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train · 44.6k rows

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348d5a26-213b-4d11-9d77-0942fc9320b8	recursive-euclidean-distance-based-robust	2303.11337	null	https://arxiv.org/abs/2303.11337v1	https://arxiv.org/pdf/2303.11337v1.pdf	Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning	Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by training the local model with malicious data. In this paper, we propose a novel robust aggregation approach based on recursive Euclidean distance calculation. Our approach measures the distance of the local models from the previous global model and assigns weights accordingly. Local models far away from the global model are assigned smaller weights to minimize the data poisoning effect during aggregation. Our experiments demonstrate that the proposed algorithm outperforms state-of-the-art algorithms by at least $5\%$ in accuracy while reducing time complexity by less than $55\%$. Our contribution is significant as it addresses the critical issue of malicious attacks in federated learning while improving the accuracy of the global model.	['Xiaolan Liu', 'Yogachandran Rahulamathavan', 'Charuka Herath']	2023-03-20	null	null	null	null	['data-poisoning']	['adversarial']	[-1.61802247e-01 -1.33663481e-02 -2.35492066e-01 -1.82710111e-01 -9.20341611e-01 -8.51619780e-01 4.44534898e-01 5.49993873e-01 -5.57051718e-01 7.82416880e-01 -2.50890881e-01 -3.25510859e-01 -1.44962758e-01 -9.65260863e-01 -6.87460184e-01 -8.99484098e-01 -9.95321423e-02 3.33657086e-01 5.05427942e-02 1.09028876e-01 1.00965850e-01 5.64716816e-01 -1.24927354e+00 1.79883197e-01 9.00714874e-01 1.09050548e+00 -6.92941129e-01 2.70357937e-01 -1.72894672e-01 1.12357390e+00 -7.10163057e-01 -8.54342878e-01 7.09717274e-01 -4.79664028e-01 -8.95637274e-01 -1.56167433e-01 4.69750196e-01 -1.90168142e-01 -2.57316977e-01 1.53188777e+00 2.93600321e-01 5.21489568e-02 6.63674846e-02 -1.61358738e+00 -3.48062664e-01 7.51891315e-01 -1.75584376e-01 -9.71569195e-02 1.86237749e-02 6.54577091e-02 8.32631469e-01 -5.29789865e-01 6.47660553e-01 7.50566781e-01 6.94217920e-01 5.85551977e-01 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c526904a-bb0f-4dec-a743-22490650a03c	multi-view-audio-and-music-classification	2103.02420	null	https://arxiv.org/abs/2103.02420v1	https://arxiv.org/pdf/2103.02420v1.pdf	Multi-view Audio and Music Classification	We propose in this work a multi-view learning approach for audio and music classification. Considering four typical low-level representations (i.e. different views) commonly used for audio and music recognition tasks, the proposed multi-view network consists of four subnetworks, each handling one input types. The learned embedding in the subnetworks are then concatenated to form the multi-view embedding for classification similar to a simple concatenation network. However, apart from the joint classification branch, the network also maintains four classification branches on the single-view embedding of the subnetworks. A novel method is then proposed to keep track of the learning behavior on the classification branches and adapt their weights to proportionally blend their gradients for network training. The weights are adapted in such a way that learning on a branch that is generalizing well will be encouraged whereas learning on a branch that is overfitting will be slowed down. Experiments on three different audio and music classification tasks show that the proposed multi-view network not only outperforms the single-view baselines but also is superior to the multi-view baselines based on concatenation and late fusion.	['Alfred Mertins', 'Ian McLoughlin', 'Philipp Koch', 'Lam Pham', 'Oliver Y. Chén', 'Huy Le Nguyen', 'Huy Phan']	2021-03-03	null	null	null	null	['multi-view-learning', 'music-classification']	['computer-vision', 'music']	[ 4.11248095e-02 -1.18414283e-01 -2.09734946e-01 -2.69228876e-01 -7.07995892e-01 -6.10730052e-01 3.85246724e-01 7.77706727e-02 -1.16452098e-01 2.67950267e-01 4.00002480e-01 2.73832530e-01 -1.86997905e-01 -6.88255012e-01 -4.38267291e-01 -9.81266141e-01 -3.45445052e-02 4.10760194e-01 3.57654512e-01 -1.52165085e-01 8.77610967e-02 2.56191194e-01 -1.56352782e+00 7.76454628e-01 4.03147429e-01 1.01931775e+00 -1.77865475e-01 8.80287349e-01 1.95572585e-01 6.42353177e-01 -4.07282829e-01 -4.01156515e-01 1.70637026e-01 -4.12941962e-01 -6.76477075e-01 1.99221686e-01 5.92391312e-01 3.93681042e-02 -1.96266770e-01 8.63733292e-01 8.61926377e-01 1.99612290e-01 5.10901511e-01 -1.21948981e+00 -1.66964903e-01 8.06916058e-01 -6.91389918e-01 1.42524138e-01 1.66165352e-01 -2.00046614e-01 1.46952462e+00 -1.02807140e+00 4.35626507e-01 1.26406968e+00 7.18121827e-01 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-8.45790565e-01 -9.55713212e-01 -8.00011218e-01 1.49262309e-01 4.20927674e-01 -7.57185578e-01 -5.51469684e-01 -1.78830966e-01]	[15.599387168884277, 5.236428737640381]
a355f26b-ddcd-487f-8dc5-66a2738be732	tracking-by-natural-language-specification	null	null	http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Tracking_by_Natural_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Tracking_by_Natural_CVPR_2017_paper.pdf	Tracking by Natural Language Specification	This paper strives to track a target object in a video. Rather than specifying the target in the first frame of a video by a bounding box, we propose to track the object based on a natural language specification of the target, which provides a more natural human-machine interaction as well as a means to improve tracking results. We define three variants of tracking by language specification: one relying on lingual target specification only, one relying on visual target specification based on language, and one leveraging their joint capacity. To show the potential of tracking by natural language specification we extend two popular tracking datasets with lingual descriptions and report experiments. Finally, we also sketch new tracking scenarios in surveillance and other live video streams that become feasible with a lingual specification of the target.	['Arnold W. M. Smeulders', 'Efstratios Gavves', 'Zhenyang Li', 'Ran Tao', 'Cees G. M. Snoek']	2017-07-01	null	null	null	cvpr-2017-7	['referring-expression-segmentation']	['computer-vision']	[ 9.41877365e-02 7.49777481e-02 -4.88668770e-01 -2.73317307e-01 -5.02199948e-01 -1.07084811e+00 9.63057280e-01 -3.08624148e-01 -4.04675901e-01 4.32149768e-01 2.30709538e-01 -4.12896395e-01 1.09848395e-01 -4.61584955e-01 -8.36648762e-01 -4.36028361e-01 -1.99969113e-01 3.45721245e-01 7.29233801e-01 1.19707994e-01 -2.30091214e-01 7.64047980e-01 -1.35448432e+00 4.25334215e-01 -5.20487465e-02 8.46769452e-01 1.78172126e-01 7.59266019e-01 -2.24577084e-01 8.21582675e-01 -4.87260491e-01 -4.81295466e-01 7.23351538e-01 -2.84785211e-01 -4.37743366e-01 6.16543114e-01 1.06472576e+00 -2.99569219e-01 -2.45560721e-01 1.03055727e+00 -1.60551239e-02 -2.74716288e-01 1.96729764e-01 -1.64162731e+00 -4.08455193e-01 6.83784842e-01 -5.47669709e-01 4.42221910e-02 6.49582982e-01 1.98459178e-01 8.47948849e-01 -2.81327456e-01 1.13283479e+00 1.35333395e+00 8.98151219e-01 8.91225696e-01 -1.31606603e+00 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3b88ec31-39f3-4328-a058-671bd90c019e	is-a-video-worth-n-times-n-images-a-highly	2305.09107	null	https://arxiv.org/abs/2305.09107v1	https://arxiv.org/pdf/2305.09107v1.pdf	Is a Video worth $n\times n$ Images? A Highly Efficient Approach to Transformer-based Video Question Answering	Conventional Transformer-based Video Question Answering (VideoQA) approaches generally encode frames independently through one or more image encoders followed by interaction between frames and question. However, such schema would incur significant memory use and inevitably slow down the training and inference speed. In this work, we present a highly efficient approach for VideoQA based on existing vision-language pre-trained models where we concatenate video frames to a $n\times n$ matrix and then convert it to one image. By doing so, we reduce the use of the image encoder from $n^{2}$ to $1$ while maintaining the temporal structure of the original video. Experimental results on MSRVTT and TrafficQA show that our proposed approach achieves state-of-the-art performance with nearly $4\times$ faster speed and only 30% memory use. We show that by integrating our approach into VideoQA systems we can achieve comparable, even superior, performance with a significant speed up for training and inference. We believe the proposed approach can facilitate VideoQA-related research by reducing the computational requirements for those who have limited access to budgets and resources. Our code will be made publicly available for research use.	['Jennifer Foster', 'Yvette Graham', 'Tianbo Ji', 'Chenyang Lyu']	2023-05-16	null	null	null	null	['video-question-answering']	['computer-vision']	[ 1.59395993e-01 -1.64692372e-01 7.47932419e-02 -5.53016365e-01 -1.09170759e+00 -6.44043028e-01 1.77898437e-01 -1.28375337e-01 -6.36739790e-01 5.91214359e-01 -1.34789944e-01 -7.49366581e-01 3.20312321e-01 -8.73255432e-01 -9.18880820e-01 -4.08344090e-01 1.75733984e-01 1.88784853e-01 5.54089963e-01 -9.67142805e-02 1.96470007e-01 1.74038380e-01 -1.54261315e+00 4.40472901e-01 6.26534879e-01 1.15860033e+00 3.79699141e-01 1.01076603e+00 -3.92912865e-01 1.49887621e+00 -5.64858973e-01 -8.28756332e-01 3.87792438e-01 -5.79509079e-01 -1.16814935e+00 2.51131028e-01 8.23477447e-01 -8.71489584e-01 -7.27292776e-01 9.32652831e-01 1.86000407e-01 3.21026027e-01 4.00418267e-02 -1.14454067e+00 -5.80047488e-01 1.18010901e-01 -4.99941856e-01 6.10033035e-01 4.62934554e-01 2.66911834e-01 1.02758288e+00 -8.11769128e-01 5.02800584e-01 1.07045925e+00 2.77128100e-01 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f5a473a3-5e2c-439f-8b7b-bdfb718c441a	reconsider-re-ranking-using-span-focused	2010.10757	null	https://arxiv.org/abs/2010.10757v1	https://arxiv.org/pdf/2010.10757v1.pdf	RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering	State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few predictions, but a low overall accuracy, motivating the need for answer re-ranking. We develop a simple and effective re-ranking approach (RECONSIDER) for span-extraction tasks, that improves upon the performance of large pre-trained MRC models. RECONSIDER is trained on positive and negative examples extracted from high confidence predictions of MRC models, and uses in-passage span annotations to perform span-focused re-ranking over a smaller candidate set. As a result, RECONSIDER learns to eliminate close false positive passages, and achieves a new state of the art on four QA tasks, including 45.5% Exact Match accuracy on Natural Questions with real user questions, and 61.7% on TriviaQA.	['Wen-tau Yih', 'Yashar Mehdad', 'Sewon Min', 'Srinivasan Iyer']	2020-10-21	null	null	null	null	['triviaqa']	['miscellaneous']	[ 3.72304946e-01 5.66551805e-01 6.98076710e-02 -3.97291481e-01 -2.01929522e+00 -8.03774059e-01 2.37444356e-01 6.27169371e-01 -5.74056506e-01 9.42203522e-01 6.29874110e-01 -5.19018233e-01 -2.18544304e-01 -7.38925993e-01 -7.48139799e-01 1.21217072e-01 1.11574881e-01 1.00039446e+00 8.31317723e-01 -7.69170403e-01 6.17330492e-01 -1.95303708e-01 -1.68499768e+00 1.08593965e+00 1.37488258e+00 8.33396912e-01 6.94538951e-02 1.17890918e+00 -2.82540679e-01 1.35864413e+00 -8.45442593e-01 -8.03668618e-01 -3.30271959e-01 -6.07076108e-01 -1.81910276e+00 -6.94236219e-01 9.85538006e-01 -3.06240857e-01 -1.40688658e-01 2.60516196e-01 5.83263040e-01 4.74418014e-01 5.89182615e-01 -5.19076705e-01 -7.64443815e-01 5.28799891e-01 3.05136293e-02 6.21919453e-01 1.24761081e+00 1.15865275e-01 1.65183628e+00 -9.96752858e-01 5.01676083e-01 1.14975703e+00 5.94888628e-01 7.39565432e-01 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9b7c3b4c-6637-4f2d-9af8-c84cebdb2859	does-interpretability-of-neural-networks	1912.03430	null	https://arxiv.org/abs/1912.03430v6	https://arxiv.org/pdf/1912.03430v6.pdf	An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness	Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpretation. Interestingly, recent empirical and theoretical evidence suggests these two seemingly disparate issues are actually connected. In particular, robust models tend to provide more interpretable gradients than non-robust models. However, whether this relationship works in the opposite direction remains obscure. With this paper, we seek empirical answers to the following question: can models acquire adversarial robustness when they are trained to have interpretable gradients? We introduce a theoretically inspired technique called Interpretation Regularization (IR), which encourages a model's gradients to (1) match the direction of interpretable target salience maps and (2) have small magnitude. To assess model performance and tease apart factors that contribute to adversarial robustness, we conduct extensive experiments on MNIST and CIFAR-10 with both $\ell_2$ and $\ell_\infty$ attacks. We demonstrate that training the networks to have interpretable gradients improves their robustness to adversarial perturbations. Applying the network interpretation technique SmoothGrad yields additional performance gains, especially in cross-norm attacks and under heavy perturbations. The results indicate that the interpretability of the model gradients is a crucial factor for adversarial robustness. Code for the experiments can be found at https://github.com/a1noack/interp_regularization.	