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paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id float64	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference stringlengths 12 47 ⌀	conference_url_abs stringlengths 16 198 ⌀	conference_url_pdf stringlengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
3853be1b-3804-4afd-8ff7-bf1e44f7c5bd	unsupervised-person-re-identification-a	2109.06057	null	https://arxiv.org/abs/2109.06057v2	https://arxiv.org/pdf/2109.06057v2.pdf	Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions	Person re-identification (Re-ID) has been a significant research topic in the past decade due to its real-world applications and research significance. While supervised person Re-ID methods achieve superior performance over unsupervised counterparts, they can not scale to large unlabelled datasets and new domains due to the prohibitive labelling cost. Therefore, unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID. Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature learning. The corresponding solutions are diverse and complex, with various merits and limitations. Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research. Existing person Re-ID surveys have focused on supervised methods from classifications and applications but lack detailed discussion on how the person Re-ID solutions address the underlying challenges. This survey review recent works on unsupervised person Re-ID from the perspective of challenges and solutions. Specifically, we provide an in-depth analysis of highly influential methods considering the four significant challenges in unsupervised person Re-ID: 1) lacking ground-truth identity labels to supervise person feature learning; 2) learning discriminative person features with pseudo-supervision; 3) learning cross-camera invariant person feature, and 4) the domain shift between datasets. We summarise and analyse evaluation results and provide insights on the effectiveness of the solutions. Finally, we discuss open issues and suggest some promising future research directions.	['Xiaojun Chang', 'Andy Song', 'Lina Yao', 'Chung-Hsing Yeh', 'Pengzhen Ren', 'Xiangtan Lin']	2021-09-01	null	null	null	null	['unsupervised-person-re-identification']	['computer-vision']	[ 2.90606201e-01 -1.20141976e-01 -9.76853669e-02 -5.97979426e-01 -3.48629147e-01 -5.14674723e-01 8.48926663e-01 -7.66419023e-02 -7.22916961e-01 7.38388002e-01 3.50447893e-01 6.71743631e-01 -2.50932395e-01 -4.01916295e-01 -6.44825473e-02 -6.56830370e-01 1.57767043e-01 8.68242383e-01 -2.30738476e-01 9.14646238e-02 1.59772426e-01 4.84069318e-01 -1.99934459e+00 -2.99084395e-01 8.43529463e-01 3.30022693e-01 -2.81931192e-01 4.14124429e-01 3.00959527e-01 2.79730171e-01 -8.84527564e-01 -6.94672644e-01 3.75327021e-01 -5.73631763e-01 -9.14155900e-01 4.79257047e-01 7.66981900e-01 -3.19889635e-01 -3.47410530e-01 1.12345600e+00 1.05196822e+00 4.87091273e-01 9.03470516e-01 -1.64620399e+00 -8.31050396e-01 1.07224584e-01 -5.11605918e-01 2.62209088e-01 7.52760887e-01 -1.55180648e-01 4.82245922e-01 -7.35130906e-01 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daa4fdab-5767-44c9-a903-ded0f3f3204a	pressim-an-end-to-end-framework-for-dynamic	2302.00391	null	https://arxiv.org/abs/2302.00391v1	https://arxiv.org/pdf/2302.00391v1.pdf	PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation	Ground pressure exerted by the human body is a valuable source of information for human activity recognition (HAR) in unobtrusive pervasive sensing. While data collection from pressure sensors to develop HAR solutions requires significant resources and effort, we present a novel end-to-end framework, PresSim, to synthesize sensor data from videos of human activities to reduce such effort significantly. PresSim adopts a 3-stage process: first, extract the 3D activity information from videos with computer vision architectures; then simulate the floor mesh deformation profiles based on the 3D activity information and gravity-included physics simulation; lastly, generate the simulated pressure sensor data with deep learning models. We explored two approaches for the 3D activity information: inverse kinematics with mesh re-targeting, and volumetric pose and shape estimation. We validated PresSim with an experimental setup with a monocular camera to provide input and a pressure-sensing fitness mat (80x28 spatial resolution) to provide the sensor ground truth, where nine participants performed a set of predefined yoga sequences.	['Paul Lukowicz', 'Sungho Suh', 'Bo Zhou', 'Lala Shakti Swarup Ray']	2023-02-01	null	null	null	null	['human-activity-recognition', 'pgtask', 'human-activity-recognition']	['computer-vision', 'natural-language-processing', 'time-series']	[ 4.55082297e-01 4.66688238e-02 1.45210803e-01 -9.35362875e-02 -5.24931431e-01 -1.89357981e-01 -9.08379704e-02 -2.16223925e-01 -2.95250565e-01 5.76705158e-01 4.04807091e-01 3.87306809e-01 9.16657373e-02 -8.38275313e-01 -1.05742621e+00 -2.86395967e-01 -2.77344733e-01 4.80681300e-01 -2.84160636e-02 -1.10306427e-01 1.43517759e-02 2.54588306e-01 -1.72544777e+00 -3.61139663e-02 7.74064422e-01 1.32249188e+00 2.06441656e-01 1.01554513e+00 6.77152932e-01 6.09975755e-01 -3.10957611e-01 8.34365562e-02 5.90691447e-01 -4.92380202e-01 -3.23039234e-01 4.46405470e-01 4.84920561e-01 -5.12520671e-01 -1.07893139e-01 5.39840758e-01 7.10861266e-01 3.30081493e-01 4.12282288e-01 -1.13950205e+00 -1.30384818e-01 1.45910054e-01 -4.87299979e-01 -1.89893574e-01 1.23110557e+00 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e907d6b2-eea4-4d86-bc0a-310e28ec592d	a-generalized-framework-for-edge-preserving-1	2107.07058	null	https://arxiv.org/abs/2107.07058v4	https://arxiv.org/pdf/2107.07058v4.pdf	A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing	Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, we first introduce the truncated Huber penalty function which shows strong flexibility under different parameter settings. A generalized framework is then proposed with the introduced truncated Huber penalty function. When combined with its strong flexibility, our framework is able to achieve diverse smoothing natures where contradictive smoothing behaviors can even be achieved. It can also yield the smoothing behavior that can seldom be achieved by previous methods, and superior performance is thus achieved in challenging cases. These together enable our framework capable of a range of applications and able to outperform the state-of-the-art approaches in several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, image texture removal, etc. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. A simple yet effective approach is further proposed to reduce the computational cost of our method while maintaining its performance. The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications. Our code is available at https://github.com/wliusjtu/Generalized-Smoothing-Framework.	['Michael Ng', 'Jie Yang', 'Xiaolin Huang', 'Yinjie Lei', 'Pingping Zhang', 'Wei Liu']	2021-07-15	null	null	null	null	['image-smoothing']	['computer-vision']	[ 2.75314689e-01 -1.15001872e-01 2.67855395e-02 -7.56918490e-02 -5.50384939e-01 -1.71241552e-01 5.64484000e-01 -3.40318717e-02 -2.49961197e-01 6.80361807e-01 -1.05944857e-01 -7.23438784e-02 -2.64798582e-01 -6.12362444e-01 -4.21073347e-01 -9.13525105e-01 1.40797213e-01 -2.26723239e-01 5.87080657e-01 -2.88193643e-01 4.49629575e-01 5.93915224e-01 -1.81145358e+00 -2.09679976e-01 1.28365064e+00 9.55565989e-01 7.46079326e-01 4.20846075e-01 -1.60798028e-01 3.25839967e-01 -1.05887860e-01 -1.35363623e-01 3.20300072e-01 -2.84541965e-01 -4.87369806e-01 4.81300235e-01 4.37279969e-01 -1.43468514e-01 5.92758358e-02 1.36757874e+00 4.45406049e-01 2.86352217e-01 6.01535976e-01 -8.35559905e-01 -5.08912325e-01 5.44259958e-02 -7.73438036e-01 -9.25466511e-03 2.90727735e-01 3.02022118e-02 6.50917590e-01 -8.62605870e-01 4.68895078e-01 1.00973451e+00 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0bda4210-e1ee-46c9-94d1-e56c2ef2fba6	low-rank-and-sparse-nmf-for-joint-endmembers	1703.05785	null	http://arxiv.org/abs/1703.05785v1	http://arxiv.org/pdf/1703.05785v1.pdf	Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images	Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsupervised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated $\ell_2/\ell_1$ norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.	['Konstantinos D. Koutroumbas', 'Paris V. Giampouras', 'Athanasios A. Rontogiannis']	2017-03-16	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 6.84445322e-01 -3.44409049e-01 1.21845961e-01 1.61924493e-02 -5.77609479e-01 -5.03734350e-01 5.01936197e-01 8.80872086e-02 -4.31032002e-01 9.43738282e-01 1.43649966e-01 -3.01204503e-01 -5.28051257e-01 -5.36629677e-01 -3.77090305e-01 -1.38292241e+00 2.76502222e-01 2.60096908e-01 -5.52368343e-01 1.20473139e-01 7.85358846e-02 5.69578409e-01 -1.61351287e+00 -2.68514514e-01 1.25192392e+00 8.06562662e-01 2.78475612e-01 4.88491178e-01 -9.24180821e-02 7.55509555e-01 -2.22757429e-01 4.40659933e-03 3.11511308e-01 -4.90512818e-01 -3.73287261e-01 7.27507770e-01 1.68315753e-01 -1.07684679e-01 2.19559520e-01 1.30366158e+00 3.12209457e-01 5.17082930e-01 8.24028373e-01 -8.37096810e-01 1.20493330e-01 4.23161447e-01 -8.41296256e-01 -1.99126184e-01 -9.26676020e-02 -1.08573832e-01 9.11735892e-01 -9.11045790e-01 5.93209527e-02 8.01923454e-01 4.84813690e-01 -2.34936178e-01 -1.27323532e+00 -5.55600762e-01 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d931f791-5106-418b-96ad-fb5a97548182	unsupervised-image-representation-learning	2205.15821	null	https://arxiv.org/abs/2205.15821v2	https://arxiv.org/pdf/2205.15821v2.pdf	Unsupervised Image Representation Learning with Deep Latent Particles	We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location and features of its surrounding region. To drive learning of such representations, we follow a VAE-based approach and introduce a prior for particle positions based on a spatial-softmax architecture, and a modification of the evidence lower bound loss inspired by the Chamfer distance between particles. We demonstrate that our DLP representations are useful for downstream tasks such as unsupervised keypoint (KP) detection, image manipulation, and video prediction for scenes composed of multiple dynamic objects. In addition, we show that our probabilistic interpretation of the problem naturally provides uncertainty estimates for particle locations, which can be used for model selection, among other tasks. Videos and code are available: https://taldatech.github.io/deep-latent-particles-web/	['Aviv Tamar', 'Tal Daniel']	2022-05-31	null	null	null	null	['unsupervised-facial-landmark-detection', 'image-manipulation', 'video-prediction']	['computer-vision', 'computer-vision', 'computer-vision']	[-1.64159052e-02 -1.53678458e-03 -2.78223127e-01 -4.41355318e-01 -7.77914226e-01 -6.19224429e-01 9.89280879e-01 1.60941511e-01 -2.04198718e-01 2.93128908e-01 3.53965640e-01 -4.46891710e-02 -7.11060092e-02 -6.97048962e-01 -1.25626469e+00 -8.94204855e-01 -9.68486667e-02 7.15645909e-01 3.56889725e-01 3.45613569e-01 2.83911198e-01 4.69139427e-01 -1.68366146e+00 4.79697466e-01 2.86813468e-01 1.04300046e+00 6.75019205e-01 8.29531848e-01 2.42606133e-01 7.70182073e-01 -3.56735766e-01 -1.27504200e-01 1.31249070e-01 2.52714567e-02 -5.02956331e-01 2.76869703e-02 4.22524631e-01 -2.51738667e-01 -4.38916326e-01 9.31151330e-01 1.09838799e-01 4.05153662e-01 1.01860940e+00 -1.38835895e+00 -1.06046581e+00 4.25975978e-01 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78395f0f-9d52-4356-9bc6-9811218cca80	fault-detection-in-ball-bearings	2209.11041	null	https://arxiv.org/abs/2209.11041v1	https://arxiv.org/pdf/2209.11041v1.pdf	Fault Detection in Ball Bearings	Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.	['Sarah Moll', 'Joshua Pickard']	2022-09-19	null	null	null	null	['fault-detection']	['miscellaneous']	[-1.58194434e-02 -5.16916974e-04 1.65944219e-01 1.65582210e-01 -1.27619013e-01 -1.55351330e-02 3.31962109e-01 -3.60378802e-01 -6.87909573e-02 2.90456027e-01 -2.59739518e-01 -4.39750820e-01 -3.75896811e-01 -6.77048504e-01 -6.52627528e-01 -7.53701746e-01 -4.98933077e-01 5.17215431e-01 5.46618819e-01 -3.62778634e-01 4.37458664e-01 1.00605261e+00 -1.87094808e+00 1.61029100e-01 1.10132448e-01 1.12515736e+00 1.02667145e-01 7.26432860e-01 4.80567604e-01 5.76196909e-01 -1.10117722e+00 2.26794034e-01 6.86566085e-02 -1.42344415e-01 -1.05750692e+00 3.48683178e-01 -1.77929252e-01 -1.25887156e-01 -5.41486502e-01 6.98699415e-01 5.43263555e-01 1.71808913e-01 7.46488392e-01 -1.06531036e+00 -5.03189623e-01 1.29631788e-01 -1.76690936e-01 5.15036464e-01 3.40938717e-01 6.44580871e-02 4.85708624e-01 -8.11369419e-01 4.20080721e-01 1.25046039e+00 5.60932934e-01 4.77080762e-01 -6.39850557e-01 -2.96122968e-01 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21958354-d8f4-4098-abd8-447e94481502	skin-lesion-classification-using-ensembles-of	1910.03910	null	https://arxiv.org/abs/1910.03910v1	https://arxiv.org/pdf/1910.03910v1.pdf	Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data	In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified based on dermoscopic images. For task 2, dermoscopic images and additional patient meta data have to be used. A diverse dataset of 25000 images was provided for training, containing images from eight classes. The final test set contains an additional, unknown class. We address this challenging problem with a simple, data driven approach by including external data with skin lesions types that are not present in the training set. Furthermore, multi-class skin lesion classification comes with the problem of severe class imbalance. We try to overcome this problem by using loss balancing. Also, the dataset contains images with very different resolutions. We take care of this property by considering different model input resolutions and different cropping strategies. To incorporate meta data such as age, anatomical site, and sex, we use an additional dense neural network and fuse its features with the CNN. We aggregate all our models with an ensembling strategy where we search for the optimal subset of models. Our best ensemble achieves a balanced accuracy of 74.2% using five-fold cross-validation. On the official test set our method is ranked first for both tasks with a balanced accuracy of 63.6% for task 1 and 63.4% for task 2.	['René Werner', 'Mohsin Shaikh', 'Nils Gessert', 'Maximilian Nielsen', 'Alexander Schlaefer']	2019-10-09	null	null	null	null	['skin-lesion-classification']	['medical']	[ 4.53636944e-01 1.45803511e-01 -2.19484940e-01 -3.47536236e-01 -8.88077796e-01 -3.93482834e-01 4.61759657e-01 4.04430002e-01 -6.80872977e-01 7.73217976e-01 -2.06937790e-01 -8.22414737e-03 -1.05830565e-01 -7.54645586e-01 -5.14483154e-01 -7.94856787e-01 2.62810290e-01 3.10181379e-01 4.41414237e-01 5.92133999e-02 3.49256098e-02 3.75057876e-01 -1.52439642e+00 7.53014266e-01 1.07241821e+00 1.20152700e+00 1.55624247e-03 7.39033341e-01 -8.23038351e-03 5.83530903e-01 -5.75837672e-01 -7.99627006e-01 4.45244789e-01 -2.27167815e-01 -9.22929525e-01 8.38621557e-02 9.11185861e-01 -2.93754965e-01 2.15018932e-02 1.06379735e+00 6.90969050e-01 -3.17351311e-01 8.93669307e-01 -1.02495766e+00 -2.01794237e-01 2.55012691e-01 -7.20577359e-01 -6.51869476e-02 -7.66340122e-02 2.10972577e-01 6.28417611e-01 -4.98519629e-01 9.30284083e-01 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c4dc890c-1899-4e0f-abfe-f88ad079cb3f	veil-vetting-extracted-image-labels-from-in	2303.09608	null	https://arxiv.org/abs/2303.09608v1	https://arxiv.org/pdf/2303.09608v1.pdf	VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection	The use of large-scale vision-language datasets is limited for object detection due to the negative impact of label noise on localization. Prior methods have shown how such large-scale datasets can be used for pretraining, which can provide initial signal for localization, but is insufficient without clean bounding-box data for at least some categories. We propose a technique to "vet" labels extracted from noisy captions. Our method trains a classifier that predicts if an extracted label is actually present in the image or not. Our classifier generalizes across dataset boundaries and shows promise for generalizing across categories as well. We compare the classifier to eleven baselines on five datasets, and demonstrate that it can improve weakly-supervised detection without label vetting by 80% (16.0 to 29.1 mAP when evaluated on PASCAL VOC).	['Adriana Kovashka', 'Arushi Rai']	2023-03-16	null	null	null	null	['weakly-supervised-object-detection']	['computer-vision']	[ 3.91871095e-01 5.61880358e-02 -1.06673978e-01 -7.39781260e-01 -1.44846201e+00 -1.01106858e+00 5.39357185e-01 9.29654390e-02 -9.26093459e-01 8.50745857e-01 6.14489876e-02 -2.72115767e-01 5.78779101e-01 -3.39792460e-01 -1.07104611e+00 -6.30634904e-01 1.70677617e-01 2.56264240e-01 5.79070747e-01 2.64563829e-01 -1.69604328e-02 4.03340131e-01 -1.49245787e+00 5.57942986e-01 3.73598576e-01 7.36632526e-01 3.76848191e-01 5.82028508e-01 -2.41407156e-02 6.57078385e-01 -9.64464545e-01 -1.92141771e-01 2.74721265e-01 -2.31169105e-01 -7.19102859e-01 8.77184123e-02 1.34272122e+00 -4.15185988e-01 -2.17719860e-02 1.20537221e+00 3.10861200e-01 -2.62982249e-01 7.82712281e-01 -1.10436833e+00 -5.50016642e-01 2.87463397e-01 -7.33181238e-01 1.82380944e-01 1.06208973e-01 3.18562388e-01 9.78507221e-01 -9.16877151e-01 7.83749700e-01 9.62892830e-01 1.04005635e+00 7.81449258e-01 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b9b4aab9-572e-4b18-a8fd-3d51f8fa169f	allenact-a-framework-for-embodied-ai-research	2008.12760	null	https://arxiv.org/abs/2008.12760v1	https://arxiv.org/pdf/2008.12760v1.pdf	AllenAct: A Framework for Embodied AI Research	The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities. This growth has been facilitated by the creation of a large number of simulated environments (such as AI2-THOR, Habitat and CARLA), tasks (like point navigation, instruction following, and embodied question answering), and associated leaderboards. While this diversity has been beneficial and organic, it has also fragmented the community: a huge amount of effort is required to do something as simple as taking a model trained in one environment and testing it in another. This discourages good science. We introduce AllenAct, a modular and flexible learning framework designed with a focus on the unique requirements of Embodied AI research. AllenAct provides first-class support for a growing collection of embodied environments, tasks and algorithms, provides reproductions of state-of-the-art models and includes extensive documentation, tutorials, start-up code, and pre-trained models. We hope that our framework makes Embodied AI more accessible and encourages new researchers to join this exciting area. The framework can be accessed at: https://allenact.org/	['Aniruddha Kembhavi', 'Kuo-Hao Zeng', 'Luca Weihs', 'Roozbeh Mottaghi', 'Klemen Kotar', 'Jordi Salvador', 'Unnat Jain']	2020-08-28	null	null	null	null	['embodied-question-answering']	['computer-vision']	[-1.03925928e-01 2.89141864e-01 6.96985945e-02 -2.20438540e-01 -2.39437088e-01 -7.46179402e-01 8.25163007e-01 1.40727665e-02 -5.63502729e-01 7.19664335e-01 3.42850953e-01 -2.84693003e-01 -1.20858192e-01 -7.53040731e-01 -7.76751041e-01 -5.88698208e-01 -4.71735686e-01 3.82871568e-01 3.74604948e-02 -6.89568639e-01 3.12679350e-01 2.97537267e-01 -1.90088761e+00 -6.79692999e-02 7.95361757e-01 5.07704914e-01 5.81686437e-01 7.69574463e-01 5.03762625e-02 1.30470097e+00 -2.80009449e-01 -1.38016239e-01 3.01368330e-02 -4.90132660e-01 -1.00893378e+00 -2.85287917e-01 8.93471465e-02 -5.62658608e-01 -6.19308233e-01 6.33033991e-01 6.07503176e-01 4.89918202e-01 3.75238061e-01 -1.59883356e+00 -8.34369779e-01 4.65621591e-01 9.68252346e-02 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02db9e3d-9a9b-44a1-83ed-a4c9e26d454a	ide-3d-interactive-disentangled-editing-for	2205.15517	null	https://arxiv.org/abs/2205.15517v1	https://arxiv.org/pdf/2205.15517v1.pdf	IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis	Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initialize the latent codes from the semantic and texture encoder, and further optimized them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and product high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing, and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness, and efficiency.	['Yebin Liu', 'Jue Wang', 'Lizhen Wang', 'Yichun Shi', 'Xuan Wang', 'Jingxiang Sun']	2022-05-31	null	null	null	null	['3d-aware-image-synthesis']	['computer-vision']	[ 2.92669475e-01 4.37583625e-01 3.36369336e-01 -2.73426563e-01 -4.32973564e-01 -5.32862723e-01 7.88858175e-01 -6.45306408e-01 2.92391121e-01 6.12647116e-01 3.29401463e-01 1.58617064e-01 8.76732171e-02 -1.01359665e+00 -5.73120713e-01 -5.26769698e-01 5.90476096e-01 5.66975176e-01 -1.28062321e-02 -4.24787760e-01 3.35669741e-02 7.93173671e-01 -1.65018475e+00 1.59019560e-01 9.14509535e-01 1.04107654e+00 -4.20804769e-02 5.63679099e-01 -1.27260357e-01 6.75124049e-01 -1.52408734e-01 -9.38701630e-01 3.55612248e-01 -7.75001824e-01 -5.02106071e-01 3.64885867e-01 4.60356414e-01 -4.02124554e-01 4.84458879e-02 9.24398363e-01 4.26366627e-01 -3.22153598e-01 5.26837707e-01 -1.09222448e+00 -1.02676690e+00 7.10471496e-02 -8.62491906e-01 -7.14759350e-01 4.15607363e-01 3.25586021e-01 6.48795366e-01 -9.21565294e-01 1.03022802e+00 1.43484318e+00 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25986cab-241b-4643-ba89-4e401192be67	infoseg-unsupervised-semantic-image	2110.03477	null	https://arxiv.org/abs/2110.03477v1	https://arxiv.org/pdf/2110.03477v1.pdf	InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization	We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features. The core idea of our work is to leverage recent progress in self-supervised image representation learning. Representation learning methods compute a single high-level feature capturing an entire image. In contrast, we compute multiple high-level features, each capturing image segments of one particular semantic class. To this end, we propose a novel two-step learning procedure comprising a segmentation and a mutual information maximization step. In the first step, we segment images based on local and global features. In the second step, we maximize the mutual information between local features and high-level features of their respective class. For training, we provide solely unlabeled images and start from random network initialization. For quantitative and qualitative evaluation, we use established benchmarks, and COCO-Persons, whereby we introduce the latter in this paper as a challenging novel benchmark. InfoSeg significantly outperforms the current state-of-the-art, e.g., we achieve a relative increase of 26% in the Pixel Accuracy metric on the COCO-Stuff dataset.	['Patrick Knöbelreiter', 'Robert Harb']	2021-10-07	null	null	null	null	['unsupervised-semantic-segmentation']	['computer-vision']	[ 6.83538973e-01 3.32877040e-01 -3.79499286e-01 -5.23854554e-01 -1.15531671e+00 -4.68890041e-01 5.29860079e-01 4.17036116e-01 -6.86910212e-01 4.02766138e-01 -5.84580712e-02 1.34316534e-01 -8.69110972e-03 -8.26158822e-01 -6.76129878e-01 -7.79515386e-01 1.42927289e-01 4.95894909e-01 1.60944924e-01 3.31630975e-01 2.11965546e-01 2.85393625e-01 -1.54763448e+00 3.71290714e-01 8.02149475e-01 1.24041462e+00 4.25628543e-01 3.55458766e-01 -9.00727585e-02 8.99321198e-01 -2.17107430e-01 -1.69526171e-02 7.71656027e-03 -5.45937181e-01 -1.31426334e+00 6.63089216e-01 3.16106349e-01 8.86568651e-02 -1.00586616e-01 1.16587198e+00 2.28953749e-01 1.66297793e-01 9.63582993e-01 -1.04032362e+00 -2.68236548e-01 4.63755608e-01 -7.00884640e-01 -1.22242190e-01 4.08152454e-02 3.37732993e-02 1.25217915e+00 -7.32156694e-01 7.44708478e-01 8.37329388e-01 5.22050917e-01 3.68995130e-01 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4c6aaa67-81b1-441b-a6e7-c8ba95e3ec19	neural-pitch-shifting-and-time-stretching	2110.02360	null	https://arxiv.org/abs/2110.02360v1	https://arxiv.org/pdf/2110.02360v1.pdf	Neural Pitch-Shifting and Time-Stretching with Controllable LPCNet	Modifying the pitch and timing of an audio signal are fundamental audio editing operations with applications in speech manipulation, audio-visual synchronization, and singing voice editing and synthesis. Thus far, methods for pitch-shifting and time-stretching that use digital signal processing (DSP) have been favored over deep learning approaches due to their speed and relatively higher quality. However, even existing DSP-based methods for pitch-shifting and time-stretching induce artifacts that degrade audio quality. In this paper, we propose Controllable LPCNet (CLPCNet), an improved LPCNet vocoder capable of pitch-shifting and time-stretching of speech. For objective evaluation, we show that CLPCNet performs pitch-shifting of speech on unseen datasets with high accuracy relative to prior neural methods. For subjective evaluation, we demonstrate that the quality and naturalness of pitch-shifting and time-stretching with CLPCNet on unseen datasets meets or exceeds competitive neural- or DSP-based approaches.	['Bryan Pardo', 'Juan-Pablo Caceres', 'Nicholas J. Bryan', 'Zeyu Jin', 'Max Morrison']	2021-10-05	null	null	null	null	['audio-visual-synchronization', 'audio-visual-synchronization']	['audio', 'computer-vision']	[ 2.63829499e-01 -2.31854454e-01 -4.48125824e-02 -5.15159443e-02 -7.71527946e-01 -5.90692818e-01 3.33046883e-01 1.40910484e-02 -3.14531744e-01 5.52459002e-01 2.31293753e-01 -4.11515757e-02 -3.36104222e-02 -2.04191759e-01 -5.72994292e-01 -6.77217603e-01 -2.07030937e-01 -1.83440700e-01 2.38023803e-01 -2.03668192e-01 5.85156819e-03 5.47015786e-01 -1.90986776e+00 1.60778880e-01 4.50500488e-01 9.44222331e-01 7.65729547e-02 9.85634565e-01 4.07907665e-01 2.01763764e-01 -1.12252975e+00 -1.74542800e-01 1.63448781e-01 -2.68756211e-01 -2.56764323e-01 -3.11708242e-01 7.68254161e-01 -3.71989399e-01 -5.84861040e-01 1.04888594e+00 1.11674929e+00 3.50501329e-01 3.55962574e-01 -1.19879997e+00 -3.20469379e-01 6.53976738e-01 -1.63910568e-01 3.35618377e-01 4.59845930e-01 1.23770311e-01 1.06583154e+00 -9.75296319e-01 3.66345674e-01 1.17243361e+00 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-1.01609838e+00 -7.73714483e-01 -7.90950298e-01 -7.45953560e-01 1.97974503e-01 8.39067400e-01 -3.51270199e-01 -6.56773508e-01 3.25693339e-01]	[15.50790786743164, 5.973680019378662]
