80488896e-01 5.73256686e-02 7.05127656e-01 -8.15297604e-01 5.77341437e-01 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802d6725-2791-43ec-b94b-96fadc83878e	supervised-raw-video-denoising-with-a	2003.14013	null	https://arxiv.org/abs/2003.14013v1	https://arxiv.org/pdf/2003.14013v1.pdf	Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes	In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images. In this paper, we solve this problem by creating motions for controllable objects, such as toys, and capturing each static moment for multiple times to generate clean video frames. In this way, we construct a dataset with 55 groups of noisy-clean videos with ISO values ranging from 1600 to 25600. To our knowledge, this is the first dynamic video dataset with noisy-clean pairs. Correspondingly, we propose a raw video denoising network (RViDeNet) by exploring the temporal, spatial, and channel correlations of video frames. Since the raw video has Bayer patterns, we pack it into four sub-sequences, i.e RGBG sequences, which are denoised by the proposed RViDeNet separately and finally fused into a clean video. In addition, our network not only outputs a raw denoising result, but also the sRGB result by going through an image signal processing (ISP) module, which enables users to generate the sRGB result with their favourite ISPs. Experimental results demonstrate that our method outperforms state-of-the-art video and raw image denoising algorithms on both indoor and outdoor videos.	['Jingyu Yang', 'Ronghe Chu', 'Huanjing Yue', 'Lei Liao', 'Cong Cao']	2020-03-31	supervised-raw-video-denoising-with-a-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf	cvpr-2020-6	['video-denoising']	['computer-vision']	[ 4.00138110e-01 -6.82246327e-01 3.95903498e-01 -1.63885728e-01 -8.64998698e-01 -3.79711241e-01 2.39026785e-01 -4.60597754e-01 -4.69456285e-01 7.97450662e-01 1.33107632e-01 2.30831839e-02 -7.10582510e-02 -6.00208998e-01 -9.04314756e-01 -1.18815577e+00 -9.36308280e-02 -6.22584343e-01 1.47726730e-01 -1.01890832e-01 -1.70998350e-01 1.06181592e-01 -1.65394366e+00 2.50969827e-01 7.79989302e-01 1.18523932e+00 4.78210241e-01 7.68396854e-01 2.72852451e-01 9.85113502e-01 -6.99425936e-01 -3.24443251e-01 5.47537446e-01 -6.79670632e-01 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1defe6c1-c2d9-43ea-913d-1c57872f0cc0	texture-representation-via-analysis-and	2212.09983	null	https://arxiv.org/abs/2212.09983v1	https://arxiv.org/pdf/2212.09983v1.pdf	Texture Representation via Analysis and Synthesis with Generative Adversarial Networks	We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN inversion using a novel latent domain reconstruction consistency criterion for synthesized textures, and iterative refinement with Gramian loss for real textures. We propose perceptual procedures for evaluating network capabilities, exploring the global and local behavior of latent space trajectories, and comparing with existing texture analysis-synthesis techniques.	['Thrasyvoulos N. Pappas', 'Gaurav Sharma', 'Jue Lin']	2022-12-20	null	null	null	null	['texture-classification']	['computer-vision']	[ 6.83820188e-01 2.77416885e-01 -9.51117948e-02 -1.91250771e-01 -7.14335203e-01 -7.34286249e-01 9.93940651e-01 -7.38675654e-01 3.78041804e-01 8.75932515e-01 3.56877029e-01 -1.89478412e-01 -7.81248733e-02 -8.99704456e-01 -8.27254295e-01 -1.07673800e+00 -1.72168631e-02 5.05182862e-01 -2.45085403e-01 -1.93534002e-01 -1.84282586e-01 8.41211557e-01 -1.25001323e+00 5.21845222e-01 4.35733646e-01 1.12455082e+00 -3.13843250e-01 9.72853482e-01 3.43849987e-01 1.17780674e+00 -6.25297010e-01 -2.80951232e-01 2.57350743e-01 -7.23911941e-01 -5.83456397e-01 4.51064348e-01 3.15876722e-01 -3.82071406e-01 -4.11609262e-01 1.05324709e+00 2.89293498e-01 1.10793538e-01 8.64103615e-01 -1.16563892e+00 -1.10433567e+00 4.55961883e-01 -2.16380402e-01 -4.07389045e-01 1.17975280e-01 3.18697989e-01 5.83820164e-01 -7.61245012e-01 1.26563942e+00 1.49100709e+00 6.99106216e-01 7.99867213e-01 -1.73231685e+00 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ebb65d25-e566-40c1-94d0-959cd3435997	rsfnet-a-white-box-image-retouching-approach	2303.08682	null	https://arxiv.org/abs/2303.08682v1	https://arxiv.org/pdf/2303.08682v1.pdf	RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters	Retouching images is an essential aspect of enhancing the visual appeal of photos. Although users often share common aesthetic preferences, their retouching methods may vary based on their individual preferences. Therefore, there is a need for white-box approaches that produce satisfying results and enable users to conveniently edit their images simultaneously. Recent white-box retouching methods rely on cascaded global filters that provide image-level filter arguments but cannot perform fine-grained retouching. In contrast, colorists typically use a divide-and-conquer approach, performing a series of region-specific fine-grained enhancements when using traditional tools like Davinci Resolve. We draw on this insight to develop a white-box framework for photo retouching using parallel region-specific filters, called RSFNet. Our model generates filter arguments (e.g., saturation, contrast, hue) and attention maps of regions for each filter simultaneously. Instead of cascading filters, RSFNet employs linear summations of filters, allowing for a more diverse range of filter classes that can be trained more easily. Our experiments demonstrate that RSFNet achieves state-of-the-art results, offering satisfying aesthetic appeal and greater user convenience for editable white-box retouching.	['Xuansong Xie', 'Xin Xu', 'Xiaoyang Kang', 'Peiran Ren', 'Yi Dong', 'Wenqi Ouyang']	2023-03-15	null	null	null	null	['photo-retouching', 'image-retouching']	['computer-vision', 'computer-vision']	[ 3.48239899e-01 -1.22570992e-01 2.73232609e-02 -3.83780688e-01 -7.93785080e-02 -7.85954773e-01 5.10688305e-01 4.84517924e-02 -2.44373053e-01 4.12831694e-01 3.21014732e-01 -3.48395556e-01 2.14239731e-01 -8.87119114e-01 -5.67507744e-01 -2.73917854e-01 4.55892086e-01 -4.30610269e-01 4.25986081e-01 -4.82129157e-01 3.55855852e-01 5.99796355e-01 -1.82856631e+00 4.85846311e-01 1.05226529e+00 7.99188852e-01 2.24583045e-01 9.64355826e-01 -2.98500568e-01 5.01044393e-01 -7.15479136e-01 -6.43835545e-01 3.39006662e-01 -4.38249677e-01 -3.91242892e-01 1.56705499e-01 8.95637274e-01 -5.13320446e-01 -3.38672474e-02 9.70946312e-01 3.91513169e-01 3.72467935e-01 4.10383373e-01 -1.06391346e+00 -1.61345720e+00 4.28852767e-01 -1.02240789e+00 3.14111523e-02 4.56340104e-01 4.48291957e-01 1.05930424e+00 -7.63506114e-01 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d66c2322-90c7-46aa-b2a9-2d64ef74ca72	delog-a-privacy-preserving-log-filtering	1902.04843	null	https://arxiv.org/abs/1902.04843v3	https://arxiv.org/pdf/1902.04843v3.pdf	Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms	In many software applications, logs serve as the only interface between the application and the developer. However, navigating through the logs of long-running applications is often challenging. Logs from previously successful application runs can be leveraged to automatically identify errors and provide users with only the logs that are relevant to the debugging process. We describe a privacy preserving framework which can be employed by Platform as a Service (PaaS) providers to utilize the user logs generated on the platform while protecting the potentially sensitive logged data. Further, in order to accurately and scalably parse log lines, we present a distributed log parsing algorithm which leverages Locality Sensitive Hashing (LSH). We outperform the state-of-the-art on multiple datasets. We further demonstrate the scalability of Delog on publicly available Thunderbird log dataset with close to 27,000 unique patterns and 211 million lines.	['Amey Agrawal', 'Rajat Gupta', 'Namrata Shettar', 'Darshil Kapadia', 'Vikram Agrawal', 'Rohit Karlupia', 'Abhishek Dixit']	2019-02-13	null	null	null	null	['log-parsing']	['computer-code']	[-3.07763427e-01 -4.84091431e-01 -3.97104084e-01 -4.04991657e-01 -1.31339991e+00 -1.18982089e+00 -5.12120873e-02 6.28910542e-01 -6.86183050e-02 1.88367829e-01 -8.68362486e-02 -6.33915067e-01 2.15211779e-01 -4.60124075e-01 -9.58831787e-01 -4.79205437e-02 -5.94228327e-01 3.08579672e-02 5.55457532e-01 2.90275931e-01 5.42237103e-01 3.61949503e-01 -1.36143029e+00 5.85387886e-01 6.25059843e-01 8.81232083e-01 -2.70176649e-01 7.33353078e-01 2.32135564e-01 8.77726674e-01 -6.78792655e-01 -3.02975267e-01 5.16606450e-01 3.47497873e-02 -7.18291521e-01 -4.16032165e-01 4.25977170e-01 -7.11528718e-01 -1.47162661e-01 1.06494868e+00 1.51166096e-01 -3.69254887e-01 -1.97112650e-01 -1.75148749e+00 -3.87493908e-01 5.86420238e-01 -9.01325703e-01 3.22378427e-01 6.36637986e-01 1.87351052e-02 9.34201717e-01 -4.69808251e-01 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53742da7-9a4e-44c6-bf1f-65c966fc7864	context-aware-cascade-attention-based-rnn-for	1805.12098	null	http://arxiv.org/abs/1805.12098v1	http://arxiv.org/pdf/1805.12098v1.pdf	Context-aware Cascade Attention-based RNN for Video Emotion Recognition	Emotion recognition can provide crucial information about the user in many applications when building human-computer interaction (HCI) systems. Most of current researches on visual emotion recognition are focusing on exploring facial features. However, context information including surrounding environment and human body can also provide extra clues to recognize emotion more accurately. Inspired by "sequence to sequence model" for neural machine translation, which models input and output sequences by an encoder and a decoder in recurrent neural network (RNN) architecture respectively, a novel architecture, "CACA-RNN", is proposed in this work. The proposed network consists of two RNNs in a cascaded architecture to process both context and facial information to perform video emotion classification. Results of the model were submitted to video emotion recognition sub-challenge in Multimodal Emotion Recognition Challenge (MEC2017). CACA-RNN outperforms the MEC2017 baseline (mAP of 21.7%): it achieved mAP of 45.51% on the testing set in the video only challenge.	['Jen-Hsien Chien', 'Min-Chun Yang', 'Shih-Huan Hsu', 'Man-Chin Sun']	2018-05-30	null	null	null	null	['video-emotion-recognition', 'multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'computer-vision', 'speech']	[ 4.85639006e-01 -2.12703049e-01 1.30902946e-01 -6.14885926e-01 -2.23786980e-01 -2.32528523e-01 4.18955624e-01 -4.60708916e-01 -6.53294504e-01 3.83926004e-01 3.77911538e-01 -3.01558506e-02 8.21439862e-01 -1.18852936e-01 -5.64405382e-01 -6.07836485e-01 2.55624264e-01 -3.33364129e-01 -5.64742386e-01 -2.77075887e-01 1.81906000e-02 4.33741152e-01 -1.65116501e+00 9.31955397e-01 3.71841252e-01 1.37651598e+00 1.95856243e-02 1.01507616e+00 -7.96638057e-02 1.09362733e+00 -4.27278429e-01 -4.66608137e-01 -1.25908926e-01 -7.58621037e-01 -7.62187660e-01 5.09519987e-02 -4.05931063e-02 -1.38005167e-01 -2.24814355e-01 8.30620348e-01 7.13667750e-01 3.29355508e-01 7.63314664e-01 -1.41233873e+00 -6.75393820e-01 6.34967387e-02 -6.63592279e-01 -1.16540138e-02 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6252049b-2265-4601-911e-39a8408b5219	edge-weighted-pfista-net-for-mri	2302.07468	null	https://arxiv.org/abs/2302.07468v1	https://arxiv.org/pdf/2302.07468v1.pdf	Edge-weighted pFISTA-Net for MRI Reconstruction	Deep learning based on unrolled algorithm has served as an effective method for accelerated magnetic resonance imaging (MRI). However, many methods ignore the direct use of edge information to assist MRI reconstruction. In this work, we present the edge-weighted pFISTA-Net that directly applies the detected edge map to the soft-thresholding part of pFISTA-Net. The soft-thresholding value of different regions will be adjusted according to the edge map. Experimental results of a public brain dataset show that the proposed yields reconstructions with lower error and better artifact suppression compared with the state-of-the-art deep learning-based methods. The edge-weighted pFISTA-Net also shows robustness for different undersampling masks and edge detection operators. In addition, we extend the edge weighted structure to joint reconstruction and segmentation network and obtain improved reconstruction performance and more accurate segmentation results.	['Jianpeng Cao']	2023-02-15	null	null	null	null	['edge-detection', 'mri-reconstruction']	['computer-vision', 'computer-vision']	[ 2.45094776e-01 -2.13525787e-01 1.33709639e-01 -5.32705724e-01 -4.94516224e-01 2.82669775e-02 -5.71224540e-02 -8.22171196e-02 -8.37418675e-01 7.14091182e-01 2.76696868e-02 -3.28264952e-01 -7.56622180e-02 -7.28612125e-01 -4.48485702e-01 -8.38226676e-01 -4.50500399e-01 2.55585581e-01 7.40678787e-01 4.04387340e-02 2.40707487e-01 7.06124902e-01 -7.91895092e-01 3.96088868e-01 8.62466753e-01 1.00051773e+00 6.99656725e-01 1.85665175e-01 5.96614480e-02 3.91940147e-01 -1.66221216e-01 1.00967318e-01 3.45800638e-01 -2.57543683e-01 -5.75706244e-01 -3.82717722e-03 1.18997380e-01 -5.26752114e-01 -3.49229991e-01 1.29276097e+00 9.04221475e-01 4.22430187e-02 6.21935725e-01 -6.99527085e-01 -3.03312868e-01 6.36472464e-01 -9.48790073e-01 9.45586920e-01 -4.12626773e-01 -1.56553034e-02 9.13750902e-02 -9.00533319e-01 6.84474707e-01 6.09755218e-01 1.01858008e+00 1.81591630e-01 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9acf617c-c930-4ffd-8be7-3942bdc2ba6b	a-perspective-on-neural-capacity-estimation	2203.11793	null	https://arxiv.org/abs/2203.11793v2	https://arxiv.org/pdf/2203.11793v2.pdf	A Perspective on Neural Capacity Estimation: Viability and Reliability	Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks. These estimators ar referred to as neural mutual information estimation (NMIE)s. NMIEs differ from other approaches as they are data-driven estimators. As such, they have the potential to perform well on a large class of capacity problems. In order to test the performance across various NMIEs, it is desirable to establish a benchmark encompassing the different challenges of capacity estimation. This is the objective of this paper. In particular, we consider three scenarios for benchmarking:i the classic AWGN channel, ii channels continuous inputs optical intensity and peak-power constrained AWGN channel iii channels with a discrete output, i.e., Poisson channel. We also consider the extension to the multi-terminal case with iv the AWGN and optical MAC models. We argue that benchmarking a certain NMIE across these four scenarios provides a substantive test of performance. In this paper we study the performance of mutual information neural estimator (MINE), smoothed mutual information lower-bound estimator (SMILE), and directed information neural estimator (DINE). and provide insights on the performance of other methods as well. To summarize our benchmarking results, MINE provides the most reliable performance.	['Nariman Farsad', 'Stefano Rini', 'Farhad Mirkarimi']	2022-03-22	null	null	null	null	['mutual-information-estimation']	['methodology']	[ 3.69727880e-01 -5.36856577e-02 5.65169938e-02 -1.59645900e-01 -8.04234326e-01 -2.14910775e-01 5.05649090e-01 -2.76128232e-01 -6.96020782e-01 1.25672936e+00 -3.89983878e-02 -6.06954336e-01 -7.22934186e-01 -6.20642722e-01 -5.51991343e-01 -9.53751624e-01 -7.14342058e-01 1.56103805e-01 -2.87346572e-01 2.87458509e-01 2.88001209e-01 6.58467412e-01 -1.20982003e+00 -2.96956718e-01 7.77403235e-01 1.42879903e+00 -1.20649608e-02 1.08180404e+00 5.60160428e-02 7.27402866e-01 -6.65258586e-01 -5.08107126e-01 1.06295608e-01 -4.58363146e-01 -8.55600834e-01 -3.26271474e-01 -3.11904430e-01 -5.23603201e-01 -6.65518045e-01 8.02821696e-01 8.94893765e-01 -2.03767061e-01 1.07243609e+00 -1.43241751e+00 -1.79294124e-01 7.83812881e-01 -3.39599550e-01 2.43448362e-01 -1.64320250e-03 4.23131995e-02 1.16852987e+00 -5.69701076e-01 3.73202384e-01 9.95939732e-01 8.15049410e-01 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c47f63ac-75a7-4f22-a54f-d092d6fb84f1	what-does-plate-glass-reveal-about-camera	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.pdf	What Does Plate Glass Reveal About Camera Calibration?	This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image contents. To reduce the impact of a noisy calibration cue estimated from a reflection-contaminated image, we propose two strategies: an optimization-based method that imposes part of though reliable entries on the map and a learning-based method that fully exploits all entries. We collect a dataset containing 320 samples as well as their camera parameters for evaluation. We demonstrate that our method not only facilitates a general single image camera calibration method that leverages image contents but also contributes to improving the performance of single image reflection removal. Furthermore, we show our byproduct output helps alleviate the ill-posed problem of estimating the panorama from a single image.	[' Alex C. Kot', ' Ling-Yu Duan', ' Kim-Hui Yap', ' Xudong Jiang', ' Boxin Shi', ' Zhan Lu', ' Jinnan Chen', 'Qian Zheng']	2020-06-01	null	null	null	cvpr-2020-6	['reflection-removal']	['computer-vision']	[ 5.28880358e-01 -1.75611377e-02 2.94556111e-01 -3.31120282e-01 -1.27283716e+00 -6.78050756e-01 2.96959907e-01 -4.61383432e-01 -3.25808793e-01 2.90825456e-01 1.85157314e-01 -2.38564551e-01 5.53868487e-02 -4.84795362e-01 -9.63254333e-01 -9.73803103e-01 4.98162419e-01 -5.74446656e-02 2.81422347e-01 6.69842064e-02 5.32847345e-01 3.82601559e-01 -1.24673486e+00 -6.70398548e-02 6.80564702e-01 8.95479739e-01 3.00479710e-01 5.25896251e-01 5.38190305e-01 5.88240266e-01 -5.10286868e-01 -3.05156171e-01 7.02127218e-01 -2.60323107e-01 -1.13977768e-01 5.19474506e-01 7.50766098e-01 -7.12946773e-01 -1.98687047e-01 1.11070466e+00 3.20996642e-01 -9.76425111e-02 4.62847024e-01 -7.31326759e-01 -9.92553383e-02 1.54728502e-01 -9.25584376e-01 -3.60628486e-01 2.22045228e-01 1.01625547e-01 6.05205059e-01 -8.37609351e-01 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5dcbbaca-4a14-454e-9bfc-8c2bb79f042c	fourier-analysis-on-robustness-of-graph	2305.17939	null	https://arxiv.org/abs/2305.17939v1	https://arxiv.org/pdf/2305.17939v1.pdf	Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition	Using Fourier analysis, we explore the robustness and vulnerability of graph convolutional neural networks (GCNs) for skeleton-based action recognition. We adopt a joint Fourier transform (JFT), a combination of the graph Fourier transform (GFT) and the discrete Fourier transform (DFT), to examine the robustness of adversarially-trained GCNs against adversarial attacks and common corruptions. Experimental results with the NTU RGB+D dataset reveal that adversarial training does not introduce a robustness trade-off between adversarial attacks and low-frequency perturbations, which typically occurs during image classification based on convolutional neural networks. This finding indicates that adversarial training is a practical approach to enhancing robustness against adversarial attacks and common corruptions in skeleton-based action recognition. Furthermore, we find that the Fourier approach cannot explain vulnerability against skeletal part occlusion corruption, which highlights its limitations. These findings extend our understanding of the robustness of GCNs, potentially guiding the development of more robust learning methods for skeleton-based action recognition.	['Kazuhiko Kawamoto', 'Hiroshi Kera', 'Nariki Tanaka']	2023-05-29	null	null	null	null	['skeleton-based-action-recognition', 'action-recognition-in-videos']	['computer-vision', 'computer-vision']	[ 8.71856153e-01 1.27871022e-01 1.18601536e-02 1.40553921e-01 -4.43026811e-01 -5.74940264e-01 5.43079793e-01 -3.49122226e-01 -1.81706965e-01 3.09987038e-01 3.99408937e-01 -3.80990505e-01 -1.29662335e-01 -1.01496112e+00 -9.26870763e-01 -7.84007370e-01 -4.95473295e-01 -4.11381483e-01 2.21274585e-01 -3.11627567e-01 4.17372845e-02 9.67588246e-01 -1.07115483e+00 3.31299365e-01 2.37342313e-01 8.48756969e-01 -9.79336679e-01 1.04785085e+00 5.31141222e-01 1.03423214e+00 -9.46488917e-01 -3.43080550e-01 6.88764811e-01 -5.26137114e-01 -8.55965853e-01 -3.20919529e-02 8.07386339e-01 -6.08705580e-01 -1.04809546e+00 7.93244660e-01 6.41726971e-01 2.27912515e-01 6.16083384e-01 -1.50659418e+00 -6.05126381e-01 2.47643203e-01 -3.28242868e-01 3.87139499e-01 6.02763891e-01 6.20181143e-01 4.77145851e-01 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e401c704-a568-4e91-a07f-f12b00bb45e5	two-stage-framework-for-optic-disc	2005.14284	null	https://arxiv.org/abs/2005.14284v1	https://arxiv.org/pdf/2005.14284v1.pdf	Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning	With the advancement of powerful image processing and machine learning techniques, CAD has become ever more prevalent in all fields of medicine including ophthalmology. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. The first stage is based on RCNN and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep CNN to classify the extracted disc into healthy or glaucomatous. In addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved AUC equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA. Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only AUC, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier's performance and calls for additional performance metrics to substantiate the results.	['Sheraz Ahmed', 'Wolfgang Neumeier', 'Shoaib Ahmed Siddiqui', 'Muhammad Naseer Bajwa', 'Faisal Shafait', 'Muhammad Imran Malik', 'Andreas Dengel']	2020-05-28	null	null	null	null	['optic-disc-detection']	['medical']	[ 1.20535761e-01 1.58552408e-01 -7.21101984e-02 -2.12775439e-01 -8.57419133e-01 -4.89445806e-01 2.98687994e-01 1.67399213e-01 -5.08281827e-01 8.32811832e-01 2.62269139e-01 -5.41708231e-01 -5.76936424e-01 -5.07142901e-01 -1.96155995e-01 -6.47845805e-01 -2.82804638e-01 6.74340069e-01 2.71442026e-01 3.17719728e-01 4.53322858e-01 5.40585399e-01 -2.04948235e+00 3.52107078e-01 1.09106660e+00 1.22975814e+00 1.08315587e-01 8.07954669e-01 3.16935182e-01 5.67352116e-01 -6.29596293e-01 -1.32760167e-01 5.16139746e-01 -5.15797794e-01 -9.53072011e-01 3.52343887e-01 8.67439985e-01 -2.27112904e-01 8.47075582e-02 7.95617521e-01 9.25861478e-01 -6.44924641e-01 5.12566805e-01 -3.18996340e-01 -3.21927905e-01 5.27374297e-02 -3.46144855e-01 3.83560240e-01 -1.30819827e-01 4.99095201e-01 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7d1a7b2b-6e04-4ecb-9bde-59be4593c6cc	mmdialog-a-large-scale-multi-turn-dialogue	2211.05719	null	https://arxiv.org/abs/2211.05719v3	https://arxiv.org/pdf/2211.05719v3.pdf	MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation	Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a curated set of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. MMDialog has two main and unique advantages. First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x. Second, it contains massive topics to generalize the open-domain. To build engaging dialogue system with this dataset, we propose and normalize two response producing tasks based on retrieval and generative scenarios. In addition, we build two baselines for above tasks with state-of-the-art techniques and report their experimental performance. We also propose a novel evaluation metric MM-Relevance to measure the multi-modal responses. Our dataset and scripts are available in https://github.com/victorsungo/MMDialog.	['QIngwei Lin', 'Dongyan Zhao', 'Chongyang Tao', 'Yaming Yang', 'Pu Zhao', 'Can Xu', 'Qingfeng Sun', 'Jiazhan Feng']	2022-11-10	null	null	null	null	['multimodal-intent-recognition']	['miscellaneous']	[-2.85656720e-01 -6.74448535e-03 9.52171460e-02 -6.55654669e-01 -1.31008911e+00 -6.43321991e-01 1.20124495e+00 -5.53604126e-01 -3.55329961e-01 8.85948300e-01 9.52077329e-01 2.61981547e-01 3.43047440e-01 -6.34685159e-01 -3.64970975e-02 -6.87100172e-01 4.73347366e-01 1.12606525e+00 1.03477187e-01 -6.09573007e-01 3.75776023e-01 -1.39942423e-01 -1.19322622e+00 9.40920591e-01 8.44193161e-01 8.24535668e-01 3.66185337e-01 9.01895106e-01 -1.69541135e-01 8.74896348e-01 -9.84858811e-01 -5.35408020e-01 -2.15698317e-01 -7.08216965e-01 -1.28926945e+00 6.63999692e-02 2.71784514e-01 -7.06951261e-01 -5.69972098e-01 7.66808093e-01 1.09854889e+00 3.75895888e-01 8.48103940e-01 -1.27886021e+00 -1.05364144e+00 6.67051077e-01 -5.17598927e-01 2.55144864e-01 9.92515504e-01 3.40240866e-01 8.66018713e-01 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bf71048d-02bd-43ad-b548-abd9546c6023	open-set-recognition-via-augmentation-based	2203.13238	null	https://arxiv.org/abs/2203.13238v3	https://arxiv.org/pdf/2203.13238v3.pdf	Open-set Recognition via Augmentation-based Similarity Learning	The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called closed set learning or classification. In many practical scenarios, this is not the case because there are unknowns or unseen class samples in the test data, which is called the open set scenario, and the unknowns need to be detected. This problem is referred to as the open set recognition problem and is important in safety-critical applications. We propose to detect unknowns (or unseen class samples) through learning pairwise similarities. The proposed method works in two steps. It first learns a closed set classifier using the seen classes that have appeared in training and then learns how to compare seen classes with pseudo-unseen (automatically generated unseen class samples). The pseudo-unseen generation is carried out by performing distribution shifting augmentations on the seen or training samples. We call our method OPG (Open set recognition based on Pseudo unseen data Generation). The experimental evaluation shows that the learned similarity-based features can successfully distinguish seen from unseen in benchmark datasets for open set recognition.	['Lei Shu', 'Bing Liu', 'Sepideh Esmaeilpour']	2022-03-24	null	null	null	null	['open-set-learning']	['miscellaneous']	[ 6.50645554e-01 1.09888189e-01 -4.14298140e-02 -4.78616685e-01 -6.87879145e-01 -8.18684697e-01 5.77810884e-01 2.98432738e-01 -5.09236120e-02 1.03862619e+00 -2.50598162e-01 -1.06788956e-01 -3.40001017e-01 -8.48171115e-01 -8.08755338e-01 -1.04084802e+00 1.49812967e-01 8.84793103e-01 2.35470325e-01 -3.90554853e-02 2.86605984e-01 3.69690090e-01 -2.13457274e+00 5.45404851e-01 9.89635587e-01 1.07959378e+00 -2.18519166e-01 5.02112448e-01 -2.84984559e-01 3.13343227e-01 -8.47860873e-01 1.55418053e-01 7.81129479e-01 -6.95632279e-01 -5.16722739e-01 1.74344555e-01 7.52297461e-01 -2.73932993e-01 1.78253576e-01 9.33976829e-01 5.97066879e-01 1.58037946e-01 1.15026712e+00 -1.61847365e+00 -5.74093699e-01 2.13542908e-01 -4.67899233e-01 1.83662832e-01 3.68199378e-01 -1.65161058e-01 5.93983829e-01 -9.03303564e-01 4.17624950e-01 9.21237051e-01 3.53169769e-01 8.11523020e-01 -1.13955367e+00 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d4dbf85d-035b-4e92-be5b-8c9c9e207a91	unsupervised-domain-adaptation-for-robust	1707.06265	null	http://arxiv.org/abs/1707.06265v2	http://arxiv.org/pdf/1707.06265v2.pdf	Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation	Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are presented, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.	['Yu Zhang', 'Wei-Ning Hsu', 'James Glass']	2017-07-19	null	null	null	null	['robust-speech-recognition']	['speech']	[ 6.96968675e-01 4.09465492e-01 8.61020163e-02 -6.55677736e-01 -1.24189723e+00 -7.20680833e-01 6.43379807e-01 -8.44665766e-02 -4.96182799e-01 8.03031862e-01 4.29435104e-01 -5.13856173e-01 2.62857050e-01 -2.93300450e-01 -5.54813504e-01 -8.75005364e-01 5.25770903e-01 6.16529524e-01 1.39824376e-01 -1.72299445e-01 -1.93093911e-01 3.75435501e-01 -1.49704754e+00 3.65782320e-01 9.05549169e-01 5.70761681e-01 4.16917861e-01 5.50240636e-01 -2.87641883e-01 3.50736469e-01 -1.20430791e+00 -1.89996734e-01 1.31016269e-01 -5.87865531e-01 -8.07620168e-01 6.35134995e-01 2.82369345e-01 -1.37949839e-01 -2.45043680e-01 1.00651586e+00 5.59966028e-01 5.06067455e-01 7.88432896e-01 -8.81448030e-01 -6.56809926e-01 6.78827107e-01 -5.06562926e-02 1.90974563e-01 7.22313374e-02 -1.35377973e-01 7.29907751e-01 -9.95121300e-01 4.24947709e-01 1.32935393e+00 -1.58556961e-02 1.02348006e+00 -1.23343587e+00 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-5.79703271e-01 3.12571108e-01 6.96381986e-01 -5.56223512e-01 -5.40557444e-01 2.56603956e-01]	[14.474555969238281, 6.577635765075684]