['Adam Noack', 'Isaac Ahern', 'Dejing Dou', 'Boyang Li']	2019-12-07	null	null	null	null	['network-interpretation']	['computer-vision']	[ 2.92613178e-01 3.22248489e-01 -1.01094969e-01 -5.28012395e-01 -4.98722196e-01 -6.39931977e-01 4.94832039e-01 -2.06589878e-01 -4.00179416e-01 6.82499170e-01 2.92746633e-01 -5.19944072e-01 -1.76515535e-01 -4.29878503e-01 -8.75968993e-01 -6.33885324e-01 -1.02757573e-01 6.01710938e-02 -2.21080072e-02 -4.81143117e-01 2.21267164e-01 5.97208023e-01 -9.35325265e-01 8.68340954e-02 8.24592769e-01 8.66467178e-01 -3.56058866e-01 6.39456213e-01 4.53184813e-01 9.79080498e-01 -6.71367049e-01 -6.66876733e-01 6.51925743e-01 -4.22101259e-01 -6.82592452e-01 -2.57984787e-01 7.27337003e-01 -3.50969970e-01 -5.07729232e-01 1.49509025e+00 4.44447249e-01 2.60107785e-01 6.07136250e-01 -1.42759693e+00 -1.09018922e+00 6.64339840e-01 -5.88127613e-01 5.48921227e-01 -1.40045673e-01 5.43714821e-01 9.54947054e-01 -6.93956077e-01 2.35548720e-01 1.55023575e+00 6.54137313e-01 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de56a4a5-8302-465b-a8a4-6cc7c9b3f3e3	decentralized-data-governance-as-part-of-a	2307.02357	null	https://arxiv.org/abs/2307.02357v1	https://arxiv.org/pdf/2307.02357v1.pdf	Decentralized Data Governance as Part of a Data Mesh Platform: Concepts and Approaches	Data mesh is a socio-technical approach to decentralized analytics data management. To manage this decentralization efficiently, data mesh relies on automation provided by a self-service data infrastructure platform. A key aspect of this platform is to enable decentralized data governance. Because data mesh is a young approach, there is a lack of coherence in how data mesh concepts are interpreted in the industry, and almost no work on how a data mesh platform facilitates governance. This paper presents a conceptual model of key data mesh concepts and discusses different approaches to drive governance through platform means. The insights presented are drawn from concrete experiences of implementing a fully-functional data mesh platform that can be used as a reference on how to approach data mesh platform development.	['Atif Akhtar', 'Sumedha Verma', 'Arif Wider']	2023-07-05	null	null	null	null	['management']	['miscellaneous']	[-1.01390827e+00 5.18398225e-01 -5.14411509e-01 -2.51417220e-01 -1.97332442e-01 -7.47669876e-01 9.35272634e-01 7.84329653e-01 3.69993001e-02 1.54806957e-01 8.93648684e-01 -4.62601304e-01 -4.82098579e-01 -9.52334523e-01 -1.62306845e-01 -2.55257726e-01 2.05410391e-01 4.90806311e-01 -5.52914590e-02 -6.01866305e-01 1.95337266e-01 4.35470134e-01 -1.83995903e+00 4.67543274e-01 3.93816710e-01 1.18576741e+00 -2.41984740e-01 2.37688571e-01 -4.39419985e-01 1.20934868e+00 -7.95831144e-01 -1.65617272e-01 6.41737044e-01 1.37031674e-01 -8.11133802e-01 -2.56053001e-01 9.75589454e-02 -2.64356643e-01 5.17197728e-01 5.36473155e-01 1.78984199e-02 -8.45574319e-01 1.32189780e-01 -1.70799279e+00 -3.29517692e-01 5.74052095e-01 -7.61212632e-02 5.39871603e-02 1.78937018e-01 4.05600339e-01 9.02499735e-01 -4.20807242e-01 1.08022904e+00 7.99003363e-01 6.90990746e-01 -1.49782121e-01 -1.29058731e+00 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-7.68675148e-01 3.40826243e-01 -8.06804523e-02 -2.32058227e-01 -3.11621800e-02 2.56442219e-01]	[8.99071979522705, 7.404989242553711]
d0cd290d-94fd-48ad-9f71-cdd2e981abe3	foreground-segmentation-based-on-multi	1402.2013	null	http://arxiv.org/abs/1402.2013v1	http://arxiv.org/pdf/1402.2013v1.pdf	Foreground segmentation based on multi-resolution and matting	We propose a foreground segmentation algorithm that does foreground extraction under different scales and refines the result by matting. First, the input image is filtered and resampled to 5 different resolutions. Then each of them is segmented by adaptive figure-ground classification and the best segmentation is automatically selected by an evaluation score that maximizes the difference between foreground and background. This segmentation is upsampled to the original size, and a corresponding trimap is built. Closed-form matting is employed to label the boundary region, and the result is refined by a final figure-ground classification. Experiments show the success of our method in treating challenging images with cluttered background and adapting to loose initial bounding-box.	['Xiaohan Liu', 'Xintong Yu', 'Yisong Chen']	2014-02-10	null	null	null	null	['foreground-segmentation']	['computer-vision']	[ 7.01720059e-01 1.11886345e-01 -1.32278740e-01 -3.09656739e-01 -7.63716578e-01 -5.50663829e-01 6.82235733e-02 -4.58799936e-02 -2.81108230e-01 6.40724480e-01 -3.50492060e-01 -1.75415412e-01 2.93145567e-01 -9.56372857e-01 -6.11866593e-01 -8.21543574e-01 5.25678955e-02 7.36258447e-01 1.05473661e+00 3.34500939e-01 1.90731883e-01 6.42561018e-01 -1.18072295e+00 4.59401131e-01 1.27078402e+00 9.06276703e-01 3.76678795e-01 8.89512181e-01 -5.90633571e-01 4.99300897e-01 -8.27864349e-01 -3.21784735e-01 4.64039475e-01 -6.38080239e-01 -1.17421913e+00 1.02871811e+00 3.41211736e-01 -2.61461735e-01 2.25932866e-01 1.23619258e+00 -1.66846246e-01 9.48914066e-02 3.63044590e-01 -8.79099429e-01 5.32604903e-02 4.27839220e-01 -1.04617703e+00 3.78415287e-01 -9.37454551e-02 -1.40114784e-01 5.32891750e-01 -1.00695550e+00 7.49097347e-01 1.43611407e+00 3.28412086e-01 4.29444343e-01 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-4.36602712e-01 4.58967507e-01 3.87598634e-01 -5.33826888e-01 -4.34043020e-01 6.86524332e-01]	[9.1506929397583, -0.36330392956733704]
f93e558b-28c9-4768-8392-8bb487c3a41f	learning-to-shoot-in-first-person-shooter	1806.05117	null	http://arxiv.org/abs/1806.05117v1	http://arxiv.org/pdf/1806.05117v1.pdf	Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning	While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention. A challenge in such environments is that the time that elapses between deciding to take an action and receiving a reward based on its outcome can be longer than the interval between successive decisions. We explore this in the context of a non-player character (NPC) in a modern first-person shooter game. Such games take place in 3D environments where players, both human and computer-controlled, compete by engaging in combat and completing task objectives. We investigate the use of RL to enable NPCs to gather experience from game-play and improve their shooting skill over time from a reward signal based on the damage caused to opponents. We propose a new method for RL updates and reward calculations, in which the updates are carried out periodically, after each shooting encounter has ended, and a new weighted-reward mechanism is used which increases the reward applied to actions that lead to damaging the opponent in successive hits in what we term "hit clusters".	['Frank G. Glavin', 'Michael G. Madden']	2018-06-13	null	null	null	null	['board-games']	['playing-games']	[ 3.57287347e-01 -4.01268080e-02 1.84347793e-01 5.65508306e-02 -3.83908540e-01 -5.58783114e-01 2.81666458e-01 4.67712402e-01 -9.88269508e-01 9.45712030e-01 -1.76045552e-01 -9.11151767e-02 -3.37874770e-01 -9.87181365e-01 -2.66855985e-01 -5.50346136e-01 -4.11519885e-01 6.15154028e-01 7.14561105e-01 -6.40502036e-01 6.00589156e-01 6.27201736e-01 -1.45826852e+00 4.58064536e-03 4.68092382e-01 7.82585621e-01 2.97685981e-01 8.78905714e-01 3.45472187e-01 1.24499094e+00 -1.03919661e+00 -1.55999124e-01 3.81773978e-01 -7.17459857e-01 -7.69753516e-01 3.21118683e-02 -7.07471848e-01 -2.88015246e-01 -2.06977762e-02 6.74612522e-01 7.52604127e-01 7.83511221e-01 4.81721193e-01 -1.19027293e+00 3.99902374e-01 3.94160807e-01 -4.81357545e-01 2.33512282e-01 6.59729421e-01 4.46765929e-01 7.02498436e-01 6.47911355e-02 5.10386884e-01 8.81894886e-01 2.91671664e-01 6.00656509e-01 -9.68711793e-01 -4.64683443e-01 1.53626353e-01 4.61603761e-01 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d699e548-ed80-44f9-a0f4-aa52f0797eeb	survey-of-face-detection-on-low-quality	1804.07362	null	http://arxiv.org/abs/1804.07362v1	http://arxiv.org/pdf/1804.07362v1.pdf	Survey of Face Detection on Low-quality Images	Face detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems. In this paper, we first review the current state-of-the-art face detectors and their performance on benchmark dataset FDDB, and compare the design protocols of the algorithms. Secondly, we investigate their performance degradation while testing on low-quality images with different levels of blur, noise, and contrast. Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. It inspires researchers to produce more robust design for face detection in the wild.	['Yuqian Zhou', 'Thomas Huang', 'Ding Liu']	2018-04-19	null	null	null	null	['robust-design']	['miscellaneous']	[-9.55385789e-02 -5.86145401e-01 1.33025259e-01 -3.83192807e-01 -1.60653442e-01 -5.21970093e-01 3.74788672e-01 -6.59503818e-01 -4.09935087e-01 4.35524434e-01 -2.85874844e-01 -8.68379045e-03 2.47491255e-01 -6.30838156e-01 -4.85153764e-01 -7.28605330e-01 -1.78738028e-01 8.28141645e-02 4.27440703e-01 -1.52123600e-01 -4.46800999e-02 9.96641517e-01 -1.89922786e+00 2.09938228e-01 1.89524427e-01 7.03892827e-01 1.50168940e-01 6.67044222e-01 2.61028975e-01 3.64794046e-01 -7.48403609e-01 -5.75474203e-01 4.62428898e-01 -2.69385457e-01 -2.87849337e-01 2.24955827e-01 9.58772242e-01 -8.28847706e-01 -3.53740603e-01 1.30990756e+00 9.09244955e-01 -3.89929354e-01 5.22096038e-01 -1.25998545e+00 -6.65664852e-01 -3.39416824e-02 -9.78760302e-01 6.28785670e-01 4.74848926e-01 3.59203488e-01 1.47213161e-01 -1.21829236e+00 5.48342526e-01 1.78531182e+00 7.96037078e-01 8.58372688e-01 -9.42557454e-01 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e262ed2a-ac0b-4487-bdf8-67a7fa73f630	apicontext2com-code-comment-generation-by	2303.01645	null	https://arxiv.org/abs/2303.01645v1	https://arxiv.org/pdf/2303.01645v1.pdf	APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation	Code comments are significantly helpful in comprehending software programs and also aid developers to save a great deal of time in software maintenance. Code comment generation aims to automatically predict comments in natural language given a code snippet. Several works investigate the effect of integrating external knowledge on the quality of generated comments. In this study, we propose a solution, namely APIContext2Com, to improve the effectiveness of generated comments by incorporating the pre-defined Application Programming Interface (API) context. The API context includes the definition and description of the pre-defined APIs that are used within the code snippets. As the detailed API information expresses the functionality of a code snippet, it can be helpful in better generating the code summary. We introduce a seq-2-seq encoder-decoder neural network model with different sets of multiple encoders to effectively transform distinct inputs into target comments. A ranking mechanism is also developed to exclude non-informative APIs, so that we can filter out unrelated APIs. We evaluate our approach using the Java dataset from CodeSearchNet. The findings reveal that the proposed model improves the best baseline by 1.88 (8.24 %), 2.16 (17.58 %), 1.38 (18.3 %), 0.73 (14.17 %), 1.58 (14.98 %) and 1.9 (6.92 %) for BLEU1, BLEU2, BLEU3, BLEU4, METEOR, ROUGE-L respectively. Human evaluation and ablation studies confirm the quality of the generated comments and the effect of architecture and ranking APIs.	['Fatemeh Fard', 'Ramin Shahbazi']	2023-03-03	null	null	null	null	['code-comment-generation', 'comment-generation']	['computer-code', 'natural-language-processing']	[ 2.25120991e-01 2.39996746e-01 -1.60300732e-01 -4.27349716e-01 -7.68830180e-01 -7.29639411e-01 4.36413705e-01 2.92381912e-01 -7.35263452e-02 5.40123343e-01 4.91536438e-01 -2.88589925e-01 2.44511798e-01 -6.67245030e-01 -7.32065380e-01 -1.58158675e-01 1.36809140e-01 -1.30281389e-01 1.96325406e-01 -1.75609514e-01 7.43596435e-01 -3.86422247e-01 -1.64388895e+00 7.61643052e-01 1.38494980e+00 3.74927402e-01 5.90770602e-01 8.04118156e-01 -4.92784530e-01 8.10095072e-01 -9.48044121e-01 -5.89043498e-01 -1.17434397e-01 -5.41563928e-01 -7.81196594e-01 -2.52136886e-01 -4.07675989e-02 -1.26205117e-01 5.16373813e-01 1.10352790e+00 4.61458147e-01 -2.25234851e-01 6.28906369e-01 -1.09901404e+00 -7.55769253e-01 1.19450784e+00 -7.08899617e-01 -1.88033670e-01 5.64256072e-01 4.42134850e-02 1.11770785e+00 -7.90606201e-01 7.15737104e-01 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f1ecb4a4-4176-4e6d-b23e-b035552cbf1f	improving-nonparametric-classification-via	2112.13951	null	https://arxiv.org/abs/2112.13951v2	https://arxiv.org/pdf/2112.13951v2.pdf	Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction	For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take label average over a ball around the query, are consistent but asymptotically biased particularly for a large radius of the ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale $k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and extrapolate it to the query itself. However, their theoretical optimality has been shown for the limit of the infinite number of training samples. For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to observed labels by taking only the radial distance as the explanatory variable and then extrapolate the estimated label probability to zero distance. The usefulness of the proposed method is shown theoretically and experimentally. We prove the convergence rate of the $L^2$ risk for LRR with reference to MS-$k$-NN, and our numerical experiments, including real-world datasets of daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.	['Hidetoshi Shimodaira', 'Kei Nakagawa', 'Akifumi Okuno', 'Ruixing Cao']	2021-12-28	null	null	null	null	['stock-prediction']	['time-series']	[-3.36529613e-01 -2.43968833e-02 -5.59434474e-01 -6.49120152e-01 -1.16941690e+00 -3.41484159e-01 9.30631757e-02 1.54158501e-02 -5.12554228e-01 8.26315701e-01 -4.68143970e-01 -6.22646868e-01 -5.94766021e-01 -9.02508199e-01 -8.28577518e-01 -9.24686253e-01 -5.07622719e-01 2.78614610e-01 7.73403645e-02 9.70613360e-02 4.32654768e-01 6.26141608e-01 -1.21287477e+00 -5.60442984e-01 1.01102030e+00 1.22319436e+00 -2.42160350e-01 5.43336272e-01 1.27488792e-01 5.99693477e-01 -5.07299244e-01 -3.51400882e-01 4.25602168e-01 -3.63474041e-01 -4.23733056e-01 -3.89715940e-01 3.74650925e-01 -1.13073841e-01 -1.73781589e-01 1.03560865e+00 3.13337952e-01 3.11979860e-01 1.44039571e+00 -1.21548617e+00 -9.05401707e-01 4.18423563e-01 -1.07300007e+00 1.85382992e-01 -9.43514332e-02 -3.88438493e-01 1.00322628e+00 -1.06948435e+00 9.77179781e-02 1.11958492e+00 1.31918156e+00 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113ff1cc-2475-4f99-be34-b0e54b027bd6	improving-visual-image-reconstruction-from	2306.11536	null	https://arxiv.org/abs/2306.11536v1	https://arxiv.org/pdf/2306.11536v1.pdf	Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs	The integration of deep learning and neuroscience has been advancing rapidly, which has led to improvements in the analysis of brain activity and the understanding of deep learning models from a neuroscientific perspective. The reconstruction of visual experience from human brain activity is an area that has particularly benefited: the use of deep learning models trained on large amounts of natural images has greatly improved its quality, and approaches that combine the diverse information contained in visual experiences have proliferated rapidly in recent years. In this technical paper, by taking advantage of the simple and generic framework that we proposed (Takagi and Nishimoto, CVPR 2023), we examine the extent to which various additional decoding techniques affect the performance of visual experience reconstruction. Specifically, we combined our earlier work with the following three techniques: using decoded text from brain activity, nonlinear optimization for structural image reconstruction, and using decoded depth information from brain activity. We confirmed that these techniques contributed to improving accuracy over the baseline. We also discuss what researchers should consider when performing visual reconstruction using deep generative models trained on large datasets. Please check our webpage at https://sites.google.com/view/stablediffusion-with-brain/. Code is also available at https://github.com/yu-takagi/StableDiffusionReconstruction.	