996308d8-a66a-461f-b6cd-7c9b6322628c	nonlinear-intensity-scale-and-rotation	2302.14239	null	https://arxiv.org/abs/2302.14239v1	https://arxiv.org/pdf/2302.14239v1.pdf	Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images	We present an effective method for the matching of multimodal images. Accurate image matching is the basis of various applications, such as image registration and structure from motion. Conventional matching methods fail when handling noisy multimodal image pairs with severe scale change, rotation, and nonlinear intensity distortion (NID). Toward this need, we introduce an image pyramid strategy to tackle scale change. We put forward an accurate primary orientation estimation approach to reduce the effect of image rotation at any angle. We utilize multi-scale and multi-orientation image filtering results and a feature-to-template matching scheme to ensure effective and accurate matching under large NID. Integrating these improvements significantly increases noise, scale, rotation, and NID invariant capability. Our experimental results confirm the excellent ability to achieve high-quality matches across various multimodal images. The proposed method outperforms the mainstream multimodal image matching methods in qualitative and quantitative evaluations. Our implementation is available at https://github.com/Zhongli-Fan/NISR.	['Yuxuan Liu', 'Li Zhang', 'Zhongli Fan']	2023-02-28	null	null	null	null	['template-matching']	['computer-vision']	[ 1.28646001e-01 -5.87528050e-01 -1.14164636e-01 -4.86500144e-01 -9.33150887e-01 -5.87967336e-01 4.11709458e-01 -3.31184827e-02 -4.39659536e-01 1.28980428e-01 3.94742399e-01 2.49321684e-01 -8.44915137e-02 -5.77566385e-01 -4.00022179e-01 -6.09179676e-01 3.43629956e-01 3.29666249e-02 3.14104915e-01 -3.98612112e-01 5.45620859e-01 6.62203074e-01 -1.59960926e+00 2.32960925e-01 8.45445275e-01 7.86172926e-01 1.89396232e-01 6.04304612e-01 1.44788614e-02 1.12832993e-01 -2.90727139e-01 -4.60234523e-01 4.21716034e-01 -2.00020880e-01 -6.70411706e-01 1.42748058e-01 6.49341464e-01 -3.08115363e-01 -4.24460500e-01 1.31593335e+00 1.01145256e+00 1.76682636e-01 4.27963018e-01 -1.26400435e+00 -5.48248947e-01 -1.08208984e-01 -8.92753184e-01 3.93004157e-02 9.61532652e-01 3.27629559e-02 4.44788009e-01 -1.05379236e+00 7.99250543e-01 1.32547140e+00 7.53360331e-01 2.94908971e-01 -9.20514822e-01 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8.87775779e-01 3.86798501e-01 -4.38779056e-01 -3.59190077e-01 2.46857256e-01]	[10.311230659484863, -1.7898311614990234]
c8033768-bbbb-45c7-8a8a-8b8d6c03dcc2	the-brain-tumor-segmentation-brats-mets	2306.00838	null	https://arxiv.org/abs/2306.00838v1	https://arxiv.org/pdf/2306.00838v1.pdf	The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI	Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.	['Mariam Aboian', 'Jeffrey Rudie', 'Spyridon Bakas', 'Gian Marco Conte', 'Fatima Memon', 'Umber Shafique', 'Ichiro Ikuta', 'Veronica Chiang', 'Sanjay Aneja', 'Evan Calabrese', 'Florian Kofler', 'Anahita Fathi Kazerooni', 'Ariana Familiar', 'Zeke Meier', 'Elaine Johanson', 'Ivan Ezhov', 'Marie Piraud', 'Koen van Leemput', 'Zhifan Jiang', 'Xinyang Liu', 'Chunhao Wang', 'Zachary Reitman', 'Walter Wiggins', 'Farouk Dako', 'Russel Takeshi Shinohara', 'Verena Chung', 'Timothy Bergquist', 'James Eddy', 'Keyvan Farahani', 'Juan Eugenio Iglesias', 'Hongwei Bran Li', 'Dominic LaBella', 'Syed Muhammad Anwar', 'Maruf Adewole', 'Benedikt Wiestler', 'Bjoern H Menze', 'Marius George Lingaru', 'Udunna Anazodo', 'Jake Albrecht', 'Group of Approvers', 'Neil Lall', 'Gloria Guzman Perez-Carrillo', 'Anthony Kam', 'Gerry Thompson', 'Tiffany So', 'Lei Wu', 'Satya N. Patro', 'Boyan Petrovic', 'Benjamin Y. M. Kwan', 'Derek R. Johnson', 'Justin Cramer', 'Group of Annotators', 'Connectome Students', 'Nicolo Gennaro', 'Yuri S. Velichko', 'Ayaman Nada', 'Ayda Youssef', 'Oleg Teytelboym', 'Strajit Chakrabarty', 'Pamela Lamontagne', 'Aristeidis Sotiras', 'Daniel Marcus', 'Nourel Hoda Tahon', 'Katherine E. Link', 'Ryan Maresca', 'Malte Westerhoff', 'MingDe Lin', 'Wolfgang Holler', 'Khaled Bousabarah', 'Jan Lost', 'Niklas Tillmans', 'Marc von Reppert', 'Sarah Merkaj', 'Mahdi Salimi', 'Nazanin Maleki', 'Gabriel Cassinelli Pedersen', 'Arman Avesta', 'Manpreet Kaur', 'Klara Wilms', 'Klara Osenberg', 'Rachit Saluja', 'Harrison Moy', 'Kiril Krantchev', 'Leon Jekel', 'Divya Ramakrishnan', 'Ujjwal Baid', 'Anastasia Janas', 'Ahmed W. Moawad']	2023-06-01	null	null	null	null	['tumor-segmentation', 'brain-tumor-segmentation']	['computer-vision', 'medical']	[ 2.13330895e-01 -1.57920256e-01 -2.38434896e-01 -4.84640487e-02 -1.09695208e+00 -5.32515705e-01 4.68533099e-01 6.23425543e-01 -7.15237379e-01 6.89618528e-01 3.78387898e-01 -7.34320223e-01 3.65983509e-02 -3.86788875e-01 8.50636959e-02 -9.20561671e-01 -5.70214763e-02 8.70652199e-01 3.90579551e-01 1.29382517e-02 3.90164442e-02 6.63283229e-01 -5.42900860e-01 -2.40764003e-02 7.68488526e-01 7.70521522e-01 8.27704549e-01 7.75350153e-01 -1.71789035e-01 5.37142873e-01 -4.35318381e-01 2.70334929e-01 1.35051664e-02 -5.50301135e-01 -8.65937769e-01 7.42990058e-03 2.13361576e-01 -1.99385479e-01 -1.52475268e-01 8.21472704e-01 6.36277795e-01 -7.47931600e-02 1.15011525e+00 -8.16332459e-01 2.24987686e-01 4.17855680e-01 -8.21225703e-01 9.01490331e-01 2.09810361e-02 1.74911946e-01 4.64416504e-01 -7.82553554e-01 4.94511366e-01 3.40055734e-01 6.36755645e-01 4.68252122e-01 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-2.80261159e-01 -1.05927682e+00 -8.04259121e-01 2.03851368e-02 2.61633605e-01 -7.85819113e-01 -5.72863758e-01 -3.52139473e-01]	[14.653213500976562, -2.4922115802764893]
639a0968-75f3-4d71-87d9-251a6792fa6a	data-driven-geophysics-from-dictionary	2007.06183	null	https://arxiv.org/abs/2007.06183v2	https://arxiv.org/pdf/2007.06183v2.pdf	Data-driven geophysics: from dictionary learning to deep learning	Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time; however, such methods suffer from the curse of dimensionality and may inaccurately model the subsurface. "Data-driven" techniques may overcome these issues with increasingly available geophysical data. In this article, we review the basic concepts of and recent advances in data-driven approaches from dictionary learning to deep learning in a variety of geophysical scenarios. Explorational geophysics including data processing, inversion and interpretation will be mainly focused. Artificial intelligence applications on geoscience involving deep Earth, earthquake, water resource, atmospheric science, satellite remoe sensing and space sciences are also reviewed. We present a coding tutorial and a summary of tips for beginners and interested geophysical readers to rapidly explore deep learning. Some promising directions are provided for future research involving deep learning in geophysics, such as unsupervised learning, transfer learning, multimodal deep learning, federated learning, uncertainty estimation, and activate learning.	['Siwei Yu', 'Jianwei Ma']	2020-07-13	null	null	null	null	['geophysics']	['miscellaneous']	[-2.91610241e-01 -2.06120819e-01 1.49385497e-01 -6.87560320e-01 -9.41425204e-01 -1.99373722e-01 5.66837132e-01 1.88588366e-01 -2.39277616e-01 7.89203167e-01 3.28320473e-01 -8.45465541e-01 -4.38373089e-01 -1.10136235e+00 -4.88042384e-01 -9.81073320e-01 -6.58200622e-01 7.80748904e-01 -3.33764434e-01 -3.59223604e-01 6.20569587e-01 5.91740012e-01 -1.11199617e+00 -1.46616802e-01 9.59806740e-01 1.23696196e+00 1.41752943e-01 4.34086382e-01 -3.93130422e-01 7.03917503e-01 -1.29555300e-01 4.17675287e-01 -2.28489451e-02 -1.97408646e-01 -6.12691760e-01 -3.29410583e-01 3.29534858e-02 -4.98386055e-01 -2.05875620e-01 1.06810379e+00 7.86754787e-01 5.90670288e-01 9.40016806e-01 -6.39309168e-01 -5.98485947e-01 6.25398815e-01 -7.67972350e-01 2.39465505e-01 -2.55720228e-01 -4.15350616e-01 9.86230195e-01 -1.68962169e+00 3.18551064e-02 1.04848218e+00 7.36939967e-01 3.17458749e-01 -5.65794170e-01 -4.97395694e-01 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07c2eb27-7e1c-4ee9-b880-13ec6af9300a	emora-stdm-a-versatile-framework-for	2006.06143	null	https://arxiv.org/abs/2006.06143v1	https://arxiv.org/pdf/2006.06143v1.pdf	Emora STDM: A Versatile Framework for Innovative Dialogue System Development	This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.	['Jinho D. Choi', 'James D. Finch']	2020-06-11	null	https://aclanthology.org/2020.sigdial-1.32	https://aclanthology.org/2020.sigdial-1.32.pdf	sigdial-acl-2020-7	['dialogue-management']	['natural-language-processing']	[-2.18633264e-01 5.61157465e-01 1.59933522e-01 -4.61279601e-01 -3.52616370e-01 -1.04623425e+00 8.74953032e-01 4.10747856e-01 -7.62691498e-02 6.68551862e-01 2.89483964e-01 -7.80327618e-01 -1.43077001e-01 -5.78128278e-01 5.58047831e-01 2.62728631e-01 2.69580513e-01 5.75775385e-01 5.87169528e-01 -9.78087544e-01 5.22935569e-01 6.85309112e-01 -1.44183707e+00 4.14687335e-01 8.47726345e-01 2.68148750e-01 3.07588458e-01 8.95594716e-01 -7.69843459e-01 1.14645278e+00 -9.02401209e-01 -3.06121647e-01 -3.34662646e-01 -5.39108396e-01 -1.61071634e+00 9.95997060e-03 -2.15010300e-01 -2.87002891e-01 1.19457863e-01 4.79981571e-01 4.82710272e-01 4.22810584e-01 5.16413897e-02 -1.21381474e+00 2.68119037e-01 5.31093836e-01 2.57353067e-01 -1.05588153e-01 1.19581473e+00 3.12709153e-01 5.56828260e-01 -5.36183298e-01 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9484a13b-f1f2-4ba1-ad61-fd78ed690b76	transfer-learning-with-ensembles-of-deep	2103.12068	null	https://arxiv.org/abs/2103.12068v4	https://arxiv.org/pdf/2103.12068v4.pdf	Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets	Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. However, the classification accuracy of these models still tends to be severely limited by the scarcity of representative images from malignant tumours. We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with auxiliary data in the form of metadata associated with the input images, are combined using a meta-learner. The proposed approach improves the model's ability to handle limited and imbalanced data. We demonstrate the benefits of the proposed technique using a dataset with 33126 dermoscopic images from 2056 patients. We evaluate the performance of the proposed technique in terms of the F1-measure, area under the ROC curve (AUC-ROC), and area under the PR-curve (AUC-PR), and compare it with that of seven different benchmark methods, including two recent CNN-based techniques. The proposed technique compares favourably in terms of all the evaluation metrics.	['Teemu Roos', 'Aqsa Saeed Qureshi']	2021-03-22	null	null	null	null	['skin-cancer-classification']	['medical']	[ 4.44585443e-01 -7.02845678e-03 -3.20378900e-01 -3.38827968e-01 -5.54564714e-01 -1.60641059e-01 4.88425821e-01 5.82844913e-01 -9.20688450e-01 6.79610729e-01 -6.89153746e-02 -1.73290163e-01 -3.47064942e-01 -8.42756629e-01 -4.14150923e-01 -8.85539174e-01 7.42535219e-02 1.58073269e-02 1.24866389e-01 1.03496693e-01 2.00161561e-01 1.60098255e-01 -1.51005673e+00 3.93037558e-01 1.22316098e+00 1.28975737e+00 -5.50160818e-02 6.71147764e-01 -1.13916375e-01 6.51083946e-01 -5.12844980e-01 -5.22352517e-01 1.91102058e-01 -9.75293592e-02 -4.93507296e-01 -3.12172454e-02 5.21850646e-01 -1.19117856e-01 -2.09948450e-01 9.31804419e-01 4.95879292e-01 -2.44750381e-01 5.43360949e-01 -8.69850576e-01 -1.73803940e-01 -7.02560171e-02 -5.10098934e-01 2.13724449e-01 -2.88805753e-01 1.63563788e-01 6.25851095e-01 -3.55501205e-01 6.11664295e-01 4.74089414e-01 8.73086095e-01 4.56062555e-01 -6.10889792e-01 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920a996c-85e1-49dc-98a0-710581471583	speecht5-unified-modal-encoder-decoder-pre	2110.07205	null	https://arxiv.org/abs/2110.07205v3	https://arxiv.org/pdf/2110.07205v3.pdf	SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing	Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification. We release our code and model at https://github.com/microsoft/SpeechT5.	['Furu Wei', 'Jinyu Li', 'Shujie Liu', 'Chengyi Wang', 'Yao Qian', 'Zhihua Wei', 'Yu Zhang', 'Qing Li', 'Tom Ko', 'Yu Wu', 'Shuo Ren', 'Long Zhou', 'Rui Wang', 'Junyi Ao']	2021-10-14	null	https://aclanthology.org/2022.acl-long.393	https://aclanthology.org/2022.acl-long.393.pdf	acl-2022-5	['speaker-identification']	['speech']	[ 5.59501946e-01 2.83855975e-01 -3.83074045e-01 -6.74707770e-01 -1.18463981e+00 -4.30987775e-01 6.57577217e-01 -2.79168457e-01 -1.84340805e-01 2.26746202e-01 7.41600156e-01 -6.09060287e-01 4.50426847e-01 -4.63996649e-01 -6.46142662e-01 -4.16239619e-01 5.27272642e-01 5.55564940e-01 -2.14586750e-01 -1.51794642e-01 -1.59288406e-01 -2.17390701e-01 -1.22176659e+00 7.20998287e-01 8.16940665e-01 8.95023227e-01 4.74903494e-01 7.68422902e-01 -5.38367271e-01 8.65993738e-01 -4.03287143e-01 -2.47791469e-01 -5.44838123e-02 -5.28763652e-01 -8.70727837e-01 3.74346793e-01 -2.00491697e-01 -4.07678723e-01 -6.82957649e-01 1.14078522e+00 6.11640215e-01 1.64767936e-01 6.46927893e-01 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4d1cbaf4-43c7-4078-b043-95e38e4ce227	hcam-hierarchical-cross-attention-model-for	2304.06910	null	https://arxiv.org/abs/2304.06910v1	https://arxiv.org/pdf/2304.06910v1.pdf	HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition	Emotion recognition in conversations is challenging due to the multi-modal nature of the emotion expression. We propose a hierarchical cross-attention model (HCAM) approach to multi-modal emotion recognition using a combination of recurrent and co-attention neural network models. The input to the model consists of two modalities, i) audio data, processed through a learnable wav2vec approach and, ii) text data represented using a bidirectional encoder representations from transformers (BERT) model. The audio and text representations are processed using a set of bi-directional recurrent neural network layers with self-attention that converts each utterance in a given conversation to a fixed dimensional embedding. In order to incorporate contextual knowledge and the information across the two modalities, the audio and text embeddings are combined using a co-attention layer that attempts to weigh the utterance level embeddings relevant to the task of emotion recognition. The neural network parameters in the audio layers, text layers as well as the multi-modal co-attention layers, are hierarchically trained for the emotion classification task. We perform experiments on three established datasets namely, IEMOCAP, MELD and CMU-MOSI, where we illustrate that the proposed model improves significantly over other benchmarks and helps achieve state-of-art results on all these datasets.	['Sriram Ganapathy', 'Soumya Dutta']	2023-04-14	null	null	null	null	['multimodal-emotion-recognition', 'emotion-classification', 'emotion-recognition-in-conversation', 'emotion-classification', 'multimodal-emotion-recognition']	['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech']	[ 1.12042472e-01 -6.39943630e-02 3.25021237e-01 -5.40122986e-01 -9.80786920e-01 -8.64197910e-02 4.98526394e-01 -1.32930055e-02 -4.57359791e-01 3.11254710e-01 8.24613452e-01 1.33279145e-01 2.77809441e-01 -4.64649618e-01 -4.45872366e-01 -7.32402802e-01 -3.81332748e-02 1.15063608e-01 -5.08847773e-01 -3.65681380e-01 -5.56068346e-02 8.82683992e-02 -1.79146898e+00 7.81277299e-01 3.60842049e-01 1.77692258e+00 -3.38816464e-01 1.23071849e+00 -3.72395635e-01 1.29654694e+00 -4.11741495e-01 -4.65778589e-01 -2.68234193e-01 -4.10444915e-01 -9.59834993e-01 1.94497667e-02 -1.13029949e-01 -2.90188044e-02 -2.07989842e-01 5.85980177e-01 6.91410124e-01 6.14309311e-01 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29f294d6-d9c4-4ff7-823b-60fac4aede98	explorable-tone-mapping-operators	2010.10000	null	https://arxiv.org/abs/2010.10000v1	https://arxiv.org/pdf/2010.10000v1.pdf	Explorable Tone Mapping Operators	Tone-mapping plays an essential role in high dynamic range (HDR) imaging. It aims to preserve visual information of HDR images in a medium with a limited dynamic range. Although many works have been proposed to provide tone-mapped results from HDR images, most of them can only perform tone-mapping in a single pre-designed way. However, the subjectivity of tone-mapping quality varies from person to person, and the preference of tone-mapping style also differs from application to application. In this paper, a learning-based multimodal tone-mapping method is proposed, which not only achieves excellent visual quality but also explores the style diversity. Based on the framework of BicycleGAN, the proposed method can provide a variety of expert-level tone-mapped results by manipulating different latent codes. Finally, we show that the proposed method performs favorably against state-of-the-art tone-mapping algorithms both quantitatively and qualitatively.	['Soo-Chang Pei', 'Yu-Lin Chang', 'Chia-Ping Chen', 'Yu-Lun Liu', 'Hung-Jin Lin', 'Ren Wang', 'Chien-Chuan Su']	2020-10-20	null	null	null	null	['tone-mapping']	['computer-vision']	[ 5.40187657e-01 -4.19787586e-01 -2.28136390e-01 -9.68534425e-02 -8.15774977e-01 -4.03521717e-01 5.05845964e-01 -6.75753951e-01 5.22555523e-02 6.88924670e-01 3.08850259e-01 6.37669414e-02 -3.09968460e-02 -9.43827212e-01 -3.31917942e-01 -9.13446486e-01 3.05878878e-01 -1.01559445e-01 3.71134788e-01 -7.14774907e-01 2.28030443e-01 2.77832866e-01 -1.61154425e+00 2.82097250e-01 8.13611388e-01 7.35012174e-01 5.58782101e-01 5.66404164e-01 4.32366915e-02 9.48436499e-01 -5.06967366e-01 -3.32631469e-01 4.07484442e-01 -1.01424599e+00 -3.99581164e-01 1.08759396e-01 3.40212792e-01 -3.05537045e-01 -7.26479590e-01 1.35855770e+00 6.52837336e-01 -8.59068334e-02 4.80532169e-01 -7.58373499e-01 -1.24863803e+00 3.59910965e-01 -7.19415486e-01 -9.54103321e-02 4.10277843e-01 3.32658691e-03 6.72548473e-01 -7.70795703e-01 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f7cdbd16-61c9-468e-bd86-80ee64b74133	on-using-the-ua-speech-and-torgo-databases-to	2211.08833	null	https://arxiv.org/abs/2211.08833v1	https://arxiv.org/pdf/2211.08833v1.pdf	On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches	Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robust automatic speech recognition systems, they have also been used to validate a considerable number of automatic dysarthric speech classification approaches. Such approaches typically rely on the underlying assumption that recordings from control and dysarthric speakers are collected in the same noiseless environment using the same recording setup. In this paper, we show that this assumption is violated for the UA-Speech and TORGO databases. Using voice activity detection to extract speech and non-speech segments, we show that the majority of state-of-the-art dysarthria classification approaches achieve the same or a considerably better performance when using the non-speech segments of these databases than when using the speech segments. These results demonstrate that such approaches trained and validated on the UA-Speech and TORGO databases are potentially learning characteristics of the recording environment or setup rather than dysarthric speech characteristics. We hope that these results raise awareness in the research community about the importance of the quality of recordings when developing and evaluating automatic dysarthria classification approaches.	['Ina Kodrasi', 'Parvaneh Janbakhshi', 'Guilherme Schu']	2022-11-16	null	null	null	null	['activity-detection']	['computer-vision']	[-5.21898177e-03 -5.49181625e-02 2.04795107e-01 -3.27154011e-01 -9.95803773e-01 -5.13649940e-01 6.33024216e-01 -5.22301435e-01 -4.01173025e-01 5.14005840e-01 6.63803756e-01 -6.11738637e-02 -1.50586441e-01 -2.60259926e-01 -2.11504295e-01 -7.36490905e-01 1.62365392e-01 5.08654356e-01 2.19670877e-01 -4.31509078e-01 7.15093687e-02 5.33176005e-01 -2.07993293e+00 2.02051491e-01 4.72001851e-01 5.67201674e-01 3.33106309e-01 9.27564561e-01 -7.34720901e-02 6.77436531e-01 -1.13795412e+00 5.65405712e-02 5.23349978e-02 -6.43316507e-01 -7.73541927e-01 1.97584048e-01 6.11443043e-01 -4.51957822e-01 -7.14347541e-01 1.12498093e+00 9.96923029e-01 1.25477597e-01 3.38866979e-01 -5.49145460e-01 -5.04333854e-01 3.77970099e-01 4.95558709e-01 8.32266927e-01 3.87426943e-01 2.05664888e-01 7.96928644e-01 -5.72780967e-01 5.76867819e-01 1.10865486e+00 4.19164240e-01 7.36772299e-01 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-1.03120673e+00 3.83428216e-01 5.31485379e-01 -7.24175274e-01 -8.47894549e-01 -6.68430030e-02]	[14.51404857635498, 6.331265926361084]
ae88c69b-d531-4619-8b1a-c2b40b89cf27	wwfedcbmir-world-wide-federated-content-based	2305.03383	null	https://arxiv.org/abs/2305.03383v1	https://arxiv.org/pdf/2305.03383v1.pdf	WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval	The paper proposes a Federated Content-Based Medical Image Retrieval (FedCBMIR) platform that utilizes Federated Learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR assists pathologists in diagnosing breast cancer more rapidly by identifying similar medical images and relevant patches in prior cases compared to traditional cancer detection methods. However, CBMIR in histopathology necessitates a pool of Whole Slide Images (WSIs) to train to extract an optimal embedding vector that leverages search engine performance, which may not be available in all centers. The strict regulations surrounding data sharing in medical data sets also hinder research and model development, making it difficult to collect a rich data set. The proposed FedCBMIR distributes the model to collaborative centers for training without sharing the data set, resulting in shorter training times than local training. FedCBMIR was evaluated in two experiments with three scenarios on BreaKHis and Camelyon17 (CAM17). The study shows that the FedCBMIR method increases the F1-Score (F1S) of each client to 98%, 96%, 94%, and 97% in the BreaKHis experiment with a generalized model of four magnifications and does so in 6.30 hours less time than total local training. FedCBMIR also achieves 98% accuracy with CAM17 in 2.49 hours less training time than local training, demonstrating that our FedCBMIR is both fast and accurate for both pathologists and engineers. In addition, our FedCBMIR provides similar images with higher magnification for non-developed countries where participate in the worldwide FedCBMIR with developed countries to facilitate mitosis measuring in breast cancer diagnosis. We evaluate this scenario by scattering BreaKHis into four centers with different magnifications.	['Valery Naranjo', 'Zhiming Zhao', 'Javier Oliver Moll', 'Adrián Colomer', 'Yuandou Wang', 'Zahra Tabatabaei']	2023-05-05	null	null	null	null	['whole-slide-images', 'medical-image-retrieval', 'medical-image-retrieval']	['computer-vision', 'computer-vision', 'medical']	[-1.92649826e-01 -4.35262844e-02 -4.27403361e-01 4.73918319e-02 -1.49366868e+00 -5.82042575e-01 1.10703275e-01 4.20983166e-01 -5.98804593e-01 5.24417818e-01 1.08359277e-01 -6.34382427e-01 -2.59174675e-01 -7.19723582e-01 -5.11414766e-01 -1.12654173e+00 1.46672964e-01 3.57075363e-01 5.02728298e-02 7.75716901e-02 -3.09656523e-02 6.13243282e-01 -1.18508446e+00 6.98265314e-01 5.54741502e-01 7.19905734e-01 3.18858624e-01 1.22389770e+00 -2.13617831e-01 7.36100078e-01 -5.98539054e-01 -5.85975289e-01 4.66713846e-01 -5.94369806e-02 -8.93901110e-01 -4.11007345e-01 3.90984893e-01 -4.24472660e-01 -2.71361977e-01 7.91508079e-01 7.12105751e-01 -3.28892767e-01 6.06579304e-01 -1.01678872e+00 -4.41642642e-01 3.69105816e-01 -7.32235551e-01 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e6d13c01-2715-4921-9d28-b20bb524c7f3	sequence-aware-item-recommendations-for	2304.00578	null	https://arxiv.org/abs/2304.00578v1	https://arxiv.org/pdf/2304.00578v1.pdf	Sequence-aware item recommendations for multiply repeated user-item interactions	Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boosts sales and customer engagement. The main goal of these systems is to analyse past user behaviour to predict which items are of most interest to users. They are typically built with the use of matrix-completion techniques such as collaborative filtering or matrix factorisation. However, although these approaches have achieved tremendous success in numerous real-world applications, their effectiveness is still limited when users might interact multiple times with the same items, or when user preferences change over time. We were inspired by the approach that Natural Language Processing techniques take to compress, process, and analyse sequences of text. We designed a recommender system that induces the temporal dimension in the task of item recommendation and considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, besides increasing total sales by 5% and individual customer expenditure by over 50% in an A/B live test.	['Berthold Lausen', 'Henrik Nordmark', 'Maged Ali', 'Juan Pablo Equihua']	2023-04-02	null	null	null	null	['matrix-completion', 'marketing', 'collaborative-filtering']	['methodology', 'miscellaneous', 'miscellaneous']	[ 2.59476513e-01 -3.68076712e-01 -3.19601387e-01 -5.43674588e-01 -1.85097545e-01 -5.26000202e-01 5.77934921e-01 6.80446148e-01 -7.12040246e-01 3.20011199e-01 4.14614588e-01 -4.33114409e-01 -4.91789430e-01 -6.97291493e-01 -3.09434950e-01 -3.78786445e-01 -3.77124488e-01 5.36910832e-01 8.17507133e-03 -6.05418921e-01 5.38459063e-01 3.58288795e-01 -1.80316865e+00 7.24449277e-01 5.93106329e-01 8.94739449e-01 2.21061125e-01 7.11140335e-01 -3.12456518e-01 6.33623183e-01 -2.64946550e-01 -5.48013031e-01 2.75042683e-01 -2.80801535e-01 -5.96046567e-01 3.53057861e-01 -2.11677134e-01 -1.12725116e-01 1.49265416e-02 6.62674546e-01 2.34377533e-01 6.95668280e-01 4.62313384e-01 -6.71751201e-01 -3.94359767e-01 6.98667943e-01 -3.69475514e-01 3.96195292e-01 8.20539296e-01 -4.19469744e-01 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31dcf81a-0395-4aab-91bd-3bf0242849b2	neural-sign-language-translation-based-on	1811.11436	null	https://arxiv.org/abs/1811.11436v2	https://arxiv.org/pdf/1811.11436v2.pdf	Neural Sign Language Translation based on Human Keypoint Estimation	We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive amount of dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI (short for Korea Electronics Technology Institute) sign language dataset which consists of 14,672 videos of high resolution and quality. Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting line for further research on the Korean sign language translation. Using the KETI sign language dataset, we develop a neural network model for translating sign videos into natural language sentences by utilizing the human keypoints extracted from a face, hands, and body parts. The obtained human keypoint vector is normalized by the mean and standard deviation of the keypoints and used as input to our translation model based on the sequence-to-sequence architecture. As a result, we show that our approach is robust even when the size of the training data is not sufficient. Our translation model achieves 93.28% (55.28%, respectively) translation accuracy on the validation set (test set, respectively) for 105 sentences that can be used in emergency situations. We compare several types of our neural sign translation models based on different attention mechanisms in terms of classical metrics for measuring the translation performance.	['Sang-Ki Ko', 'Choongsang Cho', 'Hyedong Jung', 'Chang Jo Kim']	2018-11-28	null	null	null	null	['sign-language-translation']	['computer-vision']	[ 1.66990891e-01 -5.51288545e-01 -3.15941513e-01 -3.28399599e-01 -7.55663216e-01 -3.61425728e-01 5.03263474e-01 -9.79738355e-01 -7.66760707e-01 6.39117718e-01 4.75090802e-01 -1.65112510e-01 2.20096424e-01 -5.49785972e-01 -7.29244828e-01 -7.58977830e-01 4.39945728e-01 3.87313962e-01 -4.25412841e-02 -2.40658179e-01 1.62924957e-02 5.45810521e-01 -1.47834587e+00 2.09060639e-01 8.34788680e-01 9.74086344e-01 1.03206951e-02 7.03613341e-01 1.40323848e-01 6.40482485e-01 -6.24627113e-01 -4.26762044e-01 5.15621781e-01 -9.12406266e-01 -5.69503307e-01 -1.50526762e-01 9.31272924e-01 -7.92350829e-01 -5.10297716e-01 1.07227695e+00 9.22791719e-01 -2.76112892e-02 6.59195602e-01 -1.44068146e+00 -8.03131580e-01 3.20374906e-01 -2.57744402e-01 -1.98622450e-01 1.36384726e-01 4.16696429e-01 8.30517888e-01 -9.70145345e-01 1.09293020e+00 1.01938236e+00 5.01795530e-01 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6809937e-4fb9-49b9-a85b-79b7cb25ec6c	energy-disaggregation-using-variational	2103.12177	null	https://arxiv.org/abs/2103.12177v2	https://arxiv.org/pdf/2103.12177v2.pdf	Energy Disaggregation using Variational Autoencoders	Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consumption of individual appliances can be estimated from the aggregate measurement. Recent disaggregation algorithms have significantly improved the performance of NILM systems. However, the generalization capability of these methods to different houses as well as the disaggregation of multi-state appliances are still major challenges. In this paper we address these issues and propose an energy disaggregation approach based on the variational autoencoders framework. The probabilistic encoder makes this approach an efficient model for encoding information relevant to the reconstruction of the target appliance consumption. In particular, the proposed model accurately generates more complex load profiles, thus improving the power signal reconstruction of multi-state appliances. Moreover, its regularized latent space improves the generalization capabilities of the model across different houses. The proposed model is compared to state-of-the-art NILM approaches on the UK-DALE and REFIT datasets, and yields competitive results. The mean absolute error reduces by 18% on average across all appliances compared to the state-of-the-art. The F1-Score increases by more than 11%, showing improvements for the detection of the target appliance in the aggregate measurement.	