ebd22bed-5d14-4e24-b94b-dbc8cea74acc	blind-predicting-similar-quality-map-for	1805.08493	null	http://arxiv.org/abs/1805.08493v2	http://arxiv.org/pdf/1805.08493v2.pdf	Blind Predicting Similar Quality Map for Image Quality Assessment	A key problem in blind image quality assessment (BIQA) is how to effectively model the properties of human visual system in a data-driven manner. In this paper, we propose a simple and efficient BIQA model based on a novel framework which consists of a fully convolutional neural network (FCNN) and a pooling network to solve this problem. In principle, FCNN is capable of predicting a pixel-by-pixel similar quality map only from a distorted image by using the intermediate similarity maps derived from conventional full-reference image quality assessment methods. The predicted pixel-by-pixel quality maps have good consistency with the distortion correlations between the reference and distorted images. Finally, a deep pooling network regresses the quality map into a score. Experiments have demonstrated that our predictions outperform many state-of-the-art BIQA methods.	['Ming Hou', 'Ping Shi', 'Yuan Zhang', 'Zefeng Ying', 'Sizhe Fu', 'Da Pan']	2018-05-22	blind-predicting-similar-quality-map-for-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Pan_Blind_Predicting_Similar_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Pan_Blind_Predicting_Similar_CVPR_2018_paper.pdf	cvpr-2018-6	['blind-image-quality-assessment']	['computer-vision']	[ 7.92410970e-02 -4.11492288e-01 4.18209791e-01 -6.20796561e-01 -1.02795029e+00 -3.36126745e-01 4.85172987e-01 -4.06716585e-01 -2.91561306e-01 5.28486431e-01 4.05917108e-01 -1.33449525e-01 -2.56339848e-01 -7.83411324e-01 -6.26192570e-01 -5.47362924e-01 2.05097422e-01 -2.55336761e-01 2.90043414e-01 -2.33484536e-01 5.35848260e-01 6.36274695e-01 -1.68014705e+00 6.19123101e-01 1.15553391e+00 1.41356254e+00 9.63173136e-02 9.57744241e-01 1.29551575e-01 9.43120778e-01 -7.33018398e-01 -6.28429711e-01 7.16716051e-01 -5.04865468e-01 -7.39184976e-01 -5.71734784e-03 7.88378775e-01 -7.28467405e-01 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99ff7318-b9af-4e43-8b79-e9d913ef82a1	a-practical-toolkit-for-multilingual-question	2305.17416	null	https://arxiv.org/abs/2305.17416v1	https://arxiv.org/pdf/2305.17416v1.pdf	A Practical Toolkit for Multilingual Question and Answer Generation	Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query. Training models for question and answer generation (QAG) is not straightforward due to the expected structured output (i.e. a list of question and answer pairs), as it requires more than generating a single sentence. This results in a small number of publicly accessible QAG models. In this paper, we introduce AutoQG, an online service for multilingual QAG, along with lmqg, an all-in-one Python package for model fine-tuning, generation, and evaluation. We also release QAG models in eight languages fine-tuned on a few variants of pre-trained encoder-decoder language models, which can be used online via AutoQG or locally via lmqg. With these resources, practitioners of any level can benefit from a toolkit that includes a web interface for end users, and easy-to-use code for developers who require custom models or fine-grained controls for generation.	['Jose Camacho-Collados', 'Fernando Alva-Manchego', 'Asahi Ushio']	2023-05-27	null	null	null	null	['reading-comprehension', 'answer-generation']	['natural-language-processing', 'natural-language-processing']	[ 5.75162731e-02 5.41121840e-01 4.07497704e-01 -4.09939826e-01 -1.63168335e+00 -9.41646576e-01 4.88185346e-01 3.09551716e-01 -1.71984538e-01 8.46652329e-01 3.81887347e-01 -9.04771745e-01 1.39045805e-01 -1.04263353e+00 -6.10708952e-01 6.35485947e-02 4.82996613e-01 8.38893175e-01 2.22368971e-01 -6.99422896e-01 2.16161758e-01 -1.73441336e-01 -1.55796814e+00 5.10788500e-01 1.35855532e+00 5.17934203e-01 5.49985945e-01 1.14489067e+00 -6.34898245e-01 8.65385771e-01 -8.65140200e-01 -8.87983680e-01 -1.88301042e-01 -7.77650476e-01 -1.24649298e+00 -5.40494621e-01 6.02892399e-01 -3.42965811e-01 2.56677181e-01 5.33011377e-01 7.57447898e-01 7.54843093e-03 3.51609081e-01 -9.78580534e-01 -1.00680542e+00 7.80741394e-01 4.33555275e-01 -1.68341994e-01 9.13194895e-01 4.13102269e-01 1.02193439e+00 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4dcb6603-0109-4aaf-9431-2ce560897aee	adaptive-graph-convolutional-networks-for	2202.06503	null	https://arxiv.org/abs/2202.06503v3	https://arxiv.org/pdf/2202.06503v3.pdf	Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos	For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information. To solve this, we propose a novel weakly supervised adaptive graph convolutional network (WAGCN) to model the complex contextual relationship among video segments. By which, we fully consider the influence of other video segments on the current one when generating the anomaly probability score for each segment. Firstly, we combine the temporal consistency as well as feature similarity of video segments to construct a global graph, which makes full use of the association information among spatial-temporal features of anomalous events in videos. Secondly, we propose a graph learning layer in order to break the limitation of setting topology manually, which can extract graph adjacency matrix based on data adaptively and effectively. Extensive experiments on two public datasets (i.e., UCF-Crime dataset and ShanghaiTech dataset) demonstrate the effectiveness of our approach which achieves state-of-the-art performance.	['Yanning Zhang', 'Peng Wang', 'Shizhou Zhang', 'Xin Zhang', 'Congqi Cao']	2022-02-14	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[-4.49131280e-02 -3.16772968e-01 -1.32714644e-01 -2.61560261e-01 -7.53244609e-02 -3.30053627e-01 2.79650748e-01 3.96974057e-01 -2.74063081e-01 2.65498757e-01 3.52211475e-01 -2.38235459e-01 -9.56045240e-02 -7.49277294e-01 -7.23416984e-01 -5.82163155e-01 -5.36190689e-01 -1.02225952e-01 5.05222142e-01 -1.28564656e-01 1.34481803e-01 2.90102512e-01 -1.17476618e+00 9.40419957e-02 1.02877676e+00 9.72414136e-01 -1.44153848e-01 4.08714950e-01 -1.10856488e-01 1.04375231e+00 -2.54031897e-01 -1.43278867e-01 8.30639377e-02 -4.80803758e-01 -5.25787175e-01 4.10150498e-01 3.25679421e-01 -6.58743203e-01 -1.04407001e+00 1.21170425e+00 2.20169723e-01 2.70622939e-01 3.65531951e-01 -1.47962332e+00 -4.53509718e-01 4.86753225e-01 -7.40593433e-01 8.14197063e-01 3.41984302e-01 3.05543214e-01 9.95878875e-01 -6.53517365e-01 5.21790743e-01 8.89993966e-01 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1.13342218e-02 -8.49266291e-01 -6.54510140e-01 9.89839315e-01 4.93528545e-01 -6.14886999e-01 -4.88124013e-01 -1.22372299e-01]	[7.842347145080566, 1.6342964172363281]
d8108ed1-8219-48ba-86c4-ee1af3f68ddb	human-machine-co-adaption-interface-via	2305.02058	null	https://arxiv.org/abs/2305.02058v1	https://arxiv.org/pdf/2305.02058v1.pdf	Human Machine Co-adaption Interface via Cooperation Markov Decision Process System	This paper aims to develop a new human-machine interface to improve rehabilitation performance from the perspective of both the user (patient) and the machine (robot) by introducing the co-adaption techniques via model-based reinforcement learning. Previous studies focus more on robot assistance, i.e., to improve the control strategy so as to fulfill the objective of Assist-As-Needed. In this study, we treat the full process of robot-assisted rehabilitation as a co-adaptive or mutual learning process and emphasize the adaptation of the user to the machine. To this end, we proposed a Co-adaptive MDPs (CaMDPs) model to quantify the learning rates based on cooperative multi-agent reinforcement learning (MARL) in the high abstraction layer of the systems. We proposed several approaches to cooperatively adjust the Policy Improvement among the two agents in the framework of Policy Iteration. Based on the proposed co-adaptive MDPs, the simulation study indicates the non-stationary problem can be mitigated using various proposed Policy Improvement approaches.	['Steven W. Su', 'Rob Duffield', 'Jun Li', 'Yaqi Li', 'Adrian Cheng', 'Kairui Guo']	2023-05-03	null	null	null	null	['multi-agent-reinforcement-learning', 'model-based-reinforcement-learning']	['methodology', 'reasoning']	[-2.19739050e-01 5.92347860e-01 -2.75650322e-01 5.83056360e-02 -2.84789562e-01 1.46276698e-01 1.27520069e-01 6.31182343e-02 -6.94257498e-01 1.15729856e+00 2.74392277e-01 -6.65917024e-02 -5.84744096e-01 -5.69986343e-01 -2.84931451e-01 -9.42246735e-01 -1.14420697e-01 6.56254470e-01 1.05938487e-01 -6.85266316e-01 1.91214472e-01 3.52988601e-01 -1.28180170e+00 -1.93517864e-01 1.16768384e+00 2.11563215e-01 7.38888621e-01 7.93734968e-01 3.31607878e-01 8.16087663e-01 -2.98357457e-01 4.26073492e-01 5.56537509e-02 -4.08950418e-01 -9.28495705e-01 2.87189424e-01 -9.78795409e-01 -6.09578907e-01 -2.20098361e-01 7.71868825e-01 1.01278389e+00 3.85291070e-01 6.83820724e-01 -1.49755979e+00 -1.60615399e-01 5.00392735e-01 -5.70511460e-01 7.22221583e-02 3.03831160e-01 3.80487651e-01 3.74176949e-01 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1.02692223e+00 -9.69623625e-01 -9.89227593e-01 -1.14952958e+00 -8.47040057e-01 1.05730250e-01 9.99635011e-02 -8.92430604e-01 -9.95170772e-02 -2.01120809e-01]	[4.224880695343018, 1.910614252090454]
1e4afd04-aea6-4ddd-ac8a-55695a1ed8df	let-s-verify-step-by-step-1	2305.20050	null	https://arxiv.org/abs/2305.20050v1	https://arxiv.org/pdf/2305.20050v1.pdf	Let's Verify Step by Step	In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or process supervision, which provides feedback for each intermediate reasoning step. Given the importance of training reliable models, and given the high cost of human feedback, it is important to carefully compare the both methods. Recent work has already begun this comparison, but many questions still remain. We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset. Our process-supervised model solves 78% of problems from a representative subset of the MATH test set. Additionally, we show that active learning significantly improves the efficacy of process supervision. To support related research, we also release PRM800K, the complete dataset of 800,000 step-level human feedback labels used to train our best reward model.	['Karl Cobbe', 'Ilya Sutskever', 'John Schulman', 'Jan Leike', 'Teddy Lee', 'Bowen Baker', 'Harri Edwards', 'Yura Burda', 'Vineet Kosaraju', 'Hunter Lightman']	2023-05-31	let-s-verify-step-by-step	https://cdn.openai.com/improving-mathematical-reasoning-with-process-supervision/Lets_Verify_Step_by_Step.pdf	https://cdn.openai.com/improving-mathematical-reasoning-with-process-supervision/Lets_Verify_Step_by_Step.pdf	preprint-2023-5	['math-word-problem-solving', 'active-learning', 'active-learning', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'methodology', 'natural-language-processing', 'reasoning', 'time-series']	[ 1.50706679e-01 3.35724264e-01 -1.98521003e-01 -7.05112398e-01 -1.18951976e+00 -6.69183791e-01 4.33798283e-01 6.37639642e-01 -5.18293858e-01 8.87500226e-01 2.73317754e-01 -5.67192614e-01 -1.75898001e-01 -8.52563441e-01 -6.47953391e-01 -4.08726037e-02 2.38388881e-01 8.16686749e-01 1.90394253e-01 -2.37623930e-01 6.30632520e-01 2.33671829e-01 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0d9fb503-d14d-43e4-9101-6a507445c87c	early-melanoma-diagnosis-with-sequential	2110.05976	null	https://arxiv.org/abs/2110.05976v1	https://arxiv.org/pdf/2110.05976v1.pdf	Early Melanoma Diagnosis with Sequential Dermoscopic Images	Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.	['ZongYuan Ge', 'Victoria Mar', 'Lei Zhang', 'Paul Bonnington', 'Catriona Mclean', 'John Kelly', 'Toan D Nguyen', 'Jennifer Nguyen', 'Zhen Yu']	2021-10-12	null	null	null	null	['melanoma-diagnosis']	['computer-vision']	[ 7.85242498e-01 -3.30683351e-01 -3.12767386e-01 -7.57982507e-02 -3.75473469e-01 -6.07594728e-01 4.90872681e-01 1.23763904e-01 -6.73833847e-01 4.88880754e-01 -2.68640935e-01 -4.03805166e-01 -3.43262494e-01 -7.08797514e-01 1.26602482e-02 -9.57961142e-01 7.36555830e-02 8.02807510e-02 4.13366497e-01 1.55021161e-01 2.83218086e-01 7.00045824e-01 -1.06886923e+00 3.98461133e-01 1.23820436e+00 5.38750947e-01 2.74071187e-01 1.19954610e+00 -2.62966193e-02 6.03441775e-01 -5.16233027e-01 -6.03710055e-01 2.24471748e-01 -5.43982744e-01 -8.41894925e-01 7.44274199e-01 4.19363201e-01 -5.89127004e-01 -2.96190172e-01 1.26364601e+00 3.11635494e-01 -4.73952383e-01 8.47557425e-01 -8.65237892e-01 -5.28044999e-01 -5.81986792e-02 -1.18768811e+00 3.08653384e-01 2.01591566e-01 4.03861105e-01 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2ab3818c-d5e7-48b0-b423-c5a9ffc9ffcb	cluster-forests	1104.2930	null	http://arxiv.org/abs/1104.2930v3	http://arxiv.org/pdf/1104.2930v3.pdf	Cluster Forests	With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure kappa. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis reveals that the kappa measure makes it possible to grow the local clustering in a desirable way---it is "noise-resistant". A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.	['Michael. I. Jordan', 'Aiyou Chen', 'Donghui Yan']	2011-04-14	null	null	null	null	['clustering-ensemble']	['graphs']	[ 1.84693381e-01 -2.39888191e-01 -2.07768127e-01 -2.89086431e-01 -7.59989798e-01 -8.12508941e-01 4.82765764e-01 1.01782451e-03 -6.84139505e-02 6.68628037e-01 1.24612838e-01 -3.43166679e-01 -4.47733432e-01 -8.94961178e-01 -2.98858434e-01 -1.48914027e+00 -3.84884655e-01 5.30998766e-01 2.83686608e-01 4.07121480e-01 3.84624720e-01 5.52495122e-01 -1.63520217e+00 2.93039888e-01 9.66480255e-01 7.82344818e-01 8.73190016e-02 6.13067508e-01 -2.83767395e-02 2.84725666e-01 -3.47805351e-01 -1.96317568e-01 4.87456530e-01 -2.84542531e-01 -1.10136747e+00 3.86166245e-01 -1.69773117e-01 5.58476031e-01 3.05324018e-01 1.03364205e+00 3.04199249e-01 7.14532733e-02 9.65172827e-01 -1.28248608e+00 -1.47471830e-01 4.90195900e-01 -8.13798308e-01 5.92998788e-02 1.30633950e-01 -6.65966198e-02 1.02751744e+00 -6.40203953e-01 7.97050536e-01 1.13470328e+00 8.21917415e-01 3.01828563e-01 -1.83898377e+00 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716c857a-82aa-4a6e-ae03-b22e399444e7	native-language-identification-using-a	null	null	https://aclanthology.org/W17-5022	https://aclanthology.org/W17-5022.pdf	Native Language Identification Using a Mixture of Character and Word N-grams	Native language identification (NLI) is the task of determining an author{'}s native language, based on a piece of his/her writing in a second language. In recent years, NLI has received much attention due to its challenging nature and its applications in language pedagogy and forensic linguistics. We participated in the NLI2017 shared task under the name UT-DSP. In our effort to implement a method for native language identification, we made use of a fusion of character and word N-grams, and achieved an optimal F1-Score of 77.64{\%}, using both essay and speech transcription datasets.	['Elham Mohammadi', 'Hadi Veisi', 'Hessam Amini']	2017-09-01	null	null	null	ws-2017-9	['native-language-identification']	['natural-language-processing']	[ 3.36103663e-02 -2.60446906e-01 -4.27424729e-01 -1.64768249e-01 -1.09786141e+00 -9.74477768e-01 8.34884644e-01 2.66679555e-01 -7.28402972e-01 6.20998144e-01 3.10247749e-01 -7.87511289e-01 1.08990431e-01 -1.64386272e-01 -2.12902695e-01 -2.35552698e-01 5.78177035e-01 3.44692409e-01 -1.90945014e-01 2.13274464e-01 7.28559613e-01 5.45074344e-01 -1.21719098e+00 3.25437859e-02 1.12043476e+00 4.67655391e-01 3.19151655e-02 7.48592377e-01 -4.84863907e-01 8.72069299e-01 -7.98566520e-01 -9.06797290e-01 -8.64587650e-02 -4.41317379e-01 -1.06878853e+00 -2.96097517e-01 6.93482280e-01 -1.86765477e-01 -4.10669446e-01 1.35973513e+00 5.34195483e-01 8.81064907e-02 8.43287289e-01 -7.86519170e-01 -7.02453017e-01 1.02563429e+00 -3.01891029e-01 5.55773556e-01 7.71307409e-01 -3.91581878e-02 1.17694676e+00 -1.08479321e+00 6.41144872e-01 1.41555560e+00 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23478c1d-efba-4fb5-a797-9d913067dbea	follownet-a-comprehensive-benchmark-for-car	2306.05381	null	https://arxiv.org/abs/2306.05381v1	https://arxiv.org/pdf/2306.05381v1.pdf	FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling	Car-following is a control process in which a following vehicle (FV) adjusts its acceleration to keep a safe distance from the lead vehicle (LV). Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. In contrast, research fields such as image recognition and object detection have benchmark datasets like ImageNet, Microsoft COCO, and KITTI. To address this gap and promote the development of microscopic traffic flow modeling, we establish a public benchmark dataset for car-following behavior modeling. The benchmark consists of more than 80K car-following events extracted from five public driving datasets using the same criteria. These events cover diverse situations including different road types, various weather conditions, and mixed traffic flows with autonomous vehicles. Moreover, to give an overview of current progress in car-following modeling, we implemented and tested representative baseline models with the benchmark. Results show that the deep deterministic policy gradient (DDPG) based model performs competitively with a lower MSE for spacing compared to traditional intelligent driver model (IDM) and Gazis-Herman-Rothery (GHR) models, and a smaller collision rate compared to fully connected neural network (NN) and long short-term memory (LSTM) models in most datasets. The established benchmark will provide researchers with consistent data formats and metrics for cross-comparing different car-following models, promoting the development of more accurate models. We open-source our dataset and implementation code in https://github.com/HKUST-DRIVE-AI-LAB/FollowNet.	['Yinhai Wang', 'Xu Han', 'Hui Zhong', 'Hongliang Lu', 'Pengqin Wang', 'Kehua Chen', 'Meixin Zhu', 'Xianda Chen']	2023-05-25	null	null	null	null	['autonomous-vehicles']	['computer-vision']	[-3.63513976e-01 -5.88510573e-01 -6.47459924e-01 -5.79445064e-01 -3.81139666e-01 -2.56800443e-01 7.62502432e-01 -3.19040358e-01 -7.54743516e-01 6.26001418e-01 -1.40289739e-01 -9.03649390e-01 -3.06397881e-02 -9.74403560e-01 -9.50576961e-01 -6.14814162e-01 -1.00800171e-01 3.22175086e-01 5.81926465e-01 -5.45393288e-01 2.43763283e-01 8.27636957e-01 -1.94789684e+00 3.71928848e-02 7.62759805e-01 1.01464069e+00 3.66845518e-01 7.44715512e-01 -1.27732798e-01 1.04091418e+00 -2.47155309e-01 -3.44444335e-01 2.10616738e-01 6.95002899e-02 -5.09354949e-01 -5.41211307e-01 4.93708521e-01 -3.23369801e-01 -1.01235294e+00 7.78692722e-01 3.20257008e-01 3.78151506e-01 5.44123292e-01 -1.86407125e+00 -5.75575113e-01 2.03473300e-01 -1.78095594e-01 5.70744693e-01 -3.44289392e-01 5.92175186e-01 4.64993596e-01 -5.59310257e-01 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e43ce53c-7df7-4828-957b-b6865fc59b8f	operation-wise-attention-network-for	2105.05515	null	https://arxiv.org/abs/2105.05515v2	https://arxiv.org/pdf/2105.05515v2.pdf	Operation-wise Attention Network for Tampering Localization Fusion	In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.	['Ioannis Kompatsiaris', 'Symeon Papadopoulos', 'Giorgos Kordopatis-Zilos', 'Polychronis Charitidis']	2021-05-12	null	null	null	null	['image-forensics']	['computer-vision']	[ 2.76340038e-01 -5.42941928e-01 3.58537465e-01 9.58980247e-03 -1.21330059e+00 -4.99754876e-01 7.31020689e-01 2.51438051e-01 -5.36247730e-01 4.25225407e-01 9.42431390e-02 -3.57642084e-01 -1.18867442e-01 -5.24524868e-01 -7.42186129e-01 -9.12335396e-01 1.43328413e-01 3.00539672e-01 2.18037277e-01 -5.59395440e-02 6.60317719e-01 7.45116889e-01 -1.36366081e+00 6.30757928e-01 7.63571441e-01 1.04371297e+00 2.58995205e-01 5.72163343e-01 -1.86814919e-01 8.80572736e-01 -9.84381974e-01 -6.51578486e-01 3.22837681e-01 2.59225257e-02 -5.70815027e-01 1.35399982e-01 6.11076415e-01 -4.40553397e-01 -4.14111882e-01 1.15228438e+00 5.20446479e-01 -8.80973488e-02 4.40105945e-01 -1.09944713e+00 -5.86362064e-01 5.13626099e-01 -7.82847822e-01 6.22264206e-01 3.55162382e-01 3.40049624e-01 4.78346556e-01 -7.67754614e-01 4.69559491e-01 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-1.11971283e+00 -4.93615657e-01 -9.86591816e-01 -4.90768135e-01 7.14815378e-01 4.14173603e-01 -8.10285687e-01 -2.73376942e-01 1.28916070e-01]	[12.410805702209473, 1.0147883892059326]