['Shinji Nishimoto', 'Yu Takagi']	2023-06-20	null	null	null	null	['image-reconstruction']	['computer-vision']	[-3.53290200e-01 -2.35472545e-01 1.85383469e-01 -2.62756288e-01 -5.68230569e-01 -3.30730915e-01 8.01522315e-01 -2.87243277e-01 -6.05847418e-01 7.51187325e-01 5.76217532e-01 -8.21564421e-02 1.48393691e-01 -5.17684937e-01 -7.08783388e-01 -8.12355399e-01 -2.64904723e-02 1.67683855e-01 7.52898529e-02 2.79977292e-01 2.78380156e-01 4.91609275e-01 -1.38876688e+00 3.17886382e-01 6.19439185e-01 7.13766992e-01 5.55834770e-01 5.25063813e-01 1.21484742e-01 9.30336177e-01 -2.19434589e-01 -2.24951506e-01 2.45968238e-01 -7.71553814e-01 -4.80272144e-01 -4.91093844e-02 1.22482702e-01 -4.23363775e-01 -5.28109431e-01 8.18260431e-01 7.99653828e-01 2.13198457e-02 6.87132657e-01 -8.53579283e-01 -7.31000364e-01 1.90213516e-01 -5.44672072e-01 8.03738952e-01 1.50491178e-01 3.37762952e-01 5.01716971e-01 -1.09878886e+00 7.23022103e-01 9.38155651e-01 2.65452415e-01 5.88705778e-01 -1.32382095e+00 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02ec9f7c-f7b7-4967-9963-ed166f656ff5	cross-domain-few-shot-meta-learning-using	2205.05831	null	https://arxiv.org/abs/2205.05831v2	https://arxiv.org/pdf/2205.05831v2.pdf	Feature Extractor Stacking for Cross-domain Few-shot Meta-learning	Cross-domain few-shot meta-learning (CDFSML) addresses learning problems where knowledge needs to be transferred from several source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSML methods generally construct a "universal model" that combines knowledge of multiple source domains into one backbone feature extractor. This enables efficient inference but necessitates re-computation of the backbone whenever a new source domain is added. Moreover, these methods often derive their universal model from a collection of backbones -- normally one for each source domain -- where these backbones are constrained to have the same architecture as the universal model. We propose feature extractor stacking (FES), a new CDFSML method for combining information from a collection of backbones that imposes no constraints on the backbones' architecture and does not require re-computing a universal model when a backbone for a new source domain becomes available. We present the basic FES algorithm, which is inspired by the classic stacking approach to meta-learning, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each backbone independently, use cross-validation to extract meta training data from the support set available for the task, and learn a simple linear meta-classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.	['Geoffrey Holmes', 'Michael Mayo', 'Bernhard Pfahringer', 'Eibe Frank', 'Hongyu Wang']	2022-05-12	null	null	null	null	['cross-domain-few-shot']	['computer-vision']	[ 4.80673224e-01 -2.60564476e-01 -4.69045013e-01 -4.30409700e-01 -1.20109594e+00 -5.27941167e-01 7.45819926e-01 1.63205966e-01 -4.81288999e-01 9.19330478e-01 -7.46022761e-02 9.86886546e-02 -3.28648299e-01 -9.29475129e-01 -1.07201910e+00 -7.58976698e-01 1.40277222e-01 8.27471018e-01 6.09391809e-01 -3.90690446e-01 1.85573846e-01 2.57589161e-01 -1.85317969e+00 7.85229623e-01 8.02380085e-01 9.99961436e-01 5.07034540e-01 5.93708694e-01 -4.43837821e-01 7.80905783e-01 -7.70422280e-01 -2.37743184e-01 1.66069970e-01 -2.93357760e-01 -9.18999314e-01 2.98531093e-02 5.47922730e-01 -1.21136326e-02 -5.85352406e-02 8.84127319e-01 2.97224283e-01 2.83014387e-01 9.21164274e-01 -1.28101289e+00 -5.21268070e-01 5.41068316e-01 -3.76090318e-01 2.76966631e-01 -7.74283856e-02 4.07607071e-02 9.26347792e-01 -7.44189262e-01 7.47965574e-01 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69e6f9a6-22b0-47fd-8f8a-c25ee2786272	iplan-intent-aware-planning-in-heterogeneous	2306.06236	null	https://arxiv.org/abs/2306.06236v1	https://arxiv.org/pdf/2306.06236v1.pdf	iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning	Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we propose a distributed multi-agent reinforcement learning (MARL) algorithm with trajectory and intent prediction in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations. We model two distinct incentives for agents' strategies: Behavioral incentives for agents' long-term planning based on their driving behavior or personality; Instant incentives for agents' short-term planning for collision avoidance based on the current traffic state. We design a two-stream inference module that allows agents to infer their opponents' incentives and incorporate their inferred information into decision-making. We perform experiments on two simulation environments, Non-Cooperative Navigation and Heterogeneous Highway. In Heterogeneous Highway, results show that, compared with centralized MARL baselines such as QMIX and MAPPO, our method yields a 4.0% and 35.7% higher episodic reward in mild and chaotic traffic, with 48.1% higher success rate and 80.6% longer survival time in chaotic traffic. We also compare with a decentralized baseline IPPO and demonstrate a higher episodic reward of 9.2% and 10.3% in mild traffic and chaotic traffic, 25.3% higher success rate, and 13.7% longer survival time.	['Dinesh Manocha', 'Amrit Singh Bedi', 'Tianrui Guan', 'Rohan Chandra', 'Xiyang Wu']	2023-06-09	null	null	null	null	['autonomous-vehicles', 'multi-agent-reinforcement-learning']	['computer-vision', 'methodology']	[-5.51787436e-01 5.23203790e-01 -3.89662027e-01 -3.52428705e-01 -8.35747361e-01 -4.08971459e-01 6.34402394e-01 2.44137552e-02 -8.58439088e-01 1.14619184e+00 3.83408740e-02 -5.34982085e-01 -2.26586670e-01 -9.65544939e-01 -6.71797693e-01 -5.00853479e-01 -5.23117185e-01 1.10719526e+00 5.47386587e-01 -6.18062794e-01 4.67100106e-02 5.49155712e-01 -1.61034977e+00 -3.53327572e-01 8.65351915e-01 5.09670734e-01 1.71461046e-01 9.42798078e-01 4.01502907e-01 1.21106601e+00 -3.06177914e-01 -1.11654207e-01 3.29909265e-01 5.38484827e-02 -4.63333189e-01 -2.08480448e-01 -2.88402885e-01 -7.54401982e-01 -6.20317280e-01 3.50224942e-01 4.00452137e-01 3.17794025e-01 7.81862557e-01 -2.00931644e+00 2.51734436e-01 5.48258007e-01 -5.11236012e-01 1.44943476e-01 5.69480620e-02 8.47506762e-01 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9a4d15cb-6293-423f-aef5-faad64214d93	2305-14952	2305.14952	null	https://arxiv.org/abs/2305.14952v1	https://arxiv.org/pdf/2305.14952v1.pdf	Focus Your Attention (with Adaptive IIR Filters)	We present a new layer in which dynamic (i.e.,input-dependent) Infinite Impulse Response (IIR) filters of order two are used to process the input sequence prior to applying conventional attention. The input is split into chunks, and the coefficients of these filters are determined based on previous chunks to maintain causality. Despite their relatively low order, the causal adaptive filters are shown to focus attention on the relevant sequence elements. The layer performs on-par with state of the art networks, with a fraction of the parameters and with time complexity that is sub-quadratic with input size. The obtained layer is favorable to layers such as Heyna, GPT2, and Mega, both with respect to the number of parameters and the obtained level of performance on multiple long-range sequence problems.	['Lior Wolf', 'Itamar Zimerman', 'Shahar Lutati']	2023-05-24	null	null	null	null	['long-range-modeling']	['natural-language-processing']	[ 5.12723505e-01 1.90665230e-01 -1.34329855e-01 -5.47032282e-02 -2.78042674e-01 -5.85715771e-01 6.76879764e-01 2.23054796e-01 -7.88708687e-01 6.36430740e-01 4.97170717e-01 -3.43581140e-01 -9.26840901e-02 -6.37932718e-01 -8.58884335e-01 -5.68284631e-01 -2.70484686e-01 2.49835595e-01 7.20895290e-01 -3.45885187e-01 5.21877050e-01 3.49801511e-01 -1.44015336e+00 6.68767214e-01 5.81817925e-01 7.77287602e-01 4.25813884e-01 1.20313060e+00 -5.65913171e-02 1.06455636e+00 -4.79658157e-01 -1.27464041e-01 9.23042744e-03 -6.48887157e-01 -1.00813878e+00 -4.19567436e-01 2.18873665e-01 -1.73670754e-01 -6.00868344e-01 8.23913753e-01 2.75476694e-01 3.96437615e-01 6.80617511e-01 -6.94859326e-01 -8.07187200e-01 1.09915435e+00 -3.90727282e-01 8.93477440e-01 2.83847302e-01 3.44859213e-01 1.17603755e+00 -9.22129035e-01 5.57249486e-01 1.48060083e+00 7.09301531e-01 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b88d89f6-93b7-42fc-b5e7-ccfd80169b78	vietnamese-open-domain-complaint-detection-in	2104.11969	null	https://arxiv.org/abs/2104.11969v3	https://arxiv.org/pdf/2104.11969v3.pdf	Vietnamese Complaint Detection on E-Commerce Websites	Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (UIT-ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.	['Phuong Phan-Dieu Ha', 'Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Luan Thanh Nguyen', 'Nhung Thi-Hong Nguyen']	2021-04-24	null	null	null	null	['toxic-comment-classification', 'vietnamese-datasets', 'complaint-comment-classification']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-4.28641707e-01 2.44537771e-01 -2.86637723e-01 -5.46159506e-01 -8.39311361e-01 -5.73807657e-01 1.73882633e-01 4.56997186e-01 -3.14147204e-01 4.89530116e-01 2.90085465e-01 -1.47645166e-02 3.29685152e-01 -5.48236251e-01 -1.37836665e-01 -2.34115288e-01 3.94881576e-01 4.67157722e-01 5.56632392e-02 -3.31946552e-01 5.56232214e-01 -1.12427875e-01 -1.25318122e+00 5.33003807e-01 1.08880591e+00 1.15767038e+00 -5.54184094e-02 2.76068330e-01 -4.09719735e-01 5.44900894e-01 -6.79581046e-01 -1.28751600e+00 -4.98883165e-02 -3.59596759e-01 -9.00047004e-01 7.69990444e-01 -7.60430098e-02 2.40499049e-01 5.70389450e-01 1.39223266e+00 3.42274994e-01 -1.85619667e-01 5.77436924e-01 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389de4a4-c4dd-4040-accb-67304931edb6	how-to-fool-radiologists-with-generative	1710.09762	null	http://arxiv.org/abs/1710.09762v2	http://arxiv.org/pdf/1710.09762v2.pdf	How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis	Discriminating lung nodules as malignant or benign is still an underlying challenge. To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to malignant and benign nodules. However, learning highly discriminative imaging features is an open problem. In this paper, our aim is to learn the most discriminative features pertaining to lung nodules by using an adversarial learning methodology. Specifically, we propose to use unsupervised learning with Deep Convolutional-Generative Adversarial Networks (DC-GANs) to generate lung nodule samples realistically. We hypothesize that imaging features of lung nodules will be discriminative if it is hard to differentiate them (fake) from real (true) nodules. To test this hypothesis, we present Visual Turing tests to two radiologists in order to evaluate the quality of the generated (fake) nodules. Extensive comparisons are performed in discerning real, generated, benign, and malignant nodules. This experimental set up allows us to validate the overall quality of the generated nodules, which can then be used to (1) improve diagnostic decisions by mining highly discriminative imaging features, (2) train radiologists for educational purposes, and (3) generate realistic samples to train deep networks with big data.	['Maria J. M. Chuquicusma', 'Sarfaraz Hussein', 'Jeremy Burt', 'Ulas Bagci']	2017-10-26	null	null	null	null	['lung-cancer-diagnosis']	['medical']	[ 3.67970377e-01 6.84591174e-01 1.45080671e-01 -1.86764508e-01 -8.33321095e-01 -5.52802503e-01 4.98405576e-01 -4.36466187e-01 -5.83769870e-04 6.09938145e-01 -1.77617744e-02 -4.94475514e-01 1.47987213e-02 -7.99608052e-01 -6.71259642e-01 -8.41630399e-01 -5.44773489e-02 9.21223640e-01 2.08868101e-01 2.59591490e-01 -2.84432769e-01 6.75917208e-01 -1.20962119e+00 5.49906492e-01 9.62462604e-01 6.42010212e-01 8.25474039e-02 9.18439627e-01 2.85554409e-01 1.04501188e+00 -5.74245870e-01 -4.95837539e-01 4.59775090e-01 -8.75368476e-01 -9.21389222e-01 3.99274349e-01 3.45701694e-01 -6.19490564e-01 -2.38721505e-01 1.14045107e+00 3.52329940e-01 -4.37795579e-01 1.07386374e+00 -1.12255156e+00 -6.23939514e-01 5.12398243e-01 -5.44888750e-02 2.54062433e-02 -5.31207137e-02 6.42793775e-01 6.50528908e-01 -5.76919198e-01 5.17917871e-01 9.68109488e-01 3.60428780e-01 9.38044071e-01 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f235f1a2-ce0e-4aab-9c8a-2842e43ccd03	spectral-unmixing-of-raman-microscopic-images	2110.13189	null	https://arxiv.org/abs/2110.13189v1	https://arxiv.org/pdf/2110.13189v1.pdf	Spectral unmixing of Raman microscopic images of single human cells using Independent Component Analysis	Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman maps is reported. A hyperspectral map of a fixed human cell was collected by a Raman micro spectrometer in a raster pattern on a 0.5um grid. Unlike previously used unsupervised machine learning techniques such as principal component analysis, ICA is based on non-Gaussianity and statistical independence of data which is the case for mixture Raman spectra. Hence, ICA is a great candidate for assembling pseudo-colour maps from the spectral hypercube of Raman spectra. Our experimental results revealed that ICA is capable of reconstructing false colour maps of Raman hyperspectral data of human cells, showing the nuclear region constituents as well as subcellular organelle in the cytoplasm and distribution of mitochondria in the perinuclear region. Minimum preprocessing requirements and label-free nature of the ICA method make it a great unmixed method for extraction of endmembers in Raman hyperspectral maps of living cells.	