['Ghyslain Gagnon', 'Mohamed Cheriet', 'Marc-André Carbonneau', 'Antoine Langevin']	2021-03-22	null	null	null	null	['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring']	['knowledge-base', 'miscellaneous', 'time-series']	[-6.99132308e-02 -1.78744644e-01 5.02270535e-02 -2.83602893e-01 -1.04117894e+00 -2.91370392e-01 4.67899561e-01 -1.74706317e-02 4.11780402e-02 5.99156618e-01 2.47027084e-01 3.02278936e-01 -1.29641324e-01 -9.76622581e-01 -5.93925953e-01 -1.24503732e+00 2.60426432e-01 4.05343711e-01 -3.40007126e-01 2.02055186e-01 -5.81931472e-01 2.59054482e-01 -1.69575679e+00 3.10073905e-02 8.59280825e-01 1.28162730e+00 3.38319272e-01 3.62306714e-01 4.00443316e-01 7.92749226e-01 -8.42830181e-01 -1.28449246e-01 -5.86797409e-02 -2.15398923e-01 -3.95269215e-01 1.00244775e-01 1.09442249e-02 -6.71900213e-01 -2.67070442e-01 1.22020221e+00 6.23162806e-01 1.24619253e-01 6.77363276e-01 -1.32198131e+00 -4.70935553e-01 9.53143775e-01 -3.23414385e-01 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9acae079-eae2-4996-8e91-a9184d12b967	multi-modal-transformer-path-prediction-for	2208.07256	null	https://arxiv.org/abs/2208.07256v1	https://arxiv.org/pdf/2208.07256v1.pdf	Multi-modal Transformer Path Prediction for Autonomous Vehicle	Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of data collected from sensors equipped on the self-driving vehicles, we propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer architecture is adopted in our model. To better utilize the lane information, the lanes which are in opposite direction to target agent are not likely to be taken by the target agent and are consequently filtered out. In addition, consecutive lane chunks are combined to ensure the lane input to be long enough for path prediction. An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.	['Wei-Shinn Ku', 'Kazuya Sakai', 'Min-Te Sun', 'Jie Zhang', 'Chia Hong Tseng']	2022-08-15	null	null	null	null	['trajectory-forecasting']	['computer-vision']	[-2.71226913e-01 3.00087556e-02 -5.18771470e-01 -5.52464068e-01 2.26405989e-02 -2.79147536e-01 5.72621346e-01 2.32637823e-02 -2.32205242e-01 6.91476762e-01 2.63063312e-01 -7.94288278e-01 -3.16285431e-01 -1.24652767e+00 -4.83472198e-01 -6.58085883e-01 1.56624373e-02 3.07287097e-01 9.20113862e-01 -5.71831286e-01 3.68075132e-01 5.81732869e-01 -1.61104953e+00 5.36557138e-02 1.00348496e+00 7.90839136e-01 3.13650817e-01 2.06839770e-01 -3.10745478e-01 7.35241532e-01 1.64555371e-01 -1.87782004e-01 3.35881144e-01 1.58998650e-02 -3.89573097e-01 -1.78453833e-01 -3.14314097e-01 -5.74234903e-01 -6.99327290e-01 8.03053141e-01 -1.08019747e-01 3.10910553e-01 5.79913855e-01 -1.68877637e+00 2.03681827e-01 5.80271423e-01 -1.54529318e-01 4.19508427e-01 1.36418641e-02 3.63847733e-01 6.38093710e-01 -5.74669659e-01 5.94192266e-01 1.24273050e+00 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19004410-3e16-49ec-bbae-452ffcf5bfbd	learning-based-defect-recognitions-for	2302.06093	null	https://arxiv.org/abs/2302.06093v1	https://arxiv.org/pdf/2302.06093v1.pdf	Learning-Based Defect Recognitions for Autonomous UAV Inspections	Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmark dataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed and will be established for the crack inspection tasks of various concrete structures. All our self-established datasets and codes are open-sourced at: https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection	['Kangcheng Liu']	2023-02-13	null	null	null	null	['crack-segmentation']	['computer-vision']	[-2.01596677e-01 1.82277486e-02 2.46446967e-01 -1.29904121e-01 -3.53732854e-01 -1.55499633e-02 -3.66541356e-01 -1.49628625e-03 -2.85843551e-01 2.16709971e-01 -4.47032005e-01 -4.36643630e-01 1.13208428e-01 -1.29375172e+00 -5.25575936e-01 -6.94762588e-01 2.29445449e-03 -8.42151493e-02 6.50853097e-01 -5.29744864e-01 5.89906752e-01 4.42978829e-01 -1.54601896e+00 4.92096022e-02 7.74327159e-01 1.09444535e+00 2.59609103e-01 7.79353917e-01 6.81204855e-01 6.82219803e-01 -5.10372281e-01 1.49372131e-01 1.08740941e-01 -1.65752526e-02 -1.15795934e+00 2.37041563e-01 2.78649658e-01 -6.70635223e-01 -7.30705857e-01 1.12586296e+00 5.22899449e-01 -2.08217353e-01 5.01557469e-01 -8.32411051e-01 -7.26677358e-01 5.47476888e-01 -5.69685459e-01 2.23976865e-01 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7fba8e13-3d55-4202-b624-b7172ff0bd51	dory-automatic-end-to-end-deployment-of-real	2008.07127	null	https://arxiv.org/abs/2008.07127v3	https://arxiv.org/pdf/2008.07127v3.pdf	DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs	The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area overheads and increase energy efficiency -- requiring explicit DMA-based memory transfers between different levels of the memory hierarchy. Mapping modern DNNs on these systems requires aggressive topology-dependent tiling and double-buffering. In this work, we propose DORY (Deployment Oriented to memoRY) - an automatic tool to deploy DNNs on low cost MCUs with typically less than 1MB of on-chip SRAM memory. DORY abstracts tiling as a Constraint Programming (CP) problem: it maximizes L1 memory utilization under the topological constraints imposed by each DNN layer. Then, it generates ANSI C code to orchestrate off- and on-chip transfers and computation phases. Furthermore, to maximize speed, DORY augments the CP formulation with heuristics promoting performance-effective tile sizes. As a case study for DORY, we target GreenWaves Technologies GAP8, one of the most advanced parallel ultra-low power MCU-class devices on the market. On this device, DORY achieves up to 2.5x better MAC/cycle than the GreenWaves proprietary software solution and 18.1x better than the state-of-the-art result on an STM32-F746 MCU on single layers. Using our tool, GAP-8 can perform end-to-end inference of a 1.0-MobileNet-128 network consuming just 63 pJ/MAC on average @ 4.3 fps - 15.4x better than an STM32-F746. We release all our developments - the DORY framework, the optimized backend kernels, and the related heuristics - as open-source software.	['Francesco Conti', 'Davide Rossi', 'Nazareno Bruschi', 'Alessio Burrello', 'Angelo Garofalo', 'Giuseppe Tagliavini']	2020-08-17	null	null	null	null	['tiling-deployment']	['computer-code']	[-2.12549001e-01 1.16138935e-01 -5.46776533e-01 -1.11257486e-01 -1.85562804e-01 -1.99519128e-01 -4.49905880e-02 -3.03497523e-01 -6.48141742e-01 7.75517166e-01 -4.27325904e-01 -9.31059241e-01 -1.27938807e-01 -9.84427273e-01 -8.96547258e-01 -6.02701068e-01 -7.89538771e-02 4.71128911e-01 5.11181712e-01 1.58300325e-01 -2.10758865e-01 4.60080028e-01 -1.66863120e+00 3.97229269e-02 3.97911042e-01 1.53519154e+00 4.47658330e-01 5.46766639e-01 -1.03189707e-01 6.05951905e-01 -6.02991521e-01 -4.35299166e-02 2.70118624e-01 3.97172160e-02 -3.20406914e-01 -5.95172465e-01 6.86159730e-01 -5.34698009e-01 -4.14422810e-01 8.21637511e-01 8.30344439e-01 -4.75810379e-01 1.55672789e-01 -1.44627035e+00 4.61876631e-01 1.01994145e+00 -4.10701275e-01 2.90525168e-01 -7.06742942e-01 1.54159933e-01 5.86485982e-01 -2.90665090e-01 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5b758b0e-9de1-44cf-8560-6bae260d4296	pedestrian-crossing-action-recognition-and	2306.01075	null	https://arxiv.org/abs/2306.01075v1	https://arxiv.org/pdf/2306.01075v1.pdf	Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints	Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossing pedestrians and predicting their future trajectories. To achieve these goals, we not only need the context information of road geometry and other traffic participants but also need fine-grained information of the human pose, motion and activity, which can be inferred from human keypoints. In this paper, we propose a novel multi-task learning framework for pedestrian crossing action recognition and trajectory prediction, which utilizes 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. Moreover, we propose to apply two auxiliary tasks and contrastive learning to enable auxiliary supervisions to improve the learned keypoints representation, which further enhances the performance of major tasks. We validate our approach on a large-scale in-house dataset, as well as a public benchmark dataset, and show that our approach achieves state-of-the-art performance on a wide range of evaluation metrics. The effectiveness of each model component is validated in a detailed ablation study.	['CongCong Li', 'Eugene Ie', 'Weilong Yang', 'Khaled S. Refaat', 'Jeonhyung Kang', 'Junhua Mao', 'Tian Lan', 'Zhishuai Zhang', 'Jonathan Stroud', 'Feiyu Chen', 'Xinwei Shi', 'Jiachen Li']	2023-06-01	null	null	null	null	['trajectory-prediction', 'autonomous-vehicles', 'action-recognition-in-videos']	['computer-vision', 'computer-vision', 'computer-vision']	[-6.90090433e-02 -4.77481395e-01 -2.50423223e-01 -5.74645579e-01 -8.57043505e-01 -4.40042228e-01 6.83929384e-01 2.08234012e-01 -6.88286066e-01 5.72140932e-01 3.75058383e-01 -1.32498875e-01 9.81291533e-02 -8.36276472e-01 -7.85736620e-01 -4.66251910e-01 -1.42542452e-01 3.25151592e-01 8.60093117e-01 -3.14248741e-01 -6.79260641e-02 6.57264590e-01 -1.78588128e+00 1.98143125e-01 8.01512480e-01 9.10742939e-01 -4.84184176e-02 6.96053267e-01 4.45929646e-01 6.59400702e-01 -1.19100079e-01 -5.75716615e-01 1.63009241e-01 2.22100332e-01 -4.17378038e-01 -6.66986108e-02 6.01943314e-01 -4.80994612e-01 -8.43356967e-01 6.07540667e-01 4.27823514e-01 3.85540187e-01 5.04417360e-01 -1.58826649e+00 2.19178006e-01 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5e1f3841-e509-43d4-b58b-97f8db42a657	prompt-based-time-series-forecasting-a-new	2210.08964	null	https://arxiv.org/abs/2210.08964v4	https://arxiv.org/pdf/2210.08964v4.pdf	PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting	This paper presents a new perspective on time series forecasting. In existing time series forecasting methods, the models take a sequence of numerical values as input and yield numerical values as output. The existing SOTA models are largely based on the Transformer architecture, modified with multiple encoding mechanisms to incorporate the context and semantics around the historical data. Inspired by the successes of pre-trained language foundation models, we pose a question about whether these models can also be adapted to solve time-series forecasting. Thus, we propose a new forecasting paradigm: prompt-based time series forecasting (PromptCast). In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes. To support and facilitate the research of this task, we also present a large-scale dataset (PISA) that includes three real-world forecasting scenarios. We evaluate different SOTA numerical-based forecasting methods and language generation models. The benchmark results with various forecasting settings demonstrate the proposed PromptCast with language generation models is a promising research direction. Additionally, in comparison to conventional numerical-based forecasting, PromptCast shows a much better generalization ability under the zero-shot setting.	['Flora D. Salim', 'Hao Xue']	2022-09-20	null	null	null	null	['weather-forecasting']	['miscellaneous']	[ 1.93556339e-01 -3.00947666e-01 -4.11075912e-02 -7.40217030e-01 -4.80686039e-01 -6.98101938e-01 1.15972745e+00 -1.33414741e-03 1.48441583e-01 6.96675599e-01 5.62543631e-01 -7.69306839e-01 -5.84984161e-02 -1.07510817e+00 -3.49762589e-01 -6.28878117e-01 -1.63671076e-01 1.95720971e-01 -3.04082543e-01 -8.21233809e-01 4.77677077e-01 3.55634272e-01 -1.78847516e+00 5.64100921e-01 8.47173870e-01 1.20428205e+00 1.74639419e-01 6.09846056e-01 -8.76928806e-01 1.08785999e+00 -8.30753207e-01 -3.19481283e-01 1.66384563e-01 -4.27711397e-01 -5.29160321e-01 -2.15863973e-01 -1.12600893e-01 -2.24510849e-01 -1.42253056e-01 5.31289697e-01 3.74453306e-01 5.36905348e-01 5.62074363e-01 -1.54984701e+00 -9.04569149e-01 8.71158242e-01 -4.85869031e-03 2.58286893e-01 6.62810266e-01 1.58381481e-02 6.78145409e-01 -8.41977358e-01 3.63809973e-01 1.30468929e+00 7.66434848e-01 3.69889289e-01 -8.34533632e-01 -5.19645572e-01 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1168ee85-7198-47e0-b313-dc41bf5e9d3e	sod-mtgan-small-object-detection-via-multi	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.pdf	SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network	Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects on large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfaction. The reason is that small objects lack sufficient detailed appearance information, which can distinguish them from the background or similar objects. To deal with small object detection problem, we propose an end-to-end multi-task generative adversarial network (MTGAN). In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi task network, which describes each super-resolution image patch with a real/fake score, object category scores, and bounding box regression off sets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training. Extensive experiments on the challenging COCO dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolution image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods.	['Yancheng Bai', 'Yongqiang Zhang', 'Mingli Ding', 'Bernard Ghanem']	2018-09-01	null	null	null	eccv-2018-9	['small-object-detection']	['computer-vision']	[ 4.17114019e-01 -5.20278104e-02 2.28840739e-01 2.36751549e-02 -8.16242158e-01 -4.10146743e-01 3.47518325e-01 -5.46379387e-01 -2.93222100e-01 7.23082542e-01 -1.04960315e-01 2.84599215e-01 3.51241231e-01 -8.82359982e-01 -9.68715787e-01 -9.44512188e-01 1.12138525e-01 3.16016793e-01 7.15003848e-01 -1.34536222e-01 -2.81551178e-03 2.90040612e-01 -1.30239379e+00 4.65513855e-01 1.01397455e+00 1.06212056e+00 6.53268695e-01 6.43487811e-01 3.14463735e-01 9.57571328e-01 -8.58059406e-01 -4.82876569e-01 4.27254379e-01 -4.47095275e-01 -2.87504613e-01 2.03289181e-01 7.69034386e-01 -7.17493534e-01 -4.43377346e-01 1.59621203e+00 5.00136018e-01 -1.90582678e-01 6.49623036e-01 -1.04648721e+00 -1.05860257e+00 5.75768352e-01 -1.01641798e+00 4.61086869e-01 8.87860656e-02 3.86411548e-01 5.72472632e-01 -1.03100574e+00 4.49775606e-01 1.55005276e+00 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671a1de4-14eb-417c-a614-79f1ef029fff	mathqa-towards-interpretable-math-word	1905.13319	null	https://arxiv.org/abs/1905.13319v1	https://arxiv.org/pdf/1905.13319v1.pdf	MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms	We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small in scale or did not offer precise operational annotations over diverse problem types. We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models. Using this representation language, our new dataset, MathQA, significantly enhances the AQuA dataset with fully-specified operational programs. We additionally introduce a neural sequence-to-program model enhanced with automatic problem categorization. Our experiments show improvements over competitive baselines in our MathQA as well as the AQuA dataset. The results are still significantly lower than human performance indicating that the dataset poses new challenges for future research. Our dataset is available at: https://math-qa.github.io/math-QA/	['Hannaneh Hajishirzi', 'Rik Koncel-Kedziorski', 'Yejin Choi', 'Saadia Gabriel', 'Peter Lin', 'Aida Amini']	2019-05-30	mathqa-towards-interpretable-math-word-1	https://aclanthology.org/N19-1245	https://aclanthology.org/N19-1245.pdf	naacl-2019-6	['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'reasoning', 'time-series']	[ 1.19245671e-01 2.61126250e-01 -2.24715292e-01 -7.93730736e-01 -1.00463724e+00 -9.95748460e-01 9.81654748e-02 3.93211007e-01 -1.75670952e-01 3.51495296e-01 2.61426419e-01 -6.85476005e-01 -3.55666243e-02 -1.06726301e+00 -1.11407208e+00 1.28339350e-01 2.13570431e-01 8.70815277e-01 -1.15008041e-01 -3.60958666e-01 1.97836518e-01 -1.04084276e-01 -1.26425159e+00 7.49434888e-01 1.08628368e+00 7.87709653e-01 8.64188299e-02 8.30807984e-01 -3.97150576e-01 1.25816250e+00 -6.56143427e-01 -5.66879153e-01 3.73813987e-01 -1.14160918e-01 -1.39031529e+00 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42760db1-cddc-4231-98bb-a12f9eb17789	multi-view-bangla-sign-language-mv-bsl	2302.11559	null	https://arxiv.org/abs/2302.11559v2	https://arxiv.org/pdf/2302.11559v2.pdf	Word level Bangla Sign Language Dataset for Continuous BSL Recognition	An robust sign language recognition system can greatly alleviate communication barriers, particularly for people who struggle with verbal communication. This is crucial for human growth and progress as it enables the expression of thoughts, feelings, and ideas. However, sign recognition is a complex task that faces numerous challenges such as same gesture patterns for multiple signs, lighting, clothing, carrying conditions, and the presence of large poses, as well as illumination discrepancies across different views. Additionally, the absence of an extensive Bangla sign language video dataset makes it even more challenging to operate recognition systems, particularly when utilizing deep learning techniques. In order to address this issue, firstly, we created a large-scale dataset called the MVBSL-W50, which comprises 50 isolated words across 13 categories. Secondly, we developed an attention-based Bi-GRU model that captures the temporal dynamics of pose information for individuals communicating through sign language. The proposed model utilizes human pose information, which has shown to be successful in analyzing sign language patterns. By focusing solely on movement information and disregarding body appearance and environmental factors, the model is simplified and can achieve a speedier performance. The accuracy of the model is reported to be 85.64%.	['Ibrahim Elwarfalli', 'Sohaib Abdullah', 'Md Mahedi Hasan', 'Md Nur Hossain', 'A. J. M. Akhtarujjaman Joha', 'Md Shamimul Islam']	2023-02-22	null	null	null	null	['sign-language-recognition']	['computer-vision']	[-1.60626695e-01 -5.82906783e-01 5.15642986e-02 -2.97380865e-01 -1.66746974e-01 -2.27728069e-01 4.26836491e-01 -7.62464046e-01 -4.28642482e-01 4.62602913e-01 5.52062511e-01 1.10395432e-01 -5.89965796e-03 -2.54583806e-01 -1.87879935e-01 -7.51506686e-01 1.64279819e-01 -3.96337286e-02 4.58356217e-02 -2.61784196e-01 2.27922544e-01 6.91889167e-01 -1.68849826e+00 1.49828810e-02 5.41871548e-01 7.35985696e-01 8.56178999e-02 5.32546937e-01 -1.80667281e-01 6.32792294e-01 -7.91467726e-01 -3.99573892e-01 5.72751984e-02 -5.00145137e-01 -2.56108671e-01 2.86394268e-01 6.51024997e-01 -6.99725568e-01 -4.84891713e-01 7.09631503e-01 8.97879601e-01 1.43938109e-01 4.86329794e-01 -9.39182937e-01 -7.13368297e-01 8.36884661e-04 -5.18041670e-01 -2.16895550e-01 4.78702754e-01 2.91167587e-01 5.84153891e-01 -7.30987728e-01 5.94676614e-01 1.20357895e+00 4.81546193e-01 8.31280529e-01 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08dc308f-1046-46fa-9d33-bc6e3de38838	deep-template-matching-for-pedestrian	2011.06798	null	https://arxiv.org/abs/2011.06798v1	https://arxiv.org/pdf/2011.06798v1.pdf	Deep Template Matching for Pedestrian Attribute Recognition with the Auxiliary Supervision of Attribute-wise Keypoints	Pedestrian Attribute Recognition (PAR) has aroused extensive attention due to its important role in video surveillance scenarios. In most cases, the existence of a particular attribute is strongly related to a partial region. Recent works design complicated modules, e.g., attention mechanism and proposal of body parts to localize the attribute corresponding region. These works further prove that localization of attribute specific regions precisely will help in improving performance. However, these part-information-based methods are still not accurate as well as increasing model complexity which makes it hard to deploy on realistic applications. In this paper, we propose a Deep Template Matching based method to capture body parts features with less computation. Further, we also proposed an auxiliary supervision method that use human pose keypoints to guide the learning toward discriminative local cues. Extensive experiments show that the proposed method outperforms and has lower computational complexity, compared with the state-of-the-art approaches on large-scale pedestrian attribute datasets, including PETA, PA-100K, RAP, and RAPv2 zs.	['Jianmin Li', 'Pengyuan Ren', 'Jiajun Zhang']	2020-11-13	null	null	null	null	['template-matching', 'pedestrian-attribute-recognition']	['computer-vision', 'computer-vision']	[-2.07159638e-01 -2.18434244e-01 -1.30792439e-01 -6.88623667e-01 -4.48768675e-01 -1.89323872e-01 5.38292408e-01 1.30924091e-01 -3.26237112e-01 7.57874370e-01 2.25905493e-01 2.40127012e-01 1.61075532e-01 -8.68136764e-01 -8.68593514e-01 -7.70242453e-01 2.10565835e-01 5.39629638e-01 6.23362601e-01 -1.56184703e-01 7.85741583e-02 3.39484990e-01 -1.63547206e+00 8.18259567e-02 7.47675657e-01 1.22530711e+00 1.37640566e-01 1.36393175e-01 9.71108153e-02 3.73171896e-01 -4.07929033e-01 -8.90433252e-01 3.18871588e-01 -1.76084355e-01 -4.93494809e-01 2.50935584e-01 7.66479194e-01 -4.55404490e-01 -5.79281747e-01 9.51890409e-01 6.59366310e-01 1.75373018e-01 6.20462179e-01 -1.49310219e+00 -3.49350810e-01 1.86719552e-01 -8.07133675e-01 9.26703140e-02 5.08982778e-01 1.42812520e-01 6.87065899e-01 -7.93671370e-01 2.86294639e-01 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051f3e2c-3491-405a-93af-489c011d509f	accurate-method-of-temporal-shift-estimation	null	null	https://ieeexplore.ieee.org/abstract/document/8478431	https://www.researchgate.net/profile/Aleksandr-Ploshkin-2/publication/328082698_ACCURATE_METHOD_OF_TEMPORAL-SHIFT_ESTIMATION_FOR_3D_VIDEO/links/5bc20f40458515a7a9e71cf2/ACCURATE-METHOD-OF-TEMPORAL-SHIFT-ESTIMATION-FOR-3D-VIDEO.pdf	ACCURATE METHOD OF TEMPORAL-SHIFT ESTIMATION FOR 3D VIDEO	Video synchronization is a fundamental computer-vision task that is necessary for a wide range of applications. A 3D video involves two streams, which show the scene from different angles concurrently, but many cases exhibit desynchronization between them. This paper investigates the problem of synchronizing the left and right stereoscopic views. We assume the temporal shift (time difference) and geometric distortion between the two streams are constant throughout each scene. We propose a temporal-shift estimation method with subframe accuracy based on a block-matching algorithm.	['Dmitriy Vatolin', 'Aleksandr Ploshkin']	2018-06-03	null	null	null	3dtv-conference-the-true-vision-capture	['video-synchronization', 'video-alignment']	['computer-vision', 'computer-vision']	[ 3.90754938e-01 -6.41651034e-01 -1.56132663e-02 -1.55268550e-01 -6.16776310e-02 -6.68963015e-01 7.10670710e-01 -4.33621377e-01 -2.67221302e-01 3.92369092e-01 -7.15443864e-02 -1.20760456e-01 3.30359012e-01 -2.87215650e-01 -5.90068877e-01 -6.91145420e-01 -4.16751504e-02 -2.49806512e-03 9.16169524e-01 -9.86808315e-02 4.75165784e-01 4.80406284e-01 -1.50744724e+00 1.58047184e-01 1.46903574e-01 8.33882689e-01 5.33947408e-01 9.75396872e-01 3.38040739e-01 5.70761263e-01 -3.03978026e-01 -1.51887804e-01 6.35889292e-01 -5.38209736e-01 -4.93278980e-01 3.94574165e-01 6.69847131e-01 -5.51762760e-01 -8.28337908e-01 1.13856840e+00 2.70480335e-01 5.43996766e-02 8.74858424e-02 -1.58484685e+00 2.09092021e-01 -2.20265910e-01 -1.04356861e+00 6.97473526e-01 9.28053439e-01 -1.29762694e-01 2.62212723e-01 -4.74810660e-01 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1.08527720e+00 -1.01636922e+00 9.32247341e-02 -2.58056581e-01 -8.63425970e-01 4.60250825e-01 7.28739142e-01 -4.28011179e-01 -4.45951730e-01 7.75792181e-01]	[9.135103225708008, -2.3803653717041016]
4f1f926f-7d7c-45d4-9f9f-5fdadb6cd60c	minimal-solutions-for-panoramic-stitching	2012.00465	null	https://arxiv.org/abs/2012.00465v1	https://arxiv.org/pdf/2012.00465v1.pdf	Minimal Solutions for Panoramic Stitching Given Gravity Prior	When capturing panoramas, people tend to align their cameras with the vertical axis, i.e., the direction of gravity. Moreover, modern devices, such as smartphones and tablets, are equipped with an IMU (Inertial Measurement Unit) that can measure the gravity vector accurately. Using this prior, the y-axes of the cameras can be aligned or assumed to be already aligned, reducing their relative orientation to 1-DOF (degree of freedom). Exploiting this assumption, we propose new minimal solutions to panoramic image stitching of images taken by cameras with coinciding optical centers, i.e., undergoing pure rotation. We consider four practical camera configurations, assuming unknown fixed or varying focal length with or without radial distortion. The solvers are tested both on synthetic scenes and on more than 500k real image pairs from the Sun360 dataset and from scenes captured by us using two smartphones equipped with IMUs. It is shown, that they outperform the state-of-the-art both in terms of accuracy and processing time.	['Zuzana Kukelova', 'Daniel Barath', 'Yaqing Ding']	2020-12-01	null	http://openaccess.thecvf.com//content/ICCV2021/html/Ding_Minimal_Solutions_for_Panoramic_Stitching_Given_Gravity_Prior_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Ding_Minimal_Solutions_for_Panoramic_Stitching_Given_Gravity_Prior_ICCV_2021_paper.pdf	iccv-2021-1	['image-stitching']	['computer-vision']	[ 3.92518759e-01 -1.80210084e-01 -5.32503501e-02 6.95060790e-02 -7.57499039e-02 -8.16897869e-01 6.82148457e-01 -5.66279590e-01 -5.08905470e-01 3.45703334e-01 -2.62412410e-02 -1.32011753e-02 2.56519198e-01 -4.43521976e-01 -8.82220030e-01 -5.71127117e-01 4.65813577e-01 4.20427114e-01 -1.22933030e-01 1.08300589e-01 3.77026439e-01 2.99671292e-01 -1.47049236e+00 -5.81658125e-01 5.99758387e-01 7.40012169e-01 6.85825646e-02 9.84646976e-01 7.40637064e-01 3.71901184e-01 -5.11601746e-01 -4.85140860e-01 7.08553970e-01 8.71559605e-02 -2.62547135e-01 9.17755783e-01 1.06578124e+00 -6.84649885e-01 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24367a40-1eff-4812-aa48-a03cfa4adffd	interactiveie-towards-assessing-the-strength	2305.14659	null	https://arxiv.org/abs/2305.14659v1	https://arxiv.org/pdf/2305.14659v1.pdf	InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction	Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; however, real-world IE do not have pre-defined schemas and it is a figure-out-as you go phenomena. To quickly bootstrap templates in a real-world setting, we need to induce template slots from documents with zero or minimal supervision. Since the purpose of question answering intersect with the goal of information extraction, we use automatic question generation to induce template slots from the documents and investigate how a tiny amount of a proxy human-supervision on-the-fly (termed as InteractiveIE) can further boost the performance. Extensive experiments on biomedical and legal documents, where obtaining training data is expensive, reveal encouraging trends of performance improvement using InteractiveIE over AI-only baseline.	['Jordan Boyd-Graber', 'Benjamin Van Durme', 'Andrew Blair-Stanek', 'Francis Ferraro', 'Aparna Garimella', 'Anandhavelu N', 'Michelle Yuan', 'Ishani Mondal']	2023-05-24	null	null	null	null	['question-generation']	['natural-language-processing']	[ 6.69187367e-01 8.58274817e-01 -3.26019108e-01 -5.00285804e-01 -1.35059130e+00 -7.02182531e-01 8.47426474e-01 7.38223940e-02 -4.29515094e-01 9.65582490e-01 3.41329634e-01 -5.95299482e-01 -1.17386103e-01 -7.98944235e-01 -9.64863718e-01 -1.64763872e-02 4.75969791e-01 1.03538322e+00 4.35181946e-01 -4.91814584e-01 -1.16729438e-02 -1.88738644e-01 -1.27843380e+00 7.64522254e-01 1.08643842e+00 6.49885476e-01 1.49623742e-02 4.25460756e-01 -4.96547788e-01 6.83190286e-01 -7.07293391e-01 -5.90075016e-01 3.97407711e-01 -4.53117013e-01 -1.19448471e+00 3.15249175e-01 3.64602059e-01 -1.21326759e-01 3.88941146e-04 7.11120069e-01 1.02426417e-01 -2.19450518e-01 6.94591761e-01 -1.04994953e+00 -2.42131755e-01 6.67045176e-01 -7.72442967e-02 8.30911323e-02 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5.31122327e-01 1.40412557e+00 4.58162069e-01 1.04127012e-01 2.05396861e-01 2.10848004e-02 -4.59505379e-01 -9.57231820e-01 -9.28825513e-03 5.95626175e-01 8.27616602e-02 -3.90483499e-01 -1.45734906e-01 3.10971469e-01]	[10.136137962341309, 8.528116226196289]