d853c3dc-02f9-4531-8ad3-3f6344273180	modified-qpsk-partition-algorithm-based-on	2010.10106	null	https://arxiv.org/abs/2010.10106v1	https://arxiv.org/pdf/2010.10106v1.pdf	Modified QPSK Partition Algorithm Based on MAP Estimation for Probabilistically-Shaped 16-QAM	Probabilistic shaping (PS) is investigated as a potential technique to approach the Shannon limit. However, it has been proved that conventional carrier phase recovery (CPR) algorithm designed for uniform distribution may have extra penalty in PS systems. In this paper, we find that the performance of QPSK partition algorithm is degenerated when PS is implemented. To solve this issue, a modified QPSK partition algorithm that jointly optimizes the amplitude decision threshold and filter weight is proposed, where the optimization of decision threshold is based on maximum a posterior probability (MAP) estimation. Different from the conventional decision methods which commonly use Euclidean distance metric, the MAP-based decision introduces the statistical characteristics of the received signals to obtain an accurate amplitude partition. In addition, the filter weight is optimized for different decision thresholds to enhance the tolerance of ASE-induced phase noise. We verify the feasibility of the proposed algorithm in a 56 GBaud PS 16-ary quadrature amplitude modulation (16-QAM) system. The proposed algorithm reduces the error of phase noise estimation by nearly half. Compared with conventional QPSK partition, the proposed algorithm could narrow the gap with theoretical mutual information (MI) by more than 0.1 bit/symbol. The channel capacity is increased by 4.2%, 4.3% and 3.6% with signal-to-noise ratio (SNR) from 8 dB to 10 dB respectively. These observations show that the proposed algorithm is a promising method to relieve the penalty of QPSK partition algorithm in PS systems.	['Yaojun Qiao', 'Yueming Lu', 'Xizi Tang', 'Mengqi Guo', 'Xuekai Xu', 'Zhongliang Sun', 'Jin Hu']	2020-10-20	null	null	null	null	['noise-estimation']	['medical']	[ 4.74608868e-01 1.52751029e-01 1.39002129e-02 8.98044705e-02 -6.05643868e-01 -2.65163273e-01 4.31361496e-02 7.42422566e-02 -4.61092800e-01 1.17038035e+00 -1.19984902e-01 -5.88734984e-01 -4.57002431e-01 -6.59973562e-01 -1.72856048e-01 -1.37734830e+00 -2.90338606e-01 -1.48051217e-01 3.60721678e-01 9.93682817e-03 6.68993413e-01 4.58046466e-01 -1.09365809e+00 -2.76233226e-01 1.30837488e+00 1.09649456e+00 3.60018790e-01 6.47140682e-01 2.00017005e-01 1.00859359e-01 -1.11282372e+00 -4.13536327e-03 1.45415589e-01 -6.78555310e-01 -1.14169233e-01 -1.92988589e-01 -3.12717468e-01 -4.87393402e-02 -5.85215807e-01 1.21877372e+00 7.95470774e-01 -1.38788417e-01 7.81823397e-01 -1.16164958e+00 -2.56064031e-02 5.07713139e-01 -9.16851461e-01 3.10071915e-01 5.62669858e-02 2.21964255e-01 2.76021093e-01 -2.40618184e-01 8.90769362e-02 8.89936566e-01 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eadc0339-a8ea-4f18-b859-3dd2c2191358	focus-effective-embedding-initialization-for	2305.14481	null	https://arxiv.org/abs/2305.14481v1	https://arxiv.org/pdf/2305.14481v1.pdf	FOCUS: Effective Embedding Initialization for Specializing Pretrained Multilingual Models on a Single Language	Using model weights pretrained on a high-resource language as a warm start can reduce the need for data and compute to obtain high-quality language models in low-resource languages. To accommodate the new language, the pretrained vocabulary and embeddings need to be adapted. Previous work on embedding initialization for such adapted vocabularies has mostly focused on monolingual source models. In this paper, we investigate the multilingual source model setting and propose FOCUS - Fast Overlapping Token Combinations Using Sparsemax, a novel embedding initialization method that outperforms previous work when adapting XLM-R. FOCUS represents newly added tokens as combinations of tokens in the overlap of the pretrained and new vocabularies. The overlapping tokens are selected based on semantic similarity in an auxiliary token embedding space. Our implementation of FOCUS is publicly available on GitHub.	['Gerard de Melo', 'Konstantin Dobler']	2023-05-23	null	null	null	null	['semantic-textual-similarity', 'semantic-similarity', 'xlm-r']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-0.433459 -0.21803297 -0.63326406 -0.2976352 -1.099041 -0.71310747 0.42788133 0.15614085 -1.0228633 0.74991244 0.70788056 -0.21072936 0.20678149 -0.59595585 -0.43984145 -0.2981911 0.05496901 0.64439297 -0.10112511 -0.5024713 -0.24194269 0.15267132 -1.1276172 0.05924038 0.932841 0.206148 0.63602906 0.45314187 -0.7885457 0.1797946 -0.4417436 -0.58965105 0.49235767 0.02283672 -0.58925307 -0.3276871 0.37603334 -0.04977392 -0.32345402 0.845963 0.67708457 0.2063704 0.37194982 -1.1401818 -1.1420419 1.5458543 -0.13797711 0.15565978 0.14819828 -0.10527565 1.2340989 -1.6163609 0.9872473 1.1231451 0.5308179 0.56606585 -1.4413711 -1.0426096 0.40241557 0.24240214 -1.8977957 -0.5637864 0.9289418 -0.20905808 1.5160983 -0.09197644 0.70164436 1.1240386 -0.19961035 0.74102485 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905f4432-cca4-4442-ab91-b32263f63a4e	deep-attention-unet-a-network-model-with	2304.10829	null	https://arxiv.org/abs/2304.10829v1	https://arxiv.org/pdf/2304.10829v1.pdf	Deep Attention Unet: A Network Model with Global Feature Perception Ability	Remote sensing image segmentation is a specific task of remote sensing image interpretation. A good remote sensing image segmentation algorithm can provide guidance for environmental protection, agricultural production, and urban construction. This paper proposes a new type of UNet image segmentation algorithm based on channel self attention mechanism and residual connection called . In my experiment, the new network model improved mIOU by 2.48% compared to traditional UNet on the FoodNet dataset. The image segmentation algorithm proposed in this article enhances the internal connections between different items in the image, thus achieving better segmentation results for remote sensing images with occlusion.	['Jiacheng Li']	2023-04-21	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[ 4.96018529e-01 -5.13612591e-02 -3.91484529e-01 -3.71936560e-01 3.93042594e-01 -3.03485394e-01 -2.04040781e-01 7.34615624e-02 -4.88080353e-01 4.93253022e-01 -1.76715046e-01 -8.59739125e-01 -2.58919686e-01 -1.48380697e+00 -4.23064798e-01 -5.85314751e-01 -1.01945862e-01 1.48592636e-01 7.25200325e-02 -2.58272558e-01 1.46504998e-01 6.28481448e-01 -1.09641731e+00 -5.22848815e-02 1.23881793e+00 8.35237563e-01 6.32522047e-01 5.55926740e-01 -4.90618289e-01 1.94234133e-01 -5.18083990e-01 3.20853412e-01 5.44329643e-01 -4.85714078e-01 -8.23103189e-01 4.06674594e-01 1.52365863e-01 -3.63642037e-01 -7.58481920e-02 1.40615976e+00 3.75337899e-01 1.48560062e-01 5.81502259e-01 -7.09969163e-01 -9.13273036e-01 1.47917616e+00 -1.15864408e+00 6.22252643e-01 -4.41747695e-01 -5.56773460e-03 7.31559515e-01 -3.36729348e-01 3.11856627e-01 1.24069989e+00 4.83750761e-01 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2121e676-53d3-4ed2-922f-5ab59c15754d	considering-image-information-and-self	2209.06417	null	https://arxiv.org/abs/2209.06417v1	https://arxiv.org/pdf/2209.06417v1.pdf	Considering Image Information and Self-similarity: A Compositional Denoising Network	Recently, convolutional neural networks (CNNs) have been widely used in image denoising. Existing methods benefited from residual learning and achieved high performance. Much research has been paid attention to optimizing the network architecture of CNN but ignored the limitations of residual learning. This paper suggests two limitations of it. One is that residual learning focuses on estimating noise, thus overlooking the image information. The other is that the image self-similarity is not effectively considered. This paper proposes a compositional denoising network (CDN), whose image information path (IIP) and noise estimation path (NEP) will solve the two problems, respectively. IIP is trained by an image-to-image way to extract image information. For NEP, it utilizes the image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output a similar estimated noise distribution for different image patches with a specific kind of noise. Finally, image information and noise distribution information will be comprehensively considered for image denoising. Experiments show that CDN achieves state-of-the-art results in synthetic and real-world image denoising. Our code will be released on https://github.com/JiaHongZ/CDN.	['Jingning Ma', 'Wenshu Yu', 'Yonggui Zhu', 'Jiahong Zhang']	2022-09-14	null	null	null	null	['noise-estimation']	['medical']	[ 1.45602301e-01 -3.72079641e-01 2.22490430e-01 -3.49859059e-01 -4.66451406e-01 8.29884876e-03 2.13187978e-01 -4.02206004e-01 -3.98870796e-01 3.38067383e-01 1.40332147e-01 -4.08232883e-02 -2.56562978e-02 -9.48693395e-01 -5.14761925e-01 -1.11542904e+00 2.14940414e-01 -4.59146649e-01 2.78265715e-01 -3.39380354e-01 1.99340433e-01 3.37833941e-01 -1.45508230e+00 3.02090168e-01 9.47404683e-01 1.15129733e+00 6.01435483e-01 4.67362106e-01 -2.48487636e-01 8.33696842e-01 -6.53525710e-01 -3.71745266e-02 4.16723162e-01 -6.44224882e-01 -3.34726244e-01 -2.12363023e-02 2.82060713e-01 -3.42318356e-01 -6.46384895e-01 1.54962611e+00 8.01384330e-01 8.26181695e-02 3.22638035e-01 -1.10007477e+00 -8.01049709e-01 4.39747602e-01 -5.57367861e-01 1.71325132e-01 -3.28981429e-01 1.09858535e-01 3.98576230e-01 -8.16574693e-01 4.41289067e-01 1.34680188e+00 9.56201315e-01 5.57721615e-01 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ed42c669-7c14-4659-889b-8cf858b91d33	recognizing-involuntary-actions-from-3d	1708.06227	null	http://arxiv.org/abs/1708.06227v1	http://arxiv.org/pdf/1708.06227v1.pdf	Recognizing Involuntary Actions from 3D Skeleton Data Using Body States	Human action recognition has been one of the most active fields of research in computer vision for last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method of action recognition which uses temporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition rate of 88.64% for eight different actions and up to 96.18% for classifying fall actions.	['Benyamin Ghojogh', 'Hoda Mohammadzade', 'Mozhgan Mokari']	2017-08-21	null	null	null	null	['3d-human-action-recognition']	['computer-vision']	[ 9.64010805e-02 -3.01412374e-01 -3.94248098e-01 -2.95294464e-01 -4.10443395e-01 -1.21787146e-01 5.83881974e-01 -2.69645393e-01 -5.85634172e-01 4.90831554e-01 3.85493517e-01 6.96084425e-02 -7.00867772e-02 -4.89422768e-01 9.09586325e-02 -7.54330039e-01 -3.22618186e-02 5.38348675e-01 5.33391893e-01 -1.78995579e-02 3.34182173e-01 7.09823191e-01 -1.71994340e+00 1.71796709e-01 2.64748394e-01 7.53483176e-01 -1.18374564e-01 6.92802131e-01 -6.26493096e-02 5.22533536e-01 -4.01426196e-01 5.92220835e-02 3.62213343e-01 -7.42913127e-01 -7.28201807e-01 3.82547617e-01 2.02971715e-02 -5.25322795e-01 -3.83845121e-01 7.06212878e-01 5.72804332e-01 5.20523489e-01 8.17492962e-01 -1.26013064e+00 -8.27066079e-02 -9.71248001e-02 -5.86141348e-01 3.78713489e-01 7.63769746e-01 2.73733325e-02 5.23842633e-01 -6.04477048e-01 4.32427168e-01 1.14768136e+00 5.14874339e-01 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2db5f232-6bd9-43af-9294-5e0822f1a3e5	sem-pos-grammatically-and-semantically	2303.14829	null	https://arxiv.org/abs/2303.14829v2	https://arxiv.org/pdf/2303.14829v2.pdf	SEM-POS: Grammatically and Semantically Correct Video Captioning	Generating grammatically and semantically correct captions in video captioning is a challenging task. The captions generated from the existing methods are either word-by-word that do not align with grammatical structure or miss key information from the input videos. To address these issues, we introduce a novel global-local fusion network, with a Global-Local Fusion Block (GLFB) that encodes and fuses features from different parts of speech (POS) components with visual-spatial features. We use novel combinations of different POS components - 'determinant + subject', 'auxiliary verb', 'verb', and 'determinant + object' for supervision of the POS blocks - Det + Subject, Aux Verb, Verb, and Det + Object respectively. The novel global-local fusion network together with POS blocks helps align the visual features with language description to generate grammatically and semantically correct captions. Extensive qualitative and quantitative experiments on benchmark MSVD and MSRVTT datasets demonstrate that the proposed approach generates more grammatically and semantically correct captions compared to the existing methods, achieving the new state-of-the-art. Ablations on the POS blocks and the GLFB demonstrate the impact of the contributions on the proposed method.	['Armin Mustafa', 'Graham Thomas', 'Robert Dawes', 'Adrian Hilton', 'Asmar Nadeem']	2023-03-26	null	null	null	null	['video-captioning']	['computer-vision']	[ 2.11064771e-01 1.96380705e-01 2.39163879e-02 -5.72441161e-01 -1.01791215e+00 -7.21882999e-01 5.92850804e-01 8.29140469e-02 -9.50426981e-02 7.71131814e-01 6.18649423e-01 -8.87811780e-02 3.36932510e-01 -3.23617369e-01 -1.11796105e+00 -5.50049245e-01 2.90742069e-01 2.46722788e-01 2.36097962e-01 -3.27816963e-01 1.12451822e-01 1.72901079e-01 -1.69275427e+00 8.34536314e-01 8.61406744e-01 9.26794708e-01 5.86704135e-01 5.03618956e-01 -2.89040208e-01 8.04426134e-01 -5.00459075e-01 -5.55385113e-01 1.26424134e-01 -5.41083157e-01 -5.96220255e-01 -7.88049325e-02 8.26210737e-01 -1.16295017e-01 -2.49942198e-01 1.17642045e+00 4.29813981e-01 5.07395752e-02 6.94047570e-01 -1.44179523e+00 -1.04486334e+00 2.70765215e-01 -6.75784588e-01 2.46315569e-01 5.84099591e-01 1.28511369e-01 1.17338192e+00 -8.08475196e-01 7.62334824e-01 1.64404726e+00 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82057e8a-74e1-40a9-bd6a-7407f5242916	cross-corpus-native-language-identification	null	null	https://aclanthology.org/W18-1605	https://aclanthology.org/W18-1605.pdf	Cross-corpus Native Language Identification via Statistical Embedding	In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus. The motivation behind this study is to investigate native language identification in the Australian academic scenario where a majority of students come from China, Indonesia, and Arabic-speaking nations. We have proposed a statistical embedding representation reporting a significant improvement over common single-layer approaches of the state of the art, identifying Chinese, Arabic, and Indonesian in a cross-corpus scenario. The proposed approach was shown to be competitive even when the data is scarce and imbalanced.	['ra', 'Julian Brooke', 'Francisco Rangel', 'Alex Uitdenbogerd', 'Paolo Rosso']	2018-06-01	null	null	null	ws-2018-6	['cross-corpus', 'native-language-identification']	['computer-vision', 'natural-language-processing']	[-1.16878025e-01 -1.16590008e-01 -2.58704036e-01 -2.78499097e-01 -9.84755754e-01 -7.18147457e-01 6.77185833e-01 3.09800714e-01 -9.12519872e-01 8.61178041e-01 3.93348277e-01 -5.64605951e-01 1.92225292e-01 -5.47363997e-01 -1.51218981e-01 -4.29562956e-01 2.13863090e-01 7.93165267e-01 -4.68502045e-01 -4.04496908e-01 4.95069116e-01 4.96976227e-01 -1.22239733e+00 -5.07608987e-02 1.15798998e+00 2.90880769e-01 -2.42986828e-02 8.34482193e-01 -4.52696025e-01 6.23091698e-01 -5.31905234e-01 -7.70429909e-01 3.05000871e-01 -1.69174731e-01 -1.08654785e+00 -4.11757678e-02 8.76177549e-01 -4.08190349e-03 -1.20408252e-01 1.07806265e+00 1.10155940e+00 -1.73845254e-02 6.12639487e-01 -7.35619485e-01 -8.65775108e-01 9.30065632e-01 -4.15497333e-01 4.05068368e-01 5.73211193e-01 -2.23407894e-01 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04527bc4-7517-4e33-bc57-374ceb8069cd	background-modeling-via-uncertainty	2006.07006	null	https://arxiv.org/abs/2006.07006v3	https://arxiv.org/pdf/2006.07006v3.pdf	Weakly-supervised Temporal Action Localization by Uncertainty Modeling	Weakly-supervised temporal action localization aims to learn detecting temporal intervals of action classes with only video-level labels. To this end, it is crucial to separate frames of action classes from the background frames (i.e., frames not belonging to any action classes). In this paper, we present a new perspective on background frames where they are modeled as out-of-distribution samples regarding their inconsistency. Then, background frames can be detected by estimating the probability of each frame being out-of-distribution, known as uncertainty, but it is infeasible to directly learn uncertainty without frame-level labels. To realize the uncertainty learning in the weakly-supervised setting, we leverage the multiple instance learning formulation. Moreover, we further introduce a background entropy loss to better discriminate background frames by encouraging their in-distribution (action) probabilities to be uniformly distributed over all action classes. Experimental results show that our uncertainty modeling is effective at alleviating the interference of background frames and brings a large performance gain without bells and whistles. We demonstrate that our model significantly outperforms state-of-the-art methods on the benchmarks, THUMOS'14 and ActivityNet (1.2 & 1.3). Our code is available at https://github.com/Pilhyeon/WTAL-Uncertainty-Modeling.	['Pilhyeon Lee', 'Yan Lu', 'Jinglu Wang', 'Hyeran Byun']	2020-06-12	null	null	null	null	['weakly-supervised-action-localization', 'weakly-supervised-temporal-action']	['computer-vision', 'computer-vision']	[ 1.71560884e-01 1.46444574e-01 -6.74152076e-01 -3.30799967e-01 -1.07730854e+00 -4.09108579e-01 4.85179543e-01 -2.21633404e-01 -1.68941662e-01 9.25949991e-01 1.52832955e-01 -2.41765771e-02 1.63460255e-01 -4.62925375e-01 -1.09004235e+00 -1.05070722e+00 -2.65744567e-01 6.55865744e-02 5.58993638e-01 5.39760113e-01 -1.21450327e-01 -2.84077935e-02 -1.29562306e+00 5.83801806e-01 5.12258291e-01 1.24542224e+00 -8.16750377e-02 5.99442422e-01 2.98959874e-02 1.73350358e+00 -6.78155422e-01 -1.24975957e-01 2.52643734e-01 -6.33839488e-01 -6.16527438e-01 4.67552841e-01 5.00092447e-01 -7.36462653e-01 -6.65557504e-01 1.28570235e+00 1.17368825e-01 2.24570289e-01 4.96307135e-01 -1.63117552e+00 -2.97825903e-01 5.84963500e-01 -7.91975141e-01 5.91716170e-01 2.20587105e-01 2.44473979e-01 8.51594687e-01 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d4d49087-b07c-4aa3-9edd-b888d049440f	deep-co-attention-based-comparators-for	1804.11027	null	http://arxiv.org/abs/1804.11027v1	http://arxiv.org/pdf/1804.11027v1.pdf	Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification	Person re-identification (re-ID) requires rapid, flexible yet discriminant representations to quickly generalize to unseen observations on-the-fly and recognize the same identity across disjoint camera views. Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring. However, the most relevant and crucial parts of each image are detected independently without referring to the dependency conditioned on one and another. Also, these region based methods rely on spatial manipulation to position the local features in comparable similarity measuring. To combat these limitations, in this paper we introduce the Deep Co-attention based Comparators (DCCs) that fuse the co-dependent representations of the paired images so as to focus on the relevant parts of both images and produce their \textit{relative representations}. Given a pair of pedestrian images to be compared, the proposed model mimics the foveation of human eyes to detect distinct regions concurrent on both images, namely co-dependent features, and alternatively attend to relevant regions to fuse them into the similarity learning. Our comparator is capable of producing dynamic representations relative to a particular sample every time, and thus well-suited to the case of re-identifying pedestrians on-the-fly. We perform extensive experiments to provide the insights and demonstrate the effectiveness of the proposed DCCs in person re-ID. Moreover, our approach has achieved the state-of-the-art performance on three benchmark data sets: DukeMTMC-reID \cite{DukeMTMC}, CUHK03 \cite{FPNN}, and Market-1501 \cite{Market1501}.	['DaCheng Tao', 'Lin Wu', 'Yang Wang', 'Junbin Gao']	2018-04-30	null	null	null	null	['foveation']	['computer-vision']	[ 1.57066043e-02 -3.72744918e-01 2.06931576e-01 -5.12620866e-01 -6.64097667e-01 -5.54342091e-01 8.22254300e-01 1.46169409e-01 -8.11909020e-01 5.76961219e-01 -8.23196843e-02 1.94601104e-01 -3.32389399e-02 -6.17721379e-01 -7.24948645e-01 -6.49098754e-01 -4.06715348e-02 3.50203097e-01 3.11347634e-01 -1.01013832e-01 1.77824393e-01 5.72724581e-01 -1.96021998e+00 1.71610385e-01 7.19997346e-01 8.30644786e-01 2.62740046e-01 6.69582248e-01 4.04747963e-01 1.88589409e-01 -4.64451075e-01 -7.63677061e-01 5.25805950e-01 -3.41300249e-01 -6.04143381e-01 1.48074761e-01 1.00837028e+00 -3.05240124e-01 -5.40489376e-01 1.29293799e+00 6.01271510e-01 4.59997594e-01 7.59247363e-01 -1.39388871e+00 -9.16635692e-01 -9.26576480e-02 -7.84513772e-01 6.96386158e-01 5.75830042e-01 4.20599133e-01 5.71362674e-01 -1.06701136e+00 4.22596395e-01 1.35669017e+00 5.92621863e-01 6.79769814e-01 -1.03196633e+00 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8.93178701e-01 3.07497710e-01 -9.29226339e-01 -2.21257225e-01 3.37751061e-02]	[14.697342872619629, 0.9457085728645325]
76685bb2-e6d6-4b03-abe2-92ccc55d3739	semeval-2015-task-5-qa-tempeval-evaluating	null	null	https://aclanthology.org/S15-2134	https://aclanthology.org/S15-2134.pdf	SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question Answering	null	['Nasrin Mostafazadeh', 'James Allen', 'Naushad UzZaman', 'Nathanael Chambers', 'Hector Llorens', 'James Pustejovsky']	2015-06-01	null	null	null	semeval-2015-6	['temporal-information-extraction']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 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0db149b2-8dd4-4a5e-9f7a-a85d3974a1e2	on-the-relationship-between-normalising-flows	null	null	https://openreview.net/forum?id=HklKEUUY_E	https://openreview.net/pdf?id=HklKEUUY_E	On the relationship between Normalising Flows and Variational- and Denoising Autoencoders	Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity. They are based on the idea of transforming a simple density into that of the data. We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning. For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows. The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE). Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits. The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space.	['Tim Salimans', 'Jasper Snoek', 'Alexey A. Gritsenko']	2019-03-27	null	null	null	iclr-workshop-deepgenstruct-2019	['normalising-flows']	['methodology']	[ 1.16526475e-02 2.14934200e-01 2.00565353e-01 -4.79372919e-01 3.12252194e-02 -3.86033267e-01 1.17355251e+00 -6.96863055e-01 -4.25496027e-02 6.41435325e-01 7.12552130e-01 -2.68387437e-01 -3.35073978e-01 -1.07102108e+00 -6.77771270e-01 -8.25695872e-01 2.01384887e-01 5.40549219e-01 -2.51655821e-02 -2.01758012e-01 5.74969761e-02 4.60028112e-01 -1.47988236e+00 1.78646415e-01 6.90742373e-01 6.32916212e-01 7.90435225e-02 8.24935555e-01 -2.29984418e-01 1.28229249e+00 -7.05344141e-01 -7.85472035e-01 1.60223171e-02 -9.06425059e-01 -7.94906557e-01 5.05618513e-01 2.03336775e-01 -5.42348862e-01 -7.70253658e-01 9.19980049e-01 -5.00492230e-02 4.45589632e-01 1.37136638e+00 -1.45159686e+00 -1.28031206e+00 6.93999350e-01 3.87757942e-02 4.93313462e-01 -2.30612576e-01 -3.67655121e-02 9.05945718e-01 -7.33939886e-01 6.46439910e-01 1.41908383e+00 6.21208370e-01 7.62556970e-01 -1.50877094e+00 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3.08831334e-01 -7.83235669e-01 -6.17483437e-01 7.32658565e-01]	[11.5596342086792, 0.033370330929756165]
4b11913d-8197-4879-a28d-6348539bfd79	hierarchical-semantic-contrast-for-scene	2303.13051	null	https://arxiv.org/abs/2303.13051v1	https://arxiv.org/pdf/2303.13051v1.pdf	Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection	Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware VAD model from normal videos. We first incorporate foreground object and background scene features with high-level semantics by taking advantage of pre-trained video parsing models. Then, building upon the autoencoder-based reconstruction framework, we introduce both scene-level and object-level contrastive learning to enforce the encoded latent features to be compact within the same semantic classes while being separable across different classes. This hierarchical semantic contrast strategy helps to deal with the diversity of normal patterns and also increases their discrimination ability. Moreover, for the sake of tackling rare normal activities, we design a skeleton-based motion augmentation to increase samples and refine the model further. Extensive experiments on three public datasets and scene-dependent mixture datasets validate the effectiveness of our proposed method.	['Xiaojin Gong', 'Shengyang Sun']	2023-03-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.pdf	cvpr-2023-1	['video-anomaly-detection']	['computer-vision']	[ 3.91724110e-01 -2.18830600e-01 -1.57559797e-01 -4.40853983e-01 -2.57527947e-01 -1.80794865e-01 5.44001222e-01 -5.21485955e-02 -6.39102012e-02 1.67352363e-01 3.69093984e-01 1.08858114e-02 7.16300402e-03 -7.41554558e-01 -8.50632906e-01 -6.37035131e-01 3.64507586e-02 9.57501456e-02 5.74426830e-01 1.95394590e-01 2.42830236e-02 4.63747919e-01 -1.67748666e+00 5.15812218e-01 1.08119822e+00 1.04117119e+00 2.68195748e-01 4.56905007e-01 -3.25643629e-01 1.23638189e+00 -2.36059472e-01 -6.31076097e-02 4.51784492e-01 -5.40494502e-01 -5.46674728e-01 8.90496016e-01 6.76384985e-01 -6.24633908e-01 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02410799-967f-4aea-aa99-69f5083f1e95	continual-causal-inference-with-incremental	2303.01775	null	https://arxiv.org/abs/2303.01775v1	https://arxiv.org/pdf/2303.01775v1.pdf	Continual Causal Inference with Incremental Observational Data	The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have been made to overcome the challenges in the academic area, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data, which is unrealistic in most industrial applications. In this paper, we investigate a new industrial problem of causal effect estimation from incrementally available observational data and present three new evaluation criteria accordingly, including extensibility, adaptability, and accessibility. We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions. Instead of having access to all seen observational data, our method only stores a limited subset of feature representations learned from previous data. Combining selective and balanced representation learning, feature representation distillation, and feature transformation, our method achieves the continual causal effect estimation for new data without compromising the estimation capability for original data. Extensive experiments demonstrate the significance of continual causal effect estimation and the effectiveness of our method.	['Sheng Li', 'Stephen Rathbun', 'Ruopeng Li', 'Zhixuan Chu']	2023-03-03	null	null	null	null	['marketing', 'selection-bias']	['miscellaneous', 'natural-language-processing']	[ 2.97210962e-01 -1.88267112e-01 -8.05284023e-01 -4.02953833e-01 -4.84209120e-01 -1.30173579e-01 6.67692363e-01 3.60231936e-01 -2.53734529e-01 1.11394989e+00 8.74992430e-01 -4.16593283e-01 -6.13912880e-01 -1.00049293e+00 -7.78315783e-01 -5.70466936e-01 -4.04212743e-01 8.37957934e-02 -1.48962140e-01 4.70475517e-02 1.63864136e-01 1.75371662e-01 -1.50994098e+00 6.62151352e-02 1.02270484e+00 6.75107360e-01 5.50204597e-04 1.59316733e-01 3.64262313e-02 7.96277344e-01 -3.20970267e-01 -8.99101943e-02 -1.61432549e-02 -1.51059568e-01 -4.72244948e-01 -4.23466861e-01 1.68566760e-02 -5.14106810e-01 -5.12322068e-01 5.81341326e-01 6.83193624e-01 -7.03502148e-02 5.87592185e-01 -1.47196639e+00 -1.06493986e+00 8.65241885e-01 -8.82235944e-01 3.09193462e-01 4.31801051e-01 -6.70246407e-02 8.90310585e-01 -6.86880708e-01 3.76711935e-01 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ad30dbc8-4638-44f6-8d37-8550269edf89	learning-pose-invariant-3d-object	2004.01347	null	https://arxiv.org/abs/2004.01347v2	https://arxiv.org/pdf/2004.01347v2.pdf	Learning Pose-invariant 3D Object Reconstruction from Single-view Images	Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data. However, most work in this direction requires multi-view images for each object instance as training supervision, which oftentimes does not apply in practice. In this paper, we relax the common multi-view assumption and explore a more challenging yet more realistic setup of learning 3D shape from only single-view images. The major difficulty lies in insufficient constraints that can be provided by single view images, which leads to the problem of pose entanglement in learned shape space. As a result, reconstructed shapes vary along input pose and have poor accuracy. We address this problem by taking a novel domain adaptation perspective, and propose an effective adversarial domain confusion method to learn pose-disentangled compact shape space. Experiments on single-view reconstruction show effectiveness in solving pose entanglement, and the proposed method achieves on-par reconstruction accuracy with state-of-the-art with higher efficiency.	