['Li-Lin Tay', 'M. Hamed Mozaffari']	2021-10-25	null	null	null	null	['image-clustering']	['computer-vision']	[ 8.29802632e-01 -4.02359128e-01 5.48092246e-01 1.06710836e-01 -2.56384671e-01 -6.70810461e-01 4.30059731e-01 -3.27938795e-01 -4.69250888e-01 7.40554810e-01 -1.11323975e-01 -2.07358330e-01 -2.72191972e-01 -6.86765552e-01 -2.43026182e-01 -1.56507242e+00 2.06498653e-02 8.96848679e-01 -4.27520424e-01 1.83011174e-01 2.95082927e-01 8.71803105e-01 -1.61963546e+00 1.82568088e-01 7.15789080e-01 4.28389251e-01 2.61510670e-01 8.43994379e-01 -4.61984724e-01 2.58500457e-01 -2.53234148e-01 5.26138365e-01 4.11003768e-01 -6.23414278e-01 -3.84151131e-01 4.85076427e-01 -2.55330235e-01 4.04515505e-01 2.16353804e-01 1.22135150e+00 3.97875398e-01 3.05671841e-01 1.33685791e+00 -1.16275787e+00 -6.21154606e-01 5.20103395e-01 -1.08258510e+00 -7.79497623e-02 -6.73129261e-02 9.53119621e-03 3.03726226e-01 -8.24705005e-01 4.83286768e-01 8.68150234e-01 1.25409797e-01 3.78068149e-01 -1.55129087e+00 -7.54795313e-01 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5e781d96-5adf-49a3-9eab-a5fe1c87a07e	cluster-head-detection-for-hierarchical-uav	2203.04311	null	https://arxiv.org/abs/2203.04311v1	https://arxiv.org/pdf/2203.04311v1.pdf	Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning	In this paper, we study the cluster head detection problem of a two-level unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters, where the inherent follow strategy (IFS) of low-level follower UAVs (FUAVs) with respect to high-level cluster head UAVs (HUAVs) is unknown. We first propose a graph attention self-supervised learning algorithm (GASSL) to detect the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same time. Then, to detect the HUAVs in the USNET with multiple UAV clusters, we develop a multi-cluster graph attention self-supervised learning algorithm (MC-GASSL) based on the GASSL. The MC-GASSL clusters the USNET with a gated recurrent unit (GRU)-based metric learning scheme and finds the HUAVs in each cluster with GASSL. Numerical results show that the GASSL can detect the HUAVs in single UAV clusters obeying various kinds of IFSs with over 98% average accuracy. The simulation results also show that the clustering purity of the USNET with MC-GASSL exceeds that with traditional clustering algorithms by at least 10% average. Furthermore, the MC-GASSL can efficiently detect all the HUAVs in USNETs with various IFSs and cluster numbers with low detection redundancies.	['Qihui Wu', 'Feifei Gao', 'Xiang Yun', 'Jun Liu', 'Zhiyu Mou']	2022-03-08	null	null	null	null	['head-detection']	['computer-vision']	[-4.03769672e-01 6.61812769e-03 -6.67102411e-02 4.34796065e-01 1.76391378e-02 -7.28690624e-01 8.94320011e-02 2.65988052e-01 -9.76575315e-02 4.75848973e-01 -7.42642283e-01 -2.34544486e-01 -4.46674705e-01 -8.06248963e-01 -4.98593181e-01 -1.11435723e+00 -8.56189668e-01 3.60707849e-01 6.53673410e-01 -2.00583175e-01 -1.23888828e-01 4.36209351e-01 -1.39992666e+00 -4.89544153e-01 6.88604355e-01 1.01093543e+00 6.18218362e-01 9.87605214e-01 6.93938494e-01 8.57342124e-01 -7.59307861e-01 2.75064766e-01 5.66080928e-01 -4.92422253e-01 -6.48196220e-01 2.01129690e-01 -1.50659397e-01 1.41090080e-02 -4.25858498e-01 1.21518648e+00 1.35811329e-01 1.62268892e-01 8.36195052e-01 -1.87961507e+00 -1.62378877e-01 5.74632645e-01 -1.05401111e+00 2.92648792e-01 -4.08630818e-02 -1.81541685e-02 9.29365516e-01 -7.66572133e-02 2.41774514e-01 1.00196850e+00 6.75453305e-01 2.00728521e-01 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0331815d-d4a3-4e93-b41c-1293160cafd9	point2vec-for-self-supervised-representation	2303.16570	null	https://arxiv.org/abs/2303.16570v1	https://arxiv.org/pdf/2303.16570v1.pdf	Point2Vec for Self-Supervised Representation Learning on Point Clouds	Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. In an in-depth analysis, we discover that the leakage of positional information reveals the overall object shape to the student even under heavy masking and thus hampers data2vec to learn strong representations for point clouds. We address this 3D-specific shortcoming by proposing point2vec, which unleashes the full potential of data2vec-like pre-training on point clouds. Our experiments show that point2vec outperforms other self-supervised methods on shape classification and few-shot learning on ModelNet40 and ScanObjectNN, while achieving competitive results on part segmentation on ShapeNetParts. These results suggest that the learned representations are strong and transferable, highlighting point2vec as a promising direction for self-supervised learning of point cloud representations.	['Bastian Leibe', 'Alexander Hermans', 'Jonas Schult', 'Karim Abou Zeid']	2023-03-29	null	null	null	null	['3d-point-cloud-classification', '3d-part-segmentation', 'few-shot-3d-point-cloud-classification']	['computer-vision', 'computer-vision', 'computer-vision']	[-7.10942000e-02 3.66825283e-01 -1.56206116e-01 -5.31335831e-01 -8.30713212e-01 -7.04461396e-01 7.91320801e-01 2.48813063e-01 7.95017462e-03 -4.53447439e-02 1.66641816e-01 -2.14323595e-01 1.17567249e-01 -1.02510333e+00 -9.83189583e-01 -6.46465123e-01 5.63786589e-02 7.20128834e-01 3.52362573e-01 -3.03850830e-01 1.72383055e-01 8.31066966e-01 -1.79921627e+00 1.78397149e-01 4.76381123e-01 9.65750098e-01 1.22709520e-01 3.25999349e-01 -8.25396419e-01 3.78130704e-01 -2.96692640e-01 -3.11485142e-01 4.06268299e-01 3.35320085e-01 -8.21781814e-01 3.47519815e-01 9.38149214e-01 -8.86603147e-02 -4.19094533e-01 9.25437212e-01 4.98825282e-01 1.04368202e-01 7.40612388e-01 -1.21289670e+00 -9.99543786e-01 3.32839906e-01 -4.92406964e-01 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82d59c78-a3d8-4564-b0f0-6bd0d35dc9cc	achieving-domain-generalization-in-underwater	2104.02230	null	https://arxiv.org/abs/2104.02230v6	https://arxiv.org/pdf/2104.02230v6.pdf	Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning	The performance of existing underwater object detection methods degrades seriously when facing domain shift caused by complicated underwater environments. Due to the limitation of the number of domains in the dataset, deep detectors easily memorize a few seen domains, which leads to low generalization ability. There are two common ideas to improve the domain generalization performance. First, it can be inferred that the detector trained on as many domains as possible is domain-invariant. Second, for the images with the same semantic content in different domains, their hidden features should be equivalent. This paper further excavates these two ideas and proposes a domain generalization framework (named DMC) that learns how to generalize across domains from Domain Mixup and Contrastive Learning. First, based on the formation of underwater images, an image in an underwater environment is the linear transformation of another underwater environment. Thus, a style transfer model, which outputs a linear transformation matrix instead of the whole image, is proposed to transform images from one source domain to another, enriching the domain diversity of the training data. Second, mixup operation interpolates different domains on the feature level, sampling new domains on the domain manifold. Third, contrastive loss is selectively applied to features from different domains to force the model to learn domain invariant features but retain the discriminative capacity. With our method, detectors will be robust to domain shift. Also, a domain generalization benchmark S-UODAC2020 for detection is set up to measure the performance of our method. Comprehensive experiments on S-UODAC2020 and two object recognition benchmarks (PACS and VLCS) demonstrate that the proposed method is able to learn domain-invariant representations, and outperforms other domain generalization methods.	['Hong Liu', 'Pinhao Song', 'Shengquan Li', 'Runwei Ding', 'Xiaochuan Zhang', 'Linhui Dai', 'Yang Chen']	2021-04-06	null	null	null	null	['image-stylization']	['computer-vision']	[ 2.10585594e-01 -2.72183806e-01 3.71688664e-01 -4.12951320e-01 -2.68605024e-01 -6.63479924e-01 4.42182690e-01 -2.77571350e-01 -5.61356723e-01 6.08776927e-01 2.20255628e-02 2.36614466e-01 -3.28867100e-02 -1.01430488e+00 -7.11190939e-01 -9.81822550e-01 -9.51836780e-02 1.22797377e-01 6.25339031e-01 -4.34886277e-01 4.80224192e-02 5.58788180e-01 -1.55812156e+00 2.06080899e-01 1.04087067e+00 9.56353962e-01 6.61656618e-01 7.77790248e-02 -1.32472590e-01 1.01604939e-01 -6.10505939e-01 -2.07341965e-02 6.55335069e-01 -5.38941443e-01 -4.26277101e-01 4.93729264e-02 3.66600364e-01 -5.45981348e-01 -7.25744128e-01 1.38574874e+00 4.30277944e-01 2.97597468e-01 8.49181414e-01 -9.56766307e-01 -7.76455402e-01 8.13015476e-02 -1.90130830e-01 8.74686912e-02 2.48905629e-01 2.78601330e-02 6.64379179e-01 -9.44369793e-01 7.42420077e-01 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30b69c69-17d8-4fc1-ae59-2b4751afe9f8	a-convolutional-decoder-for-point-clouds	1906.11478	null	https://arxiv.org/abs/1906.11478v1	https://arxiv.org/pdf/1906.11478v1.pdf	A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization	Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks to generate 3D shapes, none of them reach the level of quality that deep learning synthesis approaches for images provide. In this work we present a method for a convolutional point cloud decoder/generator that makes use of recent advances in the domain of image synthesis. Namely, we use Adaptive Instance Normalization and offer an intuition on why it can improve training. Furthermore, we propose extensions to the minimization of the commonly used Chamfer distance for auto-encoding point clouds. In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results. The results are evaluated in an auto-encoding setup to offer both qualitative and quantitative analysis. The proposed decoder is validated by an extensive ablation study and is able to outperform current state of the art results in a number of experiments. We show the applicability of our method in the fields of point cloud upsampling, single view reconstruction, and shape synthesis.	['Moritz Ibing', 'Leif Kobbelt', 'Isaak Lim']	2019-06-27	null	null	null	null	['point-cloud-generation']	['computer-vision']	[ 3.48210812e-01 1.73311338e-01 3.42659682e-01 -3.97968709e-01 -6.33454263e-01 -5.17980516e-01 9.46328342e-01 7.20397756e-02 -6.33078292e-02 3.78674775e-01 1.35185793e-01 -1.33636519e-01 1.29759638e-02 -1.10569072e+00 -1.10247493e+00 -3.72861415e-01 1.23132885e-01 7.90092766e-01 1.39291301e-01 -5.36795795e-01 1.55886516e-01 1.18584180e+00 -1.81592667e+00 2.93245882e-01 4.90185082e-01 1.06026483e+00 6.93229586e-02 4.68325555e-01 -1.65621683e-01 2.09785700e-01 -4.55609679e-01 -5.56248248e-01 6.10093832e-01 -1.77275106e-01 -6.89779401e-01 3.01571667e-01 7.67717659e-01 -4.45063502e-01 2.40403023e-02 6.96874321e-01 7.38515675e-01 -1.52261585e-01 7.24194348e-01 -1.07631445e+00 -3.98482352e-01 2.86562860e-01 -1.62824005e-01 -2.55525559e-01 1.89781755e-01 1.91568121e-01 7.65597045e-01 -1.08146310e+00 9.08653557e-01 1.23081720e+00 7.50147343e-01 4.52306092e-01 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5.04248321e-01 2.53941894e-01 -7.64389336e-01 -1.09235890e-01 1.25413895e-01]	[8.69271183013916, -3.463822364807129]
b00423f8-5938-40fd-bc6c-51fb543fe1bc	tribert-full-body-human-centric-audio-visual	2110.13412	null	https://arxiv.org/abs/2110.13412v1	https://arxiv.org/pdf/2110.13412v1.pdf	TriBERT: Full-body Human-centric Audio-visual Representation Learning for Visual Sound Separation	The recent success of transformer models in language, such as BERT, has motivated the use of such architectures for multi-modal feature learning and tasks. However, most multi-modal variants (e.g., ViLBERT) have limited themselves to visual-linguistic data. Relatively few have explored its use in audio-visual modalities, and none, to our knowledge, illustrate them in the context of granular audio-visual detection or segmentation tasks such as sound source separation and localization. In this work, we introduce TriBERT -- a transformer-based architecture, inspired by ViLBERT, which enables contextual feature learning across three modalities: vision, pose, and audio, with the use of flexible co-attention. The use of pose keypoints is inspired by recent works that illustrate that such representations can significantly boost performance in many audio-visual scenarios where often one or more persons are responsible for the sound explicitly (e.g., talking) or implicitly (e.g., sound produced as a function of human manipulating an object). From a technical perspective, as part of the TriBERT architecture, we introduce a learned visual tokenization scheme based on spatial attention and leverage weak-supervision to allow granular cross-modal interactions for visual and pose modalities. Further, we supplement learning with sound-source separation loss formulated across all three streams. We pre-train our model on the large MUSIC21 dataset and demonstrate improved performance in audio-visual sound source separation on that dataset as well as other datasets through fine-tuning. In addition, we show that the learned TriBERT representations are generic and significantly improve performance on other audio-visual tasks such as cross-modal audio-visual-pose retrieval by as much as 66.7% in top-1 accuracy.	['Leonid Sigal', 'Mengyu Yang', 'Tanzila Rahman']	2021-10-26	null	null	null	null	['pose-retrieval']	['computer-vision']	[ 3.51070464e-02 -3.90082598e-01 1.48831487e-01 -1.48186460e-01 -1.51320434e+00 -9.05327559e-01 7.66869485e-01 6.88760579e-02 -3.61821890e-01 1.38124436e-01 5.32459438e-01 1.67329069e-02 -2.58039594e-01 -2.82195151e-01 -8.68830979e-01 -5.46292782e-01 -1.73386917e-01 3.13673615e-01 8.05724710e-02 -1.56703189e-01 -1.07848860e-01 3.97147894e-01 -1.80552328e+00 6.54486716e-01 3.58491699e-04 1.23110306e+00 -4.90079783e-02 8.63397002e-01 1.68637820e-02 4.94526535e-01 -6.02889597e-01 -1.07454292e-01 8.03283453e-02 -2.88290977e-01 -8.35558772e-01 -5.45991622e-02 9.40394759e-01 -9.20784697e-02 -3.07250291e-01 4.45438623e-01 9.47585404e-01 3.18955034e-01 6.51417732e-01 -1.48726559e+00 -6.68748617e-01 6.90522730e-01 -6.28842890e-01 1.13762856e-01 6.22328401e-01 2.66828209e-01 1.62468874e+00 -1.21501970e+00 2.09216267e-01 1.47083855e+00 8.02996039e-01 4.17596281e-01 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-6.96223259e-01 6.72461927e-01 8.84568155e-01 -9.42749381e-01 -5.82669497e-01 2.42098402e-02]	[14.762632369995117, 4.942720890045166]