65a9b470-6dc5-40eb-824c-0e26c262d5ea	qmrnet-quality-metric-regression-for-eo-image	2210.06618	null	https://arxiv.org/abs/2210.06618v2	https://arxiv.org/pdf/2210.06618v2.pdf	QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution	Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both Full-Reference and No-Reference Image Quality Assessment (IQA) metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict quality (as a No-Reference metric) by training on any property of the image (i.e. its resolution, its distortions...) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW which has been developed for evaluating image quality, detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features like blur, sharpness, snr, rer and ground sampling distance (GSD) and obtain validation medRs below 1.0 (out of N=50) and recall rates above 95\%. Overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as Loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.	['Katalin Takáts', 'Javier Marín', 'David Vilaseca', 'Clara Garcia-Moll', 'Laura Riordan-Chen', 'Eva Mohedano', 'Pau Gallés', 'David Berga']	2022-10-12	null	null	null	null	['no-reference-image-quality-assessment']	['computer-vision']	[ 2.96281189e-01 -3.17562968e-01 3.48563939e-01 -3.96688551e-01 -6.31689012e-01 -4.14354980e-01 5.60505331e-01 -1.36948943e-01 -3.99056971e-01 8.46698344e-01 1.61978081e-01 -6.42105117e-02 -7.30397403e-01 -9.58717942e-01 -2.20692098e-01 -6.47997618e-01 -6.39631212e-01 1.79678112e-01 3.85497361e-01 -4.76368725e-01 3.85336548e-01 1.00275934e+00 -2.02532220e+00 2.25931793e-01 1.02238607e+00 1.12898421e+00 5.78018248e-01 1.23520851e+00 3.98916245e-01 8.90816271e-01 -5.78554928e-01 2.05528423e-01 5.13990641e-01 -2.45895520e-01 -8.23830247e-01 -3.26872081e-01 6.66614234e-01 -3.43585193e-01 -2.63070583e-01 1.02317619e+00 8.55584502e-01 1.27724826e-01 5.76378047e-01 -7.25495696e-01 -5.19112945e-01 6.80564642e-02 -4.19122487e-01 9.32929814e-01 2.04380706e-01 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5d3a93b3-92ba-489c-86e3-0ac23254a7fc	large-language-models-fail-on-trivial	2302.08399	null	https://arxiv.org/abs/2302.08399v5	https://arxiv.org/pdf/2302.08399v5.pdf	Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks	Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on belief attribution in Theory-of-Mind tasks. These tasks have shown both successes and failures. We consider in particular a recent purported success case, and show that small variations that maintain the principles of ToM turn the results on their head. We argue that in general, the zero-hypothesis for model evaluation in intuitive psychology should be skeptical, and that outlying failure cases should outweigh average success rates. We also consider what possible future successes on Theory-of-Mind tasks by more powerful LLMs would mean for ToM tasks with people.	['Tomer Ullman']	2023-02-16	null	null	null	null	['common-sense-reasoning']	['reasoning']	[-1.13696866e-01 6.79314792e-01 -2.50396460e-01 -4.18209016e-01 4.10841359e-03 -4.60895300e-02 6.05186522e-01 4.48728055e-01 -5.15141547e-01 3.64766300e-01 3.15807730e-01 -8.46316874e-01 -4.24864024e-01 -8.22004735e-01 -3.45052063e-01 -2.02113420e-01 1.88684881e-01 8.08533430e-01 -1.25290789e-02 -4.73917007e-01 7.38473296e-01 1.12921394e-01 -1.05470133e+00 2.18179539e-01 7.20912993e-01 1.45071715e-01 -9.84353051e-02 3.44350368e-01 2.20218599e-01 1.37001228e+00 2.38340497e-02 -9.85454440e-01 -1.28530756e-01 -3.73606980e-01 -1.22944963e+00 1.06973108e-02 4.06744987e-01 -4.51606572e-01 -6.38256073e-02 1.07377756e+00 1.48472026e-01 1.74134433e-01 6.69082224e-01 -1.33076608e+00 -1.16433203e+00 9.67696905e-01 -5.98389089e-01 2.57032365e-01 5.06683886e-01 5.97396791e-01 1.17049825e+00 -4.19755638e-01 3.24139684e-01 1.86876941e+00 8.29478860e-01 3.96188527e-01 -1.36631072e+00 -5.32772660e-01 2.59197444e-01 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8bbe27bc-0baf-437a-94e0-79ce6390a90b	scanpath-prediction-in-panoramic-videos-via	2305.02536	null	https://arxiv.org/abs/2305.02536v2	https://arxiv.org/pdf/2305.02536v2.pdf	Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization	Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and diversity of the output. Most previous methods fail to give a complete treatment of these characteristics, and thus are prone to errors. In this paper, we present a simple new criterion for scanpath prediction based on principles from lossy data compression. This criterion suggests minimizing the expected code length of quantized scanpaths in a training set, which corresponds to fitting a discrete conditional probability model via maximum likelihood. Specifically, the probability model is conditioned on two modalities: a viewport sequence as the deformation-reduced visual input and a set of relative historical scanpaths projected onto respective viewports as the aligned path input. The probability model is parameterized by a product of discretized Gaussian mixture models to capture the uncertainty and the diversity of scanpaths from different users. Most importantly, the training of the probability model does not rely on the specification of "ground-truth" scanpaths for imitation learning. We also introduce a proportional-integral-derivative (PID) controller-based sampler to generate realistic human-like scanpaths from the learned probability model. Experimental results demonstrate that our method consistently produces better quantitative scanpath results in terms of prediction accuracy (by comparing to the assumed "ground-truths") and perceptual realism (through machine discrimination) over a wide range of prediction horizons. We additionally verify the perceptual realism improvement via a formal psychophysical experiment and the generalization improvement on several unseen panoramic video datasets.	['Kede Ma', 'Kanglong Fan', 'Mu Li']	2023-05-04	null	null	null	null	['scanpath-prediction', 'data-compression']	['computer-vision', 'time-series']	[ 4.09770161e-01 2.29113385e-01 -3.21951747e-01 -1.65850133e-01 -7.13302493e-01 -4.85378027e-01 5.67032456e-01 -5.98186851e-01 1.38975844e-01 4.67898965e-01 -4.80655488e-03 -2.54834089e-02 -3.89723659e-01 -5.62215209e-01 -1.04664743e+00 -5.91675878e-01 -2.23056540e-01 6.07488930e-01 1.57783434e-01 1.20079694e-02 2.06159785e-01 5.39629340e-01 -1.69844687e+00 1.56472340e-01 9.43019807e-01 1.02936661e+00 4.69912082e-01 5.74951291e-01 5.44276893e-01 4.53373194e-01 -2.43210420e-01 -3.93140197e-01 6.88197732e-01 -4.18776453e-01 -4.44505572e-01 3.62986118e-01 3.26217979e-01 -6.86496377e-01 -4.20435756e-01 1.00320554e+00 9.25532952e-02 1.25083268e-01 8.53393674e-01 -1.28687048e+00 -5.43040819e-02 1.74538568e-02 -5.53277254e-01 -4.73736107e-01 5.73314011e-01 3.53982270e-01 8.21248233e-01 -5.95128119e-01 1.04197311e+00 1.17623699e+00 4.95611906e-01 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3275ad05-6550-48e8-9310-f646031f91b0	sjtu-nlp-at-semeval-2018-task-9-neural	1805.10465	null	http://arxiv.org/abs/1805.10465v1	http://arxiv.org/pdf/1805.10465v1.pdf	SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings	This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance.	['Jiangtong Li', 'Bingjie Tang', 'Zhuosheng Zhang', 'Hai Zhao']	2018-05-26	sjtu-nlp-at-semeval-2018-task-9-neural-1	https://aclanthology.org/S18-1147	https://aclanthology.org/S18-1147.pdf	semeval-2018-6	['hypernym-discovery']	['natural-language-processing']	[-4.09291908e-02 4.52270806e-01 -4.74101454e-01 -1.47793695e-01 -1.28809392e-01 -1.37009680e-01 5.78756094e-01 4.40151721e-01 -8.67549539e-01 5.01543820e-01 7.18683183e-01 -3.53263736e-01 -2.65008062e-01 -1.08145976e+00 -1.47918448e-01 -2.60044128e-01 -2.21464321e-01 9.05344665e-01 -1.67150244e-01 -5.73096991e-01 7.09325001e-02 1.10670030e-01 -1.59385991e+00 7.40226805e-02 5.41650712e-01 5.30075788e-01 1.11121617e-01 3.27398449e-01 -5.32077909e-01 4.43696827e-01 -4.39432472e-01 -4.62239027e-01 3.62803116e-02 1.34484515e-01 -1.20460808e+00 -7.65170038e-01 2.65424132e-01 1.14421561e-01 -7.00857520e-01 1.08507824e+00 5.56676626e-01 5.47166109e-01 7.44642496e-01 -1.31493306e+00 -1.22211075e+00 1.10569215e+00 1.55155554e-01 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2520446b-2c17-4951-a384-a820590ec5bb	robust-model-training-and-generalisation-with	2006.06599	null	https://arxiv.org/abs/2006.06599v2	https://arxiv.org/pdf/2006.06599v2.pdf	Robust model training and generalisation with Studentising flows	Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood. We discuss how these methods can be further improved based on insights from robust (in particular, resistant) statistics. Specifically, we propose to endow flow-based models with fat-tailed latent distributions such as multivariate Student's $t$, as a simple drop-in replacement for the Gaussian distribution used by conventional normalising flows. While robustness brings many advantages, this paper explores two of them: 1) We describe how using fatter-tailed base distributions can give benefits similar to gradient clipping, but without compromising the asymptotic consistency of the method. 2) We also discuss how robust ideas lead to models with reduced generalisation gap and improved held-out data likelihood. Experiments on several different datasets confirm the efficacy of the proposed approach in both regards.	['Gustav Eje Henter', 'Simon Alexanderson']	2020-06-11	null	null	null	null	['normalising-flows']	['methodology']	[-3.31681073e-01 4.71231863e-02 -2.26113573e-01 -3.05205911e-01 -6.43559933e-01 -8.29008341e-01 7.72441983e-01 -2.12415427e-01 -3.35217029e-01 1.07810438e+00 2.99368322e-01 -5.49685717e-01 -6.10261679e-01 -8.51943791e-01 -6.00941420e-01 -7.61149228e-01 -3.97768110e-01 3.58617097e-01 2.63537139e-01 1.65858418e-01 2.76693493e-01 7.29810953e-01 -1.20516622e+00 -2.63662010e-01 1.02174044e+00 8.92083764e-01 -3.48612964e-01 7.58771420e-01 -2.88262814e-01 8.84781778e-01 -5.25838614e-01 -7.91231394e-01 3.96826804e-01 -2.00290186e-03 -5.40393412e-01 -1.91191465e-01 3.56756806e-01 -6.68562591e-01 -4.81937945e-01 8.08556318e-01 3.87659431e-01 3.12824517e-01 1.18717933e+00 -1.53278279e+00 -5.68535328e-01 5.73816359e-01 -4.15841967e-01 1.94507882e-01 -5.11753932e-02 2.98152953e-01 1.03295124e+00 -5.76498628e-01 2.28509352e-01 1.48497224e+00 8.76714706e-01 3.60115886e-01 -1.20462286e+00 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dfed1901-667e-4056-b48e-04ef24a05cba	from-neural-re-ranking-to-neural-ranking	null	null	https://dl.acm.org/citation.cfm?id=3271800	https://ciir-publications.cs.umass.edu/getpdf.php?id=1302	From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing	The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with efficiency. Current neural ranking models are implemented as multistage rankers: for efficiency reasons, the neural model only re-ranks the top ranked documents retrieved by a first-stage efficient ranker in response to a given query. Neural ranking models learn dense representations causing essentially every query term to match every document term, making it highly inefficient or intractable to rank the whole collection. The reliance on a first stage ranker creates a dual problem: First, the interaction and combination effects are not well understood. Second, the first stage ranker serves as a “gate-keeper” or filter, effectively blocking the potential of neural models to uncover new relevant documents. In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document. This representation captures the semantic relationship between the query and documents, but is also sparse enough to enable constructing an inverted index for the whole collection. We parameterize the sparsity of the model to yield a retrieval model as efficient as conventional term based models. Our model gains in efficiency without loss of effectiveness: it not only outperforms the existing term matching baselines, but also performs similarly to the recent re-ranking based neural models with dense representations. Our model can also take advantage of pseudo-relevance feedback for further improvements. More generally, our results demonstrate the importance of sparsity in neuralIR models and show that dense representations can be pruned effectively, giving new insights about essential semantic features and their distributions.	['Erik Learned-Miller', 'W. Bruce Croft', 'Mostafa Dehghani', 'Hamed Zamani', 'and Jaap Kamps']	2018-10-22	null	null	null	27th-acm-international-conference-on	['ad-hoc-information-retrieval']	['natural-language-processing']	[ 3.49009156e-01 -1.55922174e-01 -6.08325124e-01 -2.94828564e-01 -1.08223212e+00 -6.39290214e-01 7.50512779e-01 1.99481517e-01 -3.34004343e-01 3.64249915e-01 6.96841359e-01 -1.69677183e-01 -6.25817895e-01 -8.16523910e-01 -7.78953075e-01 -4.18369740e-01 -2.44923756e-01 8.39029908e-01 1.47003978e-01 -3.90641838e-01 4.71260130e-01 3.46133649e-01 -1.95729554e+00 6.50825262e-01 6.99545681e-01 8.48125696e-01 4.42973584e-01 3.98173511e-01 -1.89682096e-01 7.88801551e-01 -4.97113138e-01 -1.18630059e-01 2.99275368e-01 -1.88792516e-02 -8.29504192e-01 -5.55800915e-01 7.22329438e-01 -7.92558372e-01 -7.76827216e-01 8.26229513e-01 4.83498037e-01 4.86807615e-01 7.12171853e-01 -7.57912338e-01 -1.07133985e+00 8.80783617e-01 -5.47713578e-01 2.45125890e-01 2.58869112e-01 -6.48608267e-01 1.68658102e+00 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3.84222001e-01 7.15201437e-01 -7.41532445e-01 -7.40764737e-01 -8.62286985e-01 -5.15563071e-01 3.22261751e-01 4.61631835e-01 -5.68530977e-01 -4.23477590e-01 -3.87949288e-01]	[11.440435409545898, 7.579082489013672]
5e16e900-6547-43e8-b073-70c9a83deee3	imitating-task-and-motion-planning-with	2305.16309	null	https://arxiv.org/abs/2305.16309v1	https://arxiv.org/pdf/2305.16309v1.pdf	Imitating Task and Motion Planning with Visuomotor Transformers	Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contrast, Task and Motion Planning (TAMP) can autonomously generate large-scale datasets of diverse demonstrations. In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation. To that end, we present a novel imitation learning system called OPTIMUS that trains large-scale visuomotor Transformer policies by imitating a TAMP agent. OPTIMUS introduces a pipeline for generating TAMP data that is specifically curated for imitation learning and can be used to train performant transformer-based policies. In this paper, we present a thorough study of the design decisions required to imitate TAMP and demonstrate that OPTIMUS can solve a wide variety of challenging vision-based manipulation tasks with over 70 different objects, ranging from long-horizon pick-and-place tasks, to shelf and articulated object manipulation, achieving 70 to 80% success rates. Video results at https://mihdalal.github.io/optimus/	['Dieter Fox', 'Ruslan Salakhutdinov', 'Ankur Handa', 'Caelan Garrett', 'Ajay Mandlekar', 'Murtaza Dalal']	2023-05-25	null	null	null	null	['robot-manipulation', 'motion-planning']	['robots', 'robots']	[-2.05358073e-01 -1.26731303e-03 -2.36254916e-01 -1.15186602e-01 -6.91199064e-01 -7.67411649e-01 5.80940902e-01 -3.79949510e-01 -4.46073681e-01 8.22934628e-01 -2.73940355e-01 -3.53262812e-01 4.13070954e-02 -4.00230914e-01 -1.26441324e+00 -5.00121176e-01 -5.28765880e-02 1.00693500e+00 3.80188704e-01 -3.73598188e-01 3.38047057e-01 5.34627378e-01 -1.64333427e+00 8.35980996e-02 9.40628648e-01 4.57502574e-01 9.49239314e-01 9.27874506e-01 3.56864095e-01 9.40570295e-01 -5.95918655e-01 2.97391236e-01 4.02770579e-01 -1.63649693e-01 -7.46360302e-01 -1.11170486e-01 1.36300266e-01 -6.90723062e-01 -5.21199942e-01 6.11295998e-01 3.94940525e-01 1.46895230e-01 6.39246047e-01 -1.91109955e+00 -4.16531146e-01 6.13568246e-01 -6.91080838e-02 -1.61205903e-01 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5e486e28-8c28-4147-ae6b-147f6dc208ee	location-aware-single-image-reflection	2012.07131	null	https://arxiv.org/abs/2012.07131v2	https://arxiv.org/pdf/2012.07131v2.pdf	Location-aware Single Image Reflection Removal	This paper proposes a novel location-aware deep-learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells if a region is reflection-dominated or transmission-dominated, and it is used as a cue for the network to control the feature flow when predicting the reflection and transmission layers. We design our network as a recurrent network to progressively refine reflection removal results at each iteration. The novelty is that we leverage Laplacian kernel parameters to emphasize the boundaries of strong reflections. It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results. Extensive experiments verify the superior performance of the proposed method over state-of-the-art approaches. Our code and the pre-trained model can be found at https://github.com/zdlarr/Location-aware-SIRR.	['Rynson W. H. Lau', 'Weiwei Xu', 'Hujun Bao', 'Yin Yang', 'Ke Xu', 'Zheng Dong']	2020-12-13	null	http://openaccess.thecvf.com//content/ICCV2021/html/Dong_Location-Aware_Single_Image_Reflection_Removal_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Dong_Location-Aware_Single_Image_Reflection_Removal_ICCV_2021_paper.pdf	iccv-2021-1	['reflection-removal']	['computer-vision']	[ 2.42396042e-01 9.09599140e-02 1.07112683e-01 -1.48477823e-01 -7.06827939e-01 7.40283867e-03 4.91424650e-01 -3.89936537e-01 -7.14874268e-02 2.38545880e-01 5.02294004e-01 -4.32539493e-01 9.20386165e-02 -9.33032155e-01 -5.87829053e-01 -9.18946981e-01 1.55275449e-01 -1.73848301e-01 4.21045542e-01 -1.92042395e-01 3.94130886e-01 4.69308048e-01 -9.05274630e-01 4.59124058e-01 9.35076356e-01 8.96506071e-01 7.44860843e-02 6.18918836e-01 2.84626484e-01 8.89926434e-01 -3.09606642e-01 5.12384586e-02 3.98760229e-01 -3.48556966e-01 -1.09251253e-01 -4.39444005e-01 1.81518093e-01 -4.44479495e-01 -7.71800876e-01 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1e0c3e92-3745-42bd-97a8-ec6246685418	asfm-net-asymmetrical-siamese-feature	2104.09587	null	https://arxiv.org/abs/2104.09587v3	https://arxiv.org/pdf/2104.09587v3.pdf	ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion	We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net. Specifically, the Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, which can capture detailed shape prior. Then we design an iterative refinement unit to generate complete shapes with fine-grained details by integrating prior information. Experiments are conducted on the PCN dataset and the Completion3D benchmark, demonstrating the state-of-the-art performance of the proposed ASFM-Net. Our method achieves the 1st place in the leaderboard of Completion3D and outperforms existing methods with a large margin, about 12%. The codes and trained models are released publicly at https://github.com/Yan-Xia/ASFM-Net.	['Uwe Stilla', 'Kailang Cao', 'Rui Song', 'Wei Li', 'Yan Xia', 'Yaqi Xia']	2021-04-19	null	null	null	null	['point-cloud-completion']	['computer-vision']	[-1.94946036e-01 3.06041986e-02 6.15292341e-02 -3.07681948e-01 -9.10287201e-01 -2.93104321e-01 6.47919416e-01 -4.80538815e-01 -5.27773835e-02 3.21635336e-01 1.19539164e-01 1.18432216e-01 -2.97439657e-02 -6.83664799e-01 -1.03685582e+00 -4.43454295e-01 2.83839643e-01 7.98948586e-01 -5.98841347e-03 4.45364751e-02 2.29281202e-01 8.63092721e-01 -1.37134993e+00 -9.83675718e-02 9.13513780e-01 1.04025102e+00 5.51727653e-01 3.46655816e-01 -1.06113054e-01 2.47736320e-01 -9.83034596e-02 -3.24898660e-01 6.41207874e-01 2.56058127e-01 -6.04224801e-01 2.77544081e-01 3.87492597e-01 -5.11367381e-01 -6.31408632e-01 1.00374377e+00 2.40553886e-01 1.29710317e-01 7.26903021e-01 -1.29557037e+00 -9.74827528e-01 7.77562782e-02 -7.50870347e-01 -4.50098962e-01 2.37797856e-01 1.89489499e-01 8.58324587e-01 -1.59391272e+00 7.36886203e-01 1.30568433e+00 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42f0334b-827e-49a3-bef2-b1b5b2833ec9	kernel-embedding-of-maps-for-sequential	1805.11380	null	http://arxiv.org/abs/1805.11380v1	http://arxiv.org/pdf/1805.11380v1.pdf	Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter	In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles. Particles are pushed forward from the prior to the posterior density using a sequence of mappings that minimizes the Kullback-Leibler divergence between the posterior and the sequence of intermediate densities. The sequence of mappings represents a gradient flow. A key ingredient of the mappings is that they are embedded in a reproducing kernel Hilbert space, which allows for a practical and efficient algorithm. The embedding provides a direct means to calculate the gradient of the Kullback-Leibler divergence leading to quick convergence using well-known gradient-based stochastic optimization algorithms. Evaluation of the method is conducted in the chaotic Lorenz-63 system, the Lorenz-96 system, which is a coarse prototype of atmospheric dynamics, and an epidemic model that describes cholera dynamics. No resampling is required in the mapping particle filter even for long recursive sequences. The number of effective particles remains close to the total number of particles in all the experiments.	['Peter Jan vanLeeuwen', 'Manuel Pulido']	2018-05-29	null	null	null	null	['sequential-bayesian-inference']	['time-series']	[-3.49163532e-01 -4.23047543e-01 5.09967029e-01 -6.64132833e-02 -6.51159212e-02 -2.33215272e-01 9.40734446e-01 8.87164250e-02 -9.20563638e-01 1.10778260e+00 -1.32676259e-01 -1.58776209e-01 -1.62202835e-01 -1.04514754e+00 -4.43804592e-01 -8.03757906e-01 -6.64463162e-01 6.39431953e-01 4.82417226e-01 -2.67049491e-01 8.95611569e-02 5.58045208e-01 -1.62565458e+00 -5.99444091e-01 9.05842066e-01 6.67302608e-01 5.17552316e-01 1.10514593e+00 4.23528142e-02 3.11321199e-01 -5.03376067e-01 1.94145720e-02 2.47509927e-01 -7.66616523e-01 -3.28869253e-01 -1.42031997e-01 -2.92428643e-01 -1.77900925e-01 4.86315191e-02 1.43140638e+00 3.25653464e-01 5.68026006e-01 1.00405908e+00 -7.47080326e-01 -2.02404857e-01 1.24828123e-01 6.67492114e-03 4.24233347e-01 5.34128249e-02 -8.83531645e-02 5.06529450e-01 -8.18844557e-01 5.22548735e-01 1.15512478e+00 8.82952452e-01 3.52116525e-01 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c267105c-a40d-4222-9f42-f2066cfcb71a	data-free-quantization-through-weight	1906.04721	null	https://arxiv.org/abs/1906.04721v3	https://arxiv.org/pdf/1906.04721v3.pdf	Data-Free Quantization Through Weight Equalization and Bias Correction	We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and tasks. 8-bit fixed-point quantization is essential for efficient inference on modern deep learning hardware. However, quantizing models to run in 8-bit is a non-trivial task, frequently leading to either significant performance reduction or engineering time spent on training a network to be amenable to quantization. Our approach relies on equalizing the weight ranges in the network by making use of a scale-equivariance property of activation functions. In addition the method corrects biases in the error that are introduced during quantization. This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call. For common architectures, such as the MobileNet family, we achieve state-of-the-art quantized model performance. We further show that the method also extends to other computer vision architectures and tasks such as semantic segmentation and object detection.	['Max Welling', 'Mart van Baalen', 'Tijmen Blankevoort', 'Markus Nagel']	2019-06-11	data-free-quantization-through-weight-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.pdf	iccv-2019-10	['data-free-quantization', 'data-free-quantization']	['computer-vision', 'methodology']	[ 3.16802800e-01 6.20621592e-02 -2.03980282e-01 -6.97885513e-01 -7.28863895e-01 -6.10515237e-01 4.71808434e-01 1.75390705e-01 -9.78789687e-01 3.17026228e-01 -3.95544618e-01 -7.15383530e-01 2.23444894e-01 -7.00515985e-01 -9.01244104e-01 -5.45021832e-01 2.24332158e-02 4.51294243e-01 5.90790749e-01 -1.01686850e-01 2.09506229e-01 6.02791548e-01 -1.45124638e+00 9.56592187e-02 3.34714830e-01 1.43883729e+00 1.25576943e-01 1.11345541e+00 -9.82728321e-03 4.86736566e-01 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6c07e8d7-16fb-466b-aa98-17b1286daadb	nanonet-real-time-polyp-segmentation-in-video	2104.11138	null	https://arxiv.org/abs/2104.11138v1	https://arxiv.org/pdf/2104.11138v1.pdf	NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy	Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.	['Pål Halvorsen', 'Thomas de Lange', 'Dag Johansen', 'Håvard D. Johansen', 'Michael A. Riegler', 'Sharib Ali', 'Nikhil Kumar Tomar', 'Debesh Jha']	2021-04-22	null	null	null	null	['instrument-recognition']	['audio']	[-1.66190177e-01 1.60578396e-02 -1.43281907e-01 -1.22901157e-01 -5.53339124e-01 -8.01852047e-01 -2.44566262e-01 5.11282980e-01 -4.43319201e-01 1.44848034e-01 -2.85246164e-01 -7.04638898e-01 -3.74044552e-02 -8.49645674e-01 -5.70062160e-01 -6.72611892e-01 -4.06542808e-01 3.13806355e-01 3.90806049e-01 2.78782308e-01 -1.60744280e-01 3.31168741e-01 -9.17490184e-01 3.80857050e-01 1.04152846e+00 1.03071773e+00 3.22757721e-01 8.57115567e-01 1.90214202e-01 3.18287045e-01 -4.07280862e-01 -1.41273111e-01 3.47521603e-01 -2.33812645e-01 -5.47986984e-01 -3.40221310e-03 7.42297396e-02 -4.78252590e-01 -2.16264993e-01 1.21337020e+00 5.22321284e-01 -1.79042533e-01 1.87226593e-01 -4.94569749e-01 -7.04529360e-02 6.79326713e-01 -4.14594382e-01 1.92801520e-01 1.21078581e-01 2.41620824e-01 4.87120539e-01 -2.97842510e-02 3.29990268e-01 7.24575341e-01 8.30008566e-01 5.46319842e-01 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4c173503-1d80-4473-9ab0-b9350373e7f2	using-offline-data-to-speed-up-reinforcement	2304.09825	null	https://arxiv.org/abs/2304.09825v1	https://arxiv.org/pdf/2304.09825v1.pdf	Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments	One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large numbers of interactions with the environment. Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study to investigate whether agents can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments. We consider two settings of using IL from offline data for RL: (1) pre-training a policy before online RL training and (2) concurrently training a policy with online RL and IL from offline data. We analyse the impact of the quality (optimality of trajectories) and diversity (number of trajectories and covered level) of available offline trajectories on the effectiveness of both approaches. Across four well-known sparse reward tasks in the MiniGrid environment, we find that using IL for pre-training and concurrently during online RL training both consistently improve the sample-efficiency while converging to optimal policies. Furthermore, we show that pre-training a policy from as few as two trajectories can make the difference between learning an optimal policy at the end of online training and not learning at all. Our findings motivate the widespread adoption of IL for pre-training and concurrent IL in procedurally generated environments whenever offline trajectories are available or can be generated.	['Javier Del Ser', 'Stefano V. Albrecht', 'Esther Villar-Rodriguez', 'Lukas Schäfer', 'Alain Andres']	2023-04-18	null	null	null	null	['offline-rl']	['playing-games']	[-2.38444790e-01 -1.50609594e-02 -1.21495627e-01 1.13312483e-01 -7.05712378e-01 -1.03768539e+00 8.51415098e-01 3.98051649e-01 -9.18763578e-01 1.01388705e+00 8.14314261e-02 -5.90841711e-01 -1.04266062e-01 -7.71033585e-01 -1.03428614e+00 -6.97540760e-01 -6.49166346e-01 6.98085666e-01 1.40769929e-01 -1.15078084e-01 1.70901373e-01 8.73034596e-01 -1.66236365e+00 -1.70741171e-01 9.80318964e-01 4.75838065e-01 4.97981101e-01 9.07553494e-01 1.80268794e-01 8.96393716e-01 -5.01285553e-01 5.05596459e-01 7.57209241e-01 -4.90404516e-01 -3.75871420e-01 1.60194755e-01 -1.51804820e-01 -7.23774552e-01 -5.54897964e-01 6.66360974e-01 5.91875970e-01 7.43409395e-01 4.22456294e-01 -1.15230501e+00 -9.37417373e-02 5.00594735e-01 -1.95969597e-01 3.17776084e-01 2.14959845e-01 1.03018761e+00 5.43015718e-01 -2.99929321e-01 7.10565984e-01 1.03051221e+00 4.05960649e-01 2.94330776e-01 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099b07eb-64a1-4e31-95b0-db7ea7ea6a69	hold-on-honey-men-at-work-a-semi-supervised	null	null	https://aclanthology.org/2021.acl-srw.19	https://aclanthology.org/2021.acl-srw.19.pdf	``Hold on honey, men at work'': A semi-supervised approach to detecting sexism in sitcoms	Television shows play an important role inpropagating societal norms. Owing to the popularity of the situational comedy (sitcom) genre, it contributes significantly to the over-all development of society. In an effort to analyze the content of television shows belong-ing to this genre, we present a dataset of dialogue turns from popular sitcoms annotated for the presence of sexist remarks. We train a text classification model to detect sexism using domain adaptive learning. We apply the model to our dataset to analyze the evolution of sexist content over the years. We propose a domain-specific semi-supervised architecture for the aforementioned detection of sexism.Through extensive experiments, we show that our model often yields better classification performance over generic deep learn-ing based sentence classification that does not employ domain-specific training. We find that while sexism decreases over time on average,the proportion of sexist dialogue for the most sexist sitcom actually increases. A quantitative analysis along with a detailed error analysis presents the case for our proposed methodology	['Zeerak Waseem', 'Arijit Ghosh Chowdhury', 'Tanvi Anand', 'Smriti Singh']	2021-08-01	null	null	null	acl-2021-5	['sentence-classification']	['natural-language-processing']	[ 1.30804449e-01 4.70237255e-01 -3.00989211e-01 -9.02557373e-01 -7.59372354e-01 -6.92771554e-01 1.16854310e+00 5.83161175e-01 -3.08956891e-01 7.39496291e-01 6.23545647e-01 -3.07334840e-01 7.02511147e-02 -8.21884811e-01 -8.15476418e-01 -5.36317050e-01 2.09455475e-01 7.88596928e-01 -1.84063271e-01 -8.21286380e-01 4.22642142e-01 -1.46877915e-01 -1.18084860e+00 5.58194458e-01 9.01546836e-01 9.49629128e-01 -5.35765290e-01 6.77639723e-01 -1.90883070e-01 7.77587175e-01 -9.79302287e-01 -9.21520829e-01 -1.13691047e-01 -7.06018150e-01 -1.06118274e+00 3.02974790e-01 9.41013873e-01 -3.52442503e-01 -2.67017722e-01 8.70522439e-01 3.31543922e-01 -1.80168554e-01 7.85852134e-01 -9.59449351e-01 -2.25664496e-01 1.43297100e+00 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e564c319-e6c8-4f6f-8f12-e44cbded7dab	an-optimization-based-deep-equilibrium-model	2306.06378	null	https://arxiv.org/abs/2306.06378v1	https://arxiv.org/pdf/2306.06378v1.pdf	An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees	In this paper, we propose a novel methodology for addressing the hyperspectral image deconvolution problem. This problem is highly ill-posed, and thus, requires proper priors (regularizers) to model the inherent spectral-spatial correlations of the HSI signals. To this end, a new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. To tackle this problem, an effective solver is proposed using the half quadratic splitting methodology. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium (DEQ) framework, resulting in an interpretable architecture, with clear explainability to its parameters and convergence properties with practical benefits. The proposed model is a first attempt to handle the classical HSI degradation problem with different blurring kernels and noise levels via a single deep equilibrium model with significant computational efficiency. Extensive numerical experiments validate the superiority of the proposed methodology over other state-of-the-art methods. This superior restoration performance is achieved while requiring 99.85\% less computation time as compared to existing methods.	['Kostas Berberidis', 'Dimitris Ampeliotis', 'Alexandros Gkillas']	2023-06-10	null	null	null	null	['image-deconvolution']	['computer-vision']	[ 4.51510429e-01 -1.83250234e-01 3.59618694e-01 -7.24490033e-03 -5.29718459e-01 -1.31891906e-01 1.58272654e-01 -2.78552115e-01 -2.11821333e-01 1.07028341e+00 -5.69158196e-02 2.09366786e-03 -6.64302886e-01 -4.53015983e-01 -4.55568463e-01 -1.29758871e+00 6.31374121e-02 -1.45912796e-01 -3.43221843e-01 -1.32776052e-01 1.78941086e-01 4.54337686e-01 -1.36787593e+00 -3.02814037e-01 1.70982778e+00 1.08082390e+00 4.88475293e-01 3.20650876e-01 2.86583155e-01 7.11247206e-01 -2.88537532e-01 3.08015831e-02 4.14886117e-01 -4.79332566e-01 -5.90104461e-01 5.83519578e-01 2.27379367e-01 -3.13058883e-01 -2.42193475e-01 1.48621202e+00 4.97687012e-01 4.86914247e-01 6.08072519e-01 -7.79744446e-01 -7.55641341e-01 1.38306007e-01 -8.63635600e-01 5.73850237e-02 -9.85910445e-02 8.54258016e-02 7.52501249e-01 -1.17612720e+00 1.01165064e-01 7.97996938e-01 7.31010497e-01 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135f9817-4be5-4065-abd9-aa725cee69a6	adaptive-video-highlight-detection-by	2007.09598	null	https://arxiv.org/abs/2007.09598v1	https://arxiv.org/pdf/2007.09598v1.pdf	Adaptive Video Highlight Detection by Learning from User History	Recently, there is an increasing interest in highlight detection research where the goal is to create a short duration video from a longer video by extracting its interesting moments. However, most existing methods ignore the fact that the definition of video highlight is highly subjective. Different users may have different preferences of highlight for the same input video. In this paper, we propose a simple yet effective framework that learns to adapt highlight detection to a user by exploiting the user's history in the form of highlights that the user has previously created. Our framework consists of two sub-networks: a fully temporal convolutional highlight detection network $H$ that predicts highlight for an input video and a history encoder network $M$ for user history. We introduce a newly designed temporal-adaptive instance normalization (T-AIN) layer to $H$ where the two sub-networks interact with each other. T-AIN has affine parameters that are predicted from $M$ based on the user history and is responsible for the user-adaptive signal to $H$. Extensive experiments on a large-scale dataset show that our framework can make more accurate and user-specific highlight predictions.	['Yang Wang', 'Mahesh Kumar Krishna Reddy', 'Mrigank Rochan', 'Linwei Ye']	2020-07-19	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3702_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660256.pdf	eccv-2020-8	['highlight-detection']	['computer-vision']	[ 2.03020945e-01 -3.44364822e-01 -9.30229127e-02 -5.75287521e-01 -5.59889555e-01 -4.30691242e-01 2.33663604e-01 4.48717969e-03 -5.07893145e-01 2.73268640e-01 1.90152630e-01 1.06350094e-01 3.07050824e-01 -6.44160032e-01 -8.22757900e-01 -5.83612502e-01 -5.32433093e-01 -5.34311473e-01 5.32720268e-01 -1.84261822e-03 3.15408438e-01 2.46324107e-01 -1.50125813e+00 5.89098811e-01 5.91358662e-01 1.28998423e+00 3.89144689e-01 8.49447310e-01 2.51813084e-01 1.10044909e+00 -6.93745017e-01 -1.46408811e-01 5.07985651e-01 -6.22597694e-01 -3.25328380e-01 -3.27447942e-03 5.82947731e-01 -5.37325621e-01 -6.51263475e-01 8.69202971e-01 6.95815086e-01 5.79744697e-01 1.90578192e-01 -1.25966084e+00 -3.49753797e-01 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0601c22a-3cf9-41db-a94a-d3382e2d618e	intrinsic-image-transfer-for-illumination	2107.00704	null	https://arxiv.org/abs/2107.00704v2	https://arxiv.org/pdf/2107.00704v2.pdf	Intrinsic Image Transfer for Illumination Manipulation	This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets.	['Haihui Wang', 'Qianying Zhang', 'Michael Ruzhansky', 'Junqing Huang']	2021-07-01	null	null	null	null	['intrinsic-image-decomposition']	['computer-vision']	[ 1.23551106e+00 -1.19675383e-01 1.45959765e-01 -3.26403618e-01 -4.11962062e-01 -2.83417851e-01 5.49670875e-01 -4.63088959e-01 -3.94342273e-01 6.88018441e-01 -8.29816461e-02 1.19081654e-01 -3.25547814e-01 -7.36577690e-01 -8.75911653e-01 -1.22206688e+00 5.09479225e-01 -2.04789206e-01 -2.59255528e-01 -3.12512249e-01 3.52390051e-01 6.96839511e-01 -1.99796653e+00 3.51230614e-02 1.07745433e+00 1.12204206e+00 1.97861567e-01 5.88867188e-01 -1.06642274e-02 4.58561242e-01 -2.04329520e-01 -3.68441045e-01 4.45892155e-01 -5.63081324e-01 -5.45607090e-01 5.03084838e-01 7.14408517e-01 -4.15346622e-01 -8.45487118e-02 1.15600753e+00 4.26741809e-01 1.44714072e-01 5.65416932e-01 -9.41813767e-01 -7.14493155e-01 -2.51623243e-01 -7.60519445e-01 -4.66782779e-01 3.36482704e-01 1.22749597e-01 7.21372843e-01 -8.57893407e-01 6.21493995e-01 1.07732427e+00 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884806a2-c84a-4f32-8455-9a4e85e349ae	an-empirical-study-on-relation-extraction-in	2112.05910	null	https://arxiv.org/abs/2112.05910v1	https://arxiv.org/pdf/2112.05910v1.pdf	An Empirical Study on Relation Extraction in the Biomedical Domain	Relation extraction is a fundamental problem in natural language processing. Most existing models are defined for relation extraction in the general domain. However, their performance on specific domains (e.g., biomedicine) is yet unclear. To fill this gap, this paper carries out an empirical study on relation extraction in biomedical research articles. Specifically, we consider both sentence-level and document-level relation extraction, and run a few state-of-the-art methods on several benchmark datasets. Our results show that (1) current document-level relation extraction methods have strong generalization ability; (2) existing methods require a large amount of labeled data for model fine-tuning in biomedicine. Our observations may inspire people in this field to develop more effective models for biomedical relation extraction.	['Yongkang Li']	2021-12-11	null	null	null	null	['document-level-relation-extraction']	['natural-language-processing']	[ 4.41115648e-01 4.18336123e-01 -7.03067839e-01 -3.64856541e-01 -6.94357634e-01 -2.92033792e-01 4.80815440e-01 9.10338759e-01 -4.02472198e-01 1.10879910e+00 1.49480060e-01 -6.96417212e-01 -1.34419248e-01 -9.66213763e-01 -3.27052146e-01 -3.69678885e-01 -1.20520659e-01 4.56952333e-01 1.66273013e-01 -1.66466057e-01 1.47740752e-01 5.42182028e-01 -9.06546831e-01 3.50515306e-01 9.38314855e-01 5.65403640e-01 -1.88064992e-01 3.95293117e-01 -3.09071690e-01 7.92187452e-01 -6.37197375e-01 -5.15797853e-01 -2.15049595e-01 -5.30141830e-01 -1.36526299e+00 -1.35773793e-01 -3.51109803e-01 1.82497963e-01 -1.00232407e-01 1.18557131e+00 3.19473743e-01 -2.61181086e-01 7.88626611e-01 -1.02177989e+00 -5.11965334e-01 9.40854132e-01 -4.75133419e-01 4.35324788e-01 4.38966990e-01 -1.84556782e-01 9.81613338e-01 -5.31287193e-01 9.59109843e-01 9.62097347e-01 4.70331222e-01 4.81927872e-01 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6e8ab5bf-864b-4e1a-9029-d50ad38c4379	probing-script-knowledge-from-pre-trained	2204.10176	null	https://arxiv.org/abs/2204.10176v1	https://arxiv.org/pdf/2204.10176v1.pdf	Probing Script Knowledge from Pre-Trained Models	Script knowledge is critical for humans to understand the broad daily tasks and routine activities in the world. Recently researchers have explored the large-scale pre-trained language models (PLMs) to perform various script related tasks, such as story generation, temporal ordering of event, future event prediction and so on. However, it's still not well studied in terms of how well the PLMs capture the script knowledge. To answer this question, we design three probing tasks: inclusive sub-event selection, starting sub-event selection and temporal ordering to investigate the capabilities of PLMs with and without fine-tuning. The three probing tasks can be further used to automatically induce a script for each main event given all the possible sub-events. Taking BERT as a case study, by analyzing its performance on script induction as well as each individual probing task, we conclude that the stereotypical temporal knowledge among the sub-events is well captured in BERT, however the inclusive or starting sub-event knowledge is barely encoded.	['Lifu Huang', 'Mo Yu', 'Xingyu Zhang', 'Zijian Jin']	2022-04-16	null	null	null	null	['story-generation']	['natural-language-processing']	[ 1.90818071e-01 4.16905619e-02 -1.88680097e-01 -6.14719808e-01 -3.05303872e-01 -8.45935524e-01 1.13230538e+00 2.96707571e-01 -1.62942082e-01 7.84286141e-01 7.41411328e-01 -3.33877325e-01 -2.13843569e-01 -6.64552748e-01 -6.15259767e-01 -1.85011953e-01 -1.97069407e-01 7.64875233e-01 5.66996634e-01 -1.43252730e-01 2.98237234e-01 5.11589348e-01 -1.46931720e+00 8.07537675e-01 7.08620965e-01 3.41779500e-01 2.86404967e-01 7.22888052e-01 -8.36649090e-02 1.53575492e+00 -7.41501212e-01 -4.33913022e-01 -3.47119689e-01 -9.17952657e-01 -1.18349433e+00 3.39993387e-02 -5.37919402e-02 -5.44384420e-01 -3.92161995e-01 3.09371054e-01 3.34135920e-01 4.48017657e-01 5.34200907e-01 -9.94362056e-01 -8.41705054e-02 1.18015230e+00 -5.87467737e-02 4.95597512e-01 8.16261053e-01 2.21530408e-01 1.08767188e+00 -2.14957267e-01 9.03452933e-01 1.05021358e+00 6.21120751e-01 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add04f0a-2b5d-46b4-85b1-69e69f0b4aaf	lidar-in-the-loop-hyperparameter-optimization	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.pdf	LiDAR-in-the-Loop Hyperparameter Optimization	LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these DSPs are input to downstream 3D vision models -- both, in the form of training datasets or as input at inference time. Existing LiDAR DSPs are composed of cascades of parameterized operations; modifying configuration parameters results in significant changes in the point clouds and consequently the output of downstream methods. Existing methods treat LiDAR systems as fixed black boxes and construct downstream task networks more robust with respect to measurement fluctuations. Departing from this approach, the proposed method directly optimizes LiDAR sensing and DSP parameters for downstream tasks. To investigate the optimization of LiDAR system parameters, we devise a realistic LiDAR simulation method that generates raw waveforms as input to a LiDAR DSP pipeline. We optimize LiDAR parameters for both 3D object detection IoU losses and depth error metrics by solving a nonlinear multi-objective optimization problem with a 0th-order stochastic algorithm. For automotive 3D object detection models, the proposed method outperforms manual expert tuning by 39.5% mean Average Precision (mAP).	['Felix Heide', 'Nicolas Robidoux', 'Mario Bijelic', 'Dominik Scheuble', 'Félix Goudreault']	2023-01-01	null	null	null	cvpr-2023-1	['hyperparameter-optimization']	['methodology']	[ 2.42344141e-01 -3.61076891e-01 3.07289660e-01 -6.18846714e-01 -8.12308669e-01 -7.47147262e-01 3.94162744e-01 1.07872277e-01 -5.06967485e-01 2.37032667e-01 -7.16739535e-01 -5.53892612e-01 4.61556576e-02 -8.49978089e-01 -7.80585110e-01 -4.34605271e-01 1.30857944e-01 7.35458374e-01 4.30826873e-01 2.27066964e-01 4.59973067e-01 1.14075327e+00 -2.05233645e+00 -3.54459524e-01 6.60816371e-01 1.39927173e+00 2.51219213e-01 9.53295112e-01 -2.51765639e-01 -1.50774583e-01 -7.10946977e-01 -4.56904098e-02 6.68475389e-01 3.76958936e-01 3.87089640e-01 -9.38326493e-02 6.52097166e-01 -4.05811250e-01 -5.60992993e-02 1.06045949e+00 8.75218570e-01 -7.84715358e-03 7.43118644e-01 -1.39857280e+00 -1.59633473e-01 1.02080666e-01 -4.95589018e-01 4.35093567e-02 -6.25723824e-02 7.02278316e-01 6.11659527e-01 -1.27949679e+00 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cad4a705-a4a1-48e7-9ba6-7ab1148bfee8	task-oriented-feature-distillation	null	null	http://proceedings.neurips.cc/paper/2020/hash/a96b65a721e561e1e3de768ac819ffbb-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/a96b65a721e561e1e3de768ac819ffbb-Paper.pdf	Task-Oriented Feature Distillation	Feature distillation, a primary method in knowledge distillation, always leads to significant accuracy improvements. Most existing methods distill features in the teacher network through a manually designed transformation. In this paper, we propose a novel distillation method named task-oriented feature distillation (TOFD) where the transformation is convolutional layers that are trained in a data-driven manner by task loss. As a result, the task-oriented information in the features can be captured and distilled to students. Moreover, an orthogonal loss is applied to the feature resizing layer in TOFD to improve the performance of knowledge distillation. Experiments show that TOFD outperforms other distillation methods by a large margin on both image classification and 3D classification tasks. Codes have been released in Github.	['Chenglong Bao', 'Kaisheng Ma', 'Zuoqiang Shi', 'Yukang Shi', 'Linfeng Zhang']	2020-12-01	null	null	null	neurips-2020-12	['3d-classification']	['computer-vision']	[ 3.07188910e-02 1.89904884e-01 -8.35578069e-02 -5.94073117e-01 -4.78261024e-01 -4.85979438e-01 7.12004185e-01 7.93097392e-02 -5.22416115e-01 6.84929252e-01 1.43538713e-01 -2.47800097e-01 -4.43593003e-02 -8.79453123e-01 -8.39145422e-01 -7.18157828e-01 5.29341698e-01 7.90967271e-02 1.49341106e-01 -8.66186917e-02 1.95305124e-02 5.53987622e-01 -1.24652433e+00 -4.76451479e-02 1.20190513e+00 1.02794242e+00 1.73486546e-01 2.87069380e-01 -1.86157718e-01 7.27327228e-01 -4.02092308e-01 -4.10346478e-01 4.99022484e-01 -3.87599617e-01 -7.22336650e-01 -2.40478486e-01 5.10053992e-01 -4.45184261e-01 -5.15644610e-01 9.85541821e-01 6.34996176e-01 3.20787311e-01 7.30267346e-01 -1.10769093e+00 -9.26716924e-01 7.58674264e-01 -5.01075387e-01 -4.61196415e-02 2.05047697e-01 3.06744371e-02 7.63624668e-01 -1.23010170e+00 3.35047871e-01 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d2846b4a-def2-4eaf-aa83-9857dc6b2d00	tsa-inf-at-semeval-2017-task-4-an-ensemble-of	null	null	https://aclanthology.org/S17-2135	https://aclanthology.org/S17-2135.pdf	TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis	This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsystem is a gated recurrent neural network implementation. Additionally, the third system integrates opinion lexicons directly into a convolution neural network architecture. The resulting ensemble of the three architectures achieved a top ten ranking with a macro-averaged recall of 64.3{\%}. Additional results comparing variations of the submitted system are not conclusive enough to determine a best architecture, but serve as a benchmark for further implementations.	['Jasper Friedrichs', 'Amit Ajit Deshmane']	2017-08-01	null	null	null	semeval-2017-8	['twitter-sentiment-analysis']	['natural-language-processing']	[-9.63199809e-02 1.32466525e-01 1.66358098e-01 -6.53859138e-01 -6.19774878e-01 -5.02669632e-01 6.72642052e-01 2.60489970e-01 -7.25781143e-01 4.31156516e-01 3.58282715e-01 -6.25451505e-01 2.85471588e-01 -6.08595312e-01 -4.43648845e-01 -4.61059451e-01 2.62844592e-01 6.10393584e-01 -1.76106356e-02 -9.45912123e-01 2.70517617e-01 1.55684769e-01 -1.26733756e+00 1.09614873e+00 2.04252839e-01 1.41252196e+00 -4.82410520e-01 9.25666094e-01 -2.13435218e-02 1.10846853e+00 -8.92338634e-01 -7.25579560e-01 1.61721915e-01 -2.87351850e-02 -9.73179042e-01 -4.40348357e-01 4.92157519e-01 -1.73966870e-01 4.49213944e-02 6.73299789e-01 5.83717763e-01 1.01099133e-01 6.30079746e-01 -6.63203895e-01 -7.62624502e-01 9.56882536e-01 -5.21048605e-02 3.17432523e-01 9.97236669e-02 -9.80824158e-02 1.42766571e+00 -1.11772811e+00 3.28922957e-01 1.05580151e+00 8.58992159e-01 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b0b0f9ad-0f4e-46ad-bddf-cdfbcff1df60	alfred-a-system-for-prompted-weak-supervision	2305.18623	null	https://arxiv.org/abs/2305.18623v1	https://arxiv.org/pdf/2305.18623v1.pdf	Alfred: A System for Prompted Weak Supervision	Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models. Alfred provides a simple Python interface for the key steps of this emerging paradigm, with a high-throughput backend for large-scale data labeling. Users can quickly create, evaluate, and refine their prompt-based weak supervision sources; map the results to weak labels; and resolve their disagreements with a label model. Alfred enables a seamless local development experience backed by models served from self-managed computing clusters. It automatically optimizes the execution of prompts with optimized batching mechanisms. We find that this optimization improves query throughput by 2.9x versus a naive approach. We present two example use cases demonstrating Alfred on YouTube comment spam detection and pet breeds classification. Alfred is open source, available at https://github.com/BatsResearch/alfred.	['Stephen Bach', 'Peilin Yu']	2023-05-29	null	null	null	null	['spam-detection']	['natural-language-processing']	[-4.61210638e-01 5.34595214e-02 -4.27019298e-01 -7.90243268e-01 -8.08350027e-01 -7.44470477e-01 5.06524861e-01 3.47918034e-01 -4.27265793e-01 4.38161463e-01 3.44579786e-01 -5.64694583e-01 2.98071325e-01 -5.01579523e-01 -6.25703931e-01 -2.22968459e-01 4.06796038e-01 7.51885712e-01 5.52726090e-01 -3.64602447e-01 1.59034148e-01 3.76357213e-02 -1.58314991e+00 7.41511822e-01 8.24855268e-01 4.92676109e-01 1.77069485e-01 8.61537218e-01 -1.51007175e-01 1.49752831e+00 -6.87079310e-01 -5.16451657e-01 3.07610929e-01 2.82956898e-01 -1.19733143e+00 -5.29189110e-01 6.30192935e-01 -4.42707747e-01 1.04024291e-01 8.21150005e-01 5.36500096e-01 -6.44923598e-02 -2.96860486e-02 -1.61163664e+00 -8.83174956e-01 7.54327118e-01 -4.39069360e-01 2.09693208e-01 5.83939254e-01 5.01556277e-01 1.25166142e+00 -8.24405849e-01 7.27182865e-01 1.20340729e+00 6.96004033e-01 6.34324014e-01 -1.33058751e+00 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1c647844-5385-4c57-9fb2-371ca8e30280	universal-sentence-encoder	1803.11175	null	http://arxiv.org/abs/1803.11175v2	http://arxiv.org/pdf/1803.11175v2.pdf	Universal Sentence Encoder	We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.	['Yun-Hsuan Sung', 'Mario Guajardo-Cespedes', 'Sheng-yi Kong', 'Yinfei Yang', 'Brian Strope', 'Nan Hua', 'Daniel Cer', 'Steve Yuan', 'Rhomni St. John', 'Ray Kurzweil', 'Noah Constant', 'Nicole Limtiaco', 'Chris Tar']	2018-03-29	null	null	null	null	['conversational-response-selection', 'subjectivity-analysis']	['natural-language-processing', 'natural-language-processing']	[ 2.44707763e-01 6.57740980e-02 -4.27247137e-01 -5.85673273e-01 -1.26268375e+00 -6.21517837e-01 7.06057370e-01 3.03496778e-01 -8.94126475e-01 8.28462481e-01 5.43167651e-01 -6.23981714e-01 3.55169773e-02 -8.42823029e-01 -8.10370207e-01 -1.89422995e-01 -2.32947111e-01 6.04056120e-01 1.35440558e-01 -2.96356201e-01 3.97215664e-01 4.44747582e-02 -9.66186047e-01 4.50312823e-01 7.56240189e-01 6.67330742e-01 8.28181878e-02 9.65939522e-01 -1.72937840e-01 3.57492894e-01 -5.83920300e-01 -5.31911075e-01 -5.90575337e-02 -2.52963603e-01 -1.10692203e+00 -4.21361923e-01 6.83919847e-01 -3.64363968e-01 -4.70893770e-01 7.87938952e-01 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ec9b2bbd-1726-4fa6-bf3e-0825c995d6c9	learn-to-decompose-cascaded-decomposition	2207.07973	null	https://arxiv.org/abs/2207.07973v1	https://arxiv.org/pdf/2207.07973v1.pdf	Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition	Most existing compound facial expression recognition (FER) methods rely on large-scale labeled compound expression data for training. However, collecting such data is labor-intensive and time-consuming. In this paper, we address the compound FER task in the cross-domain few-shot learning (FSL) setting, which requires only a few samples of compound expressions in the target domain. Specifically, we propose a novel cascaded decomposition network (CDNet), which cascades several learn-to-decompose modules with shared parameters based on a sequential decomposition mechanism, to obtain a transferable feature space. To alleviate the overfitting problem caused by limited base classes in our task, a partial regularization strategy is designed to effectively exploit the best of both episodic training and batch training. By training across similar tasks on multiple basic expression datasets, CDNet learns the ability of learn-to-decompose that can be easily adapted to identify unseen compound expressions. Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods. Code is available at: https://github.com/zouxinyi0625/CDNet.	['Hanzi Wang', 'Si Chen', 'Jing-Hao Xue', 'Yan Yan', 'Xinyi Zou']	2022-07-16	null	null	null	null	['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'facial-expression-recognition']	['computer-vision', 'computer-vision', 'computer-vision']	[-2.72762915e-03 -3.67342204e-01 -1.46442920e-01 -7.88929999e-01 -9.23707664e-01 -2.47306257e-01 8.00028518e-02 -5.19823194e-01 -2.18102142e-01 6.75987124e-01 -1.56195745e-01 3.44568372e-01 1.35550365e-01 -3.71224284e-01 -4.59215820e-01 -8.99228871e-01 -8.23511044e-04 7.06598014e-02 -4.04073834e-01 -4.12337154e-01 -5.08402407e-01 4.23854262e-01 -1.54582691e+00 4.40944284e-01 6.48527443e-01 1.37733018e+00 -1.49591684e-01 2.58162171e-01 -3.37170847e-02 9.17480826e-01 -4.35079396e-01 -5.48753321e-01 2.32536212e-01 -5.25436103e-01 -5.33112049e-01 3.68051887e-01 4.87568587e-01 -5.08774638e-01 -2.45599598e-01 1.10841501e+00 7.54820824e-01 1.32246822e-01 3.13795418e-01 -1.51522768e+00 -3.82741094e-01 -1.58006400e-02 -8.09514105e-01 2.13305783e-02 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f09fb7a4-c29b-45ae-b9aa-884afaf94007	anomaly-detection-for-an-e-commerce-pricing	1902.09566	null	https://arxiv.org/abs/1902.09566v5	https://arxiv.org/pdf/1902.09566v5.pdf	Anomaly Detection for an E-commerce Pricing System	Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system. In this paper, we describe unsupervised and supervised anomaly detection approaches we developed and deployed for a large-scale online pricing system at Walmart. Our system detects anomalies both in batch and real-time streaming settings, and the items flagged are reviewed and actioned based on priority and business impact. We found that having the right architecture design was critical to facilitate model performance at scale, and business impact and speed were important factors influencing model selection, parameter choice, and prioritization in a production environment for a large-scale system. We conducted analyses on the performance of various approaches on a test set using real-world retail data and fully deployed our approach into production. We found that our approach was able to detect the most important anomalies with high precision.	