['Tieniu Tan', 'Jing Dong', 'Bo Peng', 'Wei Wang']	2020-04-03	null	null	null	null	['3d-object-reconstruction']	['computer-vision']	[ 8.41463730e-02 -5.82131669e-02 -5.95894866e-02 -1.80097774e-01 -8.79142642e-01 -9.36588407e-01 4.64154184e-01 -4.01077420e-01 -1.08515799e-01 5.27448416e-01 1.74170479e-01 -5.35510108e-02 -7.64547512e-02 -7.08631158e-01 -8.96456122e-01 -8.81875515e-01 5.68474531e-01 7.46713758e-01 -5.14887720e-02 -9.97261703e-02 9.95263737e-03 5.41133881e-01 -9.85184073e-01 9.36534926e-02 5.09595275e-01 7.06534922e-01 1.87012702e-01 3.04845363e-01 8.81496817e-02 5.81833199e-02 -1.92803532e-01 -6.57604098e-01 6.22727752e-01 -2.90739805e-01 -6.28128111e-01 4.32509035e-01 6.54902279e-01 -5.59122622e-01 -3.85661572e-01 1.16256225e+00 5.51203728e-01 -2.46668518e-01 6.70109332e-01 -1.03009629e+00 -7.26254880e-01 1.36474863e-01 -7.88400054e-01 -8.62509161e-02 3.17951500e-01 -1.53025582e-01 1.03139317e+00 -1.14267743e+00 7.83720553e-01 1.09718335e+00 4.58966315e-01 6.41087532e-01 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ee9edb43-85a1-4028-8978-5356f880ea0f	multilingual-sequence-to-sequence-speech	1810.03459	null	http://arxiv.org/abs/1810.03459v1	http://arxiv.org/pdf/1810.03459v1.pdf	Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling	Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance comparable to the models trained with twice more training data.	['Shinji Watanabe', 'Matthew Wiesner', 'Murali Karthick Baskar', 'Sri Harish Mallidi', 'Takaaki Hori', 'Jaejin Cho', 'Ruizhi Li', 'Nelson Yalta', 'Martin Karafiat']	2018-10-04	null	null	null	null	['sequence-to-sequence-speech-recognition']	['speech']	[ 2.92407181e-02 1.13157727e-01 -6.79011643e-02 -5.50872922e-01 -1.55632353e+00 -6.82674229e-01 5.23407280e-01 -4.77241516e-01 -6.54422939e-01 9.43786383e-01 6.22767806e-01 -6.80576801e-01 6.85495675e-01 -2.07684398e-01 -6.95642710e-01 -5.15560448e-01 2.45280534e-01 7.37841964e-01 5.30466512e-02 -5.71188569e-01 -7.26793781e-02 3.09974402e-01 -7.52037227e-01 3.89765561e-01 6.34319305e-01 1.39726534e-01 6.27276063e-01 9.01767910e-01 -1.09525241e-01 1.05878067e+00 -8.37549865e-01 -3.75264466e-01 1.69701979e-01 -6.50502503e-01 -7.38954186e-01 -3.19644421e-01 5.87993801e-01 -9.36400965e-02 -4.34998721e-01 8.28228414e-01 8.95340502e-01 2.46367112e-01 1.42311752e-01 -2.84892261e-01 -5.30117810e-01 1.07609963e+00 -1.40073195e-01 4.56013829e-01 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5881450c-196d-417f-9b5a-2b6778deeaa8	video-instance-segmentation-using-inter-frame	2106.03299	null	https://arxiv.org/abs/2106.03299v1	https://arxiv.org/pdf/2106.03299v1.pdf	Video Instance Segmentation using Inter-Frame Communication Transformers	We propose a novel end-to-end solution for video instance segmentation (VIS) based on transformers. Recently, the per-clip pipeline shows superior performance over per-frame methods leveraging richer information from multiple frames. However, previous per-clip models require heavy computation and memory usage to achieve frame-to-frame communications, limiting practicality. In this work, we propose Inter-frame Communication Transformers (IFC), which significantly reduces the overhead for information-passing between frames by efficiently encoding the context within the input clip. Specifically, we propose to utilize concise memory tokens as a mean of conveying information as well as summarizing each frame scene. The features of each frame are enriched and correlated with other frames through exchange of information between the precisely encoded memory tokens. We validate our method on the latest benchmark sets and achieved the state-of-the-art performance (AP 44.6 on YouTube-VIS 2019 val set using the offline inference) while having a considerably fast runtime (89.4 FPS). Our method can also be applied to near-online inference for processing a video in real-time with only a small delay. The code will be made available.	['Seon Joo Kim', 'Seoung Wug Oh', 'Miran Heo', 'Sukjun Hwang']	2021-06-07	null	http://proceedings.neurips.cc/paper/2021/hash/6f2688a5fce7d48c8d19762b88c32c3b-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/6f2688a5fce7d48c8d19762b88c32c3b-Paper.pdf	neurips-2021-12	['video-instance-segmentation']	['computer-vision']	[ 3.51556152e-01 -1.81749761e-01 -2.89742589e-01 -4.51504827e-01 -1.00396478e+00 -4.91645694e-01 3.70312512e-01 1.98924959e-01 -5.56081355e-01 5.50844729e-01 2.44432613e-02 -2.15655133e-01 1.80959001e-01 -7.72697151e-01 -9.98572230e-01 -4.27128673e-01 -2.44789764e-01 2.53857493e-01 9.26232100e-01 2.59687990e-01 8.39920342e-02 3.48622173e-01 -1.62218797e+00 8.39027762e-01 4.35677201e-01 1.36993229e+00 4.26864415e-01 1.03811371e+00 -2.63016701e-01 1.08069587e+00 -5.97935677e-01 -5.25232077e-01 1.82901248e-01 -3.98977220e-01 -9.15537536e-01 2.06949770e-01 7.32815444e-01 -8.18593204e-01 -4.05159771e-01 6.64740980e-01 3.28920633e-01 6.53439984e-02 -1.50742993e-01 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238987f7-46bf-4d9b-9ca6-a46419c5cc3d	typeface-completion-with-generative	1811.03762	null	http://arxiv.org/abs/1811.03762v2	http://arxiv.org/pdf/1811.03762v2.pdf	Typeface Completion with Generative Adversarial Networks	The mood of a text and the intention of the writer can be reflected in the typeface. However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese. In this paper, we propose a Typeface Completion Network (TCN) which takes one character as an input, and automatically completes the entire set of characters in the same style as the input characters. Unlike existing models proposed for image-to-image translation, TCN embeds a character image into two separate vectors representing typeface and content. Combined with a reconstruction loss from the latent space, and with other various losses, TCN overcomes the inherent difficulty in designing a typeface. Also, compared to previous image-to-image translation models, TCN generates high quality character images of the same typeface with a much smaller number of model parameters. We validate our proposed model on the Chinese and English character datasets, which is paired data, and the CelebA dataset, which is unpaired data. In these datasets, TCN outperforms recently proposed state-of-the-art models for image-to-image translation. The source code of our model is available at https://github.com/yongqyu/TCN.	['Yookyung Koh', 'Junhyun Lee', 'Jinhyuk Lee', 'Inyeop Lee', 'Jaewoo Kang', 'Yonggyu Park']	2018-11-09	null	null	null	null	['font-style-transfer', 'typeface-completion']	['computer-vision', 'computer-vision']	[ 3.74144644e-01 -3.12137753e-01 -1.20322891e-01 -4.30758357e-01 -3.98253649e-01 -7.65614510e-01 5.78727067e-01 -5.15917838e-01 -4.06087160e-01 4.33964401e-01 2.80581146e-01 -1.66705817e-01 6.11692250e-01 -6.71130836e-01 -9.46105957e-01 -5.70002556e-01 9.99584496e-01 4.58821595e-01 -3.53217542e-01 -1.13608450e-01 2.60655940e-01 1.42151907e-01 -1.10986066e+00 5.67732930e-01 9.61948931e-01 9.24766958e-01 5.29100299e-01 4.86279994e-01 -2.87320763e-01 6.77714169e-01 -4.07823950e-01 -9.23101544e-01 2.22964242e-01 -6.41574621e-01 -5.04010379e-01 4.82681900e-01 7.77549565e-01 -5.39647818e-01 -5.83852470e-01 1.29834175e+00 4.98563021e-01 -2.89502352e-01 7.28258610e-01 -1.22283638e+00 -1.50738883e+00 5.28769910e-01 -5.59015512e-01 -6.59829199e-01 1.99202165e-01 9.24605876e-02 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74faf6b6-a452-4b33-867a-e5d0ba16a78e	a-template-based-abstractive-meeting	null	null	https://aclanthology.org/W14-4407	https://aclanthology.org/W14-4407.pdf	A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships	null	['Giuseppe Carenini', 'Yashar Mehdad', 'Tatsuro Oya', 'Raymond Ng']	2014-06-01	null	null	null	ws-2014-6	['meeting-summarization']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 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f07fb436-8404-40ee-9eae-6cd14fab04eb	deep-reinforcement-learning-in-quantitative	2106.00123	null	https://arxiv.org/abs/2106.00123v1	https://arxiv.org/pdf/2106.00123v1.pdf	Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review	Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be to a force to be reckon with in many complex games like Chess and Go. We can look at the stock market historical price series and movements as a complex imperfect information environment in which we try to maximize return - profit and minimize risk. This paper reviews the progress made so far with deep reinforcement learning in the subdomain of AI in finance, more precisely, automated low-frequency quantitative stock trading. Many of the reviewed studies had only proof-of-concept ideals with experiments conducted in unrealistic settings and no real-time trading applications. For the majority of the works, despite all showing statistically significant improvements in performance compared to established baseline strategies, no decent profitability level was obtained. Furthermore, there is a lack of experimental testing in real-time, online trading platforms and a lack of meaningful comparisons between agents built on different types of DRL or human traders. We conclude that DRL in stock trading has showed huge applicability potential rivalling professional traders under strong assumptions, but the research is still in the very early stages of development.	['Tidor-Vlad Pricope']	2021-05-31	null	null	null	null	['algorithmic-trading']	['time-series']	[-8.46947789e-01 -1.77269638e-01 -1.37180984e-01 -1.66006405e-02 -4.04544741e-01 -7.44520783e-01 7.55499601e-01 -9.78438333e-02 -7.38642633e-01 1.16233385e+00 -3.15709859e-01 -4.32963729e-01 -3.42220873e-01 -1.03982544e+00 -6.09413087e-01 -6.34968042e-01 -6.26918912e-01 1.01861537e+00 3.35824698e-01 -9.14291441e-01 6.49538994e-01 2.42058381e-01 -1.31794739e+00 4.81787417e-03 2.10853979e-01 1.51330018e+00 -3.32916141e-01 4.73017633e-01 -3.93669337e-01 1.41185844e+00 -9.97791409e-01 -7.54288316e-01 9.57430840e-01 -5.04386425e-01 -2.97655195e-01 -2.78949022e-01 -2.37400115e-01 -6.76397681e-01 -4.71435674e-03 1.05832529e+00 5.69816351e-01 -3.87620479e-01 2.35911980e-01 -1.39732730e+00 -5.82263589e-01 8.95168424e-01 -5.73283017e-01 5.60696781e-01 8.09409190e-03 4.49190795e-01 1.28330481e+00 -4.53412563e-01 2.25234538e-01 9.64408696e-01 6.38504148e-01 1.47006616e-01 -8.60393167e-01 -9.78283405e-01 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896683e2-e0f8-48b1-be6a-a4ff0da185fe	sample-level-deep-convolutional-neural	1703.01789	null	http://arxiv.org/abs/1703.01789v2	http://arxiv.org/pdf/1703.01789v2.pdf	Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms	Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains. This approach was applied to musical signals as well but has been not fully explored yet. To this end, we propose sample-level deep convolutional neural networks which learn representations from very small grains of waveforms (e.g. 2 or 3 samples) beyond typical frame-level input representations. Our experiments show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million Song Dataset. In addition, we visualize filters learned in a sample-level DCNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency along layer, such as mel-frequency spectrogram that is widely used in music classification systems.	['Keunhyoung Luke Kim', 'Jiyoung Park', 'Juhan Nam', 'Jongpil Lee']	2017-03-06	null	null	null	null	['music-auto-tagging', 'music-classification']	['music', 'music']	[ 2.56276459e-01 -1.82645142e-01 1.61896840e-01 -1.93978310e-01 -8.35762918e-01 -7.90034711e-01 5.02135336e-01 5.46691306e-02 -2.64202386e-01 3.16291541e-01 5.46866953e-01 2.79087752e-01 -2.56386399e-01 -7.33708084e-01 -7.87889957e-01 -4.86199349e-01 -6.20678842e-01 2.15391517e-01 2.09537745e-01 -2.66791940e-01 2.53138453e-01 3.02938610e-01 -1.77350128e+00 1.05677426e+00 2.09579527e-01 1.32465339e+00 -2.49624718e-02 1.14153028e+00 1.04580380e-01 8.95241916e-01 -9.40708101e-01 -3.59828062e-02 3.21185589e-01 -5.80692947e-01 -7.27529824e-01 -2.46083781e-01 8.38165760e-01 -2.21915379e-01 -4.44881767e-01 7.64634132e-01 8.39852631e-01 1.43842757e-01 5.79284966e-01 -5.83267033e-01 -5.43552101e-01 1.43287241e+00 -2.91467816e-01 4.51332927e-01 3.42090577e-02 -2.06966680e-02 1.35941267e+00 -7.97811568e-01 2.76228726e-01 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21423684-46c4-4e62-bfc2-15fe93a65304	renderme-360-a-large-digital-asset-library	2305.13353	null	https://arxiv.org/abs/2305.13353v1	https://arxiv.org/pdf/2305.13353v1.pdf	RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars	Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still face great obstacles in real-world scenarios. One of the vital causes is inadequate datasets -- 1) current public datasets can only support researchers to explore high-fidelity head avatars in one or two task directions; 2) these datasets usually contain digital head assets with limited data volume, and narrow distribution over different attributes. In this paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive advance in head avatar research. It contains massive data assets, with 243+ million complete head frames, and over 800k video sequences from 500 different identities captured by synchronized multi-view cameras at 30 FPS. It is a large-scale digital library for head avatars with three key attributes: 1) High Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K cameras in 360 degrees. 2) High Diversity: The collected subjects vary from different ages, eras, ethnicities, and cultures, providing abundant materials with distinctive styles in appearance and geometry. Moreover, each subject is asked to perform various motions, such as expressions and head rotations, which further extend the richness of assets. 3) Rich Annotations: we provide annotations with different granularities: cameras' parameters, matting, scan, 2D/3D facial landmarks, FLAME fitting, and text description. Based on the dataset, we build a comprehensive benchmark for head avatar research, with 16 state-of-the-art methods performed on five main tasks: novel view synthesis, novel expression synthesis, hair rendering, hair editing, and talking head generation. Our experiments uncover the strengths and weaknesses of current methods. RenderMe-360 opens the door for future exploration in head avatars.	['Kwan-Yee Lin', 'Dahua Lin', 'Wayne Wu', 'Chen Qian', 'Chen Change Loy', 'Ziwei Liu', 'Bo Dai', 'Lei Yang', 'Shengqi Liu', 'Siming Fan', 'Yuxin Wang', 'Wei Cheng', 'Huiwen Luo', 'Jingtan Piao', 'Long Zhuo', 'Dongwei Pan']	2023-05-22	null	null	null	null	['talking-head-generation', 'image-matting', 'novel-view-synthesis']	['computer-vision', 'computer-vision', 'computer-vision']	[-2.30720818e-01 7.04564378e-02 5.78910261e-02 -3.34797800e-01 -6.50724590e-01 -3.22396100e-01 4.82034266e-01 -7.97964573e-01 -6.59677610e-02 5.38955271e-01 7.08531797e-01 5.26252389e-01 5.29268682e-01 -3.18959534e-01 -4.84456956e-01 -7.83475757e-01 2.15150341e-01 6.04081869e-01 4.85396432e-03 -5.34263551e-01 -2.70773798e-01 6.47161961e-01 -1.85999405e+00 -1.40909478e-01 2.69392192e-01 8.57483566e-01 -2.91928530e-01 6.93344831e-01 4.51339960e-01 6.45707369e-01 -6.43976450e-01 -9.59903121e-01 2.29233652e-01 -3.56415361e-01 -5.83150029e-01 3.97518814e-01 7.12568045e-01 -6.69452608e-01 -6.07607424e-01 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401269a8-4b42-44d8-9be6-70b7b6fa83e2	data-assemble-leveraging-multiple-datasets	2109.12265	null	https://arxiv.org/abs/2109.12265v4	https://arxiv.org/pdf/2109.12265v4.pdf	Label-Assemble: Leveraging Multiple Datasets with Partial Labels	The success of deep learning relies heavily on large labeled datasets, but we often only have access to several small datasets associated with partial labels. To address this problem, we propose a new initiative, "Label-Assemble", that aims to unleash the full potential of partial labels from an assembly of public datasets. We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection. This discovery will be particularly crucial in novel disease diagnosis, where positive examples are hard to collect, yet negative examples are relatively easier to assemble. For example, assembling existing labels from NIH ChestX-ray14 (available since 2017) significantly improves the accuracy of COVID-19 diagnosis from 96.3% to 99.3%. In addition to diagnosis, assembling labels can also improve disease detection, e.g., the detection of pancreatic ductal adenocarcinoma (PDAC) can greatly benefit from leveraging the labels of Cysts and PanNets (two other types of pancreatic abnormalities), increasing sensitivity from 52.1% to 84.0% while maintaining a high specificity of 98.0%.	['Elliot K. Fishman', 'Yongyi Lu', 'Zengle Zhu', 'Bowen Li', 'Zongwei Zhou', 'Alan L. Yuille', 'Mintong Kang']	2021-09-25	null	null	null	null	['covid-19-detection']	['medical']	[ 9.12310034e-02 3.36681038e-01 -5.26480615e-01 -3.77757221e-01 -1.31156468e+00 -8.37715268e-01 2.72626907e-01 6.37832224e-01 -4.13601816e-01 8.40863526e-01 1.46082550e-01 -5.85964799e-01 2.80361064e-02 -8.12130809e-01 -5.98951817e-01 -7.37015247e-01 -1.06093064e-01 6.55511081e-01 -3.27807277e-01 4.49936092e-01 -3.54044914e-01 3.36513340e-01 -9.75375652e-01 4.97676462e-01 8.73830259e-01 7.42675245e-01 9.59575996e-02 5.13357997e-01 -1.25624448e-01 6.34229183e-01 -4.24566686e-01 -2.01589346e-01 1.08258739e-01 -1.45491093e-01 -6.71466768e-01 -1.19516850e-01 3.14325571e-01 -6.14033818e-01 1.93293571e-01 9.10032928e-01 3.93403679e-01 -6.67273521e-01 7.54550993e-01 -1.01390421e+00 -5.35946667e-01 6.40833497e-01 -6.60102010e-01 -1.91038683e-01 -1.53360233e-01 4.47981536e-01 1.21171498e+00 -4.57996637e-01 9.33859706e-01 6.90405071e-01 1.03792012e+00 6.40289843e-01 -1.25877607e+00 -9.05012548e-01 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2.92139769e-01 4.93007034e-01 -5.32614768e-01 -4.34536725e-01 1.26544863e-01]	[15.073539733886719, -2.85795521736145]
710b1ec5-6422-4383-b06e-82354dc0b1ee	enhancing-dynamic-mode-decomposition-workflow	2208.07767	null	https://arxiv.org/abs/2208.07767v1	https://arxiv.org/pdf/2208.07767v1.pdf	Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression	Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition (DMD) can extract coherent structures from spatio-temporal data generated from dynamical systems and infer different scenarios for said systems. The spatio-temporal data comes as snapshots containing spatial information for each time instant. In modern engineering applications, the generation of high-dimensional snapshots can be time and/or resource-demanding. In the present study, we consider two strategies for enhancing DMD workflow in large numerical simulations: (i) snapshots compression to relieve disk pressure; (ii) the use of in situ visualization images to reconstruct the dynamics (or part of) in runtime. We evaluate our approaches with two 3D fluid dynamics simulations and consider DMD to reconstruct the solutions. Results reveal that snapshot compression considerably reduces the required disk space. We have observed that lossy compression reduces storage by almost $50\%$ with low relative errors in the signal reconstructions and other quantities of interest. We also extend our analysis to data generated on-the-fly, using in-situ visualization tools to generate image files of our state vectors during runtime. On large simulations, the generation of snapshots may be slow enough to use batch algorithms for inference. Streaming DMD takes advantage of the incremental SVD algorithm and updates the modes with the arrival of each new snapshot. We use streaming DMD to reconstruct the dynamics from in-situ generated images. We show that this process is efficient, and the reconstructed dynamics are accurate.	['Alvaro L. G. A. Coutinho', 'José J. Camata', 'Malú Grave', 'Gabriel F. Barros']	2022-08-16	null	null	null	null	['data-compression']	['time-series']	[ 5.11500090e-02 -4.93370682e-01 5.52846134e-01 2.59981185e-01 -4.13405985e-01 -6.55845344e-01 6.67651057e-01 1.54551640e-01 -3.94067585e-01 7.37398207e-01 -6.51628152e-02 -4.80827510e-01 -3.74262959e-01 -6.84679210e-01 -5.95506549e-01 -9.52811360e-01 -6.62668347e-01 6.17240965e-01 1.44953832e-01 8.61041155e-03 3.74079227e-01 9.20294225e-01 -1.79918623e+00 2.87463009e-01 5.20356417e-01 7.89531052e-01 3.26729119e-01 1.30739677e+00 -1.50929347e-01 5.92764974e-01 -4.69489485e-01 5.81565559e-01 2.98323989e-01 -4.91398215e-01 -5.02047539e-01 3.14260572e-02 -1.05075464e-01 -4.79700983e-01 -3.70483607e-01 5.88352919e-01 7.13338017e-01 3.56247276e-01 4.71760869e-01 -6.46731615e-01 3.27162564e-01 1.00068294e-01 -5.90477169e-01 5.53571165e-01 3.75876814e-01 6.87542498e-01 2.13670537e-01 -9.85697210e-01 9.04526532e-01 1.05898821e+00 6.31638706e-01 1.33323148e-01 -1.62044084e+00 -2.21311867e-01 -3.35417628e-01 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533a3e79-9ea8-4019-ac85-861aa26e8ba2	twitch-plays-pokemon-machine-learns-twitch	1902.06208	null	http://arxiv.org/abs/1902.06208v1	http://arxiv.org/pdf/1902.06208v1.pdf	Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data	With the increasing importance of online communities, discussion forums, and customer reviews, Internet "trolls" have proliferated thereby making it difficult for information seekers to find relevant and correct information. In this paper, we consider the problem of detecting and identifying Internet trolls, almost all of which are human agents. Identifying a human agent among a human population presents significant challenges compared to detecting automated spam or computerized robots. To learn a troll's behavior, we use contextual anomaly detection to profile each chat user. Using clustering and distance-based methods, we use contextual data such as the group's current goal, the current time, and the username to classify each point as an anomaly. A user whose features significantly differ from the norm will be classified as a troll. We collected 38 million data points from the viral Internet fad, Twitch Plays Pokemon. Using clustering and distance-based methods, we develop heuristics for identifying trolls. Using MapReduce techniques for preprocessing and user profiling, we are able to classify trolls based on 10 features extracted from a user's lifetime history.	['Albert Haque']	2019-02-17	null	null	null	null	['contextual-anomaly-detection']	['miscellaneous']	[-1.43541634e-01 -5.50316930e-01 -3.39547843e-02 -1.27965525e-01 -1.81271106e-01 -6.58533037e-01 7.57953167e-01 8.76492977e-01 -5.34901977e-01 5.31913161e-01 -4.64868546e-02 -4.73562330e-01 -1.84890240e-01 -6.35067403e-01 -1.20790012e-01 -4.59545642e-01 -2.16637775e-01 7.99666345e-01 3.28769952e-01 -2.68601239e-01 7.94550776e-01 3.99632782e-01 -1.27897060e+00 3.10555935e-01 8.85547996e-01 6.36800706e-01 -8.72279704e-02 9.09190953e-01 -3.50846440e-01 1.08696961e+00 -1.15565920e+00 -2.48736545e-01 1.89586222e-01 -5.92952371e-01 -8.31307948e-01 1.44020647e-01 3.08929205e-01 -2.21735418e-01 -1.57345861e-01 9.41873372e-01 1.44433886e-01 2.16727152e-01 7.02656269e-01 -1.54923570e+00 -9.46595371e-02 1.78837150e-01 -7.58067191e-01 8.95882308e-01 7.28350461e-01 -1.22917645e-01 1.09732032e+00 -2.82852769e-01 8.73126626e-01 1.22442842e+00 8.09870005e-01 3.54299575e-01 -1.15108681e+00 -5.88715851e-01 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01014767-6b73-4efc-ad8e-865d7715ba39	multi-density-sketch-to-image-translation	2006.10649	null	https://arxiv.org/abs/2006.10649v1	https://arxiv.org/pdf/2006.10649v1.pdf	Multi-Density Sketch-to-Image Translation Network	Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as powerful tools in the art design industry. However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to micro structures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and generation of images. Moreover, our method has been successfully verified on various datasets for different applications including face editing, multi-modal sketch-to-photo translation, and anime colorization, providing coarse-to-fine levels of controls to these applications.	['Zhifeng Tan', 'Sam Kwong', 'Jing Liao', 'Jialu Huang']	2020-06-18	null	null	null	null	['sketch-to-image-translation']	['computer-vision']	[ 3.34124148e-01 -2.36323029e-01 -1.27070099e-01 -1.38949275e-01 -3.16443443e-01 -6.50281608e-01 8.40589464e-01 -5.49122691e-01 8.12292695e-02 6.24551892e-01 -1.93060189e-01 -7.06067532e-02 -1.21017814e-01 -1.06048024e+00 -6.29138291e-01 -6.65901601e-01 5.76360464e-01 6.40775919e-01 1.15864091e-01 -9.42252055e-02 1.35560080e-01 8.47122431e-01 -1.60362148e+00 2.04073191e-01 7.45279312e-01 7.87216842e-01 2.01184839e-01 5.44075608e-01 -6.53223038e-01 9.95006561e-02 -5.15446484e-01 -4.73756462e-01 1.83364183e-01 -4.30175036e-01 5.14224097e-02 2.70795435e-01 5.41694403e-01 -2.50383109e-01 -8.05617422e-02 1.14394677e+00 3.80375594e-01 -6.20143823e-02 1.08621180e+00 -1.48201263e+00 -9.30759668e-01 1.72019243e-01 -9.00797665e-01 -6.08188391e-01 2.80284971e-01 5.88861108e-02 5.81119299e-01 -1.04877341e+00 7.21072197e-01 1.82893753e+00 2.68580407e-01 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-3.20579201e-01 -9.87407744e-01 -9.45412517e-01 6.51792586e-01 -7.27786049e-02 -8.89088809e-01 -1.53472170e-01 2.06103727e-01]	[12.09615707397461, -0.3913973271846771]