08e041a7-1f84-49d8-a605-83fa381d3dc0	discriminative-functional-connectivity	1402.5684	null	http://arxiv.org/abs/1402.5684v2	http://arxiv.org/pdf/1402.5684v2.pdf	Discriminative Functional Connectivity Measures for Brain Decoding	We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning method, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using a functional neighbourhood concept. In order to define the functional neighbourhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighbouring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-nearest neighbour (k-nn) and Support Vector Machine (SVM), are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62%-71% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40%-48%, for ten semantic categories.	['Orhan Firat', 'Mete Ozay', 'Fatos T. Yarman Vural', 'Ilke Oztekin']	2014-02-23	null	null	null	null	['brain-decoding', 'brain-decoding']	['medical', 'miscellaneous']	[ 2.66949505e-01 -9.81413573e-02 -2.24229530e-01 -4.62546021e-01 -2.55658239e-01 -2.48283967e-01 6.07915163e-01 6.34823859e-01 -6.22864366e-01 6.56005323e-01 2.74726957e-01 2.55181909e-01 -8.43886733e-01 -1.06338406e+00 -4.16319102e-01 -7.65468359e-01 -3.87623370e-01 4.96287137e-01 2.57805526e-01 1.29284695e-01 7.98290253e-01 7.37163305e-01 -1.82288647e+00 8.19828928e-01 7.37855196e-01 1.32031357e+00 4.01331335e-01 -8.04953501e-02 -4.65041548e-01 3.27605128e-01 -3.78529966e-01 3.22577775e-01 -2.28449851e-02 -3.34594250e-01 -9.77457702e-01 -2.65250623e-01 1.30005449e-01 4.39632863e-01 6.77033737e-02 8.93814206e-01 2.21026152e-01 5.37626743e-01 1.31788790e+00 -6.04190648e-01 -3.57135534e-01 4.40255940e-01 -3.51623923e-01 8.21880817e-01 3.71458381e-01 -4.66677785e-01 8.45244825e-01 -1.26875854e+00 6.74826682e-01 1.22662616e+00 4.25489664e-01 1.06155023e-01 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156a1fe2-0648-4a06-9a3f-a0bc302db011	dosa-a-system-to-accelerate-annotations-on	2211.04934	null	https://arxiv.org/abs/2211.04934v1	https://arxiv.org/pdf/2211.04934v1.pdf	DoSA : A System to Accelerate Annotations on Business Documents with Human-in-the-Loop	Business documents come in a variety of structures, formats and information needs which makes information extraction a challenging task. Due to these variations, having a document generic model which can work well across all types of documents and for all the use cases seems far-fetched. For document-specific models, we would need customized document-specific labels. We introduce DoSA (Document Specific Automated Annotations), which helps annotators in generating initial annotations automatically using our novel bootstrap approach by leveraging document generic datasets and models. These initial annotations can further be reviewed by a human for correctness. An initial document-specific model can be trained and its inference can be used as feedback for generating more automated annotations. These automated annotations can be reviewed by human-in-the-loop for the correctness and a new improved model can be trained using the current model as pre-trained model before going for the next iteration. In this paper, our scope is limited to Form like documents due to limited availability of generic annotated datasets, but this idea can be extended to a variety of other documents as more datasets are built. An open-source ready-to-use implementation is made available on GitHub https://github.com/neeleshkshukla/DoSA.	['Amit Vaid', 'Raghu Katikeri', 'Msp Raja', 'Neelesh K Shukla']	2022-11-09	null	null	null	null	['document-ai', 'key-information-extraction']	['natural-language-processing', 'natural-language-processing']	[ 8.68421644e-02 4.76951182e-01 -3.18595976e-01 -6.32564425e-01 -7.59962022e-01 -9.46986914e-01 8.68001282e-01 4.36137170e-01 5.28454408e-02 6.89309180e-01 1.36138439e-01 -4.45098400e-01 -1.69795737e-01 -5.69810808e-01 -5.06127000e-01 -2.38669798e-01 3.00611526e-01 9.22231019e-01 5.02042890e-01 -1.02181196e-01 4.68298644e-01 2.33127266e-01 -1.44556189e+00 7.78413653e-01 7.36704051e-01 5.91930270e-01 4.88101542e-01 7.46994257e-01 -6.78217888e-01 6.65612757e-01 -5.06698906e-01 -5.22542894e-01 2.02733383e-01 -3.94394279e-01 -1.15934241e+00 2.25823402e-01 1.82912081e-01 -2.22183347e-01 4.57074970e-01 7.36466944e-01 2.23021120e-01 -2.46314809e-01 5.95410526e-01 -1.13774633e+00 -4.27733004e-01 9.68846858e-01 -1.18047245e-01 -4.11894739e-01 4.95641649e-01 -1.87598765e-01 9.84797835e-01 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fcaadeac-dc5b-46e2-b36d-404c7178f234	lungbrn-a-smart-digital-stethoscope-for	null	null	https://ieeexplore.ieee.org/document/8919021	https://yongfu-li.github.io/papers/LungBRN_A_Smart_Digital_Stethoscope_for_Detecting_Respiratory_Disease_Using_bi-ResNet_Deep_Learning_Algorithm.pdf	LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm	Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can be used to provide an immediate diagnostic for respiratory-related diseases such as chronic obstructive pulmonary. In this work, we have developed an improved bi-ResNet deep learning architecture, LungBRN, which uses STFT and wavelet feature extraction techniques to mprove the accuracy compared to the state-of-the-art works. To ensure a fair evaluation, we have adopted the official benchmark standards and the “train-and-test” dataset splitting method stated in the ICBHI 2017 challenge. As a result, we are able to achieve a performance of 50.16%, which is the best result in terms of accuracy compared to all participating teams from ICBHI 2017.	['Jian Zhao and Guoxing Wang', 'Yongfu Li', 'Yuhang Zhang', 'Qing Yu', 'Xinzi Xu', 'Yi Ma']	2019-12-05	null	null	null	ieee-biomedical-circuits-and-systems-biocas	['sound-classification']	['audio']	[-2.09739301e-02 2.15919688e-02 3.49086598e-02 1.97573006e-01 -7.11465776e-01 -8.65631178e-02 9.33694169e-02 1.86731011e-01 -6.72303796e-01 6.92860663e-01 2.16119632e-01 -6.17643833e-01 -2.80249804e-01 -7.27626860e-01 -3.80404711e-01 -6.05907679e-01 1.70662384e-02 6.43660963e-01 1.91212177e-01 -1.92903560e-02 -3.34264457e-01 5.90722084e-01 -1.36890864e+00 5.13577580e-01 4.37807113e-01 1.15435612e+00 3.71149145e-02 8.69646728e-01 1.70922413e-01 8.57033074e-01 -5.52759528e-01 -1.01507567e-02 1.63752332e-01 -3.80418807e-01 -1.00206518e+00 -6.94715798e-01 3.14959615e-01 -6.84202969e-01 -4.00342077e-01 3.06887358e-01 1.10898983e+00 4.11894917e-02 4.69712496e-01 -4.91864264e-01 1.26642913e-01 3.26171905e-01 1.96842879e-01 5.53157568e-01 3.95938396e-01 8.08222517e-02 6.45362318e-01 -8.16087723e-01 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d84ad23f-9e2b-422f-9e40-4718c8f91d9c	self-motivated-multi-agent-exploration	2301.02083	null	https://arxiv.org/abs/2301.02083v1	https://arxiv.org/pdf/2301.02083v1.pdf	Self-Motivated Multi-Agent Exploration	In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-exploration and team collaboration. However, agents can hardly accomplish the team task without coordination and they would be trapped in a local optimum where easy cooperation is accessed without enough individual exploration. Recent works mainly concentrate on agents' coordinated exploration, which brings about the exponentially grown exploration of the state space. To address this issue, we propose Self-Motivated Multi-Agent Exploration (SMMAE), which aims to achieve success in team tasks by adaptively finding a trade-off between self-exploration and team cooperation. In SMMAE, we train an independent exploration policy for each agent to maximize their own visited state space. Each agent learns an adjustable exploration probability based on the stability of the joint team policy. The experiments on highly cooperative tasks in StarCraft II micromanagement benchmark (SMAC) demonstrate that SMMAE can explore task-related states more efficiently, accomplish coordinated behaviours and boost the learning performance.	['De-Chuan Zhan', 'Yang Yu', 'Lei Yuan', 'Jiahan Cao', 'Shaowei Zhang']	2023-01-05	null	null	null	null	['starcraft-ii', 'smac-1', 'starcraft', 'smac']	['playing-games', 'playing-games', 'playing-games', 'playing-games']	[-6.13157570e-01 1.29746497e-01 -6.30822182e-02 1.97397858e-01 -4.87528622e-01 -3.04784983e-01 4.86616492e-01 1.93774626e-01 -6.21343315e-01 1.11437488e+00 -1.65043578e-01 -5.93096018e-02 -3.22336197e-01 -6.16711259e-01 -3.33017558e-01 -1.20728385e+00 -5.73664784e-01 7.18883514e-01 2.44740516e-01 -7.45450616e-01 1.83216721e-01 -1.28971100e-01 -1.38296449e+00 -4.08952594e-01 1.35952938e+00 5.60535848e-01 6.33282006e-01 5.23284912e-01 1.19396448e-01 9.47847784e-01 -7.92297363e-01 3.52592498e-01 3.34721386e-01 -4.66879487e-01 -8.59462500e-01 2.66552866e-01 -7.38192141e-01 9.63018835e-02 1.44769803e-01 9.96875763e-01 5.17689347e-01 3.82948458e-01 3.26705635e-01 -1.56979632e+00 -8.06992725e-02 1.06827319e+00 -9.89224374e-01 1.85137600e-01 4.96857949e-02 4.96715665e-01 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51390ecb-f035-4412-abcc-f136e2e519e5	tap-dlnd-10-a-corpus-for-document-level	1802.06950	null	http://arxiv.org/abs/1802.06950v1	http://arxiv.org/pdf/1802.06950v1.pdf	TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection	Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc. Important though the problem is, we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework. To bridge this gap, we present here a resource for benchmarking the techniques for document level novelty detection. We create the resource via event-specific crawling of news documents across several domains in a periodic manner. We release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern.	['Amitra Salam', 'Swati Tiwari', 'Asif Ekbal', 'Tirthankar Ghosal', 'Pushpak Bhattacharyya']	2018-02-20	tap-dlnd-10-a-corpus-for-document-level-2	https://aclanthology.org/L18-1559	https://aclanthology.org/L18-1559.pdf	lrec-2018-5	['extractive-document-summarization']	['natural-language-processing']	[ 1.65114999e-01 1.94040313e-01 -3.25915337e-01 4.59896326e-02 -1.06220710e+00 -8.75042379e-01 1.22507858e+00 1.02302754e+00 -3.60342622e-01 1.02388120e+00 7.17852235e-01 -6.34352714e-02 -4.84341741e-01 -5.37354231e-01 -5.12329459e-01 -4.46832031e-01 -1.54980332e-01 5.00889599e-01 3.48883331e-01 -7.43204281e-02 1.39675140e+00 6.24278188e-01 -1.84524715e+00 5.36886930e-01 9.47409749e-01 5.38407385e-01 -9.26280469e-02 9.64582384e-01 -4.99712944e-01 8.64717305e-01 -1.42922997e+00 -3.70938361e-01 -1.31840929e-01 -7.75386095e-01 -1.13640451e+00 -2.70982802e-01 5.14072418e-01 3.45514596e-01 -8.55513066e-02 1.16044366e+00 4.20952380e-01 1.04305789e-01 8.85122061e-01 -1.06260192e+00 -3.74052465e-01 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5ecdcb37-84a7-42d0-a245-902da67498ff	anchor-based-adversarially-robust-zero-shot	2301.13096	null	https://arxiv.org/abs/2301.13096v2	https://arxiv.org/pdf/2301.13096v2.pdf	Language-Driven Anchors for Zero-Shot Adversarial Robustness	Deep neural networks are known to be susceptible to adversarial attacks. In this work, we focus on improving adversarial robustness in the challenging zero-shot image classification setting. To address this issue, we propose LAAT, a novel Language-driven, Anchor-based Adversarial Training strategy. LAAT utilizes a text encoder to generate fixed anchors (normalized feature embeddings) for each category and then uses these anchors for adversarial training. By leveraging the semantic consistency of the text encoders, LAAT can enhance the adversarial robustness of the image model on novel categories without additional examples. We identify the large cosine similarity problem of recent text encoders and design several effective techniques to address it. The experimental results demonstrate that LAAT significantly improves zero-shot adversarial performance, outperforming previous state-of-the-art adversarially robust one-shot methods. Moreover, our method produces substantial zero-shot adversarial robustness when models are trained on large datasets such as ImageNet-1K and applied to several downstream datasets.	['Xiaolin Hu', 'Bo Zhang', 'Zhanhao Hu', 'Yining Liu', 'Wei zhang', 'Xiao Li']	2023-01-30	null	null	null	null	['adversarial-defense']	['adversarial']	[ 2.74206847e-01 1.31133720e-01 4.96050790e-02 -2.56174505e-01 -9.60849881e-01 -8.39225233e-01 7.83561349e-01 -1.84722930e-01 -4.66385543e-01 4.52966213e-01 2.40711719e-01 -2.71262914e-01 2.19542608e-01 -7.58807540e-01 -1.09559917e+00 -5.45099556e-01 8.94197226e-02 8.11584946e-03 4.42832261e-01 -4.17404354e-01 7.16299110e-04 3.27395916e-01 -9.89758909e-01 1.39871001e-01 6.43639922e-01 9.46727633e-01 -3.32283646e-01 7.26343036e-01 2.13844597e-01 1.09270132e+00 -8.67962956e-01 -1.10132968e+00 6.48660004e-01 -3.41107279e-01 -6.08698308e-01 -2.94770926e-01 7.29741275e-01 -7.37245262e-01 -1.14207709e+00 1.46060646e+00 7.13597059e-01 1.75238252e-01 7.22874105e-01 -1.58225822e+00 -1.32059455e+00 7.86169887e-01 -9.97214839e-02 2.19018221e-01 -4.07447368e-02 3.77998501e-01 7.58864462e-01 -8.07785034e-01 5.93410015e-01 1.32335651e+00 8.35116744e-01 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d649496a-c945-4294-b2f2-2d0ccee236c6	towards-efficient-and-exact-map-inference-for	null	null	http://openaccess.thecvf.com/content_cvpr_2013/html/Kappes_Towards_Efficient_and_2013_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2013/papers/Kappes_Towards_Efficient_and_2013_CVPR_paper.pdf	Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization	Discrete graphical models (also known as discrete Markov random fields) are a major conceptual tool to model the structure of optimization problems in computer vision. While in the last decade research has focused on fast approximative methods, algorithms that provide globally optimal solutions have come more into the research focus in the last years. However, large scale computer vision problems seemed to be out of reach for such methods. In this paper we introduce a promising way to bridge this gap based on partial optimality and structural properties of the underlying problem factorization. Combining these preprocessing steps, we are able to solve grids of size 2048 x 2048 in less than 90 seconds. On the hitherto unsolvable Chinese character dataset of Nowozin et al. we obtain provably optimal results in 56% of the instances and achieve competitive runtimes on other recent benchmark problems. While in the present work only generalized Potts models are considered, an extension to general graphical models seems to be feasible.	['Christoph Schnorr', 'Gerhard Reinelt', 'Markus Speth', 'Jorg Hendrik Kappes']	2013-06-01	null	null	null	cvpr-2013-6	['2048']	['playing-games']	[ 2.30143756e-01 2.28617758e-01 -4.20023613e-02 -2.52205402e-01 -7.16464996e-01 -5.44966996e-01 6.06793284e-01 5.36441207e-01 -6.06113732e-01 8.45084608e-01 -3.02598774e-01 -3.67777526e-01 -3.66591781e-01 -8.42622876e-01 -6.40904307e-01 -8.11350346e-01 -1.18308030e-01 8.93835485e-01 4.38602239e-01 9.77943167e-02 4.36855644e-01 6.83839142e-01 -1.35727656e+00 1.93937555e-01 8.65878403e-01 8.09572220e-01 3.56131375e-01 8.59674156e-01 -2.32081518e-01 5.15143335e-01 -1.48732424e-01 -3.28000993e-01 3.28412056e-01 -4.07897115e-01 -1.07543349e+00 3.92455727e-01 6.35734260e-01 2.10076228e-01 -3.04299027e-01 1.37275481e+00 2.68188328e-01 -1.39024094e-01 5.22139013e-01 -1.19379270e+00 -4.49620605e-01 6.48651600e-01 -7.50223398e-01 -4.19346057e-02 1.09847806e-01 -2.17625439e-01 1.05847371e+00 -4.09453392e-01 8.68296564e-01 1.19707191e+00 5.78914046e-01 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-6.02193654e-01 -7.45329201e-01 -1.31418914e-01 5.03245294e-02 -5.22395968e-01 -1.49007425e-01 9.75482985e-02]	[9.27899169921875, 0.0793975442647934]