['Mátyás A. Sustik', 'Jagdish Ramakrishnan', 'Elham Shaabani', 'Chao Li']	2019-02-25	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[-3.33034366e-01 -3.30161601e-01 1.72088876e-01 -5.67957222e-01 -6.22031808e-01 -6.95602775e-01 9.61782485e-02 1.09379745e+00 -3.65204424e-01 8.89683068e-02 -2.06297100e-01 -3.92200530e-01 -3.55542570e-01 -1.00033987e+00 -7.12585330e-01 -2.97182322e-01 -7.74715245e-01 8.97901118e-01 4.70077664e-01 -5.07507205e-01 5.47190309e-01 4.59686548e-01 -1.61417985e+00 3.05615813e-01 3.81678849e-01 1.43354070e+00 -3.57014030e-01 7.99372911e-01 -7.03345845e-03 8.97360027e-01 -6.95500553e-01 -3.51654828e-01 8.08519185e-01 5.84949814e-02 2.73087285e-02 -3.14816907e-02 1.21053636e-01 -4.95486766e-01 3.04536611e-01 6.25885725e-01 2.85623133e-01 2.18746644e-02 2.10939810e-01 -1.59012616e+00 -3.26561555e-02 6.37013078e-01 -6.04324698e-01 7.60297596e-01 2.30495512e-01 3.24186325e-01 1.17417264e+00 -5.31384170e-01 1.43786266e-01 7.59071887e-01 8.34532499e-01 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c60ba4a9-71d7-4b2e-b00f-2c26bff6f705	probabilistic-human-mesh-recovery-in-3d	2304.06024	null	https://arxiv.org/abs/2304.06024v1	https://arxiv.org/pdf/2304.06024v1.pdf	Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views	Automatic perception of human behaviors during social interactions is crucial for AR/VR applications, and an essential component is estimation of plausible 3D human pose and shape of our social partners from the egocentric view. One of the biggest challenges of this task is severe body truncation due to close social distances in egocentric scenarios, which brings large pose ambiguities for unseen body parts. To tackle this challenge, we propose a novel scene-conditioned diffusion method to model the body pose distribution. Conditioned on the 3D scene geometry, the diffusion model generates bodies in plausible human-scene interactions, with the sampling guided by a physics-based collision score to further resolve human-scene inter-penetrations. The classifier-free training enables flexible sampling with different conditions and enhanced diversity. A visibility-aware graph convolution model guided by per-joint visibility serves as the diffusion denoiser to incorporate inter-joint dependencies and per-body-part control. Extensive evaluations show that our method generates bodies in plausible interactions with 3D scenes, achieving both superior accuracy for visible joints and diversity for invisible body parts. The code will be available at https://sanweiliti.github.io/egohmr/egohmr.html.	['Siyu Tang', 'Darren Cosker', 'Sadegh Aliakbarian', 'Yan Zhang', 'Qianli Ma', 'Siwei Zhang']	2023-04-12	null	null	null	null	['human-mesh-recovery']	['computer-vision']	[-1.29924580e-01 3.82465124e-01 3.20433766e-01 -2.43259400e-01 -3.06502581e-01 -2.82862961e-01 3.65108877e-01 -4.38498527e-01 -1.24686860e-01 3.55802000e-01 4.79590476e-01 4.98476118e-01 -1.63019210e-01 -6.87107325e-01 -5.36135912e-01 -4.80136782e-01 -2.39981804e-02 1.00305140e+00 4.04330790e-01 -6.22478902e-01 -1.85839102e-01 5.45672596e-01 -1.58411145e+00 -8.31047148e-02 9.19242144e-01 6.87393367e-01 2.67620236e-01 1.03963435e+00 2.00625107e-01 3.53338987e-01 -4.40460354e-01 -7.68376470e-01 3.67504746e-01 -3.20251137e-01 -3.97880912e-01 2.19025463e-01 5.65929770e-01 -3.35016906e-01 -4.37889785e-01 8.39106202e-01 8.96013558e-01 5.02882898e-01 7.09261060e-01 -1.21298015e+00 -2.54008267e-02 1.05161361e-01 -8.12834442e-01 -1.17055967e-01 1.03572333e+00 2.31290355e-01 4.87997293e-01 -7.56610990e-01 8.80819976e-01 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d478f2fe-8ce2-45ba-9b89-ab1a88546de0	non-local-latent-relation-distillation-for-1	2204.01971	null	https://arxiv.org/abs/2204.01971v2	https://arxiv.org/pdf/2204.01971v2.pdf	Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation	Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient. To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target. We propose to infer image-to-pose via two explicit mappings viz. image-to-latent and latent-to-pose where the latter is a pre-learned decoder obtained from a prior-enforcing generative adversarial auto-encoder. Next, we introduce relation distillation as a means to align the unpaired cross-modal samples i.e. the unpaired target videos and unpaired 3D pose sequences. To this end, we propose a new set of non-local relations in order to characterize long-range latent pose interactions unlike general contrastive relations where positive couplings are limited to a local neighborhood structure. Further, we provide an objective way to quantify non-localness in order to select the most effective relation set. We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks.	['R. Venkatesh Babu', 'Anirban Chakraborty', 'Varun Jampani', 'Pradyumna YM', 'Anirudh Jamkhandi', 'Siddharth Seth', 'Jogendra Nath Kundu']	2022-04-05	non-local-latent-relation-distillation-for	http://proceedings.neurips.cc/paper/2021/hash/018b59ce1fd616d874afad0f44ba338d-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/018b59ce1fd616d874afad0f44ba338d-Paper.pdf	neurips-2021-12	['unsupervised-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[ 2.56288350e-01 2.77039111e-01 -1.32161127e-02 -5.55937409e-01 -1.04203999e+00 -6.80616796e-01 7.59443581e-01 -4.46431071e-01 -3.26889426e-01 6.46954954e-01 2.44863048e-01 3.42537105e-01 1.57123268e-01 -5.13195038e-01 -1.30146217e+00 -6.28372133e-01 6.02692552e-02 8.15633535e-01 -4.03574854e-02 -3.36514264e-01 -3.27835500e-01 3.46386880e-01 -1.31227887e+00 1.11067347e-01 5.75879574e-01 7.82646775e-01 7.04503059e-02 6.50390267e-01 5.99717677e-01 4.78433907e-01 -4.81848568e-01 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ee22648c-e58c-4712-addc-42edaa5fe317	learning-spatiotemporal-frequency-transformer-1	2212.14046	null	https://arxiv.org/abs/2212.14046v1	https://arxiv.org/pdf/2212.14046v1.pdf	Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution	Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a ``divided attention'' which conducts a joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.	['Dongmei Fu', 'Chang Xu', 'Daochang Liu', 'Jianlong Fu', 'Huan Yang', 'Zhongwei Qiu']	2022-12-27	null	null	null	null	['video-super-resolution', 'video-enhancement']	['computer-vision', 'computer-vision']	[ 4.22850490e-01 -5.30907571e-01 -2.82130450e-01 -3.27801406e-02 -1.08903825e+00 -1.70767531e-01 2.51869678e-01 -5.61556697e-01 2.77354002e-01 7.38572657e-01 6.81080341e-01 2.62291789e-01 -1.83424801e-01 -5.35140038e-01 -8.25804353e-01 -8.38488162e-01 -5.15596122e-02 -6.11578107e-01 1.26334578e-01 -4.41344023e-01 2.52991527e-01 2.60461211e-01 -1.80903602e+00 6.28005624e-01 1.04265356e+00 1.03880739e+00 6.21064067e-01 9.24400330e-01 4.22600448e-01 9.90094304e-01 -4.90970194e-01 6.56388104e-02 1.36181489e-01 -5.44874430e-01 -7.40653217e-01 2.32277438e-01 5.39093018e-01 -5.79483271e-01 -8.25789452e-01 1.28891957e+00 5.27561128e-01 2.87543058e-01 3.28855246e-01 -6.12233996e-01 -1.31728637e+00 2.97467917e-01 -9.24703360e-01 9.16182518e-01 7.24547327e-01 2.13631243e-01 7.06040680e-01 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645e12c1-2413-43ea-a5ef-b90ed4aa1935	two-stage-is-enough-a-concise-deep-unfolding	2201.05810	null	https://arxiv.org/abs/2201.05810v2	https://arxiv.org/pdf/2201.05810v2.pdf	Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing	We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure. Yet, we aim to build a flexible and concise model using minimum stages. Different from existing deep unfolding networks used for inverse problems, where more stages are used for higher performance but without flexibility to different masks and scales, hereby we show that a 2-stage deep unfolding network can lead to the state-of-the-art (SOTA) results (with a 1.7dB gain in PSNR over the single stage model, RevSCI) in VCS. The proposed method possesses the properties of adaptation to new masks and ready to scale to large data without any additional training thanks to the advantages of deep unfolding. Furthermore, we extend the proposed model for color VCS to perform joint reconstruction and demosaicing. Experimental results demonstrate that our 2-stage model has also achieved SOTA on color VCS reconstruction, leading to a >2.3dB gain in PSNR over the previous SOTA algorithm based on plug-and-play framework, meanwhile speeds up the reconstruction by >17 times. In addition, we have found that our network is also flexible to the mask modulation and scale size for color VCS reconstruction so that a single trained network can be applied to different hardware systems. The code and models will be released to the public.	['Xin Yuan', 'Xiaoyu Yang', 'Siming Zheng']	2022-01-15	null	null	null	null	['video-compressive-sensing']	['computer-vision']	[ 4.26642329e-01 2.54235119e-02 2.55405694e-01 -8.70315582e-02 -7.13448167e-01 -1.81487530e-01 2.87736148e-01 -7.36944020e-01 -2.64587134e-01 3.89463693e-01 1.35674030e-01 -4.70253021e-01 -1.53781176e-02 -4.77661014e-01 -1.03213215e+00 -6.35748029e-01 -2.60138780e-01 -1.92581892e-01 2.48796299e-01 -2.65295535e-01 3.63707803e-02 5.74060917e-01 -1.22730005e+00 4.02171284e-01 6.19456828e-01 1.32946932e+00 6.49826229e-01 5.72804093e-01 2.91875601e-01 7.12575853e-01 -2.39161953e-01 -2.69766361e-01 8.15188587e-01 -4.50239062e-01 -3.62887919e-01 1.62026331e-01 3.32719892e-01 -8.60496581e-01 -5.41795075e-01 8.95442843e-01 5.60002744e-01 -2.11618409e-01 9.54781547e-02 -6.72459364e-01 -7.78726280e-01 5.30737996e-01 -7.23217249e-01 -8.64305794e-02 2.52935678e-01 9.61498450e-03 7.32467949e-01 -1.25099659e+00 5.96983254e-01 1.08629382e+00 8.53761971e-01 4.73485351e-01 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16d5b81b-8268-4e7d-8774-4cccb1dff5f0	cabm-content-aware-bit-mapping-for-single	2304.06454	null	https://arxiv.org/abs/2304.06454v1	https://arxiv.org/pdf/2304.06454v1.pdf	CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input	With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released.	['Shunli Zhang', 'Yurong Chen', 'Yandong Guo', 'Jiaming Liu', 'Ming Lu', 'Senmao Tian']	2023-04-13	null	http://openaccess.thecvf.com//content/CVPR2023/html/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.pdf	cvpr-2023-1	['image-super-resolution']	['computer-vision']	[ 2.43258983e-01 -2.35667884e-01 -5.44722617e-01 -4.91461933e-01 -5.71682453e-01 -3.36036712e-01 -9.32222456e-02 -2.19480768e-01 -4.12190795e-01 7.01868951e-01 3.24403793e-02 -2.04723537e-01 -1.86814278e-01 -1.17180681e+00 -6.85366869e-01 -7.68295169e-01 3.09513420e-01 -2.76327543e-02 6.56738520e-01 -2.52629421e-03 3.61414880e-01 4.41311330e-01 -1.35391879e+00 6.27064943e-01 8.39692652e-01 1.47945964e+00 4.87459987e-01 4.94247168e-01 -3.52140754e-01 4.57519144e-01 -6.95524275e-01 -3.02187264e-01 3.95059347e-01 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a628746f-ce9b-4ecd-a2cc-cbdfa3e4f092	sharingan-combining-synthetic-and-real-data-1	2006.04026	null	https://arxiv.org/abs/2006.04026v1	https://arxiv.org/pdf/2006.04026v1.pdf	SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation	We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.	['Hao Zhou', 'Koutilya PNVR', 'David Jacobs']	2020-06-07	sharingan-combining-synthetic-and-real-data	http://openaccess.thecvf.com/content_CVPR_2020/html/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.pdf	cvpr-2020-6	['surface-normals-estimation']	['computer-vision']	[ 5.44112325e-01 3.98821324e-01 1.23825073e-01 -8.98223817e-01 -7.45329201e-01 -4.40209895e-01 5.70966125e-01 -7.81436920e-01 -3.99000078e-01 7.63369083e-01 -1.80871129e-01 -2.50681918e-02 3.06743562e-01 -8.05027366e-01 -1.04357016e+00 -3.93380553e-01 1.18645534e-01 4.91187304e-01 2.39212200e-01 -6.32529706e-03 2.05170084e-02 9.33060348e-01 -1.59507835e+00 4.76431519e-01 4.55542147e-01 1.05308497e+00 2.27881476e-01 5.65486014e-01 1.61603063e-01 6.74768746e-01 -4.37896609e-01 -3.32630128e-01 6.66864872e-01 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4c0376b9-5781-49b0-aaf0-af2a6f361535	what-if-we-enrich-day-ahead-solar-irradiance	2306.01112	null	https://arxiv.org/abs/2306.01112v1	https://arxiv.org/pdf/2306.01112v1.pdf	What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?	Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on employing purely time series-based methodologies for solar forecasting, only a limited number of studies have taken into account factors such as cloud cover or the surrounding physical context. In this paper, we put forth a deep learning architecture designed to harness spatio-temporal context using satellite data, to attain highly accurate \textit{day-ahead} time-series forecasting for any given station, with a particular emphasis on forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology to extract a distribution for each time step prediction, which can serve as a very valuable measure of uncertainty attached to the forecast. When evaluating models, we propose a testing scheme in which we separate particularly difficult examples from easy ones, in order to capture the model performances in crucial situations, which in the case of this study are the days suffering from varying cloudy conditions. Furthermore, we present a new multi-modal dataset gathering satellite imagery over a large zone and time series for solar irradiance and other related physical variables from multiple geographically diverse solar stations. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective integration of solar power into the grid.	['Yoshua Bengio', 'Loubna Benabbou', 'Tianle Yuan', 'Stefano Massaroli', 'Dan Assouline', 'Ghait Boukachab', 'Oussama Boussif']	2023-06-01	null	null	null	null	['solar-irradiance-forecasting']	['time-series']	[ 5.45779802e-02 -5.18990576e-01 9.28666070e-02 -2.68463939e-01 -6.53354526e-01 -8.73833954e-01 7.89662361e-01 2.10142527e-02 1.75172061e-01 1.18433249e+00 3.08290403e-02 -6.82705045e-01 -4.69189942e-01 -1.23593152e+00 -5.73061109e-01 -1.36899948e+00 -4.45488235e-03 -8.20686296e-02 -4.89122480e-01 -3.01560074e-01 1.22847743e-01 7.61696637e-01 -1.71402025e+00 -4.37182277e-01 1.31938970e+00 9.01333690e-01 2.89483935e-01 4.49121743e-01 6.94646165e-02 3.04361761e-01 -8.50097358e-01 1.31507844e-01 4.67781037e-01 -4.59425300e-01 2.85601113e-02 -9.88223851e-02 3.17744851e-01 -1.47585601e-01 4.30361293e-02 9.12922442e-01 8.28968525e-01 2.13208690e-01 7.15044618e-01 -1.13209605e+00 -3.23989868e-01 3.37078422e-01 -4.15448993e-01 5.17539680e-01 -1.32102352e-02 4.95295882e-01 7.13693202e-01 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1.38277709e+00 -1.15909672e+00 -5.32125473e-01 -1.06194794e+00 -7.10321128e-01 -3.48536670e-02 5.21810591e-01 -4.82679307e-01 -8.22570980e-01 -2.06890032e-01]	[6.356087684631348, 2.768005847930908]
1b8c6626-ada0-4099-bc65-ae8fffda6850	ecg-signal-super-resolution-by-considering	2012.03803	null	https://arxiv.org/abs/2012.03803v2	https://arxiv.org/pdf/2012.03803v2.pdf	SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification	A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances.	['Kai-Chun Liu', 'Yu Tsao', 'Chun-Yen Shen', 'Guo-Yuan Li', 'Chih-Han Huang', 'Jhih-Yu Chen', 'Huan-Hsin Tseng', 'Yuan-Hong Tsai', 'Tsai-Min Chen']	2020-12-07	null	null	null	null	['electrocardiography-ecg']	['methodology']	[ 2.36560106e-01 -6.28216088e-01 1.80637762e-01 -9.87065881e-02 -1.07826626e+00 -1.86426565e-01 -2.04267889e-01 1.29882768e-01 -3.60577971e-01 8.63509119e-01 -1.81526378e-01 -9.00344551e-02 -5.16641498e-01 -6.73303902e-01 -2.86360234e-01 -9.19932187e-01 -2.34719202e-01 -2.14247271e-01 -1.77151471e-01 4.70751561e-02 -1.92697391e-01 5.65938592e-01 -1.49285686e+00 7.42673874e-01 1.31710827e+00 1.44558752e+00 -5.82394116e-02 8.19572031e-01 3.88200402e-01 5.01780212e-01 -9.16883528e-01 -7.35502243e-02 8.90629143e-02 -7.88869143e-01 -2.00612277e-01 -5.94443917e-01 -1.02119118e-01 -3.21706086e-01 2.08178282e-01 6.56182885e-01 1.29189909e+00 -4.41065073e-01 3.84573400e-01 -1.03366578e+00 -2.00302601e-01 3.35579664e-01 -3.47033083e-01 4.59752351e-01 4.30281371e-01 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f6c30f72-6a3e-4503-9a86-6262050f8370	scalable-distributed-ai-frameworks-leveraging	2304.13738	null	https://arxiv.org/abs/2304.13738v1	https://arxiv.org/pdf/2304.13738v1.pdf	Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency	In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of scalable, distributed AI frameworks leveraging cloud computing for enhanced deep learning performance and efficiency. We first provide an overview of popular AI frameworks and cloud services, highlighting their respective strengths and weaknesses. Next, we delve into the critical aspects of data storage and management in cloud-based AI systems, discussing data preprocessing, feature engineering, privacy, and security. We then explore parallel and distributed training techniques for AI models, focusing on model partitioning, communication strategies, and cloud-based training architectures. In subsequent chapters, we discuss optimization strategies for AI workloads in the cloud, covering load balancing, resource allocation, auto-scaling, and performance benchmarking. We also examine AI model deployment and serving in the cloud, outlining containerization, serverless deployment options, and monitoring best practices. To ensure the cost-effectiveness of cloud-based AI solutions, we present a thorough analysis of costs, optimization strategies, and case studies showcasing successful deployments. Finally, we summarize the key findings of this study, discuss the challenges and limitations of cloud-based AI, and identify emerging trends and future research opportunities in the field.	['Neelesh Mungoli']	2023-04-26	null	null	null	null	['feature-engineering']	['methodology']	[-3.24403018e-01 -5.81224024e-01 7.99840242e-02 -4.14251298e-01 -2.87573248e-01 -6.28438234e-01 1.22502171e-01 2.02193484e-01 -3.76793772e-01 3.81031305e-01 -1.85797781e-01 -5.32705151e-02 -4.96467412e-01 -9.12260890e-01 -3.37103426e-01 -8.39701474e-01 -4.61020738e-01 1.13927197e+00 -4.52722669e-01 1.41731724e-01 4.37355824e-02 1.05801380e+00 -2.01659298e+00 2.42968127e-01 7.35960662e-01 1.49924052e+00 -2.39637837e-01 7.75637746e-01 -2.66933113e-01 8.81439924e-01 -1.23584270e+00 -4.48297560e-01 6.33020222e-01 2.72827178e-01 -7.08133757e-01 -5.14744259e-02 6.08382642e-01 -4.27575141e-01 -2.13455349e-01 6.96330309e-01 8.08353543e-01 -8.02220404e-03 1.69300511e-01 -2.03548527e+00 -4.70640630e-01 5.23088574e-01 2.74713174e-03 3.82770628e-01 -7.91255161e-02 3.45536143e-01 5.69025517e-01 -6.08692527e-01 4.13656980e-01 5.64706862e-01 5.24120748e-01 5.28968275e-01 -7.29954958e-01 -9.60618854e-01 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e93c2a61-c220-45bb-9c3f-c0d821fa0501	parcel3d-shape-reconstruction-from-single-rgb	2304.08994	null	https://arxiv.org/abs/2304.08994v1	https://arxiv.org/pdf/2304.08994v1.pdf	Parcel3D: Shape Reconstruction from Single RGB Images for Applications in Transportation Logistics	We focus on enabling damage and tampering detection in logistics and tackle the problem of 3D shape reconstruction of potentially damaged parcels. As input we utilize single RGB images, which corresponds to use-cases where only simple handheld devices are available, e.g. for postmen during delivery or clients on delivery. We present a novel synthetic dataset, named Parcel3D, that is based on the Google Scanned Objects (GSO) dataset and consists of more than 13,000 images of parcels with full 3D annotations. The dataset contains intact, i.e. cuboid-shaped, parcels and damaged parcels, which were generated in simulations. We work towards detecting mishandling of parcels by presenting a novel architecture called CubeRefine R-CNN, which combines estimating a 3D bounding box with an iterative mesh refinement. We benchmark our approach on Parcel3D and an existing dataset of cuboid-shaped parcels in real-world scenarios. Our results show, that while training on Parcel3D enables transfer to the real world, enabling reliable deployment in real-world scenarios is still challenging. CubeRefine R-CNN yields competitive performance in terms of Mesh AP and is the only model that directly enables deformation assessment by 3D mesh comparison and tampering detection by comparing viewpoint invariant parcel side surface representations. Dataset and code are available at https://a-nau.github.io/parcel3d.	['Kai Furmans', 'Laura Dörr', 'Felix Hertlein', 'Alexander Naumann']	2023-04-18	null	null	null	null	['3d-shape-reconstruction']	['computer-vision']	[ 3.91522832e-02 9.62826163e-02 3.70997041e-01 1.26030028e-01 -6.60252988e-01 -9.89169538e-01 4.12896842e-01 3.12057883e-01 7.80887390e-03 3.35544020e-01 -3.05665225e-01 -3.18088502e-01 1.30197525e-01 -1.34908164e+00 -1.26982474e+00 -3.71886104e-01 -3.44043881e-01 7.75906980e-01 1.28022924e-01 -3.66541415e-01 1.16557494e-01 1.30140960e+00 -1.56428432e+00 3.17435086e-01 7.30147600e-01 1.47390449e+00 5.50474860e-02 7.10938573e-01 2.44281083e-01 -7.08735287e-02 -6.33782029e-01 -5.19732177e-01 7.75154471e-01 4.23385322e-01 -3.56539488e-01 2.37457722e-01 4.96995717e-01 -6.16077244e-01 -2.19086260e-01 6.47198498e-01 3.87946993e-01 -3.50040585e-01 6.65387094e-01 -1.26977289e+00 -4.24931318e-01 -2.35498086e-01 -4.54877585e-01 -1.90350592e-01 5.59520841e-01 1.20874122e-01 3.41793001e-01 -9.68122363e-01 7.97013104e-01 1.17451358e+00 1.08395910e+00 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87227949-4c8d-4846-9e55-5a00e73afb9b	multivariate-time-series-imputation-with-1	1907.04155	null	https://arxiv.org/abs/1907.04155v5	https://arxiv.org/pdf/1907.04155v5.pdf	GP-VAE: Deep Probabilistic Time Series Imputation	Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learning fall short in giving reliable confidence estimates and lack interpretability. We propose a new deep sequential latent variable model for dimensionality reduction and data imputation. Our modeling assumption is simple and interpretable: the high dimensional time series has a lower-dimensional representation which evolves smoothly in time according to a Gaussian process. The non-linear dimensionality reduction in the presence of missing data is achieved using a VAE approach with a novel structured variational approximation. We demonstrate that our approach outperforms several classical and deep learning-based data imputation methods on high-dimensional data from the domains of computer vision and healthcare, while additionally improving the smoothness of the imputations and providing interpretable uncertainty estimates.	['Gunnar Rätsch', 'Dmitry Baranchuk', 'Vincent Fortuin', 'Stephan Mandt']	2019-07-09	null	null	null	null	['multivariate-time-series-imputation']	['time-series']	[ 9.98777524e-02 2.20598504e-01 -1.04870729e-01 -6.54840648e-01 -1.00463986e+00 -1.36188507e-01 5.61909854e-01 4.81281988e-02 -2.46486604e-01 1.08423197e+00 5.33493698e-01 -1.25647232e-01 -5.39737403e-01 -6.33354127e-01 -8.95341218e-01 -8.95697653e-01 1.07065403e-04 8.73687327e-01 -8.49407732e-01 2.56957293e-01 -1.78103179e-01 1.20010853e-01 -1.24332249e+00 -3.55568379e-02 1.10085297e+00 8.65310490e-01 -4.24001098e-01 2.44218469e-01 -1.18336745e-01 5.98141670e-01 -1.18851759e-01 -4.53605860e-01 1.38057247e-01 -3.18999738e-01 -3.68313938e-01 -2.18761027e-01 4.34024334e-02 -4.13951844e-01 -2.91083544e-01 7.43492186e-01 3.96714687e-01 6.92568943e-02 9.86676991e-01 -1.44268405e+00 -7.33852327e-01 3.94745976e-01 -6.69019043e-01 -3.79077226e-01 -1.13181826e-02 -1.75609946e-01 5.36413014e-01 -6.86111629e-01 5.60102224e-01 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b3ed94f9-4004-43ec-b221-7e3013c95c8d	bear-physics-principled-building-environment	2211.14744	null	https://arxiv.org/abs/2211.14744v1	https://arxiv.org/pdf/2211.14744v1.pdf	BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning	Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dynamical model and energy management task simulation and evaluation platform has arguably slowed the progress in developing advanced and dedicated reinforcement learning (RL) and control algorithms for building operation tasks. Here we propose "BEAR", a physics-principled Building Environment for Control And Reinforcement Learning. The platform allows researchers to benchmark both model-based and model-free controllers using a broad collection of standard building models in Python without co-simulation using external building simulators. In this paper, we discuss the design of this platform and compare it with other existing building simulation frameworks. We demonstrate the compatibility and performance of BEAR with different controllers, including both model predictive control (MPC) and several state-of-the-art RL methods with two case studies.	['Yize Chen', 'Yuanyuan Shi', 'Chi Zhang']	2022-11-27	null	null	null	null	['energy-management']	['time-series']	[-4.09648269e-01 -2.52711356e-01 -1.27469927e-01 2.62790889e-01 -3.95298541e-01 -5.10095060e-01 3.96597117e-01 9.15561244e-02 1.16399966e-01 1.16723764e+00 -2.78574318e-01 -2.92097569e-01 -3.87645215e-01 -1.25475991e+00 -5.17816544e-01 -1.00144875e+00 -3.72883111e-01 5.28920054e-01 1.05119534e-01 -6.57212913e-01 -3.54218870e-01 4.54468876e-01 -1.97998929e+00 -5.62884033e-01 6.85925007e-01 4.82322276e-01 5.75739145e-01 1.01290274e+00 7.37216711e-01 8.61649096e-01 -3.13496917e-01 1.02826333e+00 1.68527618e-01 -3.48869085e-01 -5.22467375e-01 -2.83255965e-01 -4.78294790e-01 -2.29583487e-01 -4.08887327e-01 1.87736899e-01 1.07538688e+00 6.44567788e-01 2.16089517e-01 -1.40964961e+00 -2.22824261e-01 1.27466336e-01 9.01650712e-02 1.72237396e-01 4.65847343e-01 7.51431704e-01 5.85739851e-01 2.37438738e-01 1.32800788e-01 8.64470661e-01 8.29662919e-01 7.55114436e-01 -1.30057585e+00 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e9944785-38e9-434d-9632-e4dce3f90b6c	mitigating-approximate-memorization-in	2305.01550	null	https://arxiv.org/abs/2305.01550v1	https://arxiv.org/pdf/2305.01550v1.pdf	Mitigating Approximate Memorization in Language Models via Dissimilarity Learned Policy	Large Language models (LLMs) are trained on large amounts of data, which can include sensitive information that may compromise per- sonal privacy. LLMs showed to memorize parts of the training data and emit those data verbatim when an adversary prompts appropriately. Previous research has primarily focused on data preprocessing and differential privacy techniques to address memorization or prevent verbatim memorization exclusively, which can give a false sense of privacy. However, these methods rely on explicit and implicit assumptions about the structure of the data to be protected, which often results in an incomplete solution to the problem. To address this, we propose a novel framework that utilizes a reinforcement learning approach (PPO) to fine-tune LLMs to mitigate approximate memorization. Our approach utilizes a negative similarity score, such as BERTScore or SacreBLEU, as a reward signal to learn a dissimilarity policy. Our results demonstrate that this framework effectively mitigates approximate memorization while maintaining high levels of coherence and fluency in the generated samples. Furthermore, our framework is robust in mitigating approximate memorization across various circumstances, including longer context, which is known to increase memorization in LLMs.	