76b665dc-6849-4818-9c5c-143262b76126	pixel-objectness-learning-to-segment-generic	1808.04702	null	http://arxiv.org/abs/1808.04702v2	http://arxiv.org/pdf/1808.04702v2.pdf	Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos	We propose an end-to-end learning framework for segmenting generic objects in both images and videos. Given a novel image or video, our approach produces a pixel-level mask for all "object-like" regions---even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning an object/background label to each pixel, implemented using a deep fully convolutional network. When applied to a video, our model further incorporates a motion stream, and the network learns to combine both appearance and motion and attempts to extract all prominent objects whether they are moving or not. Beyond the core model, a second contribution of our approach is how it leverages varying strengths of training annotations. Pixel-level annotations are quite difficult to obtain, yet crucial for training a deep network approach for segmentation. Thus we propose ways to exploit weakly labeled data for learning dense foreground segmentation. For images, we show the value in mixing object category examples with image-level labels together with relatively few images with boundary-level annotations. For video, we show how to bootstrap weakly annotated videos together with the network trained for image segmentation. Through experiments on multiple challenging image and video segmentation benchmarks, our method offers consistently strong results and improves the state-of-the-art for fully automatic segmentation of generic (unseen) objects. In addition, we demonstrate how our approach benefits image retrieval and image retargeting, both of which flourish when given our high-quality foreground maps. Code, models, and videos are at:http://vision.cs.utexas.edu/projects/pixelobjectness/	['Kristen Grauman', 'Suyog Dutt Jain', 'Bo Xiong']	2018-08-11	null	null	null	null	['image-retargeting', 'foreground-segmentation']	['computer-vision', 'computer-vision']	[ 6.64671004e-01 1.64740726e-01 -3.61649156e-01 -3.03100109e-01 -1.12143505e+00 -9.26706612e-01 4.79913831e-01 -2.45273158e-01 -3.82705629e-01 3.99025917e-01 -1.07828021e-01 -2.20670536e-01 4.55935866e-01 -4.58279401e-01 -1.29932284e+00 -7.10806310e-01 -1.11745141e-01 3.74908388e-01 7.33724177e-01 1.91880777e-01 1.20655164e-01 4.34615999e-01 -1.44203985e+00 5.86147726e-01 3.97955149e-01 9.79523540e-01 3.24287474e-01 9.87925589e-01 -2.11995076e-02 9.48998749e-01 -3.03390771e-01 -2.88562447e-01 6.42227650e-01 -2.95677334e-01 -1.26740801e+00 7.36546218e-01 9.72478330e-01 -7.19276488e-01 -3.08548331e-01 9.54780936e-01 -4.74628359e-02 1.38223037e-01 5.66949904e-01 -1.26662934e+00 -3.69913399e-01 5.57508826e-01 -7.92327225e-01 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5910f8ad-21c0-44a2-8611-ecfc1f407085	fairgen-towards-fair-graph-generation	2303.17743	null	https://arxiv.org/abs/2303.17743v1	https://arxiv.org/pdf/2303.17743v1.pdf	FairGen: Towards Fair Graph Generation	There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsupervised in nature and are typically trained to minimize the expected graph reconstruction loss, which would result in the representation disparity issue in the generated graphs, i.e., the protected groups (often minorities) contribute less to the objective and thus suffer from systematically higher errors. In this paper, we aim to tailor graph generation to downstream mining tasks by leveraging label information and user-preferred parity constraint. In particular, we start from the investigation of representation disparity in the context of graph generative models. To mitigate the disparity, we propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the `easy' concepts to the `hard' ones. In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen (1) obtains performance on par with state-of-the-art graph generative models across six network properties, (2) mitigates the representation disparity issues in the generated graphs, and (3) substantially boosts the model performance by up to 17% in downstream tasks via data augmentation.	['Jingrui He', 'Yada Zhu', 'Jiejun Xu', 'Hanghang Tong', 'Dawei Zhou', 'Lecheng Zheng']	2023-03-30	null	null	null	null	['graph-reconstruction']	['graphs']	[ 4.65368241e-01 6.32919371e-01 -5.28243780e-01 -2.53998160e-01 -3.61210495e-01 -3.08319092e-01 6.53221846e-01 2.32460067e-01 9.06184018e-02 8.71408999e-01 1.08811162e-01 -5.35931766e-01 -1.48715973e-01 -1.32830143e+00 -6.21866107e-01 -6.61460876e-01 -2.28245586e-01 5.87457120e-01 -8.29683021e-02 -2.13196903e-01 -1.90219413e-02 3.40203643e-02 -1.12134850e+00 -1.86759859e-01 1.34510064e+00 7.41336286e-01 -8.22222978e-02 1.12496637e-01 6.84327856e-02 7.55062401e-01 -4.15601224e-01 -9.70971525e-01 2.50208586e-01 -6.34482265e-01 -6.24831259e-01 3.48570108e-01 1.14010803e-01 7.31608318e-03 -4.57934737e-01 1.23168886e+00 4.48445737e-01 8.36449638e-02 7.58325994e-01 -1.43914807e+00 -8.60471964e-01 1.08923137e+00 -7.52342761e-01 -2.71679014e-01 -6.10547327e-03 8.78586397e-02 1.45547295e+00 -4.06967670e-01 7.26664960e-01 1.37154841e+00 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f8a60459-6878-480a-98b9-115532f19e21	videberta-a-powerful-pre-trained-language	2301.10439	null	https://arxiv.org/abs/2301.10439v2	https://arxiv.org/pdf/2301.10439v2.pdf	ViDeBERTa: A powerful pre-trained language model for Vietnamese	This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture. Although many successful pre-trained language models based on Transformer have been widely proposed for the English language, there are still few pre-trained models for Vietnamese, a low-resource language, that perform good results on downstream tasks, especially Question answering. We fine-tune and evaluate our model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. The empirical results demonstrate that ViDeBERTa with far fewer parameters surpasses the previous state-of-the-art models on multiple Vietnamese-specific natural language understanding tasks. Notably, ViDeBERTa_base with 86M parameters, which is only about 23% of PhoBERT_large with 370M parameters, still performs the same or better results than the previous state-of-the-art model. Our ViDeBERTa models are available at: https://github.com/HySonLab/ViDeBERTa.	['Tu Vu', 'Truong Son Hy', 'Anh Nguyen', 'Nhut Huy Pham', 'Cong Dao Tran']	2023-01-25	null	null	null	null	['part-of-speech-tagging']	['natural-language-processing']	[-6.36127591e-01 8.53169486e-02 -1.89329222e-01 -6.05990648e-01 -1.38584375e+00 -8.14109266e-01 4.19254303e-01 1.06609657e-01 -9.03919876e-01 8.33166540e-01 4.36252862e-01 -8.81034672e-01 4.21010554e-01 -7.02205360e-01 -6.17060065e-01 -1.57392144e-01 1.07107766e-01 8.18022966e-01 3.21811199e-01 -8.01540732e-01 -2.05569267e-01 4.30056229e-02 -8.37902904e-01 5.20332634e-01 1.23589051e+00 2.63324767e-01 1.93114251e-01 7.63217568e-01 -3.28011185e-01 7.91025102e-01 -6.00864947e-01 -9.05716181e-01 -1.06642924e-01 -2.38883361e-01 -1.30345511e+00 -5.15934408e-01 2.58071303e-01 -4.20920551e-01 -3.92902911e-01 5.83634198e-01 4.65789407e-01 -1.04003206e-01 3.65660012e-01 -1.09231615e+00 -1.01614726e+00 9.78626668e-01 -9.67392772e-02 3.76736283e-01 3.43745083e-01 1.99226081e-01 1.16810226e+00 -1.18703651e+00 8.14490139e-01 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38f83262-79fa-4cfb-bcb4-4b66be0e1180	occdepth-a-depth-aware-method-for-3d-semantic	2302.13540	null	https://arxiv.org/abs/2302.13540v1	https://arxiv.org/pdf/2302.13540v1.pdf	OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion	3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems. It is challenging to estimate the complete geometry and semantics of a scene solely from visual images, and accurate depth information is crucial for restoring 3D geometry. In this paper, we propose the first stereo SSC method named OccDepth, which fully exploits implicit depth information from stereo images (or RGBD images) to help the recovery of 3D geometric structures. The Stereo Soft Feature Assignment (Stereo-SFA) module is proposed to better fuse 3D depth-aware features by implicitly learning the correlation between stereo images. In particular, when the input are RGBD image, a virtual stereo images can be generated through original RGB image and depth map. Besides, the Occupancy Aware Depth (OAD) module is used to obtain geometry-aware 3D features by knowledge distillation using pre-trained depth models. In addition, a reformed TartanAir benchmark, named SemanticTartanAir, is provided in this paper for further testing our OccDepth method on SSC task. Compared with the state-of-the-art RGB-inferred SSC method, extensive experiments on SemanticKITTI show that our OccDepth method achieves superior performance with improving +4.82% mIoU, of which +2.49% mIoU comes from stereo images and +2.33% mIoU comes from our proposed depth-aware method. Our code and trained models are available at https://github.com/megvii-research/OccDepth.	['Shuchang Zhou', 'Chen Hu', 'Weixin Xu', 'Zheng Gong', 'Mingrui Chen', 'Weizhou Liu', 'Ruihang Miao']	2023-02-27	null	null	null	null	['3d-semantic-scene-completion']	['computer-vision']	[ 2.21940964e-01 1.60087422e-01 -4.33300734e-02 -5.49664199e-01 -6.68012679e-01 -4.64671999e-01 5.98930836e-01 -1.59057498e-01 -3.48375201e-01 3.49754900e-01 2.23357901e-01 -2.05271974e-01 1.86633363e-01 -1.04889405e+00 -9.16200042e-01 -5.61803877e-01 5.06303549e-01 4.94402945e-01 4.50152159e-01 -4.10836875e-01 4.23056751e-01 4.72250581e-01 -1.86122000e+00 9.84594822e-02 9.20710266e-01 1.20293367e+00 6.44055843e-01 4.58274662e-01 -3.26301396e-01 7.30690181e-01 1.16116544e-02 6.94414973e-02 4.22658563e-01 -6.54339716e-02 -7.59080887e-01 1.13597468e-01 5.11515737e-01 -6.47152305e-01 -6.55178845e-01 9.72555101e-01 2.63146162e-01 2.98217744e-01 4.27638203e-01 -1.10112321e+00 -2.17613265e-01 -1.19210608e-01 -5.91445982e-01 -8.84334221e-02 4.76404130e-01 2.46808767e-01 5.87983191e-01 -9.88625050e-01 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2ed13589-eda7-4e82-8501-9d874ce6fc8c	hub-at-semeval-2021-task-7-fusion-of-albert	null	null	https://aclanthology.org/2021.semeval-1.160	https://aclanthology.org/2021.semeval-1.160.pdf	hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense	This paper introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We successfully submitted the test set prediction results of the two subtasks in the task. The goal of the task is to perform humor detection, grade evaluation, and offensive evaluation on each English text data in the data set. Tasks can be divided into two types of subtasks. One is a text classification task, and the other is a text regression task. What we need to do is to use our method to detect the humor and offensive information of the sentence as accurately as possible. The methods used in the results submitted by our team are mainly composed of ALBERT, CNN, and Tf-Idf algorithms. The result evaluation indicators submitted by the classification task are F1 score and Accuracy. The result evaluation index of the regression task submission is the RMSE. The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0.921 (F1), task1a 0.9364 (Accuracy), task1b 0.6288 (RMSE), task1c 0.5333 (F1), task1c 0.0.5591 (Accuracy), and task2 0.5027 (RMSE) respectively.	['Yang Bai', 'Bo Huang']	2021-08-01	null	null	null	semeval-2021	['humor-detection']	['natural-language-processing']	[-3.84212494e-01 -6.69100285e-02 1.50011033e-01 -7.91206583e-02 -4.35212821e-01 -4.47421491e-01 5.93672812e-01 2.44356513e-01 -2.87403435e-01 7.37862825e-01 5.99350572e-01 -8.48187581e-02 1.01982757e-01 -6.02629006e-01 -1.72410414e-01 -4.19582725e-01 2.97681034e-01 2.79404402e-01 9.45252329e-02 -7.33273089e-01 9.76457298e-01 -9.12084803e-03 -1.33226550e+00 9.51974809e-01 1.00212884e+00 1.11672115e+00 -1.36807740e-01 1.02044344e+00 1.47922888e-01 1.78687310e+00 -9.21163559e-01 -5.88942528e-01 -1.63441747e-01 -5.85069537e-01 -1.24963725e+00 -2.33147457e-01 1.16733387e-01 -8.39497894e-02 -2.12474182e-01 1.06676841e+00 5.59383273e-01 3.91758621e-01 7.50288844e-01 -1.29852700e+00 -5.91294587e-01 6.67956233e-01 -3.40863109e-01 2.02182040e-01 5.96316755e-01 1.53855300e-02 9.89837766e-01 -1.28262234e+00 5.02053618e-01 8.93023670e-01 7.57200181e-01 4.88702476e-01 -5.53443909e-01 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-5.92514873e-01 6.37426436e-01 -6.49051070e-02 -4.04464632e-01 -5.60134828e-01 5.39372377e-02]	[8.862120628356934, 11.072988510131836]
76404b7d-420d-43cb-9af6-dc4a8260de55	mamadroid2-0-the-holes-of-control-flow-graphs	2202.13922	null	https://arxiv.org/abs/2202.13922v1	https://arxiv.org/pdf/2202.13922v1.pdf	MaMaDroid2.0 -- The Holes of Control Flow Graphs	Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary changes malicious samples such that those samples will be misclassified as benign. This paper fully inspects a well-known Android malware detection system, MaMaDroid, which analyzes the control flow graph of the application. Changes to the portion of benign samples in the train set and models are considered to see their effect on the classifier. The changes in the ratio between benign and malicious samples have a clear effect on each one of the models, resulting in a decrease of more than 40% in their detection rate. Moreover, adopted ML models are implemented as well, including 5-NN, Decision Tree, and Adaboost. Exploration of the six models reveals a typical behavior in different cases, of tree-based models and distance-based models. Moreover, three novel attacks that manipulate the CFG and their detection rates are described for each one of the targeted models. The attacks decrease the detection rate of most of the models to 0%, with regards to different ratios of benign to malicious apps. As a result, a new version of MaMaDroid is engineered. This model fuses the CFG of the app and static analysis of features of the app. This improved model is proved to be robust against evasion attacks targeting both CFG-based models and static analysis models, achieving a detection rate of more than 90% against each one of the attacks.	['Amit Dvir', 'Enrico Mariconti', 'Chen Hajaj', 'Harel Berger']	2022-02-28	null	null	null	null	['android-malware-detection']	['miscellaneous']	[ 1.46281824e-01 3.73980477e-02 -4.14164603e-01 4.36938629e-02 -1.65941179e-01 -9.85628903e-01 7.91835427e-01 -1.58493258e-02 -1.78197369e-01 4.46978837e-01 -4.11840200e-01 -8.35111499e-01 1.02507034e-02 -9.62204099e-01 -7.81661332e-01 -5.10718882e-01 -2.51097202e-01 5.63659891e-02 8.21786106e-01 -2.75331408e-01 4.35191363e-01 6.10481620e-01 -1.75737906e+00 4.66959000e-01 6.41556323e-01 8.45061362e-01 -4.45006222e-01 8.31028223e-01 3.68046276e-02 2.50616342e-01 -1.07753408e+00 -6.57674849e-01 3.34674686e-01 4.87569273e-02 -4.79144037e-01 -4.91533399e-01 8.47633108e-02 -3.51084441e-01 -1.54242367e-02 1.34764814e+00 5.65022677e-02 -5.59378803e-01 5.55673420e-01 -1.52188897e+00 -9.79975313e-02 5.29213011e-01 -6.12366617e-01 1.60558775e-01 6.63070381e-01 3.37844908e-01 4.63657945e-01 -2.49173671e-01 3.89846742e-01 1.17700076e+00 5.98742247e-01 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79684ad9-6d86-4714-a094-087e173032b3	ibiscape-a-simulated-benchmark-for-multi	2206.13455	null	https://arxiv.org/abs/2206.13455v2	https://arxiv.org/pdf/2206.13455v2.pdf	IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments	The development process of high-fidelity SLAM systems depends on their validation upon reliable datasets. Towards this goal, we propose IBISCape, a simulated benchmark that includes data synchronization and acquisition APIs for telemetry from heterogeneous sensors: stereo-RGB/DVS, Depth, IMU, and GPS, along with the ground truth scene segmentation and vehicle ego-motion. Our benchmark is built upon the CARLA simulator, whose back-end is the Unreal Engine rendering a high dynamic scenery simulating the real world. Moreover, we offer 34 multi-modal datasets suitable for autonomous vehicles navigation, including scenarios for scene understanding evaluation like accidents, along with a wide range of frame quality based on a dynamic weather simulation class integrated with our APIs. We also introduce the first calibration targets to CARLA maps to solve the unknown distortion parameters problem of CARLA simulated DVS and RGB cameras. Finally, using IBISCape sequences, we evaluate four ORB-SLAM3 systems (monocular RGB, stereo RGB, Stereo Visual Inertial (SVI), and RGB-D) performance and BASALT Visual-Inertial Odometry (VIO) system on various sequences collected in simulated large-scale dynamic environments. Keywords: benchmark, multi-modal, datasets, Odometry, Calibration, DVS, SLAM	['Samia Bouchafa', 'Désiré Sidibé', 'Fabien Bonardi', 'Abanob Soliman']	2022-06-27	null	null	null	null	['scene-segmentation']	['computer-vision']	[-4.83868927e-01 -4.38285142e-01 3.34950149e-01 -5.89939177e-01 -4.77442861e-01 -8.69347036e-01 7.40432680e-01 -2.78140008e-01 -5.00065625e-01 6.12948656e-01 -2.69977570e-01 -3.61809283e-01 -4.95683812e-02 -8.72247815e-01 -9.32938755e-01 -5.63376665e-01 -1.24068804e-01 9.96509790e-01 4.70201403e-01 -6.28365636e-01 1.56304628e-01 8.50644410e-01 -1.82510507e+00 -6.10230923e-01 7.50819683e-01 1.11284006e+00 2.62782753e-01 7.95082450e-01 2.24129051e-01 4.14299846e-01 -3.38849813e-01 -2.49294966e-01 7.50565410e-01 -1.15889991e-02 -2.65899003e-01 -9.34389904e-02 6.05634391e-01 -4.55424845e-01 -5.13851941e-01 9.35991585e-01 5.64947724e-01 -2.34674662e-03 1.06361337e-01 -1.91775119e+00 2.41937667e-01 -5.30371189e-01 -4.05685157e-02 2.14382280e-02 8.24132502e-01 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5d61fbb3-3b2b-4eae-9c91-8210517124d3	saibersoc-synthetic-attack-injection-to	2010.08453	null	https://arxiv.org/abs/2010.08453v1	https://arxiv.org/pdf/2010.08453v1.pdf	SAIBERSOC: Synthetic Attack Injection to Benchmark and Evaluate the Performance of Security Operation Centers	In this paper we introduce SAIBERSOC, a tool and methodology enabling security researchers and operators to evaluate the performance of deployed and operational Security Operation Centers (SOCs) (or any other security monitoring infrastructure). The methodology relies on the MITRE ATT&CK Framework to define a procedure to generate and automatically inject synthetic attacks in an operational SOC to evaluate any output metric of interest (e.g., detection accuracy, time-to-investigation, etc.). To evaluate the effectiveness of the proposed methodology, we devise an experiment with $n=124$ students playing the role of SOC analysts. The experiment relies on a real SOC infrastructure and assigns students to either a BADSOC or a GOODSOC experimental condition. Our results show that the proposed methodology is effective in identifying variations in SOC performance caused by (minimal) changes in SOC configuration. We release the SAIBERSOC tool implementation as free and open source software.	['Luca Allodi', 'Ganduulga Gankhuyag', 'Michele Campobasso', 'Martin Rosso']	2020-10-16	null	null	null	null	['cyber-attack-investigation']	['miscellaneous']	[-7.41657522e-03 -3.77746046e-01 2.41264120e-01 -1.61212876e-01 -4.32150990e-01 -9.26944613e-01 2.10673407e-01 3.04965436e-01 -1.64073557e-01 8.93846810e-01 -6.91950321e-01 -7.76885152e-01 -9.77348909e-03 -7.32134223e-01 -4.71286267e-01 -4.03785735e-01 -3.57055783e-01 2.02646717e-01 7.64746785e-01 -2.79608607e-01 5.93810439e-01 7.71385312e-01 -1.52887702e+00 -9.54825282e-02 4.59522009e-01 8.56256425e-01 -3.88658255e-01 8.43977392e-01 6.92206621e-01 2.83705592e-01 -1.32032585e+00 -7.61965811e-02 3.39260966e-01 3.86645794e-02 -4.32539016e-01 -2.64936715e-01 -2.26765692e-01 3.51650678e-02 2.06299603e-01 1.21068335e+00 6.20692790e-01 -1.94399536e-01 1.40389889e-01 -1.69298637e+00 5.21934032e-01 7.63243794e-01 -3.16558480e-01 4.95312750e-01 5.07904828e-01 2.33520657e-01 5.86834550e-01 -4.23048407e-01 4.05115217e-01 8.86662185e-01 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5db2e9a4-a691-47d3-9283-71a606e838ba	complex-mixer-for-medmnist-classification	2304.10054	null	https://arxiv.org/abs/2304.10054v1	https://arxiv.org/pdf/2304.10054v1.pdf	Complex Mixer for MedMNIST Classification Decathlon	With the development of the medical image field, researchers seek to develop a class of datasets to block the need for medical knowledge, such as \text{MedMNIST} (v2). MedMNIST (v2) includes a large number of small-sized (28 $\times$ 28 or 28 $\times$ 28 $\times$ 28) medical samples and the corresponding expert annotations (class label). The existing baseline model (Google AutoML Vision, ResNet-50+3D) can reach an average accuracy of over 70\% on MedMNIST (v2) datasets, which is comparable to the performance of expert decision-making. Nevertheless, we note that there are two insurmountable obstacles to modeling on MedMNIST (v2): 1) the raw images are cropped to low scales may cause effective recognition information to be dropped and the classifier to have difficulty in tracing accurate decision boundaries; 2) the labelers' subjective insight may cause many uncertainties in the label space. To address these issues, we develop a Complex Mixer (C-Mixer) with a pre-training framework to alleviate the problem of insufficient information and uncertainty in the label space by introducing an incentive imaginary matrix and a self-supervised scheme with random masking. Our method (incentive learning and self-supervised learning with masking) shows surprising potential on both the standard MedMNIST (v2) dataset, the customized weakly supervised datasets, and other image enhancement tasks.	['Xiuyi Jia', 'Zhuoran Zheng']	2023-04-20	null	null	null	null	['image-enhancement', 'automl']	['computer-vision', 'methodology']	[ 4.08999026e-01 5.52135825e-01 -2.38996267e-01 -5.55382609e-01 -9.64692056e-01 -2.98955232e-01 2.10511521e-01 -1.52166691e-02 -5.44929862e-01 6.28365695e-01 8.83966908e-02 -2.81694680e-01 -2.42414102e-01 -5.22681713e-01 -2.94175595e-01 -5.24300158e-01 1.85834363e-01 3.58684599e-01 4.94452864e-02 -1.37041077e-01 -1.14863262e-01 -5.03314324e-02 -1.46416366e+00 3.24837238e-01 1.10961282e+00 1.23030388e+00 3.88945311e-01 3.71765584e-01 1.80309415e-01 1.20779896e+00 -6.05730534e-01 -5.41538656e-01 5.71446717e-01 -3.13443065e-01 -8.87674689e-01 2.69814789e-01 3.56157124e-01 -3.72469515e-01 -1.28550693e-01 1.23707354e+00 6.81107700e-01 -3.60551812e-02 7.88762748e-01 -1.15946531e+00 -8.93726707e-01 5.43762505e-01 -8.28811228e-01 1.12491153e-01 -1.31215200e-01 3.80155534e-01 8.39000285e-01 -8.55768681e-01 7.62432098e-01 9.60120380e-01 1.06313264e+00 6.35523319e-01 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8ca034cb-c88e-43b3-8701-2e73378b763d	human-in-the-loop-automatic-program-repair	1912.07758	null	https://arxiv.org/abs/1912.07758v1	https://arxiv.org/pdf/1912.07758v1.pdf	Human-In-The-Loop Automatic Program Repair	We introduce Learn2fix, the first human-in-the-loop, semi-automatic repair technique when no bug oracle--except for the user who is reporting the bug--is available. Our approach negotiates with the user the condition under which the bug is observed. Only when a budget of queries to the user is exhausted, it attempts to repair the bug. A query can be thought of as the following question: "When executing this alternative test input, the program produces the following output; is the bug observed"? Through systematic queries, Learn2fix trains an automatic bug oracle that becomes increasingly more accurate in predicting the user's response. Our key challenge is to maximize the oracle's accuracy in predicting which tests are bug-exposing given a small budget of queries. From the alternative tests that were labeled by the user, test-driven automatic repair produces the patch. Our experiments demonstrate that Learn2fix learns a sufficiently accurate automatic oracle with a reasonably low labeling effort (lt. 20 queries). Given Learn2fix's test suite, the GenProg test-driven repair tool produces a higher-quality patch (i.e., passing a larger proportion of validation tests) than using manual test suites provided with the repair benchmark.	['Van-Thuan Pham', 'Marcel Böhme', 'Charaka Geethal']	2019-12-16	null	null	null	null	['program-repair', 'program-repair']	['computer-code', 'reasoning']	[-2.07965195e-01 4.75277364e-01 -3.08982521e-01 -3.89424562e-01 -1.70272052e+00 -9.21289802e-01 -4.50853735e-01 1.85807467e-01 2.31908023e-01 7.71106660e-01 -3.62834483e-01 -8.02664101e-01 9.44878608e-02 -9.01256561e-01 -9.15603817e-01 -1.69199914e-01 -1.64806396e-01 5.67351162e-01 3.96937102e-01 1.82358935e-01 3.22644651e-01 -2.97202557e-01 -1.41566312e+00 1.75074473e-01 1.02878916e+00 7.10209787e-01 1.19379349e-01 1.09029686e+00 3.54565084e-01 8.79671156e-01 -1.17188263e+00 -3.62851024e-01 1.37324467e-01 -6.48365617e-01 -1.14175403e+00 1.30008146e-01 2.87720203e-01 -4.86489177e-01 3.24873328e-01 1.31991708e+00 7.07344711e-03 -3.60742897e-01 -6.73713237e-02 -1.24955881e+00 -4.27359343e-01 5.79608619e-01 -2.02965457e-02 2.25465968e-01 9.20428574e-01 7.37198532e-01 1.31547058e+00 -6.67320848e-01 7.26392329e-01 6.87396824e-01 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-1.00501561e+00 -9.81218815e-01 -2.10180148e-01 -1.00143641e-01 6.30828619e-01 -3.78810078e-01 -7.69731283e-01 8.67169797e-02]	[7.626348972320557, 7.71376371383667]