35484adf-3ca1-4944-b95e-ffce70da98b6	on-a-relation-between-the-rate-distortion	2307.00246	null	https://arxiv.org/abs/2307.00246v1	https://arxiv.org/pdf/2307.00246v1.pdf	On a Relation Between the Rate-Distortion Function and Optimal Transport	We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function defined via an extremal entropic OT distance is equivalent to the rate-distortion function. We numerically verify this result as well as previous results that connect the Monge and Kantorovich problems to optimal scalar quantization. Thus, we unify solving scalar quantization and rate-distortion functions in an alternative fashion by using their respective optimal transport solvers.	['Shirin Saeedi Bidokhti', 'Hamed Hassani', 'Eric Lei']	2023-07-01	null	null	null	null	['quantization']	['methodology']	[-1.09411187e-01 2.56780475e-01 -1.86734617e-01 -1.42033890e-01 -5.77744186e-01 -8.55580151e-01 4.66167510e-01 1.79585651e-01 -5.99313915e-01 1.11299348e+00 2.39990026e-01 -4.06027734e-01 -5.56790352e-01 -3.92292351e-01 -5.88342071e-01 -9.13902700e-01 -2.17051163e-01 1.41960666e-01 -1.47436112e-01 -4.44339097e-01 5.83640218e-01 5.98129988e-01 -8.49984884e-01 -2.34949887e-01 1.13592875e+00 1.28861594e+00 -1.14451632e-01 7.92966068e-01 1.76651180e-01 4.10167843e-01 -1.91116855e-01 -5.12243688e-01 6.01351619e-01 -7.61066377e-01 -1.39107871e+00 1.05312755e-02 3.39294583e-01 -2.80967653e-01 -5.01313686e-01 1.31009424e+00 1.20697275e-01 4.60539609e-01 1.02671707e+00 -1.20632923e+00 -1.25225806e+00 1.75004527e-01 1.88668203e-02 3.76445115e-01 1.36286825e-01 9.75406468e-02 1.39330411e+00 -6.29561305e-01 6.11662984e-01 9.97085571e-01 5.19321859e-01 6.57786965e-01 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21fc1d7d-2056-4bb9-9cbf-8a637787e9f0	neural-body-fitting-unifying-deep-learning	1808.05942	null	http://arxiv.org/abs/1808.05942v1	http://arxiv.org/pdf/1808.05942v1.pdf	Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation	Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fitting	['Gerard Pons-Moll', 'Christoph Lassner', 'Peter V. Gehler', 'Mohamed Omran', 'Bernt Schiele']	2018-08-17	null	null	null	null	['monocular-3d-human-pose-estimation']	['computer-vision']	[-3.04684192e-02 4.23892766e-01 -2.99048066e-01 -4.85264271e-01 -8.27089608e-01 -6.08511329e-01 1.44813895e-01 -1.72281340e-01 -3.05805534e-01 4.34596509e-01 2.35354900e-01 1.28727630e-01 1.91034079e-01 -3.80381227e-01 -1.12036860e+00 -2.53302217e-01 -3.32490206e-02 9.01265264e-01 2.02268854e-01 -1.56721652e-01 -4.16961044e-01 4.09959257e-01 -1.24425185e+00 -3.65845084e-01 5.54639459e-01 1.22858584e+00 -1.51421815e-01 5.99842966e-01 3.10074151e-01 1.23501308e-01 -2.58693844e-01 -5.45495808e-01 6.14884853e-01 -1.87556788e-01 -9.29333925e-01 2.29095533e-01 7.18296170e-01 -4.68241274e-01 -8.13662931e-02 6.93295062e-01 7.04819798e-01 1.61099806e-01 6.37788177e-01 -1.20199716e+00 -1.76158249e-02 1.33308887e-01 -4.28317696e-01 -3.75755548e-01 4.29379582e-01 1.08146042e-01 5.98600268e-01 -7.50658154e-01 7.84943700e-01 1.36502385e+00 1.25025606e+00 8.84507179e-01 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fc602566-534f-4401-86dd-71f82e962306	automatic-covid-19-disease-diagnosis-using-1d	2112.07285	null	https://arxiv.org/abs/2112.07285v1	https://arxiv.org/pdf/2112.07285v1.pdf	Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice	The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.	['Alphonse Pja', 'Kranthi Kumar Lella']	2021-12-14	null	null	null	null	['sound-classification']	['audio']	[ 7.21014813e-02 -1.21486656e-01 2.22082034e-01 4.64682244e-02 -1.21680349e-01 -2.28671521e-01 1.84953868e-01 -3.31326388e-02 -4.05011624e-01 3.77703279e-01 2.65537620e-01 -4.95741278e-01 -5.79800494e-02 -7.99073517e-01 -1.23696618e-01 -5.59344471e-01 1.79251637e-02 1.37258187e-01 -2.85062809e-02 -1.02529936e-01 -1.51649565e-01 5.86238623e-01 -1.39624929e+00 5.13037682e-01 6.22244179e-01 9.22746241e-01 2.84962147e-01 1.40564227e+00 -1.27324507e-01 6.95343196e-01 -1.01486897e+00 -1.18717492e-01 -4.56170663e-02 -6.60633981e-01 -6.84854209e-01 -5.66581368e-01 -1.41742408e-01 -4.37088072e-01 -4.01709586e-01 8.37085903e-01 9.81432378e-01 -1.83944374e-01 7.97877252e-01 -9.28444266e-01 -5.61207771e-01 2.81079262e-01 5.89415282e-02 7.06707954e-01 2.29270533e-01 2.70235300e-01 5.08450925e-01 -8.03278804e-01 8.63424465e-02 1.24533880e+00 1.07090020e+00 7.38260984e-01 -4.53903705e-01 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2.51869023e-01 9.26432610e-01 -1.74216062e-01 -5.99103510e-01 4.68038730e-02]	[14.525776863098145, 3.883249282836914]
1b804c48-1245-4476-b64c-1cb63b5e2781	implementation-of-an-automatic-sign-language	1403.6392	null	http://arxiv.org/abs/1403.6392v2	http://arxiv.org/pdf/1403.6392v2.pdf	Implementation of an Automatic Sign Language Lexical Annotation Framework based on Propositional Dynamic Logic	In this paper, we present the implementation of an automatic Sign Language (SL) sign annotation framework based on a formal logic, the Propositional Dynamic Logic (PDL). Our system relies heavily on the use of a specific variant of PDL, the Propositional Dynamic Logic for Sign Language (PDLSL), which lets us describe SL signs as formulae and corpora videos as labeled transition systems (LTSs). Here, we intend to show how a generic annotation system can be constructed upon these underlying theoretical principles, regardless of the tracking technologies available or the input format of corpora. With this in mind, we generated a development framework that adapts the system to specific use cases. Furthermore, we present some results obtained by our application when adapted to one distinct case, 2D corpora analysis with pre-processed tracking information. We also present some insights on how such a technology can be used to analyze 3D real-time data, captured with a depth device.	['Arturo Curiel', 'Christophe Collet']	2014-03-25	null	null	null	null	['formal-logic']	['reasoning']	[ 2.09682196e-01 5.27013302e-01 -7.59525076e-02 -2.31163770e-01 -1.78552210e-01 -7.82839060e-01 9.44641888e-01 -2.97049880e-01 -2.28861779e-01 4.62982684e-01 1.88226521e-01 -3.62593830e-01 -4.57975596e-01 -5.49662650e-01 -3.13645065e-01 -1.77172646e-01 -1.88486412e-01 5.39260864e-01 8.71739626e-01 -3.37336123e-01 -5.38109094e-02 6.84971631e-01 -2.07768583e+00 3.65190774e-01 2.47251257e-01 7.31905937e-01 -1.30137190e-01 7.79658198e-01 -3.18747759e-01 1.09263265e+00 -1.66277617e-01 -3.29767883e-01 2.93492019e-01 -3.84354740e-01 -9.62292254e-01 -4.91616204e-02 4.37100232e-01 -2.50244617e-01 -1.82453647e-01 9.77041185e-01 1.20864078e-01 -2.68347859e-01 3.93716246e-01 -1.50583410e+00 1.21814370e-01 6.63091123e-01 4.62523818e-01 -5.17813265e-01 9.39723134e-01 4.73442823e-01 8.34006608e-01 -2.06570297e-01 1.38435626e+00 1.18542671e+00 7.79013395e-01 8.54545593e-01 -1.08651912e+00 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-5.96894741e-01 2.62059480e-01 2.05104694e-01 -7.86917388e-01 -2.52629161e-01 5.74247360e-01]	[9.134469032287598, -6.406210899353027]
9116608a-194f-4ec5-bfe6-c9a4030b6bbe	pushing-the-limits-of-self-supervised-resnets	2201.05119	null	https://arxiv.org/abs/2201.05119v2	https://arxiv.org/pdf/2201.05119v2.pdf	Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?	Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings. Building on prior theoretical insights from ReLIC [Mitrovic et al., 2021], we include additional inductive biases into self-supervised learning. We propose a new self-supervised representation learning method, ReLICv2, which combines an explicit invariance loss with a contrastive objective over a varied set of appropriately constructed data views to avoid learning spurious correlations and obtain more informative representations. ReLICv2 achieves $77.1\%$ top-$1$ accuracy on ImageNet under linear evaluation on a ResNet50, thus improving the previous state-of-the-art by absolute $+1.5\%$; on larger ResNet models, ReLICv2 achieves up to $80.6\%$ outperforming previous self-supervised approaches with margins up to $+2.3\%$. Most notably, ReLICv2 is the first unsupervised representation learning method to consistently outperform the supervised baseline in a like-for-like comparison over a range of ResNet architectures. Using ReLICv2, we also learn more robust and transferable representations that generalize better out-of-distribution than previous work, both on image classification and semantic segmentation. Finally, we show that despite using ResNet encoders, ReLICv2 is comparable to state-of-the-art self-supervised vision transformers.	['Jovana Mitrovic', 'Charles Blundell', 'Razvan Pascanu', 'Lars Buesing', 'Brian McWilliams', 'Ioana Bica', 'Nenad Tomasev']	2022-01-13	null	null	null	null	['self-supervised-image-classification', 'semi-supervised-image-classification']	['computer-vision', 'computer-vision']	[ 4.20556188e-01 3.46074730e-01 -4.54954475e-01 -6.09178424e-01 -9.85138834e-01 -4.50919122e-01 6.11829400e-01 -1.25535488e-01 -6.33593738e-01 7.86883652e-01 2.21503794e-01 -5.39120957e-02 -1.36071384e-01 -8.02644491e-01 -9.38724101e-01 -4.40926433e-01 -7.00128004e-02 5.54025471e-01 1.56722903e-01 -2.18240440e-01 -3.97787243e-02 1.83306947e-01 -1.61888099e+00 2.13007331e-01 6.46682084e-01 1.26935935e+00 3.01560648e-02 3.54597479e-01 1.47638798e-01 1.14971328e+00 -3.54215980e-01 -1.71396583e-01 2.69389391e-01 -2.93525010e-01 -1.09867859e+00 -1.20452099e-01 9.79004383e-01 -3.97685915e-02 -3.39336246e-01 1.07169092e+00 4.41130608e-01 2.46599868e-01 9.17917073e-01 -8.41884136e-01 -1.09421182e+00 9.01111126e-01 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e518baa6-735c-4330-a24a-c1ee735addd2	dptnet-a-dual-path-transformer-architecture	2208.09878	null	https://arxiv.org/abs/2208.09878v1	https://arxiv.org/pdf/2208.09878v1.pdf	DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection	The prosperity of deep learning contributes to the rapid progress in scene text detection. Among all the methods with convolutional networks, segmentation-based ones have drawn extensive attention due to their superiority in detecting text instances of arbitrary shapes and extreme aspect ratios. However, the bottom-up methods are limited to the performance of their segmentation models. In this paper, we propose DPTNet (Dual-Path Transformer Network), a simple yet effective architecture to model the global and local information for the scene text detection task. We further propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path. Moreover, a bi-directional interaction module across the two paths is developed to provide complementary clues in the channel and spatial dimensions. We also upgrade the concentration operation by adding an extra multi-head attention layer to it. Our DPTNet achieves state-of-the-art results on the MSRA-TD500 dataset, and provides competitive results on other standard benchmarks in terms of both detection accuracy and speed.	['Hanzi Wang', 'Wei Liu', 'Hongfa Wang', 'Chunchao Guo', 'Yan Yan', 'Jie Jiang', 'Jingyu Lin']	2022-08-21	null	null	null	null	['scene-text-detection']	['computer-vision']	[ 8.94252909e-04 -4.67577815e-01 9.59574655e-02 -2.01107845e-01 -4.77826834e-01 -3.37848693e-01 7.18671024e-01 5.34415580e-02 -2.69385815e-01 -1.28614539e-02 1.78211972e-01 -3.48852724e-01 3.19539100e-01 -8.89378250e-01 -4.07941073e-01 -6.77187741e-01 4.59835768e-01 3.63931537e-01 9.30841804e-01 -3.76705587e-01 4.64302868e-01 2.69836187e-01 -1.24047875e+00 4.00416613e-01 1.05538356e+00 8.83119226e-01 4.22511995e-01 7.52577126e-01 -5.40284932e-01 8.07211399e-01 -3.40179831e-01 -3.81890833e-01 3.11639830e-02 -3.10803980e-01 -6.56158030e-01 1.36663809e-01 4.02630925e-01 -5.53241313e-01 -7.26180971e-01 9.53184783e-01 6.45259202e-01 -1.17301941e-01 5.88574648e-01 -9.53877330e-01 -6.25610888e-01 7.08908975e-01 -1.21176744e+00 4.41626489e-01 3.12137991e-01 1.40176967e-01 1.12904108e+00 -1.00050902e+00 1.51490852e-01 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5962c919-7648-44cc-8ec3-be0f93a47443	dddm-vc-decoupled-denoising-diffusion-models	2305.15816	null	https://arxiv.org/abs/2305.15816v1	https://arxiv.org/pdf/2305.15816v1.pdf	DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion	Diffusion-based generative models have exhibited powerful generative performance in recent years. However, as many attributes exist in the data distribution and owing to several limitations of sharing the model parameters across all levels of the generation process, it remains challenging to control specific styles for each attribute. To address the above problem, this paper presents decoupled denoising diffusion models (DDDMs) with disentangled representations, which can control the style for each attribute in generative models. We apply DDDMs to voice conversion (VC) tasks to address the challenges of disentangling and controlling each speech attribute (e.g., linguistic information, intonation, and timbre). First, we use a self-supervised representation to disentangle the speech representation. Subsequently, the DDDMs are applied to resynthesize the speech from the disentangled representations for denoising with respect to each attribute. Moreover, we also propose the prior mixup for robust voice style transfer, which uses the converted representation of the mixed style as a prior distribution for the diffusion models. The experimental results reveal that our method outperforms publicly available VC models. Furthermore, we show that our method provides robust generative performance regardless of the model size. Audio samples are available https://hayeong0.github.io/DDDM-VC-demo/.	['Seong-Whan Lee', 'Sang-Hoon Lee', 'Ha-Yeong Choi']	2023-05-25	null	null	null	null	['voice-conversion', 'style-transfer', 'voice-conversion']	['audio', 'computer-vision', 'speech']	[-7.35099539e-02 -1.21055685e-01 2.62832157e-02 -1.87576607e-01 -8.60216796e-01 -8.19642723e-01 7.35685229e-01 -6.40189886e-01 1.44642398e-01 6.14739120e-01 8.17139447e-01 1.02198444e-01 -4.30880897e-02 -8.86865318e-01 -3.34334403e-01 -1.06959295e+00 6.13266110e-01 3.89813989e-01 -5.43067455e-01 -3.07077408e-01 -2.75126845e-01 1.58305869e-01 -1.28247666e+00 6.21389411e-02 1.12031591e+00 7.04571843e-01 3.11878562e-01 7.53507495e-01 4.41122353e-02 5.19091368e-01 -8.36358190e-01 -5.08425415e-01 4.85264771e-02 -8.41691136e-01 -1.50042415e-01 1.59464166e-01 8.13648030e-02 -2.91793764e-01 -6.06086075e-01 1.03750014e+00 8.72333169e-01 1.26879737e-02 9.51732457e-01 -1.24105525e+00 -1.32073104e+00 7.82397509e-01 -2.89264232e-01 -3.61782089e-02 4.73734587e-02 1.26737848e-01 1.13607430e+00 -1.01700485e+00 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8.83761466e-01 -7.83450186e-01 -8.73483121e-01 -1.08680773e+00 -8.40879083e-01 2.73251593e-01 4.18264419e-01 -7.38155663e-01 -5.45328975e-01 1.36845008e-01]	[14.97694206237793, 6.492598056793213]