['Aly M. Kassem']	2023-05-02	null	null	null	null	['memorization']	['natural-language-processing']	[ 2.34374449e-01 2.18083024e-01 -1.98041216e-01 -5.04679918e-01 -7.43327737e-01 -6.52047455e-01 5.62463105e-01 4.60501820e-01 -8.99212360e-01 8.48723829e-01 1.01537257e-01 -3.40896875e-01 2.42191702e-01 -8.85882020e-01 -9.73608553e-01 -4.94434237e-01 1.54612735e-01 -1.68594196e-01 -3.42221141e-01 -7.47779384e-02 5.63373208e-01 3.98354024e-01 -1.20026755e+00 4.06893283e-01 1.01803863e+00 8.33327115e-01 -1.56242505e-01 4.80766624e-01 -1.90128665e-02 7.43863821e-01 -9.09598470e-01 -8.12312126e-01 6.27205491e-01 -2.92643338e-01 -7.35469341e-01 -5.80359161e-01 4.42893565e-01 -4.98743236e-01 -2.41879985e-01 1.19268477e+00 5.05322278e-01 2.48801306e-01 2.70614177e-01 -1.24348068e+00 -6.22199714e-01 6.65828407e-01 -5.16891778e-01 9.51213315e-02 5.54329574e-01 3.31509531e-01 7.56187141e-01 -5.02867937e-01 3.30287784e-01 1.27262855e+00 6.91094995e-01 8.33433092e-01 -1.41749418e+00 -1.15498519e+00 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f75c7bfc-3eb5-496f-885a-028cbf4ea89b	cascading-multiway-attentions-for-document	null	null	https://aclanthology.org/I17-1064	https://aclanthology.org/I17-1064.pdf	Cascading Multiway Attentions for Document-level Sentiment Classification	Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not comprehensively consider what roles the three kinds of information play in text modeling. In this paper, to reasonably use all the information, we present the idea that user, product and their combination can all influence the generation of attentions to words and sentences, when judging the sentiment of a document. With this idea, we propose a cascading multiway attention (CMA) model, where multiple ways of using user and product information are cascaded to influence the generation of attentions on the word and sentence layers. Then, sentences and documents are well modeled by multiple representation vectors, which provide rich information for sentiment classification. Experiments on IMDB and Yelp datasets demonstrate the effectiveness of our model.	['Xu sun', 'Dehong Ma', 'Houfeng Wang', 'Xiaodong Zhang', 'Sujian Li']	2017-11-01	cascading-multiway-attentions-for-document-1	https://aclanthology.org/I17-1064	https://aclanthology.org/I17-1064.pdf	ijcnlp-2017-11	['product-recommendation']	['miscellaneous']	[-2.85513327e-02 -1.60395011e-01 -3.63349229e-01 -7.65235066e-01 -1.79771990e-01 -4.17208374e-01 7.61390388e-01 2.28390723e-01 -2.58063078e-01 3.55152875e-01 7.21662879e-01 -4.75528121e-01 3.58533531e-01 -9.47939992e-01 -4.79372114e-01 -4.80982840e-01 5.19475341e-01 1.06064603e-02 -1.09546773e-01 -6.84496224e-01 4.93856013e-01 -8.31446871e-02 -1.31938708e+00 7.61929035e-01 7.28495836e-01 9.67101991e-01 1.56504691e-01 6.36095285e-01 -6.33241355e-01 1.18478274e+00 -8.05633724e-01 -7.80904710e-01 -2.59027779e-01 -3.55233103e-01 -7.54719496e-01 2.83690184e-01 -6.88652620e-02 -3.31846952e-01 8.07031468e-02 1.00618458e+00 3.35389256e-01 -4.06109020e-02 8.20602417e-01 -9.94561613e-01 -1.35723245e+00 1.13680947e+00 -8.14115703e-01 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a1e8cc0b-f97c-44c7-bcaf-b4e0b63c244c	evaluating-generatively-synthesized-diabetic	2208.05593	null	https://arxiv.org/abs/2208.05593v2	https://arxiv.org/pdf/2208.05593v2.pdf	Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery	Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers' diagnostic capacity. The imbalance can be addressed using Generative Adversarial Networks (GANs) to augment the datasets with synthetic images. Generating synthetic images is advantageous if high-quality and diversified images are produced. To evaluate the quality and diversity of synthetic images, several evaluation metrics, such as Multi-Scale Structural Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr\'echet Inception Distance (FID) are used. Understanding the effectiveness of each metric in evaluating the quality and diversity of GAN-based synthetic images is critical to select images for augmentation. To date, there has been limited analysis of the appropriateness of these metrics in the context of biomedical imagery. This work contributes an empirical assessment of these evaluation metrics as applied to synthetic Proliferative DR imagery generated by a Deep Convolutional GAN (DCGAN). Furthermore, the metrics' capacity to indicate the quality and diversity of synthetic images and a correlation with classifier performance is undertaken. This enables a quantitative selection of synthetic imagery and an informed augmentation strategy. Results indicate that FID is suitable for evaluating the quality, while MS-SSIM and CD are suitable for evaluating the diversity of synthetic imagery. Furthermore, the superior performance of Convolutional Neural Network (CNN) and EfficientNet classifiers, as indicated by the F1 and AUC scores, for the augmented datasets demonstrates the efficacy of synthetic imagery to augment the imbalanced dataset.	["Ruairi O'Reilly", 'Mubashir Husain Rehmani', 'Muhammad Muneeb Saad', 'Cristina-Madalina Dragan']	2022-08-10	null	null	null	null	['ms-ssim']	['computer-vision']	[ 6.21785581e-01 2.05239937e-01 -5.36173508e-02 -2.92924404e-01 -7.16101468e-01 -3.50951970e-01 5.54463267e-01 -7.09335180e-03 -3.00519437e-01 8.82024944e-01 3.48356575e-01 -1.37648270e-01 -2.19662994e-01 -7.56233096e-01 -3.95358086e-01 -8.39219928e-01 1.28414586e-01 2.17922822e-01 -3.59388322e-01 -1.65155232e-01 1.46555305e-01 7.62522697e-01 -1.73405743e+00 4.26301658e-01 1.24828792e+00 8.72244835e-01 -6.07098006e-02 6.57497644e-01 9.97377858e-02 8.65753829e-01 -1.03824377e+00 -3.12743485e-01 4.05623645e-01 -7.22555041e-01 -5.01798689e-01 8.74063447e-02 6.35064483e-01 -3.86138916e-01 -1.75254405e-01 7.04705298e-01 8.93707633e-01 -3.72884125e-01 7.47433960e-01 -1.09526551e+00 -4.07654077e-01 2.98218012e-01 -2.72216529e-01 4.26675022e-01 1.51568115e-01 5.59290051e-01 4.34121400e-01 -4.41063076e-01 7.57595181e-01 9.23267186e-01 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6.81164443e-01 -1.71921536e-01 -3.69139165e-01 -9.79531646e-01 -5.02278328e-01 4.60237443e-01 2.68439353e-01 -4.62066829e-01 -4.22805101e-01 1.10375971e-01]	[14.355939865112305, -2.0093917846679688]
8c73ef8c-7176-4b18-94aa-9eac68749dbd	distribution-regularized-self-supervised	2206.09683	null	https://arxiv.org/abs/2206.09683v1	https://arxiv.org/pdf/2206.09683v1.pdf	Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation	This paper proposes a novel pixel-level distribution regularization scheme (DRSL) for self-supervised domain adaptation of semantic segmentation. In a typical setting, the classification loss forces the semantic segmentation model to greedily learn the representations that capture inter-class variations in order to determine the decision (class) boundary. Due to the domain shift, this decision boundary is unaligned in the target domain, resulting in noisy pseudo labels adversely affecting self-supervised domain adaptation. To overcome this limitation, along with capturing inter-class variation, we capture pixel-level intra-class variations through class-aware multi-modal distribution learning (MMDL). Thus, the information necessary for capturing the intra-class variations is explicitly disentangled from the information necessary for inter-class discrimination. Features captured thus are much more informative, resulting in pseudo-labels with low noise. This disentanglement allows us to perform separate alignments in discriminative space and multi-modal distribution space, using cross-entropy based self-learning for the former. For later, we propose a novel stochastic mode alignment method, by explicitly decreasing the distance between the target and source pixels that map to the same mode. The distance metric learning loss, computed over pseudo-labels and backpropagated from multi-modal modeling head, acts as the regularizer over the base network shared with the segmentation head. The results from comprehensive experiments on synthetic to real domain adaptation setups, i.e., GTA-V/SYNTHIA to Cityscapes, show that DRSL outperforms many existing approaches (a minimum margin of 2.3% and 2.5% in mIoU for SYNTHIA to Cityscapes).	['Mohsen Ali', 'Yu-Tseh Chi', 'Rehan Hafiz', 'Hamza Rawal', 'Javed Iqbal']	2022-06-20	null	null	null	null	['self-learning']	['natural-language-processing']	[ 5.55875719e-01 2.00696155e-01 -4.58701849e-01 -4.87887740e-01 -1.08513737e+00 -7.15907753e-01 3.20635319e-01 -5.71776778e-02 -5.09529710e-01 7.16468990e-01 -9.74052176e-02 1.97872251e-01 2.47466937e-02 -7.10240722e-01 -6.83467746e-01 -1.20625365e+00 3.15109491e-01 5.56643069e-01 3.30796629e-01 1.11653954e-01 -9.13645402e-02 2.62969911e-01 -1.51260233e+00 3.18934411e-01 1.11789072e+00 1.00035203e+00 1.71377480e-01 2.87303388e-01 -1.92726672e-01 2.20909789e-01 -5.08018374e-01 -1.77263513e-01 3.17855388e-01 -5.14083385e-01 -6.93873942e-01 3.09164107e-01 6.33910358e-01 8.67622159e-03 -4.85512465e-02 1.31391490e+00 5.65358698e-01 2.62182653e-01 1.09269261e+00 -1.01537108e+00 -3.02876294e-01 3.74878705e-01 -8.39857757e-01 2.18855795e-02 -1.56624198e-01 2.21967399e-01 1.13379812e+00 -4.70818907e-01 6.72488272e-01 1.13954759e+00 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2e3392cf-58ee-4b99-b544-c583db7a3f4d	mau-a-motion-aware-unit-for-video-prediction	null	null	http://proceedings.neurips.cc/paper/2021/hash/e25cfa90f04351958216f97e3efdabe9-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/e25cfa90f04351958216f97e3efdabe9-Paper.pdf	MAU: A Motion-Aware Unit for Video Prediction and Beyond	Accurately predicting inter-frame motion information plays a key role in video prediction tasks. In this paper, we propose a Motion-Aware Unit (MAU) to capture reliable inter-frame motion information by broadening the temporal receptive field of the predictive units. The MAU consists of two modules, the attention module and the fusion module. The attention module aims to learn an attention map based on the correlations between the current spatial state and the historical spatial states. Based on the learned attention map, the historical temporal states are aggregated to an augmented motion information (AMI). In this way, the predictive unit can perceive more temporal dynamics from a wider receptive field. Then, the fusion module is utilized to further aggregate the augmented motion information (AMI) and current appearance information (current spatial state) to the final predicted frame. The computation load of MAU is relatively low and the proposed unit can be easily applied to other predictive models. Moreover, an information recalling scheme is employed into the encoders and decoders to help preserve the visual details of the predictions. We evaluate the MAU on both video prediction and early action recognition tasks. Experimental results show that the MAU outperforms the state-of-the-art methods on both tasks.	['Wen Gao', 'Xiang Xinguang', 'Yan Ye', 'Siwei Ma', 'Shanshe Wang', 'Xinfeng Zhang', 'Zheng Chang']	2021-12-01	null	https://openreview.net/forum?id=qwtfY-3ibt7	https://openreview.net/pdf?id=qwtfY-3ibt7	neurips-2021-12	['video-prediction']	['computer-vision']	[ 3.15246463e-01 -1.61547825e-01 -3.33142698e-01 -2.56427705e-01 -2.74510354e-01 2.05152318e-01 5.47814965e-01 -2.09329024e-01 -2.64334142e-01 5.44845164e-01 4.81443942e-01 1.81364626e-01 2.50677228e-01 -6.61644995e-01 -6.58368587e-01 -9.49130177e-01 -4.82454710e-02 -2.51175076e-01 8.74287963e-01 1.61888659e-01 3.28712493e-01 1.73195422e-01 -1.57446527e+00 7.37353623e-01 6.78385556e-01 1.37755287e+00 9.82357144e-01 5.85997403e-01 5.66922240e-02 1.56157362e+00 -8.40940252e-02 1.86468050e-01 -5.77094331e-02 -5.86966872e-01 -6.13619804e-01 2.83096135e-01 8.44877735e-02 -7.06007957e-01 -6.38037026e-01 8.26531231e-01 2.58595705e-01 6.55753553e-01 2.31510520e-01 -9.15091038e-01 -5.28555453e-01 2.27602616e-01 -4.88518476e-01 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91148b81-e450-4ca3-9fbe-f2ad692eca7a	semi-supervised-clustering-for-short-text-via	1602.06797	null	http://arxiv.org/abs/1602.06797v2	http://arxiv.org/pdf/1602.06797v2.pdf	Semi-supervised Clustering for Short Text via Deep Representation Learning	In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the k-means clustering process together, and optimize the objective with both labeled data and unlabeled data iteratively until convergence through three steps: (1) assign each short text to its nearest centroid based on its representation from the current neural networks; (2) re-estimate the cluster centroids based on cluster assignments from step (1); (3) update neural networks according to the objective by keeping centroids and cluster assignments fixed. Experimental results on four datasets show that our method works significantly better than several other text clustering methods.	['Abraham Ittycheriah', 'Zhiguo Wang', 'Haitao Mi']	2016-02-22	semi-supervised-clustering-for-short-text-via-1	https://aclanthology.org/K16-1004	https://aclanthology.org/K16-1004.pdf	conll-2016-8	['text-clustering', 'short-text-clustering']	['natural-language-processing', 'natural-language-processing']	[-6.52268827e-02 -1.74222440e-01 -4.72403377e-01 -8.26261461e-01 -5.20299196e-01 -6.71604276e-01 2.83133060e-01 3.43436778e-01 -6.03984177e-01 2.75070041e-01 4.31112349e-01 -1.36238545e-01 -1.22656167e-01 -5.27543128e-01 -2.37327769e-01 -7.64045298e-01 2.88582504e-01 1.05228710e+00 -1.25354121e-03 3.37978601e-01 5.08495569e-01 2.82467037e-01 -1.38194430e+00 3.03568929e-01 1.08390975e+00 7.12321341e-01 1.36339962e-01 5.59231877e-01 -6.28294885e-01 1.08124137e+00 -5.02540648e-01 1.78866591e-02 -9.79483966e-03 -5.39379835e-01 -1.21432269e+00 5.74200392e-01 -1.01811782e-01 -8.20422471e-02 -3.05061340e-01 1.13523114e+00 2.46730477e-01 8.16551328e-01 1.14226258e+00 -1.11192226e+00 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a05a63cd-a4e3-4fa6-8aba-28a0d519bb5b	robust-reference-based-super-resolution-via	2106.01863	null	https://arxiv.org/abs/2106.01863v1	https://arxiv.org/pdf/2106.01863v1.pdf	Robust Reference-based Super-Resolution via C2-Matching	Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g. scale and rotation) and the resolution gap (e.g. HR and LR). To tackle these challenges, we propose C2-Matching in this work, which produces explicit robust matching crossing transformation and resolution. 1) For the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) For the resolution gap, we adopt a teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue. In addition, to faithfully evaluate the performance of Ref-SR under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. Extensive experiments demonstrate that our proposed C2-Matching significantly outperforms state of the arts by over 1dB on the standard CUFED5 benchmark. Notably, it also shows great generalizability on WR-SR dataset as well as robustness across large scale and rotation transformations.	['Ziwei Liu', 'Chen Change Loy', 'Xintao Wang', 'Kelvin C. K. Chan', 'Yuming Jiang']	2021-06-03	null	http://openaccess.thecvf.com//content/CVPR2021/html/Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper.pdf	cvpr-2021-1	['reference-based-super-resolution']	['computer-vision']	[ 5.07177114e-01 -1.78708658e-01 -1.02736175e-01 -2.52751112e-01 -1.27365255e+00 -2.88189232e-01 6.46271706e-01 -5.00327528e-01 -1.93763614e-01 5.76464117e-01 4.49221164e-01 1.16225533e-01 -1.09533988e-01 -7.61016309e-01 -9.09171700e-01 -7.78477013e-01 3.71321231e-01 -4.45905030e-02 3.63609165e-01 -5.22461832e-01 2.37554356e-01 4.63561237e-01 -1.51614988e+00 3.12519640e-01 1.19643676e+00 9.69164729e-01 4.15459871e-01 2.29635343e-01 1.86356306e-01 4.59887445e-01 -3.03167403e-01 -1.99287072e-01 6.68770790e-01 -3.80704373e-01 -7.20686257e-01 5.03794961e-02 9.50780749e-01 -4.75841731e-01 -4.43846166e-01 1.17822611e+00 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942bad4b-c30b-499b-b5bd-2baecf657fc4	multi-image-steganography-using-deep-neural	2101.00350	null	https://arxiv.org/abs/2101.00350v1	https://arxiv.org/pdf/2101.00350v1.pdf	Multi-Image Steganography Using Deep Neural Networks	Steganography is the science of hiding a secret message within an ordinary public message. Over the years, steganography has been used to encode a lower resolution image into a higher resolution image by simple methods like LSB manipulation. We aim to utilize deep neural networks for the encoding and decoding of multiple secret images inside a single cover image of the same resolution.	['Yugant Rana', 'Mansi Anand', 'Japsimar Singh Wahi', 'Abhishek Das']	2021-01-02	null	null	null	null	['image-steganography']	['computer-vision']	[ 1.33080792e+00 3.45213771e-01 1.01009332e-01 -2.15061858e-01 -4.12689418e-01 -3.21230978e-01 5.01589000e-01 -5.01306713e-01 -3.09543729e-01 7.14565575e-01 -7.20570832e-02 -4.21873569e-01 5.59524655e-01 -1.36222816e+00 -7.96947300e-01 -7.89512038e-01 -4.18076187e-01 -3.39477301e-01 2.61324316e-01 -6.48849905e-01 4.40499693e-01 5.56807518e-01 -1.13962817e+00 9.39670444e-01 -1.36685476e-01 1.00766623e+00 -2.25444091e-03 9.82235670e-01 1.67302527e-02 7.92582512e-01 -8.36983681e-01 -4.32401121e-01 6.52928233e-01 -7.67563045e-01 -7.88795054e-01 8.98526013e-02 1.50615335e-01 -5.14308631e-01 -7.45941460e-01 1.44743693e+00 1.32685274e-01 -6.91807508e-01 1.02049738e-01 -5.92069209e-01 -9.20247674e-01 8.89037549e-01 -3.97103578e-01 3.77809927e-02 3.35226893e-01 -1.20807961e-01 4.93134260e-01 -2.26244286e-01 7.45384336e-01 1.28472698e+00 5.02922595e-01 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81677405-05cb-449b-b9f8-0fdc8b3faa5b	adversarial-training-for-low-resource	2306.06384	null	https://arxiv.org/abs/2306.06384v1	https://arxiv.org/pdf/2306.06384v1.pdf	Adversarial Training For Low-Resource Disfluency Correction	Disfluencies commonly occur in conversational speech. Speech with disfluencies can result in noisy Automatic Speech Recognition (ASR) transcripts, which affects downstream tasks like machine translation. In this paper, we propose an adversarially-trained sequence-tagging model for Disfluency Correction (DC) that utilizes a small amount of labeled real disfluent data in conjunction with a large amount of unlabeled data. We show the benefit of our proposed technique, which crucially depends on synthetically generated disfluent data, by evaluating it for DC in three Indian languages- Bengali, Hindi, and Marathi (all from the Indo-Aryan family). Our technique also performs well in removing stuttering disfluencies in ASR transcripts introduced by speech impairments. We achieve an average 6.15 points improvement in F1-score over competitive baselines across all three languages mentioned. To the best of our knowledge, we are the first to utilize adversarial training for DC and use it to correct stuttering disfluencies in English, establishing a new benchmark for this task.	['Pushpak Bhattacharyya', 'Preethi Jyothi', 'Vineet Bhat']	2023-06-10	null	null	null	null	['automatic-speech-recognition']	['speech']	[ 1.91818580e-01 1.12590738e-01 2.39180267e-01 -2.94942081e-01 -1.15614963e+00 -8.89741004e-01 3.63318980e-01 -5.83108842e-01 -3.42308939e-01 1.04486775e+00 6.11684620e-01 -6.30058467e-01 8.72412145e-01 -2.20047142e-02 -7.29078293e-01 -4.05022204e-01 9.52164605e-02 4.89315718e-01 9.93293896e-03 -5.00487328e-01 -4.26254451e-01 1.57250047e-01 -8.46573651e-01 3.94510299e-01 1.17186594e+00 2.28553697e-01 3.71090651e-01 7.46104062e-01 2.68654257e-01 9.91132736e-01 -1.15791571e+00 -4.39345479e-01 3.55793089e-02 -6.61351323e-01 -1.10830176e+00 8.73595253e-02 3.09386224e-01 -2.44746953e-01 -3.97591174e-01 8.95761728e-01 5.58536410e-01 1.44055694e-01 3.28173310e-01 -5.25087357e-01 -7.03469932e-01 9.36638057e-01 9.47985426e-02 6.31447852e-01 4.87589598e-01 3.82273018e-01 7.53269851e-01 -9.97791588e-01 7.51248598e-01 1.26082587e+00 4.29149538e-01 1.33759201e+00 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038c3421-0123-435a-a8ea-8cd032ee60c1	soft-language-clustering-for-multilingual	2306.07610	null	https://arxiv.org/abs/2306.07610v1	https://arxiv.org/pdf/2306.07610v1.pdf	Soft Language Clustering for Multilingual Model Pre-training	Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME including text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer.	['Jie zhou', 'Yunbo Cao', 'Binghuai Lin', 'Fandong Meng', 'Yi Jing', 'Yongjing Yin', 'Yufan Jiang', 'Jiali Zeng']	2023-06-13	null	null	null	null	['clustering', 'zero-shot-cross-lingual-transfer', 'text-classification', 'cross-lingual-transfer']	['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 9.82724726e-02 -2.71759421e-01 -4.94006783e-01 -3.49069953e-01 -1.68442237e+00 -8.80004227e-01 5.82372069e-01 3.96073192e-01 -9.77655649e-01 8.60287189e-01 3.54581743e-01 -7.53934026e-01 2.86306620e-01 -5.05996644e-01 -8.44245315e-01 -9.48680639e-02 1.27713367e-01 6.68348849e-01 1.19831786e-01 -4.21772420e-01 -1.13815539e-01 -1.26585752e-01 -9.50189948e-01 5.87409556e-01 1.25651371e+00 4.57737029e-01 7.11619914e-01 4.68469501e-01 -4.46986020e-01 6.69544935e-01 -4.24350113e-01 -4.47231352e-01 -2.05606356e-01 -1.79747939e-01 -9.71983016e-01 -3.47073108e-01 5.90149879e-01 7.28758611e-03 -1.49783105e-01 7.51961350e-01 6.29109025e-01 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4b1c6f28-ce25-4be5-93fe-72538041e0fe	neural-abstructions-abstractions-that-support	2107.09285	null	https://arxiv.org/abs/2107.09285v1	https://arxiv.org/pdf/2107.09285v1.pdf	Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning	Although virtual agents are increasingly situated in environments where natural language is the most effective mode of interaction with humans, these exchanges are rarely used as an opportunity for learning. Leveraging language interactions effectively requires addressing limitations in the two most common approaches to language grounding: semantic parsers built on top of fixed object categories are precise but inflexible and end-to-end models are maximally expressive, but fickle and opaque. Our goal is to develop a system that balances the strengths of each approach so that users can teach agents new instructions that generalize broadly from a single example. We introduce the idea of neural abstructions: a set of constraints on the inference procedure of a label-conditioned generative model that can affect the meaning of the label in context. Starting from a core programming language that operates over abstructions, users can define increasingly complex mappings from natural language to actions. We show that with this method a user population is able to build a semantic parser for an open-ended house modification task in Minecraft. The semantic parser that results is both flexible and expressive: the percentage of utterances sourced from redefinitions increases steadily over the course of 191 total exchanges, achieving a final value of 28%.	['Li Fei-Fei', 'Christopher D. Manning', 'Kaylee Burns']	2021-07-20	null	null	null	null	['grounded-language-learning']	['natural-language-processing']	[ 8.34822431e-02 8.09481144e-01 2.41182446e-02 -6.46884680e-01 -2.28619978e-01 -9.03022587e-01 1.04591656e+00 1.59255862e-01 -5.14476299e-01 6.83678389e-01 4.53339100e-01 -3.67537707e-01 2.42502004e-01 -1.02392972e+00 -7.37317085e-01 -8.79015401e-02 -5.96844852e-02 7.35212922e-01 1.97450846e-01 -7.03601062e-01 6.37645125e-02 2.65524983e-01 -1.70115352e+00 4.06588614e-01 8.93293381e-01 1.01308197e-01 5.38568914e-01 6.53324783e-01 -6.07172847e-01 9.52894688e-01 -7.23015368e-01 -4.39205289e-01 4.44464013e-02 -4.65649605e-01 -1.12100828e+00 5.98822124e-02 2.58418381e-01 -2.88908035e-01 -4.94528525e-02 9.62481916e-01 5.25534488e-02 5.13512433e-01 4.35328394e-01 -1.29644513e+00 -7.61225879e-01 1.23405278e+00 2.41885126e-01 -5.83917163e-02 7.55816281e-01 5.58900714e-01 1.13797069e+00 -3.81111413e-01 1.02394044e+00 1.62222648e+00 4.32607055e-01 1.01431441e+00 -1.42894781e+00 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6.61095321e-01 1.88399985e-01 -7.21138477e-01 -2.53731042e-01 3.35615814e-01]	[4.131354808807373, 1.1748504638671875]
b02389f6-d6bd-4dda-9c5a-22ce9c886f06	shift-of-perspective-identification-within	1906.02430	null	https://arxiv.org/abs/1906.02430v4	https://arxiv.org/pdf/1906.02430v4.pdf	Shift-of-Perspective Identification Within Legal Cases	Arguments, counter-arguments, facts, and evidence obtained via documents related to previous court cases are of essential need for legal professionals. Therefore, the process of automatic information extraction from documents containing legal opinions related to court cases can be considered to be of significant importance. This study is focused on the identification of sentences in legal opinion texts which convey different perspectives on a certain topic or entity. We combined several approaches based on semantic analysis, open information extraction, and sentiment analysis to achieve our objective. Then, our methodology was evaluated with the help of human judges. The outcomes of the evaluation demonstrate that our system is successful in detecting situations where two sentences deliver different opinions on the same topic or entity. The proposed methodology can be used to facilitate other information extraction tasks related to the legal domain. One such task is the automated detection of counter arguments for a given argument. Another is the identification of opponent parties in a court case.	['Thejan Rupasinghe', 'Amal Shehan Perera', 'Nisansa de Silva', 'Gathika Ratnayaka', 'Viraj Salaka Gamage', 'Menuka Warushavithana']	2019-06-06	null	null	null	null	['open-information-extraction']	['natural-language-processing']	[ 5.41859269e-01 1.86276853e-01 -1.70349717e-01 -4.25384343e-01 -1.08811247e+00 -8.93758833e-01 7.89865017e-01 8.39755416e-01 -5.12338042e-01 1.04714036e+00 5.06151378e-01 -8.17116439e-01 -4.66741979e-01 -6.47044659e-01 -2.54358053e-01 -5.38874328e-01 5.85253119e-01 3.40599805e-01 4.27770615e-01 -2.64658898e-01 1.15103567e+00 5.33238351e-01 -1.16873252e+00 7.66714156e-01 9.55263734e-01 6.51054025e-01 -1.84975043e-01 2.37505749e-01 -6.85702026e-01 1.17135489e+00 -1.06237197e+00 -1.08431661e+00 8.21708515e-02 -2.03870356e-01 -9.66206908e-01 -6.25303462e-02 2.70443887e-01 -1.29003733e-01 3.52153748e-01 1.31216466e+00 5.27419925e-01 -3.28265727e-01 6.92500293e-01 -8.73228669e-01 -2.23195404e-01 9.14911330e-01 -5.11422098e-01 6.82240427e-01 5.22687674e-01 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7.33238399e-01 1.40670943e+00 2.31235117e-01 3.41034293e-01 1.38960227e-01 2.09696293e-01 -3.16936284e-01 -9.26613688e-01 -3.15443933e-01 6.38081729e-01 2.45461445e-02 -5.03784537e-01 -4.16144371e-01 -1.01868734e-01]	[9.581527709960938, 9.477852821350098]