182e93bc-377e-41bc-bfcc-8d5c0058be25	functional-constrained-optimization-for-risk	2210.05108	null	https://arxiv.org/abs/2210.05108v1	https://arxiv.org/pdf/2210.05108v1.pdf	Functional Constrained Optimization for Risk Aversion and Sparsity Control	Risk and sparsity requirements often need to be enforced simultaneously in many applications, e.g., in portfolio optimization, assortment planning, and treatment planning. Properly balancing these potentially conflicting requirements entails the formulation of functional constrained optimization with either convex or nonconvex objectives. In this paper, we focus on projection-free methods that can generate a sparse trajectory for solving these challenging functional constrained optimization problems. Specifically, for the convex setting, we propose a Level Conditional Gradient (LCG) method, which leverages a level-set framework to update the approximation of the optimal value and an inner conditional gradient oracle (CGO) for solving mini-max subproblems. We show that the method achieves $\mathcal{O}\big(\frac{1}{\epsilon^2}\log\frac{1}{\epsilon}\big)$ iteration complexity for solving both smooth and nonsmooth cases without dependency on a possibly large size of optimal dual Lagrange multiplier. For the nonconvex setting, we introduce the Level Inexact Proximal Point (IPP-LCG) method and the Direct Nonconvex Conditional Gradient (DNCG) method. The first approach taps into the advantage of LCG by transforming the problem into a series of convex subproblems and exhibits an $\mathcal{O}\big(\frac{1}{\epsilon^3}\log\frac{1}{\epsilon}\big)$ iteration complexity for finding an ($\epsilon,\epsilon$)-KKT point. The DNCG is the first single-loop projection-free method, with iteration complexity bounded by $\mathcal{O}\big(1/\epsilon^4\big)$ for computing a so-called $\epsilon$-Wolfe point. We demonstrate the effectiveness of LCG, IPP-LCG and DNCG by devising formulations and conducting numerical experiments on two risk averse sparse optimization applications: a portfolio selection problem with and without cardinality requirement, and a radiation therapy planning problem in healthcare.	['H. Edwin Romeijn', 'Guanghui Lan', 'Yi Cheng']	2022-10-11	null	null	null	null	['portfolio-optimization']	['time-series']	[ 2.79180139e-01 3.67043912e-01 -7.31087625e-02 1.04995638e-01 -1.24699116e+00 -3.49446088e-01 -1.31920278e-01 3.59903485e-01 -5.21683276e-01 9.63151395e-01 1.32790193e-01 -7.12032318e-01 -8.81724656e-01 -6.81177855e-01 -7.81874418e-01 -9.40349162e-01 -4.07360196e-01 3.32143217e-01 -3.83507341e-01 -1.63458720e-01 2.84379095e-01 3.04540306e-01 -1.00258970e+00 -1.12926893e-01 1.24811590e+00 1.52695549e+00 8.16063136e-02 3.39434206e-01 3.02006304e-01 4.13588136e-01 -2.98567880e-02 -2.22297803e-01 5.87710023e-01 -4.67866153e-01 -4.49135214e-01 2.57090122e-01 6.32786378e-02 -1.06342547e-02 1.79563612e-01 1.26771188e+00 5.36339521e-01 3.16631913e-01 6.85355902e-01 -1.01597238e+00 3.89795452e-02 1.34616926e-01 -8.85881603e-01 7.77287185e-02 2.63777912e-01 1.18341260e-01 8.60675991e-01 -1.09080863e+00 2.66895920e-01 6.64008081e-01 8.52813125e-01 4.55378890e-02 -1.00526655e+00 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c73022dc-308a-4595-a823-f86354eebc7f	why-do-deepfake-detectors-fail	2302.13156	null	https://arxiv.org/abs/2302.13156v1	https://arxiv.org/pdf/2302.13156v1.pdf	Why Do Deepfake Detectors Fail?	Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors.	['Simon Woo', 'Kristen Moore', 'Alsharif Abuadbba', 'Shahroz Tariq', 'Binh Le']	2023-02-25	null	null	null	null	['face-swapping']	['computer-vision']	[-1.21050151e-02 -3.45133960e-01 9.76540223e-02 -1.51508898e-02 -5.26259959e-01 -7.51679301e-01 9.24045205e-01 2.96008676e-01 -2.88504511e-01 5.02888381e-01 2.86120445e-01 -3.93854171e-01 3.59601587e-01 -6.60806775e-01 -3.92433912e-01 -3.64777178e-01 4.31224629e-02 -1.22936293e-01 5.45953929e-01 -1.99819244e-02 5.18899143e-01 5.25154471e-01 -1.44672453e+00 3.98320168e-01 4.11710501e-01 8.43915284e-01 -3.61273021e-01 7.47443676e-01 1.49726078e-01 5.97046018e-01 -1.14377999e+00 -7.59723902e-01 2.12921515e-01 -4.06680971e-01 -5.25310993e-01 -2.43262112e-01 7.07545578e-01 -8.94468009e-01 -6.88345611e-01 1.13156486e+00 6.46200001e-01 -2.92395532e-01 3.27477008e-01 -1.41404903e+00 -5.84306061e-01 3.33793193e-01 -7.16499209e-01 5.62868774e-01 3.13804597e-01 3.34081173e-01 4.10417587e-01 -1.00960946e+00 3.85151803e-01 1.34387159e+00 9.58006680e-01 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258344d8-508d-4620-8ec8-767b7ef1d07f	mdm-molecular-diffusion-model-for-3d-molecule	2209.05710	null	https://arxiv.org/abs/2209.05710v1	https://arxiv.org/pdf/2209.05710v1.pdf	MDM: Molecular Diffusion Model for 3D Molecule Generation	Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances, especially when generating large molecules. At the same time, the generated molecules lack enough diversity. This paper proposes a novel diffusion model to address those two challenges. First, interatomic relations are not in molecules' 3D point cloud representations. Thus, it is difficult for existing generative models to capture the potential interatomic forces and abundant local constraints. To tackle this challenge, we propose to augment the potential interatomic forces and further involve dual equivariant encoders to encode interatomic forces of different strengths. Second, existing diffusion-based models essentially shift elements in geometry along the gradient of data density. Such a process lacks enough exploration in the intermediate steps of the Langevin dynamics. To address this issue, we introduce a distributional controlling variable in each diffusion/reverse step to enforce thorough explorations and further improve generation diversity. Extensive experiments on multiple benchmarks demonstrate that the proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks. We also conduct case studies to help understand the physicochemical properties of the generated molecules.	['Ka-Chun Wong', 'Tingyang Xu', 'Hengtong Zhang', 'Lei Huang']	2022-09-13	null	null	null	null	['3d-molecule-generation']	['medical']	[ 2.16055095e-01 -1.49328172e-01 -2.43905276e-01 -1.10951148e-01 -7.31886506e-01 -6.79073215e-01 7.62137651e-01 2.97079265e-01 -6.14871830e-02 1.33918357e+00 1.94998264e-01 -4.23163414e-01 -6.85706362e-03 -1.17483985e+00 -8.73961806e-01 -1.03783774e+00 2.14181006e-01 5.02484322e-01 -5.79522774e-02 -3.55727077e-01 4.80057240e-01 5.44234753e-01 -1.09801173e+00 9.88282412e-02 1.45096409e+00 3.80595863e-01 2.22581521e-01 2.29840994e-01 -3.07502866e-01 2.88317740e-01 -6.75576925e-01 -3.32720399e-01 2.40222201e-01 -7.51363635e-01 -4.66394931e-01 -1.61415815e-01 8.38340968e-02 -1.68981344e-01 -1.85411304e-01 1.10138726e+00 8.96392584e-01 4.04366970e-01 1.03369570e+00 -7.96355128e-01 -1.03288007e+00 5.64494252e-01 -6.28248334e-01 1.31072141e-02 3.05089772e-01 5.42318821e-01 7.49699652e-01 -1.02824450e+00 7.44808435e-01 1.21656346e+00 4.38271761e-01 7.79754996e-01 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2233a86a-7b5e-45f3-814d-aebd03545a1d	personalized-predictive-asr-for-latency	2305.13794	null	https://arxiv.org/abs/2305.13794v1	https://arxiv.org/pdf/2305.13794v1.pdf	Personalized Predictive ASR for Latency Reduction in Voice Assistants	Streaming Automatic Speech Recognition (ASR) in voice assistants can utilize prefetching to partially hide the latency of response generation. Prefetching involves passing a preliminary ASR hypothesis to downstream systems in order to prefetch and cache a response. If the final ASR hypothesis after endpoint detection matches the preliminary one, the cached response can be delivered to the user, thus saving latency. In this paper, we extend this idea by introducing predictive automatic speech recognition, where we predict the full utterance from a partially observed utterance, and prefetch the response based on the predicted utterance. We introduce two personalization approaches and investigate the tradeoff between potential latency gains from successful predictions and the cost increase from failed predictions. We evaluate our methods on an internal voice assistant dataset as well as the public SLURP dataset.	['Ariya Rastrow', 'Mohammed Hethnawi', 'Maarten Van Segbroeck', 'Di He', 'Andreas Schwarz']	2023-05-23	null	null	null	null	['response-generation']	['natural-language-processing']	[ 5.29392481e-01 5.04917562e-01 -1.18374281e-01 -4.51013714e-01 -1.29672492e+00 -4.39092636e-01 2.15262234e-01 1.30358115e-01 -5.71868956e-01 4.76774722e-01 7.13018596e-01 -5.31808794e-01 2.49727473e-01 -2.62311816e-01 -2.74769634e-01 -5.15370667e-01 -2.26767778e-01 5.42937398e-01 5.88641286e-01 -2.61867996e-02 8.78031552e-02 6.60027444e-01 -1.43073177e+00 8.08880389e-01 3.97853196e-01 8.00660312e-01 6.16481960e-01 1.41706479e+00 -1.47764564e-01 8.85145068e-01 -7.43340909e-01 1.87088773e-01 -1.27865180e-01 -2.88376629e-01 -1.00067449e+00 9.96599272e-02 -3.12754363e-01 -7.99633384e-01 -6.78590417e-01 4.62980777e-01 6.81425750e-01 4.75660443e-01 -2.56409980e-02 -1.09298325e+00 1.26836091e-01 8.96755397e-01 -1.89488202e-01 5.96750438e-01 8.52462411e-01 1.34772569e-01 1.06786430e+00 -6.81843698e-01 4.27026898e-01 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6616c3ab-472a-42b6-b6d5-50be62fbaa49	a-method-for-expressing-and-displaying-the	1904.11786	null	https://arxiv.org/abs/1904.11786v1	https://arxiv.org/pdf/1904.11786v1.pdf	A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones	Maintenance work zones on the road network have impacts on the normal travelling of vehicles, which increase the risk of traffic accidents. The traffic characteristic analysis in maintenance work zones is a basis for maintenance work zone related research such as layout design, traffic control and safety assessment. Due to the difficulty in vehicle microscopic behaviour data acquisition, traditional traffic characteristic analysis mainly focuses on macroscopic characteristics. With the development of data acquisition technology, it becomes much easier to obtain a large amount of microscopic behaviour data nowadays, which lays a good foundation for analysing the traffic characteristics from a new point of view. This paper puts forward a method for expressing and displaying the vehicle behaviour distribution in maintenance work zones. Using portable vehicle microscopic behaviour data acquisition devices, lots of data can be obtained. Based on this data, an endpoint detection technology is used to automatically extract the segments in behaviour data with violent fluctuations, which are segments where vehicles take behaviours such as acceleration or turning. Using the support vector machine classification method, the specific types of behaviours of the segments extracted can be identified, and together with a data combination method, a total of ten types of behaviours can be identified. Then the kernel density analysis is used to cluster different types of behaviours of all passing vehicles to show the distribution on maps. By this method, how vehicles travel through maintenance work zones, and how different vehicle behaviours distribute in maintenance work zones can be displayed intuitively on maps, which is a novel traffic characteristic and can shed light to maintenance work zone related researches such as safety assessment and design method.	['Ping Wang', 'Saravanan Gurupackiam', 'Zhepu Xu', 'Qun Yang']	2019-04-25	null	null	null	null	['layout-design']	['computer-vision']	[-1.66472316e-01 -6.88054323e-01 -1.79675385e-01 -1.21775486e-01 1.13931052e-01 -2.36993685e-01 6.16719723e-01 3.88535947e-01 -1.12729199e-01 3.87731910e-01 -1.50939137e-01 -7.60698557e-01 -7.80668259e-01 -1.25183511e+00 -1.09313942e-01 -1.20920599e+00 -1.46185532e-01 3.85225147e-01 6.80315316e-01 -5.08347750e-01 3.64837676e-01 9.43828166e-01 -1.93424666e+00 4.19811122e-02 7.49320209e-01 8.43740642e-01 3.47587615e-01 3.96983862e-01 -5.34680426e-01 4.60666895e-01 -6.57909095e-01 -3.25893946e-02 -2.64010429e-01 -2.34913722e-01 -3.10620606e-01 6.09765537e-02 -5.63996375e-01 -7.80982384e-03 -3.31365198e-01 1.05559647e+00 1.43549010e-01 3.54970098e-01 1.07702029e+00 -1.43212712e+00 -2.56941795e-01 -1.18828520e-01 -5.07465839e-01 4.64451343e-01 4.03813235e-02 1.74420297e-01 1.51201129e-01 -1.87158301e-01 3.32307927e-02 1.30029714e+00 3.46207827e-01 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9ebf0d61-8748-49b5-8d68-33ffd03304c8	sina-bert-a-pre-trained-language-model-for-1	null	null	https://openreview.net/forum?id=YSPukpxgWsU	https://openreview.net/pdf?id=YSPukpxgWsU	SINA-BERT: A Pre-Trained Language Model for Analysis of Medical Texts in Persian	We have released SINA-BERT, a language model pre-trained on BERT to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT utilizes pre-training on a large-scale corpus of medical contents including formal and informal texts collected from various online resources in order to improve the performance on health-care related tasks. We employ SINA-BERT to complete following representative tasks: categorization of medical questions, medical sentiment analysis, medical named entity recognition, and medical question retrieval. For each task, we have developed Persian annotated data sets for training and evaluation and learnt a representation for the data of each task especially complex and long medical questions. With the same architecture being used in each task, SINA-BERT outperforms BERT-based models that were previously made available in the Persian language.	['Anonymous']	2021-05-16	null	null	null	acl-arr-may-2021-5	['medical-named-entity-recognition']	['natural-language-processing']	[-5.91218509e-02 6.55010164e-01 -2.76730537e-01 -4.52622294e-01 -1.32229555e+00 -4.18208420e-01 2.01677740e-01 6.51595473e-01 -1.13259459e+00 8.91147554e-01 5.65408111e-01 -7.10644603e-01 -2.04474851e-01 -4.32769865e-01 -1.26747340e-01 -1.04633853e-01 -2.83432566e-02 1.32784343e+00 1.37384906e-01 -3.25478703e-01 -9.54840109e-02 4.13369872e-02 -6.87936172e-02 8.03871214e-01 1.03353810e+00 5.02973676e-01 2.93779671e-01 8.12152445e-01 -1.46676779e-01 1.30122566e+00 -6.38870656e-01 -9.30250585e-01 -1.91218555e-01 -2.16304883e-01 -1.47263968e+00 -3.01504761e-01 -2.78178066e-01 1.27923638e-01 -7.15524107e-02 5.19980490e-01 6.67365909e-01 -3.22432458e-01 9.24890339e-01 -5.74367225e-01 -6.75850153e-01 9.44910765e-01 -7.73533508e-02 3.65756750e-01 3.90274882e-01 -1.75773993e-01 1.08258271e+00 -4.07597899e-01 1.04208231e+00 1.34931731e+00 7.88159013e-01 1.06640875e+00 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04d8bb3c-5d96-4f06-90c1-97a418c2a000	a-deep-learning-based-gpr-forward-solver-for	2207.06527	null	https://arxiv.org/abs/2207.06527v1	https://arxiv.org/pdf/2207.06527v1.pdf	A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects	The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver.	['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Jiwei Qian', 'Hai-Han Sun', 'Yee Hui Lee', 'Qiqi Dai']	2022-07-13	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[ 4.89680260e-01 3.17729525e-02 8.21297228e-01 -5.19575715e-01 -1.17621601e+00 2.84298211e-01 -1.23723492e-01 -6.36712974e-03 -1.62127331e-01 7.78345108e-01 -1.79942608e-01 -5.49673319e-01 -3.24078113e-01 -1.15063071e+00 -7.65364408e-01 -1.00367308e+00 -5.30842185e-01 4.23244029e-01 -4.22738679e-02 -2.34299451e-01 3.66179734e-01 5.51134646e-01 -1.36567998e+00 3.69438201e-01 9.75673974e-01 1.64669061e+00 6.67284191e-01 6.55606270e-01 8.63945708e-02 8.24806035e-01 1.18753286e-02 1.23265207e-01 5.15284650e-02 -1.59291849e-01 -5.81353784e-01 -3.81282657e-01 -2.49262467e-01 -5.52175224e-01 -2.93938845e-01 8.27605367e-01 8.71068001e-01 9.81486663e-02 6.69184983e-01 -6.14429891e-01 -2.69290864e-01 5.59474051e-01 -9.02544796e-01 1.29200697e-01 3.37424248e-01 -2.68496245e-01 2.95206219e-01 -1.06853783e+00 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6.62003636e-01 -1.28455591e+00 -7.82486022e-01 -9.80607450e-01 -1.12705421e+00 -5.35499513e-01 4.72896606e-01 -9.97740746e-01 -3.83973897e-01 -1.08383581e-01]	[6.85938835144043, 1.87955904006958]
5dbc4c4f-1a3e-4912-86e7-72ab9bf53c85	multi-temporal-and-multi-source-remote	2012.04469	null	https://arxiv.org/abs/2012.04469v1	https://arxiv.org/pdf/2012.04469v1.pdf	Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization	Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer models across image acquisitions, one must be able to cope with datasets that are not co-registered, acquired under different illumination and atmospheric conditions, by different sensors, and with scarce ground references. Traditionally, methods based on histogram matching have been used. However, they fail when densities have very different shapes or when there is no corresponding band to be matched between the images. An alternative builds upon \emph{manifold alignment}. Manifold alignment performs a multidimensional relative normalization of the data prior to product generation that can cope with data of different dimensionality (e.g. different number of bands) and possibly unpaired examples. Aligning data distributions is an appealing strategy, since it allows to provide data spaces that are more similar to each other, regardless of the subsequent use of the transformed data. In this paper, we study a methodology that aligns data from different domains in a nonlinear way through {\em kernelization}. We introduce the Kernel Manifold Alignment (KEMA) method, which provides a flexible and discriminative projection map, exploits only a few labeled samples (or semantic ties) in each domain, and reduces to solving a generalized eigenvalue problem. We successfully test KEMA in multi-temporal and multi-source very high resolution classification tasks, as well as on the task of making a model invariant to shadowing for hyperspectral imaging.	['Gustau Camps-Valls', 'Diego Marcos', 'Devis Tuia']	2020-12-07	null	null	null	null	['remote-sensing-image-classification']	['miscellaneous']	[ 6.26152754e-01 -5.08795500e-01 1.50544196e-01 -1.93517968e-01 -5.10501802e-01 -7.09989786e-01 6.58516347e-01 2.10752890e-01 -5.27732313e-01 5.71899533e-01 -2.13387519e-01 1.21456925e-02 -6.59846902e-01 -8.55355561e-01 -4.87824529e-01 -1.12019575e+00 1.55325219e-01 5.51621377e-01 2.08049398e-02 -1.50423631e-01 4.50526550e-02 8.54198992e-01 -1.78144705e+00 -1.17246643e-01 9.57781732e-01 6.68629408e-01 5.64340770e-01 4.48844761e-01 -1.09451413e-01 2.93190721e-02 -2.58991420e-01 -2.55143754e-02 4.57399428e-01 -5.08600414e-01 -5.52011728e-01 5.55404305e-01 5.24451554e-01 2.95053363e-01 1.55671299e-01 1.41290975e+00 2.26625279e-01 3.53098303e-01 1.11187398e+00 -1.12579989e+00 -3.34339082e-01 7.26926252e-02 -6.34911954e-01 -2.03830808e-01 1.12373363e-02 -2.60359973e-01 5.17490923e-01 -6.96964085e-01 4.63097930e-01 8.84965658e-01 5.62822700e-01 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7aea6cf4-3478-40e8-bc52-24197bbb939e	infoctm-a-mutual-information-maximization	2304.03544	null	https://arxiv.org/abs/2304.03544v1	https://arxiv.org/pdf/2304.03544v1.pdf	InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling	Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method. This works as a regularization to properly align topics and prevent degenerate topic representations of words, which mitigates the repetitive topic issue. To address the low-coverage dictionary issue, we further propose a cross-lingual vocabulary linking method that finds more linked cross-lingual words for topic alignment beyond the translations of a given dictionary. Extensive experiments on English, Chinese, and Japanese datasets demonstrate that our method outperforms state-of-the-art baselines, producing more coherent, diverse, and well-aligned topics and showing better transferability for cross-lingual classification tasks.	['Anh Tuan Luu', 'Liangming Pan', 'Chaoqun Liu', 'Thong Nguyen', 'Xinshuai Dong', 'Xiaobao Wu']	2023-04-07	null	null	null	null	['topic-models']	['natural-language-processing']	[-2.06636727e-01 6.83962703e-02 -7.56937683e-01 -3.45365375e-01 -1.35208189e+00 -5.36896348e-01 6.98496699e-01 1.12451993e-01 -8.82444009e-02 6.09369338e-01 6.41890824e-01 -2.40528062e-01 8.68593380e-02 -5.34731686e-01 -5.42113423e-01 -5.92879951e-01 2.44810939e-01 5.76174557e-01 1.85757577e-01 1.27553968e-02 1.20662615e-01 -6.59567893e-01 -1.07186627e+00 2.33442530e-01 1.34191287e+00 3.08011085e-01 4.59533066e-01 -3.07466894e-01 -6.82987571e-01 -1.50391996e-01 -4.72533792e-01 -6.02880538e-01 -1.95225924e-01 -2.62736320e-01 -6.65081263e-01 1.54156163e-01 5.62731206e-01 1.43554926e-01 8.71044546e-02 1.07152653e+00 2.45613977e-01 -2.41780594e-01 8.40635717e-01 -1.29344392e+00 -5.63876152e-01 1.13825774e+00 -1.03725374e+00 -2.55751759e-01 4.44194749e-02 -5.91114998e-01 1.52242315e+00 -1.08383071e+00 6.47800207e-01 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895ff113-3012-47bd-9c83-b0e3b2633905	pure-passive-multi-person-identification-via	2104.07177	null	https://arxiv.org/abs/2104.07177v1	https://arxiv.org/pdf/2104.07177v1.pdf	PURE: Passive mUlti-peRson idEntification via Deep Footstep Separation and Recognition	Recently, \textit{passive behavioral biometrics} (e.g., gesture or footstep) have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple users at the same time. To this end, we propose \systemname\ as a passive multi-person identification system leveraging deep learning enabled footstep separation and recognition. \systemname\ passively identifies a user by deciphering the unique "footprints" in its footstep. Different from existing gait-enabled recognition systems incurring a long sensing delay to acquire many footsteps, \systemname\ can recognize a person by as few as only one step, substantially cutting the identification latency. To make \systemname\ adaptive to walking pace variations, environmental dynamics, and even unseen targets, we apply an adversarial learning technique to improve its domain generalisability and identification accuracy. Finally, \systemname\ can defend itself against replay attack, enabled by the richness of footstep and spatial awareness. We implement a \systemname\ prototype using commodity hardware and evaluate it in typical indoor settings. Evaluation results demonstrate a cross-domain identification accuracy of over 90\%.	['Jun Luo', 'Hongbo Jiang', 'Liyuan Ye', 'Peng Wang', 'Ruinan Jin', 'Chao Cai']	2021-04-15	null	null	null	null	['person-identification']	['computer-vision']	[ 4.58583593e-01 -5.09715021e-01 -3.36645216e-01 -2.46467769e-01 -6.83602810e-01 -9.06394839e-01 1.94111556e-01 -2.96456337e-01 -4.46187586e-01 8.57805729e-01 -3.79248530e-01 -3.00607532e-01 8.99229497e-02 -7.93873370e-01 -5.23315310e-01 -4.88437831e-01 5.57529293e-02 1.09956965e-01 -5.62145002e-02 -7.25472867e-02 6.17498904e-02 5.32212138e-01 -1.24641585e+00 -3.05918932e-01 6.21465564e-01 1.19946170e+00 -4.97943342e-01 8.29826057e-01 3.42242241e-01 4.78367647e-03 -8.75540495e-01 -2.54641205e-01 4.49631184e-01 -2.03475989e-02 -1.91934958e-01 -5.98604918e-01 8.00007045e-01 -8.08004737e-01 -5.48674226e-01 8.54326367e-01 9.27211881e-01 -2.14050531e-01 2.48133495e-01 -1.38913667e+00 -5.69631815e-01 3.72434229e-01 -6.08392119e-01 2.11299106e-01 9.54808831e-01 3.51220012e-01 4.19205815e-01 -6.30270004e-01 -4.73127179e-02 9.07961786e-01 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64dde0a9-327f-4182-bf29-adf1ff066cf9	training-on-polar-image-transformations	null	null	https://ieeexplore.ieee.org/document/9551998	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9551998	Training on Polar Image Transformations Improves Biomedical Image Segmentation	A key step in medical image-based diagnosis is image segmentation. A common use case for medical image segmentation is the identification of single structures of an elliptical shape. Most organs like the heart and kidneys fall into this category, as well as skin lesions, polyps, and other types of abnormalities. Neural networks have dramatically improved medical image segmentation results, but still require large amounts of training data and long training times to converge. In this paper, we propose a general way to improve neural network segmentation performance and data efficiency on medical imaging segmentation tasks where the goal is to segment a single roughly elliptically distributed object. We propose training a neural network on polar transformations of the original dataset, such that the polar origin for the transformation is the center point of the object. This results in a reduction of dimensionality as well as a separation of segmentation and localization tasks, allowing the network to more easily converge. Additionally, we propose two different approaches to obtaining an optimal polar origin: (1) estimation via a segmentation trained on non-polar images and (2) estimation via a model trained to predict the optimal origin. We evaluate our method on the tasks of liver, polyp, skin lesion, and epicardial adipose tissue segmentation. We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation. Additionally, when used as a pre-processing step, our method generally improves data efficiency across datasets and neural network architectures.	['Danilo Babin', 'Marija Habijan', 'Irena Galić', 'Marin Benčević']	2021-09-29	null	null	null	ieee-access-2021-9	['skin-cancer-segmentation', 'liver-segmentation']	['medical', 'medical']	[ 3.59113246e-01 3.17462593e-01 -1.58114687e-01 -3.13132316e-01 -7.46189475e-01 -6.13911510e-01 2.08553299e-01 4.34299588e-01 -5.20654202e-01 1.23281822e-01 -1.74342796e-01 -5.42413235e-01 2.56083190e-01 -6.66648090e-01 -7.19732106e-01 -7.08650172e-01 -5.48695624e-02 8.14500153e-01 2.69508809e-01 3.04062188e-01 -1.02887183e-01 7.28243768e-01 -6.77858829e-01 1.30009860e-01 1.00647819e+00 1.01648390e+00 7.04729035e-02 7.89922893e-01 -8.34765881e-02 1.99879900e-01 -4.20288026e-01 -1.61765292e-01 4.39187199e-01 -5.04676938e-01 -1.10033977e+00 3.37931007e-01 3.07228506e-01 -1.55518740e-01 2.29999796e-01 1.04439771e+00 5.23844838e-01 -1.47068501e-01 8.89674902e-01 -6.41335487e-01 -3.82576406e-01 6.16091788e-01 -7.38639772e-01 2.86407918e-02 -1.81862161e-01 4.08780687e-02 5.61713994e-01 -6.44914567e-01 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6.41699910e-01 3.19040626e-01 -9.33500886e-01 -4.46860701e-01 -6.56564772e-01 -2.48938143e-01 4.31770414e-01 3.44933867e-01 -1.06436126e-01 -4.88457493e-02 4.57767248e-01]	[14.557918548583984, -2.590419054031372]
d659d5b7-18ae-4614-a786-b68358bf041b	towards-building-automatic-medical	null	null	https://openreview.net/forum?id=q9uLLvoLUWD	https://openreview.net/pdf?id=q9uLLvoLUWD	Towards Building Automatic Medical Consultation System: Framework, Task and Dataset	In this paper, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and diagnosis-oriented interaction. A new medical dialogue dataset with multi-level fine-grained annotations is introduced and five evaluation tasks are established, including medical named entity recognition, dialogue act classification, symptom recognition, medical report generation and diagnosis-oriented dialogue system. We report a set of benchmark results for each track, which shows the usability of the dataset and sets a baseline for future studies.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['medical-report-generation', 'dialogue-understanding', 'medical-named-entity-recognition', 'dialogue-act-classification']	['medical', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 2.89158911e-01 1.23906219e+00 -3.06823641e-01 -8.58200371e-01 -9.00218189e-01 -3.59281689e-01 7.94262230e-01 6.31508470e-01 -3.70187253e-01 1.21740794e+00 8.78811479e-01 -3.65958482e-01 -1.20018691e-01 -3.78239274e-01 6.48678720e-01 -3.32967669e-01 -3.44899185e-02 1.39966428e+00 8.05226639e-02 -4.01529133e-01 4.27908190e-02 1.12908036e-01 -6.09426081e-01 7.74237692e-01 8.94300938e-01 6.94377005e-01 -5.64946592e-01 1.37723756e+00 -1.39654115e-01 1.51816189e+00 -1.15249193e+00 -6.03834212e-01 -5.66181719e-01 -8.61142933e-01 -1.82693160e+00 3.15556079e-01 -3.47541958e-01 -5.38133323e-01 9.55126286e-02 4.98578221e-01 9.87165391e-01 8.14831257e-02 8.00664425e-01 -8.62535238e-01 -3.55397493e-01 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eadc501e-77b1-4054-9566-e07d18427e6d	deep-density-ratio-estimation-for-change	1905.09876	null	https://arxiv.org/abs/1905.09876v1	https://arxiv.org/pdf/1905.09876v1.pdf	Deep density ratio estimation for change point detection	In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use linear combinations of kernels to approximate the density ratio function by solving a convex constrained minimization problem. Approximating the density ratio function using a deep neural network requires defining a suitable objective function to optimize. We formulate and compare objective functions that can be minimized using gradient descent and show that the network can effectively learn to approximate the density ratio function. Using our deep density ratio estimation objective function results in better performance on a seizure detection task than other (kernel and neural network based) density ratio estimation methods and other window-based change point detection algorithms. We also show that the method can still support other neural network architectures, such as convolutional networks.	['Bülent Yener', 'Lara Marcuse', 'Haidar Khan']	2019-05-23	null	null	null	null	['density-ratio-estimation']	['methodology']	[-3.88772994e-01 -4.46647406e-02 -3.39207679e-01 -4.94891524e-01 -9.86519217e-01 -1.80755749e-01 1.94905013e-01 8.93197581e-02 -6.76268220e-01 9.54097092e-01 -7.37236291e-02 -2.40112454e-01 -6.67562932e-02 -7.00945497e-01 -6.00728095e-01 -4.13874090e-01 -4.71608400e-01 6.10554814e-01 4.00020890e-02 1.62530109e-01 6.08294830e-02 5.84255457e-01 -6.60122752e-01 -9.46100205e-02 8.43569160e-01 1.46783388e+00 -7.09772632e-02 6.66793466e-01 2.22910956e-01 9.05882061e-01 -9.81508493e-01 3.42291966e-02 6.45463914e-02 -4.23061579e-01 -5.20498216e-01 -3.66655976e-01 5.42340696e-01 -9.09536362e-01 -5.84840417e-01 1.17072797e+00 6.92829311e-01 2.50837326e-01 1.30943274e+00 -1.28180480e+00 -7.29567111e-01 2.93329120e-01 -4.36820924e-01 9.26101208e-01 1.63372345e-02 -3.68024170e-01 4.65536386e-01 -7.76597440e-01 4.43449430e-02 9.03294384e-01 1.23312974e+00 4.28946435e-01 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-1.20630074e+00 -9.59512711e-01 -6.81002736e-02 6.73202097e-01 -7.17697263e-01 -6.26128793e-01 -3.71736228e-01]	[7.699991226196289, 3.715602397918701]