3b1ebd24-82eb-404a-a155-81d1f25f7887	room-geometry-estimation-from-room-impulse	1904.00869	null	http://arxiv.org/abs/1904.00869v4	http://arxiv.org/pdf/1904.00869v4.pdf	Room Geometry Estimation from Room Impulse Responses using Convolutional Neural Networks	We describe a new method to estimate the geometry of a room given room impulse responses. The method utilises convolutional neural networks to estimate the room geometry and uses the mean square error as the loss function. In contrast to existing methods, we do not require the position or distance of sources or receivers in the room. The method can be used with only a single room impulse response between one source and one receiver for room geometry estimation. The proposed estimation method can achieve an average of six centimetre accuracy. In addition, the proposed method is shown to be computationally efficient compared to state-of-the-art methods.	[]	2019-05-15	null	null	null	null	['room-impulse-response']	['audio']	[ 1.13451093e-01 1.71126246e-01 9.77030396e-01 -5.36184072e-01 -1.17159665e+00 -5.96875250e-01 3.26601446e-01 3.41015905e-01 -4.14954782e-01 4.31380928e-01 7.00409189e-02 -5.30175447e-01 -9.70035642e-02 -1.03838277e+00 -7.86705732e-01 -5.54989755e-01 -3.29389393e-01 1.24430388e-01 -1.19369172e-01 -1.53501689e-01 1.73578918e-01 6.78860307e-01 -1.00358295e+00 -1.74729168e-01 5.06369829e-01 1.35241699e+00 2.44164363e-01 1.23997045e+00 3.34522605e-01 8.32663894e-01 -8.94626379e-01 4.13683116e-01 3.81409287e-01 -1.18856721e-01 -5.42381585e-01 -2.11496398e-01 6.88711047e-01 -5.37603378e-01 -5.82280219e-01 3.80390346e-01 1.14218926e+00 5.32361805e-01 5.72191417e-01 -4.66306508e-01 -4.63809133e-01 1.75089240e-01 2.10179910e-01 -3.77298556e-02 7.13408828e-01 -4.04125720e-01 4.95300323e-01 -1.06381524e+00 -4.16222781e-01 9.96674538e-01 1.04123938e+00 1.01117469e-01 -8.37634146e-01 -4.35290605e-01 -2.54081905e-01 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-6.05776072e-01 9.83177483e-01 -2.82826185e-01 -4.93963331e-01 -1.77996904e-01]	[15.172327041625977, 5.665417671203613]
28164876-de67-46d4-8d32-893510ed5efd	hand-pose-estimation-via-multiview	2302.00988	null	https://arxiv.org/abs/2302.00988v1	https://arxiv.org/pdf/2302.00988v1.pdf	Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning	3D hand pose estimation has made significant progress in recent years. However, the improvement is highly dependent on the emergence of large-scale annotated datasets. To alleviate the label-hungry limitation, we propose a multi-view collaborative self-supervised learning framework, HaMuCo, that estimates hand pose only with pseudo labels for training. We use a two-stage strategy to tackle the noisy label challenge and the multi-view ``groupthink'' problem. In the first stage, we estimate the 3D hand poses for each view independently. In the second stage, we employ a cross-view interaction network to capture the cross-view correlated features and use multi-view consistency loss to achieve collaborative learning among views. To further enhance the collaboration between single-view and multi-view, we fuse the results of all views to supervise the single-view network. To summarize, we introduce collaborative learning in two folds, the cross-view level and the multi- to single-view level. Extensive experiments show that our method can achieve state-of-the-art performance on multi-view self-supervised hand pose estimation. Moreover, ablation studies verify the effectiveness of each component. Results on multiple datasets further demonstrate the generalization ability of our network.	['Jingyu Wang', 'Zhou Xue', 'Chao Wen', 'Xiaozheng Zheng']	2023-02-02	null	null	null	null	['3d-hand-pose-estimation', '3d-hand-pose-estimation']	['computer-vision', 'graphs']	[-2.13315710e-01 -3.31150919e-01 -4.03655946e-01 -3.30700874e-01 -1.02604103e+00 -7.18346477e-01 1.70975685e-01 -5.14569640e-01 -2.51160920e-01 4.85621125e-01 4.85587209e-01 4.28540856e-01 -8.11321139e-02 -3.03516328e-01 -4.97510910e-01 -7.70257711e-01 2.38421917e-01 5.93730152e-01 3.66592348e-01 1.31859884e-01 -2.11226523e-01 4.33856368e-01 -1.33867228e+00 4.06364173e-01 4.36243087e-01 7.81855226e-01 4.58697438e-01 5.07548511e-01 2.31005505e-01 6.83332205e-01 -2.63111562e-01 -2.10913628e-01 2.75241584e-01 -1.03771724e-01 -8.85916471e-01 4.50676799e-01 6.17560029e-01 -7.25396216e-01 -3.09108466e-01 8.65289152e-01 1.21666443e+00 -9.74516943e-02 3.66618663e-01 -1.26243854e+00 -2.02422291e-01 3.35199684e-01 -8.14863265e-01 -2.65616089e-01 4.44649309e-01 1.47628635e-01 1.12358546e+00 -1.09640491e+00 8.62130940e-01 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f7ba0939-cc37-4a86-9bc8-fe888203b753	blobgan-3d-a-spatially-disentangled-3d-aware	2303.14706	null	https://arxiv.org/abs/2303.14706v1	https://arxiv.org/pdf/2303.14706v1.pdf	BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes	3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world. However, performing realistic object-level editing of the generated images in the multi-object scenario still remains a challenge. Recently, a 2D GAN termed BlobGAN has demonstrated great multi-object editing capabilities on real-world indoor scene datasets. In this work, we propose BlobGAN-3D, which is a 3D-aware improvement of the original 2D BlobGAN. We enable explicit camera pose control while maintaining the disentanglement for individual objects in the scene by extending the 2D blobs into 3D blobs. We keep the object-level editing capabilities of BlobGAN and in addition allow flexible control over the 3D location of the objects in the scene. We test our method on real-world indoor datasets and show that our method can achieve comparable image quality compared to the 2D BlobGAN and other 3D-aware GAN baselines while being able to enable camera pose control and object-level editing in the challenging multi-object real-world scenarios.	['Peter Wonka', 'Michael Birsak', 'Yiqun Wang', 'Qian Wang']	2023-03-26	null	null	null	null	['3d-aware-image-synthesis']	['computer-vision']	[ 2.68420458e-01 1.34577081e-01 3.45262915e-01 -9.60973576e-02 -6.36108220e-01 -7.02888966e-01 5.63495338e-01 -3.99397850e-01 -1.46795763e-02 5.59447825e-01 1.60174206e-01 3.92089598e-02 6.26596436e-02 -6.05198741e-01 -1.08351827e+00 -5.95146537e-01 3.42413485e-01 6.26646340e-01 5.07693768e-01 -1.57778442e-01 -2.48164944e-02 8.39365840e-01 -1.29124868e+00 -1.08544463e-02 8.39140892e-01 9.24571514e-01 5.18262148e-01 9.11779106e-01 2.67249376e-01 5.91100931e-01 -7.59191334e-01 -3.54915977e-01 7.72406220e-01 -4.11610961e-01 -3.73120189e-01 6.37132764e-01 1.32444537e+00 -5.44377506e-01 -2.45782107e-01 6.77206218e-01 6.19394720e-01 6.36407658e-02 3.14914346e-01 -1.43398511e+00 -5.81267953e-01 -4.65687998e-02 -7.51658440e-01 -1.37669832e-01 1.96821228e-01 2.10324511e-01 6.84441209e-01 -6.36136770e-01 5.34839928e-01 1.37443841e+00 5.31535923e-01 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9a28977d-6024-491d-8fe6-c5294761751e	weakly-supervised-deep-learning-for-thoracic	1807.06067	null	http://arxiv.org/abs/1807.06067v1	http://arxiv.org/pdf/1807.06067v1.pdf	Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays	Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of the proposed model and its better performance against the state-of-the-art pipelines.	['Junzhou Huang', 'Ruoyu Li', 'Jiawen Yao', 'Chaochao Yan', 'Zheng Xu']	2018-07-16	null	null	null	null	['thoracic-disease-classification']	['computer-vision']	[ 4.10425693e-01 1.41540587e-01 -3.06415081e-01 -3.92628402e-01 -1.23906446e+00 -2.72947520e-01 2.71277726e-01 1.29149362e-01 -3.78526777e-01 5.67853987e-01 1.96273793e-02 -6.46669507e-01 -2.09296077e-01 -4.60252553e-01 -5.09172797e-01 -8.50715458e-01 1.13953814e-01 4.43004370e-01 6.53334439e-01 3.15765172e-01 -3.02145600e-01 5.69784522e-01 -8.47918928e-01 6.97276711e-01 3.90505433e-01 9.43734109e-01 7.02374935e-01 8.80646884e-01 8.46025199e-02 9.88126397e-01 -2.70917356e-01 -1.69415221e-01 2.14220688e-01 -5.51021397e-01 -7.24797964e-01 5.48541732e-02 4.42012519e-01 -5.67947567e-01 -7.29333818e-01 8.78065705e-01 6.67901278e-01 -2.73097783e-01 6.42442644e-01 -3.22240651e-01 -3.79970014e-01 4.58333939e-01 -7.67098665e-01 8.93001854e-01 -2.50690848e-01 2.29345396e-01 6.41749859e-01 -6.14537835e-01 6.10766768e-01 6.42120361e-01 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2.95171797e-01 -4.99814719e-01 -7.09590733e-01 -9.26493704e-01 -5.23913026e-01 5.31522155e-01 2.18650296e-01 -3.14392358e-01 -1.12741277e-01 1.62703246e-01]	[15.234587669372559, -2.1561169624328613]
882833c6-d1a0-407c-9cd1-3068d711ac1e	a-practical-stereo-depth-system-for-smart	2211.10551	null	https://arxiv.org/abs/2211.10551v2	https://arxiv.org/pdf/2211.10551v2.pdf	A Practical Stereo Depth System for Smart Glasses	We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a novel view generation pipeline to create 3D computational photography effects using point-of-view images captured by smart glasses. All these steps are executed on-device on the stringent compute budget of a mobile phone, and because we expect the users can use a wide range of smartphones, our design needs to be general and cannot be dependent on a particular hardware or ML accelerator such as a smartphone GPU. Although each of these steps is well studied, a description of a practical system is still lacking. For such a system, all these steps need to work in tandem with one another and fallback gracefully on failures within the system or less than ideal input data. We show how we handle unforeseen changes to calibration, e.g., due to heat, robustly support depth estimation in the wild, and still abide by the memory and latency constraints required for a smooth user experience. We show that our trained models are fast, and run in less than 1s on a six-year-old Samsung Galaxy S8 phone's CPU. Our models generalize well to unseen data and achieve good results on Middlebury and in-the-wild images captured from the smart glasses.	['Matt Uyttendaele', 'Michael F. Cohen', 'Peter Vajda', 'Zijian He', 'Jan-Michael Frahm', 'Sam Tsai', 'Yanghan Wang', 'Suhib Alsisan', 'Jonathan Lehman', 'Matthew Yu', 'Kevin Blackburn-Matzen', 'Akash Bapat', 'Daniel Scharstein', 'Jialiang Wang']	2022-11-19	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.pdf	cvpr-2023-1	['stereo-depth-estimation']	['computer-vision']	[ 4.27792460e-01 6.29012287e-02 2.70399064e-01 -4.05592918e-01 -8.32316637e-01 -5.63888311e-01 1.52023807e-01 -3.53680849e-01 -3.10476601e-01 4.27945673e-01 1.50307566e-01 -6.09385848e-01 4.83315766e-01 -5.74426770e-01 -6.96392179e-01 -3.66608351e-01 3.39476883e-01 4.78509277e-01 5.55255353e-01 -9.49918926e-02 3.49848896e-01 4.99197513e-01 -1.77015257e+00 1.14310190e-01 4.89567548e-01 1.02000427e+00 2.04554573e-01 1.32169521e+00 4.14015859e-01 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abcf2e7f-a9b1-4fe6-a052-fd7da43e2304	learning-from-sibling-mentions-with-scalable	null	null	https://aclanthology.org/2022.acl-long.147	https://aclanthology.org/2022.acl-long.147.pdf	Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing	In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance. To this end, we propose to exploit sibling mentions for enhancing the mention representations.Specifically, we present two different metrics for sibling selection and employ an attentive graph neural network to aggregate information from sibling mentions. The proposed graph model is scalable in that unseen test mentions are allowed to be added as new nodes for inference.Exhaustive experiments demonstrate the effectiveness of our sibling learning strategy, where our model outperforms ten strong baselines. Moreover, our experiments indeed prove the superiority of sibling mentions in helping clarify the types for hard mentions.	['Ruifeng Xu', 'Shuming Shi', 'Haisong Zhang', 'Lemao Liu', 'Haiyun Jiang', 'Jiayang Cheng', 'Yi Chen']	null	null	null	null	acl-2022-5	['entity-typing']	['natural-language-processing']	[ 9.98621434e-02 5.19556582e-01 -4.16724235e-01 -4.44310278e-01 -5.77043355e-01 -6.13618851e-01 5.08005679e-01 4.69906002e-01 -5.47772944e-01 9.24196482e-01 2.27520362e-01 -6.63415611e-01 -6.32387325e-02 -7.92575300e-01 -5.07383406e-01 -2.33695552e-01 -1.66655630e-01 2.69434392e-01 5.13981581e-01 -2.20422581e-01 2.82639235e-01 2.41705179e-01 -1.21141529e+00 8.50476921e-02 1.21763587e+00 3.75020713e-01 1.71028048e-01 5.49830258e-01 -2.16521442e-01 7.10800469e-01 -1.01104903e+00 -1.29870927e+00 -1.33473516e-01 2.09897812e-02 -9.68932867e-01 -5.41623652e-01 7.47556984e-01 -3.92627358e-01 -5.53047895e-01 1.11312163e+00 4.84192163e-01 3.50065649e-01 5.73714733e-01 -1.28197467e+00 -8.90945971e-01 1.40948808e+00 -3.87860745e-01 4.62977976e-01 5.52819431e-01 5.24579175e-02 1.68757463e+00 -7.98467457e-01 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635533c8-592d-4a00-b877-d2c6e76d0ade	cross-corpus-training-with-treelstm-for-the	null	null	https://openreview.net/forum?id=S1LXVnxRb	https://openreview.net/pdf?id=S1LXVnxRb	Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text	A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For specific domains, such as biomedicine, the long time and high expertise required for the development of manually annotated corpora explain that most of the existing one are relatively small (i.e., hundreds of sentences). Beside, larger corpora focusing on general or domain-specific relationships (such as citizenship or drug-drug interactions) have been developed. In this paper, we study how large annotated corpora developed for alternative tasks may improve the performances on biomedicine related tasks, for which few annotated resources are available. We experiment two deep learning-based models to extract relationships from biomedical texts with high performance. The first one combine locally extracted features using a Convolutional Neural Network (CNN) model, while the second exploit the syntactic structure of sentences using a Recursive Neural Network (RNN) architecture. Our experiments show that, contrary to the former, the latter benefits from a cross-corpus learning strategy to improve the performance of relationship extraction tasks. Indeed our approach leads to the best published performances for two biomedical RE tasks, and to state-of-the-art results for two other biomedical RE tasks, for which few annotated resources are available (less than 400 manually annotated sentences). This may be particularly impactful in specialized domains in which training resources are scarce, because they would benefit from the training data of other domains for which large annotated corpora does exist.	