4a01a872-578f-419a-a192-8a5f53f48710	variance-preserving-based-interpolation	2306.08527	null	https://arxiv.org/abs/2306.08527v1	https://arxiv.org/pdf/2306.08527v1.pdf	Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement	The goal of this study is to implement diffusion models for speech enhancement (SE). The first step is to emphasize the theoretical foundation of variance-preserving (VP)-based interpolation diffusion under continuous conditions. Subsequently, we present a more concise framework that encapsulates both the VP- and variance-exploding (VE)-based interpolation diffusion methods. We demonstrate that these two methods are special cases of the proposed framework. Additionally, we provide a practical example of VP-based interpolation diffusion for the SE task. To improve performance and ease model training, we analyze the common difficulties encountered in diffusion models and suggest amenable hyper-parameters. Finally, we evaluate our model against several methods using a public benchmark to showcase the effectiveness of our approach	['Wenbin Zhang', 'Yu Gao', 'Chin-Hui Lee', 'Jun Du', 'Zilu Guo']	2023-06-14	null	null	null	null	['speech-enhancement']	['speech']	[ 7.13033229e-02 4.95227473e-03 4.07166779e-02 -1.46694947e-03 -8.87132287e-01 -1.49934785e-02 6.84216976e-01 -1.60221875e-01 -4.23992932e-01 6.78584993e-01 4.77710754e-01 -3.88578266e-01 -2.55575866e-01 -4.35417682e-01 -3.85844439e-01 -8.62079918e-01 -3.55700642e-01 -3.76449645e-01 2.44090185e-01 -3.85642856e-01 1.67363077e-01 6.02092266e-01 -1.11513054e+00 1.79627985e-01 1.13903451e+00 8.15936804e-01 3.64742815e-01 9.07791317e-01 1.85261279e-01 6.31261587e-01 -8.62402618e-01 -4.77323085e-01 1.80392534e-01 -6.50503457e-01 -6.71339095e-01 1.91101998e-01 2.82434851e-01 -2.86829859e-01 -4.49457318e-01 1.03523231e+00 9.22894478e-01 6.53754652e-01 9.63592947e-01 -1.17943358e+00 -1.17117131e+00 3.92472804e-01 -6.21926427e-01 5.74173033e-01 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a14f04c1-fba6-4b4a-ac53-99d1a7df5c5e	generation-of-a-spanish-artificial	null	null	https://aclanthology.org/L18-1400	https://aclanthology.org/L18-1400.pdf	Generation of a Spanish Artificial Collocation Error Corpus	null	["Sara Rodr{\\'\\i}guez-Fern{\\'a}ndez", 'Leo Wanner', 'Roberto Carlini']	2018-05-01	generation-of-a-spanish-artificial-1	https://aclanthology.org/L18-1400	https://aclanthology.org/L18-1400.pdf	lrec-2018-5	['grammatical-error-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 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e5f3eedd-c9ad-424c-8b15-da272a4aaba2	unsupervised-dependency-parsing-lets-use	1504.04666	null	http://arxiv.org/abs/1504.04666v1	http://arxiv.org/pdf/1504.04666v1.pdf	Unsupervised Dependency Parsing: Let's Use Supervised Parsers	We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms. Our approach, called `iterated reranking' (IR), starts with dependency trees generated by an unsupervised parser, and iteratively improves these trees using the richer probability models used in supervised parsing that are in turn trained on these trees. Our system achieves 1.8% accuracy higher than the state-of-the-part parser of Spitkovsky et al. (2013) on the WSJ corpus.	['Phong Le', 'Willem Zuidema']	2015-04-18	unsupervised-dependency-parsing-lets-use-1	https://aclanthology.info/papers/N15-1067/n15-1067	https://www.aclweb.org/anthology/N15-1067	hlt-2015-5	['unsupervised-dependency-parsing']	['natural-language-processing']	[ 2.68945962e-01 9.69552875e-01 -5.12502730e-01 -8.94912422e-01 -1.18097603e+00 -7.86531866e-01 4.16243225e-01 5.16405940e-01 -4.58319277e-01 8.79438579e-01 6.34717464e-01 -5.75666070e-01 2.08470702e-01 -6.64860070e-01 -4.85551357e-01 -2.37334877e-01 -2.59182483e-01 9.28263366e-01 6.83800817e-01 -3.06134403e-01 5.15064038e-03 1.47550507e-02 -1.10041237e+00 3.53914291e-01 7.16253519e-01 2.19242424e-01 3.39787215e-01 9.85581756e-01 -7.69809961e-01 1.06579316e+00 -5.24390876e-01 -7.14856029e-01 -1.88923046e-01 -4.42869782e-01 -1.43136477e+00 -1.31075993e-01 4.90892678e-02 -1.38760224e-01 2.07353402e-02 8.08050990e-01 -1.19009092e-01 -1.53522134e-01 2.10252017e-01 -4.84163254e-01 -4.63712573e-01 1.70016682e+00 -2.22920805e-01 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c9ad6221-f882-44ae-b450-bf5a6bacb1c8	how-to-evaluate-the-quality-of-unsupervised	1607.01152	null	http://arxiv.org/abs/1607.01152v1	http://arxiv.org/pdf/1607.01152v1.pdf	How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?	When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms. However , in many situations, few or no data are labeled. This calls for alternative criteria one can compute on non-labeled data. In this paper, two criteria that do not require labels are empirically shown to discriminate accurately (w.r.t. ROC or PR based criteria) between algorithms. These criteria are based on existing Excess-Mass (EM) and Mass-Volume (MV) curves, which generally cannot be well estimated in large dimension. A methodology based on feature sub-sampling and aggregating is also described and tested, extending the use of these criteria to high-dimensional datasets and solving major drawbacks inherent to standard EM and MV curves.	['Nicolas Goix']	2016-07-05	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[ 5.20619750e-02 -1.36556774e-01 -1.86040401e-01 -6.37539387e-01 -5.65631211e-01 -5.25406480e-01 6.12902701e-01 8.25410724e-01 -5.51623642e-01 7.50135660e-01 -4.60245907e-01 -4.86418277e-01 -4.99921888e-01 -7.58492112e-01 4.84880209e-02 -6.04584694e-01 -2.69978821e-01 6.83976293e-01 4.54864800e-01 1.97896525e-01 4.31084067e-01 6.78193033e-01 -2.12305927e+00 8.92783776e-02 1.02666175e+00 9.55144107e-01 -3.65197212e-01 6.84043825e-01 -4.81038541e-01 1.86147124e-01 -5.88911116e-01 -1.88981250e-01 3.04587811e-01 -5.61694622e-01 -5.65002859e-01 -3.95099409e-02 3.72637272e-01 -2.48815089e-01 3.80735457e-01 9.13690090e-01 2.48372287e-01 6.28475770e-02 1.27540708e+00 -1.53415811e+00 -2.95486152e-01 1.96451038e-01 -4.64014143e-01 3.74146998e-01 5.66222131e-01 -2.69243330e-01 6.39396548e-01 -7.01611102e-01 2.96394646e-01 9.62855458e-01 7.27208495e-01 2.30185449e-01 -1.42638421e+00 -3.79520297e-01 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7a045705-6517-4034-9eef-6c01df28a1b9	two-heads-are-better-than-one-towards-better	2305.17528	null	https://arxiv.org/abs/2305.17528v1	https://arxiv.org/pdf/2305.17528v1.pdf	Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection	Both transduction and rejection have emerged as important techniques for defending against adversarial perturbations. A recent work by Tram\`er showed that, in the rejection-only case (no transduction), a strong rejection-solution can be turned into a strong (but computationally inefficient) non-rejection solution. This detector-to-classifier reduction has been mostly applied to give evidence that certain claims of strong selective-model solutions are susceptible, leaving the benefits of rejection unclear. On the other hand, a recent work by Goldwasser et al. showed that rejection combined with transduction can give provable guarantees (for certain problems) that cannot be achieved otherwise. Nevertheless, under recent strong adversarial attacks (GMSA, which has been shown to be much more effective than AutoAttack against transduction), Goldwasser et al.'s work was shown to have low performance in a practical deep-learning setting. In this paper, we take a step towards realizing the promise of transduction+rejection in more realistic scenarios. Theoretically, we show that a novel application of Tram\`er's classifier-to-detector technique in the transductive setting can give significantly improved sample-complexity for robust generalization. While our theoretical construction is computationally inefficient, it guides us to identify an efficient transductive algorithm to learn a selective model. Extensive experiments using state of the art attacks (AutoAttack, GMSA) show that our solutions provide significantly better robust accuracy.	['Somesh Jha', 'YIngyu Liang', 'Jiefeng Chen', 'Xi Wu', 'Yang Guo', 'Nils Palumbo']	2023-05-27	null	null	null	null	['adversarial-robustness']	['adversarial']	[ 5.52940547e-01 1.71123818e-01 -8.51315353e-03 -2.46787500e-02 -1.24083281e+00 -1.02960324e+00 5.69762051e-01 -2.09476352e-02 -4.47681040e-01 7.31847227e-01 -3.24070305e-01 -7.82489061e-01 -7.15038106e-02 -9.03615832e-01 -1.04335034e+00 -1.10286474e+00 -1.36947840e-01 2.24178940e-01 4.07962501e-01 -5.31515539e-01 -4.01772335e-02 6.34863377e-01 -1.29434454e+00 3.18105310e-01 4.86282140e-01 7.52025723e-01 -5.32580495e-01 1.01654255e+00 3.93297642e-01 3.81050467e-01 -6.97200954e-01 -7.47176886e-01 8.60726595e-01 -4.70950007e-01 -7.75008023e-01 -2.53525734e-01 8.29191625e-01 -7.97006786e-02 -8.46542642e-02 1.31420696e+00 7.05409110e-01 -1.90309331e-01 4.00139093e-01 -1.34815061e+00 -4.75861937e-01 8.40644300e-01 -4.01509821e-01 9.94307622e-02 8.26164931e-02 2.61723876e-01 9.96005535e-01 -4.51824725e-01 3.31421167e-01 1.31779242e+00 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4facf4e9-2569-4b98-a01f-804eb1285c61	joint-multi-scale-tone-mapping-and-denoising	2303.09071	null	https://arxiv.org/abs/2303.09071v2	https://arxiv.org/pdf/2303.09071v2.pdf	Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement	An image processing unit (IPU), or image signal processor (ISP) for high dynamic range (HDR) imaging usually consists of demosaicing, white balancing, lens shading correction, color correction, denoising, and tone-mapping. Besides noise from the imaging sensors, almost every step in the ISP introduces or amplifies noise in different ways, and denoising operators are designed to reduce the noise from these sources. Designed for dynamic range compressing, tone-mapping operators in an ISP can significantly amplify the noise level, especially for images captured in low-light conditions, making denoising very difficult. Therefore, we propose a joint multi-scale denoising and tone-mapping framework that is designed with both operations in mind for HDR images. Our joint network is trained in an end-to-end format that optimizes both operators together, to prevent the tone-mapping operator from overwhelming the denoising operator. Our model outperforms existing HDR denoising and tone-mapping operators both quantitatively and qualitatively on most of our benchmarking datasets.	['Jan P. Allebach', 'Huaijin Chen', 'Litao Hu']	2023-03-16	null	null	null	null	['demosaicking', 'image-enhancement', 'tone-mapping']	['computer-vision', 'computer-vision', 'computer-vision']	[ 6.34296298e-01 -3.97318423e-01 5.31508446e-01 -4.14444596e-01 -6.00862920e-01 -3.54800105e-01 4.53565381e-02 -3.40302616e-01 -4.25609767e-01 1.81220040e-01 3.46727878e-01 -1.68060198e-01 1.86984271e-01 -7.92743146e-01 -7.38623917e-01 -8.04206491e-01 2.61177421e-01 -1.19186617e-01 2.27724835e-01 -3.59756291e-01 -2.40216218e-02 4.20696080e-01 -1.39051306e+00 5.36702722e-02 1.15317392e+00 1.19825375e+00 4.77142394e-01 9.33636427e-01 1.55318901e-01 1.18789959e+00 -4.82792556e-01 -3.52309734e-01 7.62698710e-01 -3.15487236e-01 -3.75569128e-02 3.33440900e-01 5.51774204e-01 -9.67660308e-01 -7.84935355e-01 1.41266716e+00 7.04168499e-01 1.09063439e-01 1.12987749e-01 -7.12695837e-01 -8.52346599e-01 3.79565269e-01 -9.29350078e-01 -8.67949724e-02 4.39845137e-02 5.75104296e-01 5.65941691e-01 -6.99272573e-01 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1.02178633e+00 -9.36396122e-01 -3.09118211e-01 -8.70053172e-01 -1.04160237e+00 5.10220230e-01 6.08563960e-01 -1.56154737e-01 -7.23392248e-01 1.67923346e-01]	[10.768959999084473, -2.3701319694519043]
72f7ffd0-582f-45bc-adbd-f281b1b8e4ed	vector-space-model-as-cognitive-space-for	1708.06068	null	http://arxiv.org/abs/1708.06068v1	http://arxiv.org/pdf/1708.06068v1.pdf	Vector Space Model as Cognitive Space for Text Classification	In this era of digitization, knowing the user's sociolect aspects have become essential features to build the user specific recommendation systems. These sociolect aspects could be found by mining the user's language sharing in the form of text in social media and reviews. This paper describes about the experiment that was performed in PAN Author Profiling 2017 shared task. The objective of the task is to find the sociolect aspects of the users from their tweets. The sociolect aspects considered in this experiment are user's gender and native language information. Here user's tweets written in a different language from their native language are represented as Document - Term Matrix with document frequency as the constraint. Further classification is done using the Support Vector Machine by taking gender and native language as target classes. This experiment attains the average accuracy of 73.42% in gender prediction and 76.26% in the native language identification task.	['Soman Kp', 'Barathi Ganesh HB', 'Anand Kumar M']	2017-08-21	null	null	null	null	['gender-prediction', 'native-language-identification']	['computer-vision', 'natural-language-processing']	[-2.70248502e-01 -6.22127345e-03 -5.21913886e-01 -5.92980385e-01 -2.41596118e-01 -5.80512106e-01 1.07321906e+00 5.27816296e-01 -7.41043687e-01 5.49865663e-01 4.92685705e-01 -3.37302119e-01 -2.11426038e-02 -7.27771640e-01 2.52368599e-02 -5.64788878e-01 2.16404468e-01 6.42821848e-01 -2.99428433e-01 -3.61068726e-01 7.31495202e-01 3.45482439e-01 -1.89593542e+00 2.94126570e-01 6.06723368e-01 7.04732835e-01 4.48282734e-02 8.42788279e-01 -7.49246240e-01 7.02156842e-01 -6.86400115e-01 -4.15158302e-01 1.21087201e-01 -3.32348615e-01 -7.57484138e-01 -1.54701561e-01 2.88680166e-01 1.54835045e-01 2.08864450e-01 1.13083959e+00 3.73155177e-01 1.09983452e-01 1.17196608e+00 -1.04681718e+00 -3.08541059e-01 1.10374236e+00 -1.02274621e+00 4.55619305e-01 6.28413796e-01 -8.14454556e-01 9.92672622e-01 -9.33825314e-01 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f292a505-b7a4-4b60-92a7-1c34b6b5a6e3	cartoongan-generative-adversarial-networks	null	null	http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf	CartoonGAN: Generative Adversarial Networks for Photo Cartoonization	In this paper, we propose a solution to transforming photos of real-world scenes into cartoon style images, which is valuable and challenging in computer vision and computer graphics. Our solution belongs to learning based methods, which have recently become popular to stylize images in artistic forms such as painting. However, existing methods do not produce satisfactory results for cartoonization, due to the fact that (1) cartoon styles have unique characteristics with high level simplification and abstraction, and (2) cartoon images tend to have clear edges, smooth color shading and relatively simple textures, which exhibit significant challenges for texture-descriptor-based loss functions used in existing methods. In this paper, we propose CartoonGAN, a generative adversarial network (GAN) framework for cartoon stylization. Our method takes unpaired photos and cartoon images for training, which is easy to use. Two novel losses suitable for cartoonization are proposed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of the VGG network to cope with substantial style variation between photos and cartoons, and (2) an edge-promoting adversarial loss for preserving clear edges. We further introduce an initialization phase, to improve the convergence of the network to the target manifold. Our method is also much more efficient to train than existing methods. Experimental results show that our method is able to generate high-quality cartoon images from real-world photos (i.e., following specific artists' styles and with clear edges and smooth shading) and outperforms state-of-the-art methods.	['Yong-Jin Liu', 'Yu-Kun Lai', 'Yang Chen']	2018-06-01	null	null	null	cvpr-2018-6	['real-to-cartoon-translation']	['computer-vision']	[ 3.32455814e-01 -7.44335726e-02 2.16849908e-01 -1.07100628e-01 -3.61012936e-01 -5.06787062e-01 5.64929426e-01 -6.04350507e-01 -5.85396402e-02 8.48930717e-01 -1.53636321e-01 3.18502113e-02 2.51506299e-01 -1.02044237e+00 -9.60598469e-01 -7.01543987e-01 2.83478707e-01 3.18600446e-01 8.82996768e-02 -2.51525491e-01 9.62010920e-02 7.88474500e-01 -1.42978930e+00 2.98155636e-01 1.12064338e+00 9.32567418e-01 1.79496631e-01 3.83541226e-01 -1.78679496e-01 7.83969760e-01 -4.68688309e-01 -8.23777437e-01 5.11490822e-01 -7.39739180e-01 -3.83960396e-01 4.11446959e-01 8.34540367e-01 -2.20532864e-01 -3.34984839e-01 1.20586419e+00 3.70883942e-01 7.35604316e-02 6.95449531e-01 -1.43658471e+00 -1.05026078e+00 2.43521810e-01 -8.19284379e-01 -6.99836135e-01 1.46838486e-01 8.90605673e-02 7.84598947e-01 -8.55669737e-01 9.01794970e-01 1.56721306e+00 7.60384262e-01 6.17811501e-01 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-9.72077906e-01 7.78761029e-01 1.54380249e-02 -8.02750945e-01 -2.75299460e-01 3.16773474e-01]	[11.640449523925781, -0.6584645509719849]
7b8ab5ac-6b27-404b-a7e6-c2e59cfcbb9d	qtran-learning-to-factorize-with	1905.05408	null	https://arxiv.org/abs/1905.05408v1	https://arxiv.org/pdf/1905.05408v1.pdf	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning	We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones for decentralized execution. VDN and QMIX address only a fraction of factorizable MARL tasks due to their structural constraint in factorization such as additivity and monotonicity. In this paper, we propose a new factorization method for MARL, QTRAN, which is free from such structural constraints and takes on a new approach to transforming the original joint action-value function into an easily factorizable one, with the same optimal actions. QTRAN guarantees more general factorization than VDN or QMIX, thus covering a much wider class of MARL tasks than does previous methods. Our experiments for the tasks of multi-domain Gaussian-squeeze and modified predator-prey demonstrate QTRAN's superior performance with especially larger margins in games whose payoffs penalize non-cooperative behavior more aggressively.	['Yung Yi', 'David Earl Hostallero', 'Wan Ju Kang', 'Daewoo Kim', 'Kyunghwan Son']	2019-05-14	null	null	null	null	['smac-1']	['playing-games']	[-4.80285674e-01 1.42423600e-01 -4.26199764e-01 1.69061601e-01 -9.04649258e-01 -7.13128626e-01 3.13093543e-01 -1.09274589e-01 -7.41925359e-01 1.37241685e+00 6.94748461e-02 -5.68858981e-01 -7.46090591e-01 -4.94242281e-01 -7.96939790e-01 -1.03840733e+00 -7.55474329e-01 6.09026790e-01 -3.54613247e-03 -6.60133958e-01 -8.19675699e-02 8.55263770e-02 -1.13054979e+00 2.75688231e-01 8.79832685e-01 7.18965292e-01 1.16247654e-01 7.95984030e-01 3.81652892e-01 9.99263644e-01 -7.67001450e-01 -4.18252558e-01 7.45299935e-01 -9.57701579e-02 -6.14055991e-01 -3.78097929e-02 -9.95435789e-02 -6.36278629e-01 -1.00215316e-01 8.27064633e-01 4.94416475e-01 3.50398540e-01 6.77373648e-01 -1.92817807e+00 -5.10833383e-01 1.23627663e+00 -1.02104771e+00 5.44284508e-02 2.17782743e-02 2.43773401e-01 1.30685532e+00 -1.86754823e-01 2.61349976e-01 1.44799042e+00 6.23916388e-01 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3412bf40-ff2e-4829-bb8a-5d8096b4135d	auto-clustering-output-layer-automatic	1702.08648	null	http://arxiv.org/abs/1702.08648v2	http://arxiv.org/pdf/1702.08648v2.pdf	Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks	In this paper, we discuss a different type of semi-supervised setting: a coarse level of labeling is available for all observations but the model has to learn a fine level of latent annotation for each one of them. Problems in this setting are likely to be encountered in many domains such as text categorization, protein function prediction, image classification as well as in exploratory scientific studies such as medical and genomics research. We consider this setting as simultaneously performed supervised classification (per the available coarse labels) and unsupervised clustering (within each one of the coarse labels) and propose a novel output layer modification called auto-clustering output layer (ACOL) that allows concurrent classification and clustering based on Graph-based Activity Regularization (GAR) technique. As the proposed output layer modification duplicates the softmax nodes at the output layer for each class, GAR allows for competitive learning between these duplicates on a traditional error-correction learning framework to ultimately enable a neural network to learn the latent annotations in this partially supervised setup. We demonstrate how the coarse label supervision impacts performance and helps propagate useful clustering information between sub-classes. Comparative tests on three of the most popular image datasets MNIST, SVHN and CIFAR-100 rigorously demonstrate the effectiveness and competitiveness of the proposed approach.	['Ozsel Kilinc', 'Ismail Uysal']	2017-02-28	null	null	null	null	['protein-function-prediction']	['medical']	[ 6.56388342e-01 4.71023679e-01 -4.32527959e-01 -6.96215570e-01 -5.84689915e-01 -4.62712198e-01 5.92104018e-01 6.05700672e-01 -5.49319446e-01 7.32708693e-01 -7.24992082e-02 -1.99607402e-01 -2.38360614e-01 -5.45824885e-01 -9.34881032e-01 -9.91353154e-01 -3.01358663e-02 5.71333230e-01 2.24692389e-01 4.55486536e-01 4.78007421e-02 2.68287361e-01 -1.43222535e+00 4.91357744e-01 1.03428972e+00 9.46878731e-01 3.09695840e-01 5.24219394e-01 -3.77694294e-02 9.61897314e-01 -2.66228348e-01 -1.07306391e-01 8.61029252e-02 -2.03849614e-01 -9.91098821e-01 5.27537525e-01 3.26116920e-01 3.62337798e-01 2.71467209e-01 1.01843929e+00 1.63740501e-01 3.13087106e-01 9.67801869e-01 -1.13611603e+00 -5.24033070e-01 8.15584838e-01 -7.45294631e-01 -2.39589494e-02 -2.89180607e-01 -1.34468392e-01 1.07260036e+00 -6.30192399e-01 6.24202490e-01 1.04182196e+00 5.05699337e-01 2.89290190e-01 -1.55035174e+00 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17cad912-3685-4386-938d-fd06204266b6	a-novel-system-for-extractive-clinical-note	null	null	https://aclanthology.org/W19-1906	https://aclanthology.org/W19-1906.pdf	A Novel System for Extractive Clinical Note Summarization using EHR Data	While much data within a patient{'}s electronic health record (EHR) is coded, crucial information concerning the patient{'}s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.	['Ching-Huei Tsou', 'Jennifer Liang', 'Ananya Poddar']	2019-06-01	null	null	null	ws-2019-6	['extractive-document-summarization']	['natural-language-processing']	[ 6.58308625e-01 6.91842318e-01 2.40252152e-01 -4.57994372e-01 -1.22459424e+00 -6.79927707e-01 1.77826136e-02 1.63245893e+00 -3.11747372e-01 5.73682666e-01 1.11689377e+00 -5.76070070e-01 -3.45203668e-01 -3.70435804e-01 -4.23990972e-02 -1.88240871e-01 -2.56274015e-01 7.94101059e-01 -2.68487692e-01 2.69155025e-01 2.37232521e-01 4.55972016e-01 -5.38716495e-01 8.96249413e-01 6.56230807e-01 5.97710609e-01 -9.27729234e-02 1.11980450e+00 -1.57396480e-01 1.35717118e+00 -8.44932735e-01 -1.92174286e-01 -1.01179779e-01 -6.75750732e-01 -9.62973058e-01 2.46542796e-01 2.26381719e-01 -3.19659352e-01 -1.42672032e-01 6.32077038e-01 7.54148841e-01 -1.31017357e-01 3.77611011e-01 -3.34996283e-01 -2.28376493e-01 5.28646469e-01 -2.64514107e-02 3.67448002e-01 7.49680042e-01 2.23701283e-01 8.04062843e-01 -2.69236743e-01 1.06093562e+00 5.59941411e-01 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7.17248842e-02 -5.00709951e-01 -9.87751365e-01 -2.53289312e-01 3.29500496e-01 6.35309935e-01 4.28132355e-01 1.85359403e-01 7.23723829e-01]	[8.518792152404785, 8.601919174194336]
ae533e44-94b7-400d-ab6c-c2561ce39a78	bidirectional-semi-supervised-dual-branch-cnn	2210.08291	null	https://arxiv.org/abs/2210.08291v5	https://arxiv.org/pdf/2210.08291v5.pdf	Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions	Semi-supervised learning via teacher-student network can train a model effectively on a few labeled samples. It enables a student model to distill knowledge from the teacher's predictions of extra unlabeled data. However, such knowledge flow is typically unidirectional, having the performance vulnerable to the quality of teacher model. In this paper, we seek to robust 3D reconstruction of stereo endoscopic images by proposing a novel fashion of bidirectional learning between two learners, each of which can play both roles of teacher and student concurrently. Specifically, we introduce two self-supervisions, i.e., Adaptive Cross Supervision (ACS) and Adaptive Parallel Supervision (APS), to learn a dual-branch convolutional neural network. The two branches predict two different disparity probability distributions for the same position, and output their expectations as disparity values. The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS). Moreover, each branch also learns confidences to dynamically refine its provided supervisions. In ACS, the predicted disparity is softened into a unimodal distribution, and the lower the confidence, the smoother the distribution. In APS, the incorrect predictions are suppressed by lowering the weights of those with low confidence. With the adaptive bidirectional learning, the two branches enjoy well-tuned supervisions, and eventually converge on a consistent and more accurate disparity estimation. The extensive and comprehensive experimental results on four public datasets demonstrate our superior performance over other state-of-the-arts with a relative decrease of averaged disparity error by at least 9.76%.	['Qiang Li', 'Xin Yang', 'Dun Li', 'Ying Zhou', 'Zhiwei Wang', 'Hongkuan Shi']	2022-10-15	null	null	null	null	['disparity-estimation']	['computer-vision']	[ 1.14244424e-01 3.66322339e-01 -4.70690191e-01 -6.49041772e-01 -5.11237204e-01 -3.53009224e-01 2.23224431e-01 3.32363509e-02 -3.88897926e-01 6.82420909e-01 4.79706489e-02 -4.13481116e-01 8.24150890e-02 -9.67705667e-01 -8.74001384e-01 -1.08997977e+00 4.64445829e-01 3.00402850e-01 6.62995458e-01 5.05845696e-02 2.28090212e-02 8.41052011e-02 -1.54730582e+00 1.67374954e-01 1.25590897e+00 1.23241997e+00 2.08841637e-01 2.78767705e-01 -2.62292147e-01 7.54212022e-01 -3.67654830e-01 -1.46288514e-01 2.92318314e-01 -1.75082207e-01 -5.39462864e-01 6.63244631e-03 3.08644891e-01 -4.23432827e-01 -2.57334113e-01 1.22197855e+00 2.99183667e-01 -1.37574688e-01 4.64497447e-01 -1.02804184e+00 -5.96309781e-01 5.25934160e-01 -8.57033670e-01 -4.91275564e-02 9.71524641e-02 3.11470598e-01 5.09420812e-01 -7.05724120e-01 1.67357653e-01 1.01424479e+00 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c80758ee-23f8-499f-b383-bdd814619b80	tablesense-spreadsheet-table-detection-with	2106.13500	null	https://arxiv.org/abs/2106.13500v1	https://arxiv.org/pdf/2106.13500v1.pdf	TableSense: Spreadsheet Table Detection with Convolutional Neural Networks	Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for table detection to meet the domain-specific requirement on precise table boundary detection; third, we propose an effective uncertainty metric to guide an active learning based smart sampling algorithm, which enables the efficient build-up of a training dataset with 22,176 tables on 10,220 sheets with broad coverage of diverse table structures and layouts. Our evaluation shows that TableSense is highly effective with 91.3\% recall and 86.5\% precision in EoB-2 metric, a significant improvement over both the current detection algorithm that are used in commodity spreadsheet tools and state-of-the-art convolutional neural networks in computer vision.	['Dongmei Zhang', 'Zhouyu Fu', 'Shi Han', 'Shijie Liu', 'Haoyu Dong']	2021-06-25	null	null	null	null	['table-detection']	['miscellaneous']	[-2.11354792e-01 -2.76211977e-01 -1.74500227e-01 -1.98550642e-01 -9.92061198e-01 -9.18329239e-01 3.29476386e-01 6.43994451e-01 -7.36217201e-02 4.31843609e-01 1.19501874e-01 -4.23744291e-01 -5.83950765e-02 -1.16574776e+00 -8.47335339e-01 -1.84510484e-01 1.16080111e-02 6.06954277e-01 2.40006119e-01 -2.49474674e-01 5.55438578e-01 3.48580569e-01 -1.13190949e+00 5.11594594e-01 1.14992225e+00 1.73605549e+00 -2.52067059e-01 7.70108104e-01 -6.11631811e-01 9.95034933e-01 -8.40840876e-01 -6.70502782e-01 2.58731276e-01 8.89222398e-02 -2.95753658e-01 -1.98464021e-01 6.95396721e-01 -4.43026572e-01 -2.52643257e-01 9.70236719e-01 3.23275328e-01 -1.44402325e-01 7.86742866e-01 -9.12377656e-01 -7.58671224e-01 9.74602997e-01 -7.27351308e-01 4.71574068e-01 5.03010333e-01 -1.11657567e-01 1.21153271e+00 -9.97571766e-01 4.72450942e-01 9.12507832e-01 8.74396682e-01 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