f78e11a0-95f2-4e44-bd62-bd32c3d56933	conservative-q-learning-for-offline	2006.04779	null	https://arxiv.org/abs/2006.04779v3	https://arxiv.org/pdf/2006.04779v3.pdf	Conservative Q-Learning for Offline Reinforcement Learning	Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction. However, in practice, offline RL presents a major challenge, and standard off-policy RL methods can fail due to overestimation of values induced by the distributional shift between the dataset and the learned policy, especially when training on complex and multi-modal data distributions. In this paper, we propose conservative Q-learning (CQL), which aims to address these limitations by learning a conservative Q-function such that the expected value of a policy under this Q-function lower-bounds its true value. We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees. In practice, CQL augments the standard Bellman error objective with a simple Q-value regularizer which is straightforward to implement on top of existing deep Q-learning and actor-critic implementations. On both discrete and continuous control domains, we show that CQL substantially outperforms existing offline RL methods, often learning policies that attain 2-5 times higher final return, especially when learning from complex and multi-modal data distributions.	['Sergey Levine', 'Aurick Zhou', 'George Tucker', 'Aviral Kumar']	2020-06-08	null	http://proceedings.neurips.cc/paper/2020/hash/0d2b2061826a5df3221116a5085a6052-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/0d2b2061826a5df3221116a5085a6052-Paper.pdf	neurips-2020-12	['dqn-replay-dataset', 'dqn-replay-dataset']	['miscellaneous', 'playing-games']	[-2.53617525e-01 2.18917936e-01 -6.89264178e-01 7.44200274e-02 -1.27622497e+00 -7.09249020e-01 3.94083053e-01 3.21209431e-01 -9.37723100e-01 1.36756361e+00 1.08854480e-01 -5.58168232e-01 -2.21160904e-01 -6.37970030e-01 -9.63645279e-01 -7.44628131e-01 -3.65610808e-01 6.55881405e-01 2.34153755e-02 -2.21459836e-01 1.31685451e-01 2.99485177e-01 -1.31551039e+00 -1.92251652e-01 7.62901664e-01 1.08298039e+00 1.10626675e-01 9.09950376e-01 3.95527110e-02 8.63216996e-01 -9.21028078e-01 -3.07953265e-02 5.40102363e-01 -4.58786607e-01 -4.02314067e-01 -1.75208002e-01 1.70286804e-01 -7.63385355e-01 -8.93441141e-02 9.99531209e-01 7.88305759e-01 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64e6c4b0-c254-40ba-b2f4-24161f70d0ce	an-image-fusion-scheme-for-single-shot-high	1908.08195	null	https://arxiv.org/abs/1908.08195v1	https://arxiv.org/pdf/1908.08195v1.pdf	An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures	This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE). Single-shot imaging with SVE enables us not only to produce images without color saturation regions from a single-shot image, but also to avoid ghost artifacts in the producing ones. However, the number of exposures is generally limited to two, and moreover it is difficult to decide the optimum exposure values before the photographing. In the proposed scheme, a scene segmentation method is applied to input multi-exposure images, and then the luminance of the input images is adjusted according to both of the number of scenes and the relationship between exposure values and pixel values. The proposed method with the luminance adjustment allows us to improve the above two issues. In this paper, we focus on dual-ISO imaging as one of single-shot imaging. In an experiment, the proposed scheme is demonstrated to be effective for single-shot high dynamic range imaging with SVE, compared with conventional MEF schemes with exposure compensation.	['Sayaka Shiota', 'Hitoshi Kiya', 'Chihiro Go', 'Yuma Kinoshita']	2019-08-22	null	null	null	null	['multi-exposure-image-fusion']	['computer-vision']	[ 8.24337423e-01 -6.60790741e-01 3.86718094e-01 -2.23235071e-01 -2.97935784e-01 -2.42519036e-01 1.82949185e-01 -3.56487334e-01 -6.87768638e-01 5.33869863e-01 -2.02469915e-01 -2.90578101e-02 -2.32972220e-01 -8.52676034e-01 -2.78162360e-01 -9.10106063e-01 5.84159613e-01 -1.54039577e-01 8.02618802e-01 -4.61431108e-02 2.60519534e-01 2.80530006e-01 -1.62927604e+00 -2.03207120e-01 1.12814510e+00 8.09615850e-01 7.77023911e-01 7.00083792e-01 -1.56911299e-01 6.61715984e-01 -6.94830656e-01 2.47268341e-02 4.11677212e-01 -9.52756345e-01 -3.57055217e-01 5.10727763e-01 1.90022007e-01 -6.55873239e-01 -3.16801906e-01 1.32895315e+00 4.95234102e-01 4.27216798e-01 5.24763107e-01 -9.50178266e-01 -5.13457119e-01 1.28124848e-01 -1.09581530e+00 5.14944375e-01 1.45506144e-01 3.80652219e-01 3.55361998e-02 -5.31615436e-01 3.13131094e-01 9.62408006e-01 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f252d2c9-d12d-45ab-9163-66022d7d37b4	motionbert-unified-pretraining-for-human	2210.06551	null	https://arxiv.org/abs/2210.06551v2	https://arxiv.org/pdf/2210.06551v2.pdf	Learning Human Motion Representations: A Unified Perspective	We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. The motion representations acquired in this way incorporate geometric, kinematic, and physical knowledge about human motion, which can be easily transferred to multiple downstream tasks. We implement the motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network. It could capture long-range spatio-temporal relationships among the skeletal joints comprehensively and adaptively, exemplified by the lowest 3D pose estimation error so far when trained from scratch. Furthermore, our proposed framework achieves state-of-the-art performance on all three downstream tasks by simply finetuning the pretrained motion encoder with a simple regression head (1-2 layers), which demonstrates the versatility of the learned motion representations.	['Yizhou Wang', 'Wayne Wu', 'Libin Liu', 'Zhaoyang Liu', 'Xiaoxuan Ma', 'Wentao Zhu']	2022-10-12	null	null	null	null	['3d-pose-estimation', 'one-shot-3d-action-recognition', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[-1.60746112e-01 -2.21890919e-02 -4.47039157e-01 -7.45080933e-02 -6.41205609e-01 -3.23659390e-01 6.77882969e-01 -5.70325673e-01 -5.89896977e-01 4.21496749e-01 6.37277126e-01 2.08555788e-01 1.48428366e-01 -5.58168530e-01 -1.02116358e+00 -5.08486390e-01 -7.88415894e-02 3.82127017e-01 3.79674315e-01 -2.18797132e-01 -2.56326180e-02 3.45341742e-01 -1.45032525e+00 1.62233964e-01 3.00608218e-01 8.21815550e-01 3.89408022e-01 9.98593152e-01 3.74444097e-01 1.00174737e+00 -1.66963741e-01 -4.00692746e-02 1.87302127e-01 -3.69821280e-01 -9.18430209e-01 2.15469241e-01 2.73826569e-01 -8.23594928e-01 -1.00940120e+00 5.51850379e-01 4.18054253e-01 4.89310175e-01 5.59448957e-01 -9.21243310e-01 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05ef9c0a-b941-4190-a23c-84d81f22c657	3d-human-pose-estimation-in-multi-view	2210.11826	null	https://arxiv.org/abs/2210.11826v1	https://arxiv.org/pdf/2210.11826v1.pdf	3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections	3D human pose estimation in multi-view operating room (OR) videos is a relevant asset for person tracking and action recognition. However, the surgical environment makes it challenging to find poses due to sterile clothing, frequent occlusions, and limited public data. Methods specifically designed for the OR are generally based on the fusion of detected poses in multiple camera views. Typically, a 2D pose estimator such as a convolutional neural network (CNN) detects joint locations. Then, the detected joint locations are projected to 3D and fused over all camera views. However, accurate detection in 2D does not guarantee accurate localisation in 3D space. In this work, we propose to directly optimise for localisation in 3D by training 2D CNNs end-to-end based on a 3D loss that is backpropagated through each camera's projection parameters. Using videos from the MVOR dataset, we show that this end-to-end approach outperforms optimisation in 2D space.	['Ivo A. M. J. Broeders', 'Jelmer M. Wolterink', 'Beerend G. A. Gerats']	2022-10-21	null	null	null	null	['3d-human-pose-estimation']	['computer-vision']	[ 2.55496055e-01 1.54525355e-01 -1.36616141e-01 -8.75731930e-02 -1.10704935e+00 -6.49968684e-01 2.28362650e-01 1.79920867e-01 -9.37032223e-01 3.81536186e-01 3.08823317e-01 -9.07847658e-02 -2.08208747e-02 -1.07738636e-01 -8.60899329e-01 -4.92573351e-01 -2.34646454e-01 3.55258822e-01 -1.60839990e-01 3.93368676e-02 -3.33941355e-02 7.71122456e-01 -9.44605827e-01 3.26926075e-02 -7.44137987e-02 1.02514577e+00 1.08339861e-01 1.13289499e+00 6.07201755e-01 3.68393064e-01 -4.40826029e-01 -3.91712606e-01 5.92673302e-01 -2.58561403e-01 -4.30910826e-01 3.29173863e-01 8.58454406e-01 -5.80956638e-01 -4.54011828e-01 9.13536906e-01 9.53811109e-01 -5.32977693e-02 1.05777256e-01 -8.19216669e-01 4.45358574e-01 -2.40704089e-01 -5.08205891e-01 1.16945013e-01 8.59563649e-01 1.59887180e-01 3.15744728e-01 -7.21188724e-01 7.16297448e-01 1.04376209e+00 1.00478542e+00 7.41706491e-01 -8.52391541e-01 -3.11163723e-01 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1.07017681e-01 6.71641052e-01 -7.56066918e-01 -1.68908089e-01 -2.92077571e-01]	[6.848818302154541, -1.0308849811553955]
d7e3c8b0-5522-4372-aeef-c33adf88789c	dilation-erosion-for-single-frame-supervised	2212.06348	null	https://arxiv.org/abs/2212.06348v1	https://arxiv.org/pdf/2212.06348v1.pdf	Dilation-Erosion for Single-Frame Supervised Temporal Action Localization	To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between actions and backgrounds, i.e., action incompleteness and background false positives. To tackle the two challenges, in this work, we present the Snippet Classification model and the Dilation-Erosion module. In the Dilation-Erosion module, we expand the potential action segments with a loose criterion to alleviate the problem of action incompleteness and then remove the background from the potential action segments to alleviate the problem of action incompleteness. Relying on the single-frame annotation and the output of the snippet classification, the Dilation-Erosion module mines pseudo snippet-level ground-truth, hard backgrounds and evident backgrounds, which in turn further trains the Snippet Classification model. It forms a cyclic dependency. Furthermore, we propose a new embedding loss to aggregate the features of action instances with the same label and separate the features of actions from backgrounds. Experiments on THUMOS14 and ActivityNet 1.2 validate the effectiveness of the proposed method. Code has been made publicly available (https://github.com/LingJun123/single-frame-TAL).	['Yan Rui', 'Xiangbo Shu', 'Yang Zhao', 'Fanming Wang', 'Yan Song', 'Bin Wang']	2022-12-13	null	null	null	null	['action-localization']	['computer-vision']	[ 4.89742279e-01 3.08718443e-01 -4.23131764e-01 -2.55317837e-01 -7.67757177e-01 -2.84756958e-01 4.32952374e-01 8.79488215e-02 -4.48508501e-01 7.19758451e-01 2.09692225e-01 1.27745271e-01 2.30693340e-01 -5.64672530e-01 -5.64995944e-01 -9.45587695e-01 3.56899261e-01 -1.66812047e-01 8.49640429e-01 2.17065305e-01 -3.90266664e-02 8.33092928e-02 -1.56303000e+00 6.87797129e-01 6.96249902e-01 1.27319515e+00 1.81299478e-01 2.55406708e-01 -6.45447373e-02 1.17889452e+00 -6.81151092e-01 -5.63630201e-02 2.86467820e-01 -6.59338415e-01 -5.84770441e-01 3.89104217e-01 4.37400967e-01 -4.44728315e-01 -4.34747875e-01 1.04020858e+00 5.57650745e-01 8.41295794e-02 9.27344933e-02 -1.41346002e+00 -8.25814456e-02 4.50852901e-01 -8.56761336e-01 4.57537562e-01 2.35461786e-01 3.24087679e-01 9.38405216e-01 -1.02606535e+00 6.16369307e-01 1.12937593e+00 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edd4a0ba-56a7-4c54-bcb0-c904b08ed6b5	modeling-dynamic-heterogeneous-graph-and-node	2305.17417	null	https://arxiv.org/abs/2305.17417v1	https://arxiv.org/pdf/2305.17417v1.pdf	Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction	Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are proposed to predict a paper's future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers. First, a dynamic heterogeneous network embedding module is provided to capture the dynamic evolutionary trends of the whole academic network. Then, a node importance embedding module is proposed to capture the global consistency relationship to figure out each paper's node importance. Finally, the dynamic evolutionary trend embeddings and node importance embeddings calculated above are combined to jointly predict the future citation counts of each paper, by a log-normal distribution model according to multi-faced paper node representations. Extensive experiments on two large-scale datasets demonstrate that our model significantly improves all indicators compared to the SOTA models.	['Rui Liu', 'Haolong Guo', 'Ting Jiang', 'Chenguang Du', 'Xuehua Ming', 'Fuzhen Zhuang', 'Deqing Wang', 'Hao Geng']	2023-05-27	null	null	null	null	['network-embedding']	['methodology']	[-8.01782072e-01 -6.32921755e-02 -3.75963360e-01 1.41893998e-01 -3.06325871e-03 -5.14885902e-01 6.85143471e-01 4.54688519e-01 -8.80168471e-03 5.94959259e-01 4.18119133e-01 -3.83929938e-01 -5.62924623e-01 -1.08671975e+00 -2.44770810e-01 -6.44769490e-01 -1.30011320e-01 4.30344313e-01 8.10713843e-02 7.43213668e-02 5.16346812e-01 2.96084136e-01 -9.31431890e-01 -8.47260714e-01 1.07515550e+00 5.33993006e-01 1.25411928e-01 7.10856438e-01 -6.74625278e-01 8.02220643e-01 -7.80167282e-01 -4.95836824e-01 -1.24155089e-01 -7.66881853e-02 -3.31866533e-01 -2.39687890e-01 2.07386818e-02 1.34761468e-01 -9.09911752e-01 1.07655907e+00 4.01164472e-01 -2.15076804e-02 8.31836939e-01 -1.43014872e+00 -1.27840316e+00 1.01750350e+00 -9.20549095e-01 6.94616258e-01 -9.45199002e-03 -2.12549996e-02 1.46844530e+00 -5.63367069e-01 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09a633c3-3489-4c83-9ba4-590ef2b0fe90	tempcaps-a-capsule-network-based-embedding	null	null	https://aclanthology.org/2022.spnlp-1.3	https://aclanthology.org/2022.spnlp-1.3.pdf	TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion	Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.	['Roger Wattenhofer', 'Volker Tresp', 'Matthias Schubert', 'Yunpu Ma', 'Zifeng Ding', 'Zhen Han', 'Zhao Meng', 'Guirong Fu']	null	null	null	null	spnlp-acl-2022-5	['temporal-knowledge-graph-completion']	['knowledge-base']	[-8.24527919e-01 1.43908337e-01 -4.71829534e-01 4.55918461e-02 -7.63683170e-02 -8.92296970e-01 6.15713775e-01 4.28281218e-01 -1.15530729e-01 6.34404361e-01 6.12184286e-01 -6.13228716e-02 -8.13644826e-01 -1.15267515e+00 -7.25865722e-01 -3.56204718e-01 -7.50828922e-01 3.90718013e-01 3.92668366e-01 -1.69207469e-01 -5.79305947e-01 2.53039300e-01 -1.01483274e+00 1.55860648e-01 6.03653371e-01 8.29067111e-01 -1.58057109e-01 3.33344042e-01 -3.02021921e-01 1.34235096e+00 -1.49955183e-01 -7.82934487e-01 1.34515166e-01 1.04512699e-01 -8.99362504e-01 -2.91226625e-01 -1.62523180e-01 -7.10273385e-02 -1.28974009e+00 6.73157513e-01 1.02688499e-01 5.20818770e-01 -1.32447490e-02 -1.51707780e+00 -1.18776655e+00 1.12401748e+00 1.23054639e-01 5.24069965e-01 5.58446109e-01 7.36280829e-02 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ab0164f4-59d1-46bb-b86a-7fd0d8e9f9c1	exploiting-partially-annotated-data-for	1804.08420	null	http://arxiv.org/abs/1804.08420v2	http://arxiv.org/pdf/1804.08420v2.pdf	Exploiting Partially Annotated Data for Temporal Relation Extraction	Annotating temporal relations (TempRel) between events described in natural language is known to be labor intensive, partly because the total number of TempRels is quadratic in the number of events. As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena. In order to improve existing approaches, one possibility is to make use of the readily available, partially annotated data (P as in partial) that cover more documents. However, missing annotations in P are known to hurt, rather than help, existing systems. This work is a case study in exploring various usages of P for TempRel extraction. Results show that despite missing annotations, P is still a useful supervision signal for this task within a constrained bootstrapping learning framework. The system described in this system is publicly available.	['Qiang Ning', 'Dan Roth', 'Zhongzhi Yu', 'Chuchu Fan']	2018-04-18	null	null	null	null	['temporal-relation-extraction']	['natural-language-processing']	[ 3.54348607e-02 3.78590226e-01 -5.41024625e-01 -3.07476193e-01 -7.76069164e-01 -8.92103314e-01 6.61293507e-01 6.70252919e-01 -4.56649333e-01 1.28695130e+00 2.45878264e-01 -6.90084174e-02 5.17910086e-02 -6.01803243e-01 -5.97182930e-01 -5.10745347e-01 -7.00922385e-02 6.93530381e-01 6.39521658e-01 -1.39308691e-01 -1.32539183e-01 4.82693911e-01 -1.36886358e+00 4.04260099e-01 5.02221227e-01 5.57011127e-01 2.09842756e-01 1.51268840e-01 -4.00381416e-01 1.03596258e+00 -5.34411132e-01 -3.95718485e-01 -5.10980077e-02 -3.35291773e-01 -1.17644668e+00 3.15514486e-03 -3.02501529e-01 5.94042726e-02 -3.87610495e-02 5.47474563e-01 1.64815381e-01 3.63576710e-01 5.17476559e-01 -1.44611156e+00 1.04044586e-01 1.03940070e+00 -4.28617209e-01 3.89708251e-01 6.48159921e-01 -4.94789749e-01 1.19806886e+00 -6.98452115e-01 1.13695836e+00 1.00354552e+00 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63388cf2-7b6c-4dfa-9bd2-632f55b1ca49	deft-detection-embeddings-for-tracking	2102.02267	null	https://arxiv.org/abs/2102.02267v2	https://arxiv.org/pdf/2102.02267v2.pdf	DEFT: Detection Embeddings for Tracking	Most modern multiple object tracking (MOT) systems follow the tracking-by-detection paradigm, consisting of a detector followed by a method for associating detections into tracks. There is a long history in tracking of combining motion and appearance features to provide robustness to occlusions and other challenges, but typically this comes with the trade-off of a more complex and slower implementation. Recent successes on popular 2D tracking benchmarks indicate that top-scores can be achieved using a state-of-the-art detector and relatively simple associations relying on single-frame spatial offsets -- notably outperforming contemporary methods that leverage learned appearance features to help re-identify lost tracks. In this paper, we propose an efficient joint detection and tracking model named DEFT, or "Detection Embeddings for Tracking." Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network. An LSTM is also added to capture motion constraints. DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards while having significant advantages in robustness when applied to more challenging tracking data. DEFT raises the bar on the nuScenes monocular 3D tracking challenge, more than doubling the performance of the previous top method. Code is publicly available.	["Stephen O'Hara", 'J. Ross Beveridge', 'Peter Zhang', 'Mohamed Chaabane']	2021-02-03	null	null	null	null	['3d-multi-object-tracking']	['computer-vision']	[-9.45250243e-02 -4.72142726e-01 -3.91310692e-01 7.94227142e-03 -7.08440781e-01 -8.01858664e-01 8.02277565e-01 -6.57366961e-03 -6.68478251e-01 3.50849152e-01 -2.34368183e-02 -1.77268963e-02 3.79528664e-02 -2.61658996e-01 -7.65624166e-01 -4.94244993e-01 -7.47979581e-02 5.02499938e-01 8.60398173e-01 1.11004539e-01 -7.55528267e-03 6.89777911e-01 -1.76197720e+00 -1.49291739e-01 -6.05770051e-02 1.01739478e+00 1.15883715e-01 8.04160118e-01 1.26464263e-01 6.81290984e-01 -4.57865953e-01 -2.77793556e-01 5.68690777e-01 8.57395530e-02 -1.03971481e-01 -3.05895731e-02 1.43298495e+00 -5.52524209e-01 -6.41976118e-01 8.02683532e-01 6.46180272e-01 -2.74771675e-02 3.85168523e-01 -1.56341827e+00 -4.66419458e-01 -7.75153562e-03 -7.58513570e-01 4.45541859e-01 3.47798944e-01 3.74651283e-01 9.43285465e-01 -7.82678962e-01 8.75779748e-01 1.35967541e+00 1.46851766e+00 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3.91816556e-01 8.14500213e-01 -1.35589564e+00 -1.33285403e-01 -3.49154651e-01]	[6.369413375854492, -2.0913760662078857]
f7198d97-e8a6-4925-93f6-c7fd0eaab31d	learning-to-hallucinate-face-images-via	1708.00223	null	http://arxiv.org/abs/1708.00223v1	http://arxiv.org/pdf/1708.00223v1.pdf	Learning to Hallucinate Face Images via Component Generation and Enhancement	We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods	['Qingxiong Yang', 'Linchao Bao', 'Shengfeng He', 'Jiawei Zhang', 'Yibing Song']	2017-08-01	null	null	null	null	['face-hallucination']	['computer-vision']	[ 2.96764672e-01 5.66552401e-01 2.03385338e-01 -4.52709258e-01 -4.77619469e-01 -1.19309895e-01 6.02184772e-01 -7.86000311e-01 -6.35046214e-02 7.53576398e-01 4.89108860e-01 4.98537093e-01 5.22395611e-01 -1.06239951e+00 -8.30271482e-01 -6.07378125e-01 3.28785986e-01 2.70621628e-02 -1.50699407e-01 -3.38033855e-01 -1.50883242e-01 1.06184459e+00 -1.77177989e+00 6.56644285e-01 4.46203887e-01 1.14898682e+00 -2.51471013e-01 3.18306416e-01 -4.11562845e-02 6.55160666e-01 -4.09725636e-01 -5.83546460e-01 2.85427153e-01 -5.64869761e-01 -7.48966396e-01 7.29607880e-01 5.69787800e-01 -7.56267905e-01 -6.19003773e-01 1.38311696e+00 2.63044626e-01 -2.42099781e-02 5.85702121e-01 -1.02635384e+00 -9.97454226e-01 1.77894812e-02 -9.13211763e-01 -3.40982050e-01 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b7082c5d-6370-48b0-aa27-fb132131d497	max-margin-structured-output-regression-for	null	null	http://papers.nips.cc/paper/4794-max-margin-structured-output-regression-for-spatio-temporal-action-localization	http://papers.nips.cc/paper/4794-max-margin-structured-output-regression-for-spatio-temporal-action-localization.pdf	Max-Margin Structured Output Regression for Spatio-Temporal Action Localization	Structured output learning has been successfully applied to object localization, where the mapping between an image and an object bounding box can be well captured. Its extension to action localization in videos, however, is much more challenging, because one needs to predict the locations of the action patterns both spatially and temporally, i.e., identifying a sequence of bounding boxes that track the action in video. The problem becomes intractable due to the exponentially large size of the structured video space where actions could occur. We propose a novel structured learning approach for spatio-temporal action localization. The mapping between a video and a spatio-temporal action trajectory is learned. The intractable inference and learning problems are addressed by leveraging an efficient Max-Path search method, thus makes it feasible to optimize the model over the whole structured space. Experiments on two challenging benchmark datasets show that our proposed method outperforms the state-of-the-art methods.	['Du Tran', 'Junsong Yuan']	2012-12-01	null	null	null	neurips-2012-12	['spatio-temporal-action-localization']	['computer-vision']	[ 4.15021449e-01 -2.88420022e-01 -5.85846364e-01 -1.70570716e-01 -8.93883526e-01 -6.20590448e-01 3.48633260e-01 8.32561255e-02 -4.64138418e-01 6.19180977e-01 1.93784103e-01 1.07715249e-01 -2.54169852e-01 -3.18607301e-01 -1.11984348e+00 -8.38765740e-01 -4.09735203e-01 2.23533422e-01 6.51827633e-01 5.33258736e-01 3.54151309e-01 2.65763849e-01 -1.28859866e+00 3.24550211e-01 5.81852376e-01 1.20154297e+00 6.21626437e-01 5.07198572e-01 -8.61989707e-03 1.15593040e+00 -2.55264431e-01 1.74898610e-01 2.52565712e-01 -5.10719538e-01 -8.36829543e-01 4.80357736e-01 6.12304151e-01 -4.84938741e-01 -4.00276780e-01 1.10934186e+00 -9.81419459e-02 4.19723332e-01 4.17758733e-01 -1.39431238e+00 -2.79398739e-01 3.10551584e-01 -6.22440994e-01 2.40861177e-01 4.66070056e-01 -2.92183342e-03 9.66377676e-01 -7.67869413e-01 7.24169612e-01 1.10848761e+00 3.85801196e-01 2.33059719e-01 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b11f0b3b-d78b-42dc-a39b-b150b82cb9a5	uabcoral-a-preliminary-study-for-resolving	null	null	https://aclanthology.org/S12-1036	https://aclanthology.org/S12-1036.pdf	UABCoRAL: A Preliminary study for Resolving the Scope of Negation	null	['Binod Gyawali', 'Thamar Solorio']	2012-07-01	null	null	null	semeval-2012-7	['negation-detection']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 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4f7675a0-1b8c-4229-843e-fae7a6435787	resmlp-feedforward-networks-for-image	2105.03404	null	https://arxiv.org/abs/2105.03404v2	https://arxiv.org/pdf/2105.03404v2.pdf	ResMLP: Feedforward networks for image classification with data-efficient training	We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels, and (ii) a two-layer feed-forward network in which channels interact independently per patch. When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet. We also train ResMLP models in a self-supervised setup, to further remove priors from employing a labelled dataset. Finally, by adapting our model to machine translation we achieve surprisingly good results. We share pre-trained models and our code based on the Timm library.	['Armand Joulin', 'Gautier Izacard', 'Hervé Jégou', 'Jakob Verbeek', 'Gabriel Synnaeve', 'Edouard Grave', 'Alaaeldin El-Nouby', 'Matthieu Cord', 'Mathilde Caron', 'Piotr Bojanowski', 'Hugo Touvron']	2021-05-07	null	https://openreview.net/forum?id=K9uApq7iyyI	https://openreview.net/pdf?id=K9uApq7iyyI	neurips-2021-12	['self-supervised-image-classification']	['computer-vision']	[ 6.10153079e-01 2.55007386e-01 -5.44085130e-02 -4.43709910e-01 -8.78952861e-01 -4.83045846e-01 9.91842985e-01 -3.24404716e-01 -6.71497107e-01 5.89383483e-01 1.92869842e-01 -5.70980310e-01 5.24910092e-01 -5.08786917e-01 -1.39711368e+00 -6.56980038e-01 -5.03945015e-02 5.29478669e-01 1.59068152e-01 3.65696102e-02 -1.17378449e-02 3.06570053e-01 -1.33835936e+00 6.56153858e-01 1.43150926e-01 9.58079100e-01 1.21471539e-01 1.23971093e+00 1.33049250e-01 1.32258117e+00 -1.66493788e-01 -3.18535566e-01 4.38785344e-01 -3.00911486e-01 -1.18917227e+00 2.58076400e-01 5.90218008e-01 -1.87082961e-01 -5.24241209e-01 7.26663291e-01 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9b5a6535-7883-49fd-b562-b3bf01a05710	mfm-net-unpaired-shape-completion-network	2111.11976	null	https://arxiv.org/abs/2111.11976v3	https://arxiv.org/pdf/2111.11976v3.pdf	KTNet: Knowledge Transfer for Unpaired 3D Shape Completion	Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes. In this paper, we propose the novel KTNet to solve this task from the new perspective of knowledge transfer. KTNet elaborates a teacher-assistant-student network to establish multiple knowledge transfer processes. Specifically, the teacher network takes complete shape as input and learns the knowledge of complete shape. The student network takes the incomplete one as input and restores the corresponding complete shape. And the assistant modules not only help to transfer the knowledge of complete shape from the teacher to the student, but also judge the learning effect of the student network. As a result, KTNet makes use of a more comprehensive understanding to establish the geometric correspondence between complete and incomplete shapes in a perspective of knowledge transfer, which enables more detailed geometric inference for generating high-quality complete shapes. We conduct comprehensive experiments on several datasets, and the results show that our method outperforms previous methods of unpaired point cloud completion by a large margin.	['Bisheng Yang', 'Xiongwu Xiao', 'Yu-Shen Liu', 'Zhen Dong', 'Xin Wen', 'Wenxiao Zhang', 'Zhen Cao']	2021-11-23	null	null	null	null	['point-cloud-completion']	['computer-vision']	[-1.26702815e-01 1.81436166e-01 -5.24118692e-02 -4.28648591e-01 -5.74473500e-01 -6.37481630e-01 2.26543754e-01 -1.92402929e-01 -9.09041520e-03 5.50045550e-01 -9.07768980e-02 -2.03055099e-01 -1.24111339e-01 -1.16143811e+00 -1.11412096e+00 -6.81642056e-01 4.75014716e-01 9.00508702e-01 3.16349149e-01 -1.34508431e-01 1.29170671e-01 7.01524198e-01 -1.38347471e+00 1.01996794e-01 1.18084288e+00 8.93007994e-01 5.25589943e-01 1.23601906e-01 -4.08135742e-01 3.15457314e-01 -3.24525237e-01 -3.02664429e-01 3.38595152e-01 1.20141387e-01 -7.58517623e-01 -3.06455418e-02 4.52774346e-01 -5.83779275e-01 -4.22982872e-01 9.75576460e-01 3.70945245e-01 5.54311164e-02 8.97536457e-01 -1.36508226e+00 -1.16081977e+00 3.76641244e-01 -4.33383346e-01 -3.94758850e-01 3.22174340e-01 8.11607167e-02 5.72190940e-01 -1.22047842e+00 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3.43070984e-01 7.56832004e-01 -9.81684804e-01 -6.31809771e-01 -5.96353531e-01 -4.63151693e-01 5.31810939e-01 1.68248177e-01 -7.45345294e-01 -1.31514847e-01 9.78763551e-02]	[8.484164237976074, -3.6140987873077393]
f039879b-4c8d-45d3-8df6-d59cbd9594e3	video-acceleration-magnification	1704.04186	null	http://arxiv.org/abs/1704.04186v2	http://arxiv.org/pdf/1704.04186v2.pdf	Video Acceleration Magnification	The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly. In this work we propose a method to cope with large motions while still magnifying small changes. We make the following two observations: i) large motions are linear on the temporal scale of the small changes; ii) small changes deviate from this linearity. We ignore linear motion and propose to magnify acceleration. Our method is pure Eulerian and does not require any optical flow, temporal alignment or region annotations. We link temporal second-order derivative filtering to spatial acceleration magnification. We apply our method to moving objects where we show motion magnification and color magnification. We provide quantitative as well as qualitative evidence for our method while comparing to the state-of-the-art.	['Jan C. van Gemert', 'Silvia L. Pintea', 'Yichao Zhang']	2017-04-13	video-acceleration-magnification-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.pdf	cvpr-2017-7	['motion-magnification']	['computer-vision']	[ 5.68629622e-01 -1.13359511e-01 -6.86000362e-02 9.54047143e-02 -5.66521548e-02 -7.76092350e-01 5.89029670e-01 5.25795668e-03 -4.94534016e-01 5.41598201e-01 1.54711604e-01 -3.11864406e-01 1.14531882e-01 -5.03870726e-01 -6.88865185e-01 -3.84752482e-01 -3.52574140e-01 -2.58316785e-01 7.86584437e-01 -3.58514577e-01 4.26162809e-01 5.39161146e-01 -1.37929094e+00 4.96756285e-01 6.18787348e-01 6.39547646e-01 -1.22904755e-01 1.34108698e+00 3.41713995e-01 8.37531805e-01 -5.86899340e-01 -1.84182227e-01 4.55220819e-01 -5.52941203e-01 -7.78464556e-01 2.63283938e-01 7.20348179e-01 -6.21031225e-01 -2.86972642e-01 1.03352273e+00 3.55024815e-01 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71084e0d-5111-4ece-b276-9178327b9aca	enriched-music-representations-with-multiple	2104.00437	null	https://arxiv.org/abs/2104.00437v1	https://arxiv.org/pdf/2104.00437v1.pdf	Enriched Music Representations with Multiple Cross-modal Contrastive Learning	Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio, interactions between users and songs, or associated genre metadata. Recently, contrastive learning has led to representations that generalize better compared to traditional supervised methods. In this paper, we present a novel approach that combines multiple types of information related to music using cross-modal contrastive learning, allowing us to learn an audio feature from heterogeneous data simultaneously. We align the latent representations obtained from playlists-track interactions, genre metadata, and the tracks' audio, by maximizing the agreement between these modality representations using a contrastive loss. We evaluate our approach in three tasks, namely, genre classification, playlist continuation and automatic tagging. We compare the performances with a baseline audio-based CNN trained to predict these modalities. We also study the importance of including multiple sources of information when training our embedding model. The results suggest that the proposed method outperforms the baseline in all the three downstream tasks and achieves comparable performance to the state-of-the-art.	['Dmitry Bogdanov', 'Yuntae Kim', 'Konstantinos Drossos', 'Xavier Favory', 'Andres Ferraro']	2021-04-01	null	null	null	null	['genre-classification']	['computer-vision']	[ 2.84933537e-01 -3.47083390e-01 -3.11534584e-01 -2.95839965e-01 -1.29978979e+00 -7.12384641e-01 5.98667920e-01 4.92830873e-01 -3.39455366e-01 3.29005301e-01 8.40938270e-01 6.14882946e-01 -3.04016978e-01 -5.45539439e-01 -7.31851459e-01 -6.02351367e-01 -3.85505781e-02 2.45909870e-01 1.38229936e-01 -7.68934786e-02 2.40455270e-01 5.29729351e-02 -1.99973726e+00 7.51425326e-01 1.95710912e-01 1.39011562e+00 9.26004201e-02 4.88499910e-01 -1.74991533e-01 8.11816931e-01 -5.74547052e-01 -3.32824260e-01 -5.37244836e-03 -3.03773820e-01 -8.21092546e-01 -1.32388309e-01 6.16207421e-01 5.77637665e-02 -2.50484884e-01 6.57905936e-01 7.09451854e-01 3.10093075e-01 7.08643556e-01 -1.19680893e+00 -3.54864299e-01 1.06467688e+00 -4.16301548e-01 5.31324483e-02 5.16466498e-01 -3.84237528e-01 1.58766806e+00 -6.51461661e-01 5.93559802e-01 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6.17458582e-01 -1.28481162e+00 -2.61452913e-01 -1.16761041e+00 -7.61564374e-01 6.07942760e-01 2.25081518e-01 -9.83134151e-01 -3.25580388e-01 3.01047534e-01]	[15.633539199829102, 5.167294025421143]
e212c4c5-efa2-4d34-b654-6a2dc7d0f8d0	growsp-unsupervised-semantic-segmentation-of-1	2305.16404	null	https://arxiv.org/abs/2305.16404v1	https://arxiv.org/pdf/2305.16404v1.pdf	GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds	We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing methods which primarily rely on a large amount of human annotations for training neural networks, we propose the first purely unsupervised method, called GrowSP, to successfully identify complex semantic classes for every point in 3D scenes, without needing any type of human labels or pretrained models. The key to our approach is to discover 3D semantic elements via progressive growing of superpoints. Our method consists of three major components, 1) the feature extractor to learn per-point features from input point clouds, 2) the superpoint constructor to progressively grow the sizes of superpoints, and 3) the semantic primitive clustering module to group superpoints into semantic elements for the final semantic segmentation. We extensively evaluate our method on multiple datasets, demonstrating superior performance over all unsupervised baselines and approaching the classic fully-supervised PointNet. We hope our work could inspire more advanced methods for unsupervised 3D semantic learning.	['Bo Li', 'Bing Wang', 'Bo Yang', 'Zihui Zhang']	2023-05-25	growsp-unsupervised-semantic-segmentation-of	http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.pdf	cvpr-2023-1	['unsupervised-semantic-segmentation', '3d-semantic-segmentation']	['computer-vision', 'computer-vision']	[ 1.99441597e-01 3.84619266e-01 -2.03400657e-01 -6.46461964e-01 -8.52453470e-01 -8.21929932e-01 7.28678226e-01 4.08190936e-01 -2.47495323e-01 -1.24411145e-03 -2.31900290e-01 -2.98824042e-01 5.34327067e-02 -6.58746421e-01 -9.48192716e-01 -2.19639957e-01 -1.54006615e-01 1.03922284e+00 7.80827403e-01 5.73427901e-02 5.85755467e-01 8.87509048e-01 -1.65289974e+00 -2.11290836e-01 7.70322025e-01 1.12014437e+00 2.79779345e-01 3.91589075e-01 -5.28009951e-01 2.51712710e-01 -2.26760671e-01 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69f6c3d3-597f-4ba4-babc-56caa60e2f57	molecular-docking-studies-on-jensenone-from	2004.00217	null	http://arxiv.org/abs/2004.00217v2	http://arxiv.org/pdf/2004.00217v2.pdf	Molecular docking studies on Jensenone from eucalyptus essential oil as a potential inhibitor of COVID 19 corona virus infection	COVID-19, a member of corona virus family is spreading its tentacles across the world due to lack of drugs at present. However, the main viral proteinase (Mpro/3CLpro) has recently been regarded as a suitable target for drug design against SARS infection due to its vital role in polyproteins processing necessary for coronavirus reproduction. The present in silico study was designed to evaluate the effect of Jensenone, a essential oil component from eucalyptus oil, on Mpro by docking study. In the present study, molecular docking studies were conducted by using 1-click dock and swiss dock tools. Protein interaction mode was calculated by Protein Interactions Calculator.The calculated parameters such as binding energy, and binding site similarity indicated effective binding of Jensenone to COVID-19 proteinase. Active site prediction further validated the role of active site residues in ligand binding. PIC results indicated that, Mpro/ Jensenone complexes forms hydrophobic interactions, hydrogen bond interactions and strong ionic interactions. Therefore, Jensenone may represent potential treatment potential to act as COVID-19 Mpro inhibitor. However, further research is necessary to investigate their potential medicinal use.	[]	2020-04-17	null	null	null	null	['molecular-docking']	['medical']	[ 5.04550450e-02 -5.14164746e-01 -1.32379234e-01 -4.91115414e-02 1.26830682e-01 -5.65704763e-01 2.13845238e-01 3.99585605e-01 -4.05558914e-01 1.02245772e+00 2.77471300e-02 -4.75508302e-01 2.57034987e-01 -3.55405778e-01 -4.00203824e-01 -9.44077015e-01 -2.58914292e-01 3.80455106e-01 4.77977283e-02 -2.32892334e-01 2.70332366e-01 9.84889507e-01 -6.66917026e-01 8.07788223e-02 1.38666701e+00 1.61785513e-01 7.16708183e-01 4.39872593e-01 1.38030335e-01 2.23598834e-02 -3.41160685e-01 -3.54611017e-02 -3.04660872e-02 -3.02492380e-01 -2.08327726e-01 -3.83873224e-01 -4.69882309e-01 -3.39126140e-01 5.90690255e-01 7.56349266e-01 4.94981945e-01 -1.10849440e-01 1.09367383e+00 -8.00103366e-01 -4.04701918e-01 -3.17208529e-01 -4.05680090e-01 1.22402042e-01 4.53531653e-01 1.15871407e-01 7.29433358e-01 -1.18007123e+00 7.59341002e-01 9.13929701e-01 4.30604339e-01 2.61040151e-01 -1.07898808e+00 -7.72394836e-01 -3.71297181e-01 4.35537070e-01 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1f77b5b4-b321-4ec3-a86d-419e788b21bb	maximum-entropy-heterogeneous-agent-mirror	2306.10715	null	https://arxiv.org/abs/2306.10715v1	https://arxiv.org/pdf/2306.10715v1.pdf	Maximum Entropy Heterogeneous-Agent Mirror Learning	Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample inefficiency, brittleness regarding hyperparameters, and the risk of converging to a suboptimal Nash Equilibrium. To resolve these issues, in this paper, we propose a novel theoretical framework, named Maximum Entropy Heterogeneous-Agent Mirror Learning (MEHAML), that leverages the maximum entropy principle to design maximum entropy MARL actor-critic algorithms. We prove that algorithms derived from the MEHAML framework enjoy the desired properties of the monotonic improvement of the joint maximum entropy objective and the convergence to quantal response equilibrium (QRE). The practicality of MEHAML is demonstrated by developing a MEHAML extension of the widely used RL algorithm, HASAC (for soft actor-critic), which shows significant improvements in exploration and robustness on three challenging benchmarks: Multi-Agent MuJoCo, StarCraftII, and Google Research Football. Our results show that HASAC outperforms strong baseline methods such as HATD3, HAPPO, QMIX, and MAPPO, thereby establishing the new state of the art. See our project page at https://sites.google.com/view/mehaml.	['Yaodong Yang', 'Xiaojun Chang', 'Qiang Fu', 'Haobo Fu', 'Siyi Hu', 'Yifan Zhong', 'Jiarong Liu']	2023-06-19	null	null	null	null	['multi-agent-reinforcement-learning']	['methodology']	[-4.45618957e-01 2.45462283e-01 -5.56017637e-01 4.00288731e-01 -1.08991134e+00 -4.73262936e-01 7.58573174e-01 -4.60346416e-02 -5.84604263e-01 1.20608938e+00 3.41831923e-01 -1.42024413e-01 -2.89675862e-01 -4.02467906e-01 -6.83589399e-01 -8.21143568e-01 -3.40338409e-01 4.95776802e-01 1.21512283e-02 -6.66945040e-01 4.11365300e-01 -1.31746858e-01 -1.07142735e+00 -1.55033767e-01 1.00186455e+00 6.03531301e-01 -1.05801173e-01 6.79622471e-01 5.23810685e-01 1.32453620e+00 -3.36033493e-01 -3.87409270e-01 5.43635011e-01 -7.05504179e-01 -8.34021866e-01 -5.41050494e-01 -3.09923559e-01 -6.18297696e-01 -3.14181358e-01 9.54935670e-01 8.81695330e-01 3.92527878e-01 4.17076677e-01 -1.69138551e+00 -4.22857374e-01 1.14824224e+00 -7.82153487e-01 -8.84380490e-02 2.76907861e-01 5.34305871e-01 1.42957294e+00 -4.81685489e-01 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f7adeea0-08dc-4f62-af59-6230beba6691	unsupervised-opinion-summarization-with-1	2012.07808	null	https://arxiv.org/abs/2012.07808v1	https://arxiv.org/pdf/2012.07808v1.pdf	Unsupervised Opinion Summarization with Content Planning	The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products or movies), such training data is neither available nor can be easily sourced, motivating the development of methods which rely on synthetic datasets for supervised training. We show that explicitly incorporating content planning in a summarization model not only yields output of higher quality, but also allows the creation of synthetic datasets which are more natural, resembling real world document-summary pairs. Our content plans take the form of aspect and sentiment distributions which we induce from data without access to expensive annotations. Synthetic datasets are created by sampling pseudo-reviews from a Dirichlet distribution parametrized by our content planner, while our model generates summaries based on input reviews and induced content plans. Experimental results on three domains show that our approach outperforms competitive models in generating informative, coherent, and fluent summaries that capture opinion consensus.	['Mirella Lapata', 'Stefanos Angelidis', 'Reinald Kim Amplayo']	2020-12-14	null	null	null	null	['unsupervised-opinion-summarization']	['natural-language-processing']	[ 2.90769130e-01 7.18663394e-01 -2.00172186e-01 -5.77093899e-01 -1.23186886e+00 -7.93061078e-01 1.19705617e+00 6.05675459e-01 -1.34837002e-01 1.07077229e+00 1.07843554e+00 -3.55399996e-02 3.90821934e-01 -7.27623880e-01 -7.67368734e-01 -1.91129684e-01 3.49026769e-01 9.90485013e-01 -1.68188646e-01 -2.40531445e-01 4.40593630e-01 -1.15461349e-01 -1.55971050e+00 5.97545207e-01 1.19763398e+00 6.29817784e-01 2.48401761e-01 8.79340947e-01 -3.11030626e-01 7.63964176e-01 -1.06723678e+00 -6.50753975e-01 7.13133514e-02 -7.82283485e-01 -7.20183551e-01 3.79656345e-01 3.44094187e-01 -4.10240501e-01 3.63076806e-01 6.92081630e-01 5.64975202e-01 2.18868867e-01 9.94003475e-01 -8.62483501e-01 -6.72411442e-01 1.02242947e+00 -4.54792589e-01 -2.82984287e-01 5.53441048e-01 2.28958175e-01 1.48594284e+00 -5.16140699e-01 9.81441498e-01 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-1.15702546e+00 -8.00166309e-01 -1.22290647e+00 -2.20066026e-01 3.94133240e-01 3.61190468e-01 5.86899966e-02 -1.71150997e-01 3.84116083e-01]	[12.391692161560059, 9.343610763549805]
8aff332a-15e3-4dc4-b2b9-57e41cbbdb13	how-good-is-the-model-in-model-in-the-loop	2306.05434	null	https://arxiv.org/abs/2306.05434v1	https://arxiv.org/pdf/2306.05434v1.pdf	How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?	Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97\% recall while substantially reducing the workload required by a fully manual annotation process. Code and data can be found at https://github.com/ahmeshaf/model_in_coref	['James H. Martin', 'Nikhil Krishnaswamy', 'Adam Pollins', 'Michael Regan', 'Abhijnan Nath', 'Shafiuddin Rehan Ahmed']	2023-06-06	null	null	null	null	['coreference-resolution']	['natural-language-processing']	[ 1.05941892e-01 6.59036994e-01 -2.10371166e-01 -5.58601618e-01 -1.38289583e+00 -9.77833390e-01 5.28513491e-01 5.98157942e-01 -6.45620465e-01 7.94275999e-01 5.32589555e-01 -1.49095565e-01 -1.85225725e-01 -3.72118413e-01 -4.26122844e-01 -2.31348038e-01 1.48934096e-01 9.74426150e-01 3.55345488e-01 1.15589000e-01 2.00869352e-01 2.41462663e-01 -1.26458228e+00 4.84004110e-01 4.38462913e-01 3.07051837e-01 2.32720286e-01 6.58369422e-01 -4.82734013e-03 6.14260018e-01 -5.24614811e-01 -8.74971986e-01 -7.67360181e-02 -4.86774832e-01 -1.41455173e+00 -6.34651780e-01 2.01315999e-01 2.13351727e-01 -4.17837799e-01 9.06593680e-01 8.97797585e-01 2.64451206e-01 2.91497171e-01 -1.11345255e+00 1.36873156e-01 1.08162212e+00 -2.82845885e-01 6.65802360e-01 7.01590061e-01 -1.86522096e-01 1.09178245e+00 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937fa682-c086-4189-bfbc-c96771b9431e	rotation-synchronization-via-deep-matrix	2305.05268	null	https://arxiv.org/abs/2305.05268v1	https://arxiv.org/pdf/2305.05268v1.pdf	Rotation Synchronization via Deep Matrix Factorization	In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively. This problem is an essential task for structure from motion and simultaneous localization and mapping. We focus on the formulation of synchronization via neural networks, which has only recently begun to be explored in the literature. Inspired by deep matrix completion, we express rotation synchronization in terms of matrix factorization with a deep neural network. Our formulation exhibits implicit regularization properties and, more importantly, is unsupervised, whereas previous deep approaches are supervised. Our experiments show that we achieve comparable accuracy to the closest competitors in most scenes, while working under weaker assumptions.	['Federica Arrigoni', 'Elisa Ricci', 'Andrea Fusiello', 'Paolo Rota', 'Giacomo Zara', 'Gk Tejus']	2023-05-09	null	null	null	null	['simultaneous-localization-and-mapping', 'matrix-completion']	['computer-vision', 'methodology']	[-9.78428796e-02 1.01949545e-02 -3.14481616e-01 -2.02393401e-02 -2.98651874e-01 -4.62696135e-01 6.58432424e-01 -1.80788845e-01 -5.75079501e-01 3.71065378e-01 2.31144637e-01 1.18528761e-01 -2.26405933e-01 -2.79821366e-01 -7.26257920e-01 -7.61811435e-01 -1.29624531e-01 4.74988341e-01 -3.12425166e-01 -2.54943281e-01 1.15279406e-01 5.42411566e-01 -7.96608746e-01 -3.91427070e-01 4.83602971e-01 4.45295632e-01 8.34178030e-02 4.72314030e-01 5.29809296e-01 9.46941197e-01 -2.38296926e-01 -9.79381725e-02 2.90252298e-01 -1.91692069e-01 -9.54907238e-01 1.83541074e-01 6.57856345e-01 -2.74411827e-01 -9.40507472e-01 1.09116864e+00 2.89965928e-01 3.22725803e-01 4.89893198e-01 -1.06652057e+00 -6.73900843e-01 5.36921799e-01 -7.78569281e-01 8.56674761e-02 4.02131736e-01 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b16fc2d5-e522-49d3-9c48-ad342097444e	recursive-construction-of-stable-assemblies	2106.08928	null	https://arxiv.org/abs/2106.08928v6	https://arxiv.org/pdf/2106.08928v6.pdf	RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks	Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity. Many properties of single RNNs are well characterized theoretically, but experimental neuroscience has moved in the direction of studying multiple interacting areas, and RNN theory needs to be likewise extended. We take a constructive approach towards this problem, leveraging tools from nonlinear control theory and machine learning to characterize when combinations of stable RNNs will themselves be stable. Importantly, we derive conditions which allow for massive feedback connections between interacting RNNs. We parameterize these conditions for easy optimization using gradient-based techniques, and show that stability-constrained "networks of networks" can perform well on challenging sequential-processing benchmark tasks. Altogether, our results provide a principled approach towards understanding distributed, modular function in the brain.	['Leo Kozachkov', 'Jean-Jacques Slotine', 'Michaela Ennis']	2021-06-16	recursive-construction-of-stable-assemblies-1	https://openreview.net/forum?id=qTBC7E4c454	https://openreview.net/pdf?id=qTBC7E4c454	null	['sequential-image-classification']	['computer-vision']	[ 2.69659519e-01 9.18413624e-02 1.31101891e-01 7.39055406e-03 -1.68772861e-01 -6.20481074e-01 6.77994251e-01 -3.01196426e-01 -4.87804383e-01 6.65546060e-01 2.70929337e-01 -3.43244046e-01 -2.19897881e-01 -1.69357345e-01 -6.81895196e-01 -1.12583375e+00 -4.09521997e-01 8.75443816e-02 2.19551668e-01 -6.26014113e-01 1.71705768e-01 5.83559811e-01 -1.11929345e+00 8.84718522e-02 5.19106567e-01 4.87161547e-01 2.27463439e-01 7.68115759e-01 3.62267196e-01 8.73191953e-01 -2.95756817e-01 8.80165026e-02 3.56165290e-01 -8.34146559e-01 -7.14490533e-01 -3.68338525e-01 7.13650584e-02 2.41811529e-01 -4.26630557e-01 1.01940608e+00 6.14062309e-01 2.96555996e-01 6.32374346e-01 -7.01151311e-01 -5.34332752e-01 8.13446343e-01 -3.39997083e-01 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0b31d1eb-d953-4246-9ce5-b4b6c515d2e3	tourist-attractions-recommendation-based-on	2306.10946	null	https://arxiv.org/abs/2306.10946v4	https://arxiv.org/pdf/2306.10946v4.pdf	Att-KGCN: Tourist Attractions Recommendation System by using Attention mechanism and Knowledge Graph Convolution Network	The recommendation algorithm based on knowledge graphs is at a relatively mature stage. However, there are still some problems in the recommendation of specific areas. For example, in the tourism field, selecting suitable tourist attraction attributes process is complicated as the recommendation basis for tourist attractions. In this paper, we propose the improved Attention Knowledge Graph Convolution Network model, named ($Att-KGCN$), which automatically discovers the neighboring entities of the target scenic spot semantically. The attention layer aggregates relatively similar locations and represents them with an adjacent vector. Then, according to the tourist's preferred choices, the model predicts the probability of similar spots as a recommendation system. A knowledge graph dataset of tourist attractions used based on tourism data on Socotra Island-Yemen. Through experiments, it is verified that the Attention Knowledge Graph Convolution Network has a good effect on the recommendation of tourist attractions and can make more recommendations for tourists' choices.	['Han Cao', 'Jingjing Li', 'Ahmad A. Mubarak']	2023-06-19	null	null	null	null	['knowledge-graphs']	['knowledge-base']	[-7.79187560e-01 -2.30747536e-01 -3.00275743e-01 -5.59073806e-01 2.10015729e-01 -2.51533866e-01 2.91585922e-01 4.64639366e-02 -3.37394327e-01 4.38158959e-01 6.02476299e-01 -3.81316662e-01 -7.55120337e-01 -1.44132626e+00 -4.79145557e-01 -7.63141394e-01 -2.13465169e-01 5.57566643e-01 9.66171548e-02 -7.37341642e-01 4.02965963e-01 5.73197365e-01 -1.46629179e+00 1.74057245e-01 7.99165070e-01 9.61225986e-01 4.45474267e-01 -6.14015758e-02 -4.29289490e-01 2.27373943e-01 -2.46693313e-01 -5.27001202e-01 1.72569409e-01 -1.61620572e-01 -6.93427205e-01 -1.27544120e-01 -8.90711229e-03 -1.03498884e-01 -6.31268322e-01 1.08226919e+00 3.28339428e-01 9.06749964e-01 7.66366124e-01 -1.03581536e+00 -1.27525187e+00 1.12164676e+00 -1.92665920e-01 4.59517956e-01 -1.55250102e-01 -2.27224901e-01 1.18639433e+00 -7.32333302e-01 4.43874747e-01 1.25801528e+00 3.60227704e-01 4.61531840e-02 -4.48037744e-01 -6.00074232e-01 6.93218470e-01 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