['Chedy Raïssi', 'Yannick Toussaint', 'Legrand Joël', 'Adrien Coulet']	2018-01-01	null	null	null	iclr-2018-1	['cross-corpus', 'relationship-extraction-distant-supervised']	['computer-vision', 'natural-language-processing']	[ 2.67222226e-01 6.40523076e-01 -2.66260475e-01 -2.52453208e-01 -6.22113645e-01 -1.69053108e-01 5.43505549e-01 9.45439816e-01 -9.06869054e-01 1.44196153e+00 7.65935406e-02 -3.95241439e-01 -2.92675018e-01 -9.12658572e-01 -7.17194617e-01 -5.56951702e-01 -1.98018458e-02 7.88034022e-01 2.08381400e-01 -4.53412384e-01 -1.58888295e-01 5.12469351e-01 -1.27636278e+00 2.53595620e-01 8.62741232e-01 7.06126213e-01 3.15395653e-01 3.02102476e-01 -2.67587692e-01 7.33388841e-01 -5.82007110e-01 -6.36911809e-01 -2.20623419e-01 -4.99207616e-01 -1.15305018e+00 -4.60376531e-01 -1.50981054e-01 8.57890546e-02 -7.28417933e-02 7.91157663e-01 5.32734871e-01 -1.60396382e-01 4.67898279e-01 -6.52648151e-01 -3.32350194e-01 9.42154169e-01 -1.74107313e-01 1.05446026e-01 2.36730635e-01 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fd5c49c0-a765-432b-9f70-da9b448a84c2	vgf-net-visual-geometric-fusion-learning-for	2104.03109	null	https://arxiv.org/abs/2104.03109v1	https://arxiv.org/pdf/2104.03109v1.pdf	VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping	The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network(VGF-Net), a deep network for the fusion analysis of visual/geometric data and the construction of 2.5D height maps for simultaneous drone navigation in novel environments. Given an initial rough height map and a sequence of RGB images, our VGF-Net extracts the visual information of the scene, along with a sparse set of 3D keypoints that capture the geometric relationship between objects in the scene. Driven by the data, VGF-Net adaptively fuses visual and geometric information, forming a unified Visual-Geometric Representation. This representation is fed to a new Directional Attention Model(DAM), which helps enhance the visual-geometric object relationship and propagates the informative data to dynamically refine the height map and the corresponding keypoints. An entire end-to-end information fusion and mapping system is formed, demonstrating remarkable robustness and high accuracy on the autonomous drone navigation across complex indoor and large-scale outdoor scenes. The dataset can be found in http://vcc.szu.edu.cn/research/2021/VGFNet.	['Hui Huang', 'Ke Xie', 'Yilin Liu']	2021-04-07	null	null	null	null	['drone-navigation']	['computer-vision']	[-1.55440927e-01 -2.97971874e-01 2.85727590e-01 -6.51407242e-01 -3.64053071e-01 -4.70166087e-01 2.84887612e-01 1.05851687e-01 -3.72655004e-01 4.28525090e-01 2.24755853e-01 7.45349750e-02 -4.59475338e-01 -1.12992835e+00 -5.92824519e-01 -5.30904233e-01 -2.39182189e-02 3.37751895e-01 4.00808632e-01 -8.48223507e-01 7.16200471e-02 6.29451156e-01 -1.90937769e+00 -3.13708335e-01 9.29265738e-01 1.36350930e+00 6.39088511e-01 6.70117319e-01 5.58840036e-02 3.73272389e-01 -2.43446261e-01 -1.61703993e-02 4.63873029e-01 3.24628539e-02 -1.07370242e-01 -7.01330230e-02 6.86608732e-01 -4.36328232e-01 -7.26637363e-01 1.25786984e+00 4.87810522e-01 5.83788574e-01 2.13363454e-01 -1.34779584e+00 -8.01608086e-01 1.01734444e-01 -5.28559268e-01 1.74018219e-01 3.43167335e-01 2.68425554e-01 7.45152652e-01 -9.49278295e-01 5.26999950e-01 1.31567037e+00 3.48064721e-01 1.41959369e-01 -5.65532207e-01 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93130024-2de1-4105-b877-3f6f6dd84774	cgans-with-auxiliary-discriminative	2107.10060	null	https://arxiv.org/abs/2107.10060v5	https://arxiv.org/pdf/2107.10060v5.pdf	Conditional GANs with Auxiliary Discriminative Classifier	Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples. The fundamental reason pointed out in this paper is that the classifier of AC-GAN is generator-agnostic, which therefore cannot provide informative guidance for the generator to approach the joint distribution, resulting in a minimization of the conditional entropy that decreases the intra-class diversity. Motivated by this understanding, we propose a novel conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to resolve the above problem. Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively. Our theoretical analysis reveals that the generator can faithfully learn the joint distribution even without the original discriminator, making the proposed ADC-GAN robust to the value of the coefficient hyperparameter and the selection of the GAN loss, and stable during training. Extensive experimental results on synthetic and real-world datasets demonstrate the superiority of ADC-GAN in conditional generative modeling compared to state-of-the-art classifier-based and projection-based conditional GANs.	['Xueqi Cheng', 'Xiaoshuang Li', 'Siyuan Pan', 'HuaWei Shen', 'Qi Cao', 'Liang Hou']	2021-07-21	conditional-gans-with-auxiliary	https://openreview.net/forum?id=Yn4CPz_LRKO	https://openreview.net/pdf?id=Yn4CPz_LRKO	null	['conditional-image-generation']	['computer-vision']	[ 4.07524943e-01 8.35682303e-02 -1.65351957e-01 -1.45653889e-01 -9.19862747e-01 -5.21527410e-01 5.87543726e-01 -4.35972393e-01 8.53971988e-02 1.00168276e+00 -3.44377779e-03 4.54346314e-02 1.63421422e-01 -1.07736409e+00 -4.71667171e-01 -1.51209748e+00 5.07273018e-01 5.25601208e-01 -2.10778669e-01 1.29347950e-01 -1.55988544e-01 3.17279905e-01 -1.27168965e+00 -6.96482062e-02 1.29435027e+00 1.01739919e+00 1.24943212e-01 3.31466496e-01 1.82372898e-01 6.63523555e-01 -9.99789059e-01 -4.95861828e-01 1.06475547e-01 -1.13472295e+00 -9.76167321e-02 5.81579469e-02 -1.43543839e-01 -1.21719569e-01 -2.48023078e-01 1.05935383e+00 4.70121205e-01 -2.01892108e-02 1.25547814e+00 -1.44234776e+00 -8.21996927e-01 2.83188403e-01 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c64cafb6-c8ce-4f01-a9b2-0f5e57a5d788	training-robots-without-robots-deep-imitation	2202.09574	null	https://arxiv.org/abs/2202.09574v1	https://arxiv.org/pdf/2202.09574v1.pdf	Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer	Deep imitation learning is a promising method for dexterous robot manipulation because it only requires demonstration samples for learning manipulation skills. In this paper, deep imitation learning is applied to tasks that require force feedback, such as bottle opening. However, simple visual feedback systems, such as teleoperation, cannot be applied because they do not provide force feedback to operators. Bilateral teleoperation has been used for demonstration with force feedback; however, this requires an expensive and complex bilateral robot system. In this paper, a new master-to-robot (M2R) transfer learning system is presented that does not require robots but can still teach dexterous force feedback-based manipulation tasks to robots. The human directly demonstrates a task using a low-cost controller that resembles the kinematic parameters of the robot arm. Using this controller, the operator can naturally feel the force feedback without any expensive bilateral system. Furthermore, the M2R transfer system can overcome domain gaps between the master and robot using the gaze-based imitation learning framework and a simple calibration method. To demonstrate this, the proposed system was evaluated on a bottle-cap-opening task that requires force feedback only for the master demonstration.	['Yasuo Kuniyoshi', 'Akihiko Nagakubo', 'Yoshiyuki Ohmura', 'Heecheol Kim']	2022-02-19	null	null	null	null	['robot-manipulation']	['robots']	[-3.15551102e-01 4.42473263e-01 -5.18019274e-02 -1.32759944e-01 8.25924203e-02 -4.65587020e-01 1.59002662e-01 -6.21442258e-01 -4.60985482e-01 6.87214613e-01 -7.99602449e-01 -2.63242871e-01 -1.56479422e-02 -2.64911294e-01 -1.04269922e+00 -4.80257571e-01 2.25369573e-01 3.38161260e-01 3.42667341e-01 -6.45466030e-01 2.76879072e-01 6.34088278e-01 -1.43463576e+00 -3.56551111e-02 9.52000856e-01 7.09128380e-01 1.13249660e+00 6.05461121e-01 3.20572227e-01 5.79454482e-01 -5.40902793e-01 3.22809905e-01 6.20514512e-01 -4.95037466e-01 -3.34078968e-01 -3.72507609e-02 1.92397878e-01 -1.03891623e+00 -6.96022570e-01 8.55699718e-01 3.99188310e-01 3.22922394e-02 7.68211365e-01 -1.66344762e+00 -5.42424619e-01 5.88896513e-01 -4.21748072e-01 -6.74790263e-01 4.52091217e-01 3.92819613e-01 4.17765081e-01 -6.80195808e-01 7.47800946e-01 1.36692679e+00 2.23288909e-01 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c73a0e1c-7a0f-4fb6-b2c2-8414f1a27230	latent-transformations-for-discrete-data	2006.06346	null	https://arxiv.org/abs/2006.06346v1	https://arxiv.org/pdf/2006.06346v1.pdf	Latent Transformations for Discrete-Data Normalising Flows	Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters. Having a neural network architecture predict discrete parameters takes a non-differentiable activation function (eg, the step function) which precludes gradient-based learning. To circumvent this non-differentiability, previous work has employed biased proxy gradients, such as the straight-through estimator. We present an unbiased alternative where rather than deterministically parameterising one transformation, we predict a distribution over latent transformations. With stochastic transformations, the marginal likelihood of the data is differentiable and gradient-based learning is possible via score function estimation. To test the viability of discrete-data NFs we investigate performance on binary MNIST. We observe great challenges with both deterministic proxy gradients and unbiased score function estimation. Whereas the former often fails to learn even a shallow transformation, the variance of the latter could not be sufficiently controlled to admit deeper NFs.	['Wilker Aziz', 'Rob Hesselink']	2020-06-11	null	null	null	null	['normalising-flows']	['methodology']	[ 4.43486810e-01 2.68200010e-01 -2.28761032e-01 -6.66956365e-01 -9.45396960e-01 -8.08248699e-01 8.96071255e-01 -3.15832049e-01 -5.02471387e-01 1.08282030e+00 8.77930894e-02 -6.10409975e-01 -2.84016043e-01 -7.40532815e-01 -8.17977607e-01 -7.22439289e-01 -1.97180703e-01 6.54392123e-01 8.54476988e-02 6.82114363e-02 2.84489989e-01 6.38228953e-01 -1.00832975e+00 7.79881626e-02 7.95531034e-01 9.74914074e-01 -3.32483768e-01 1.03324652e+00 -2.80050009e-01 6.35259569e-01 -5.63550055e-01 -4.80907053e-01 6.39938593e-01 -6.77129984e-01 -7.25025356e-01 -1.40882120e-01 9.25310791e-01 -4.91561800e-01 -2.92228669e-01 1.03727281e+00 1.40471488e-01 2.00905561e-01 1.25084269e+00 -1.47385979e+00 -6.67710781e-01 4.30590868e-01 -4.89950478e-02 1.37993917e-01 -2.58029979e-02 6.17181361e-01 1.27047586e+00 -5.78031719e-01 4.81857628e-01 1.40052366e+00 8.01447213e-01 3.08254153e-01 -1.68800747e+00 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-3.34392071e-01 3.02754968e-01 7.44399250e-01 -4.82596070e-01 -8.55660021e-01 9.37499404e-02]	[7.2146735191345215, 3.839672565460205]
6d696364-ae25-4780-95b1-5ec38907c372	on-the-generalizability-of-ecg-based-stress	2210.06225	null	https://arxiv.org/abs/2210.06225v1	https://arxiv.org/pdf/2210.06225v1.pdf	On the Generalizability of ECG-based Stress Detection Models	Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that are indicators of stress. Recently, deep learning models have also been proposed to detect stress. Typically, stress models are trained and validated on the same dataset, often involving one stressful scenario. However, it is not practical to collect stress data for every scenario. So, it is crucial to study the generalizability of these models and determine to what extent they can be used in other scenarios. In this paper, we explore the generalization capabilities of Electrocardiogram (ECG)-based deep learning models and models based on handcrafted ECG features, i.e., Heart Rate Variability (HRV) features. To this end, we train three HRV models and two deep learning models that use ECG signals as input. We use ECG signals from two popular stress datasets - WESAD and SWELL-KW - differing in terms of stressors and recording devices. First, we evaluate the models using leave-one-subject-out (LOSO) cross-validation using training and validation samples from the same dataset. Next, we perform a cross-dataset validation of the models, that is, LOSO models trained on the WESAD dataset are validated using SWELL-KW samples and vice versa. While deep learning models achieve the best results on the same dataset, models based on HRV features considerably outperform them on data from a different dataset. This trend is observed for all the models on both datasets. Therefore, HRV models are a better choice for stress recognition in applications that are different from the dataset scenario. To the best of our knowledge, this is the first work to compare the cross-dataset generalizability between ECG-based deep learning models and HRV models.	['Elisabeth André', 'Pooja Prajod']	2022-10-12	null	null	null	null	['heart-rate-variability']	['medical']	[-6.90328330e-02 -3.37498486e-01 2.79942714e-02 -6.39476120e-01 -3.89079526e-02 -2.80476570e-01 -2.25048438e-02 6.05712414e-01 -4.86769825e-01 8.04794073e-01 -9.39298570e-02 -8.74107033e-02 -2.69413620e-01 -7.84165204e-01 -2.75387347e-01 -5.48492432e-01 -2.86960274e-01 -1.09998807e-02 -2.91367918e-01 -3.79224926e-01 -4.15084213e-02 4.95727241e-01 -1.41101170e+00 -7.64108896e-02 7.24010170e-01 1.01569140e+00 -2.98692137e-01 4.65643704e-01 2.83263355e-01 2.58966476e-01 -9.13149059e-01 -1.43181518e-01 5.42761013e-02 -7.34386325e-01 -4.78226721e-01 -1.33035645e-01 1.53026268e-01 4.05936912e-02 -1.77682653e-01 5.64995170e-01 1.14437580e+00 2.25734457e-01 3.83755386e-01 -1.22540450e+00 -3.82531762e-01 3.88151377e-01 -2.18939170e-01 5.33142865e-01 5.06356001e-01 6.45816028e-02 5.61362743e-01 -5.42323053e-01 2.49771938e-01 7.62243927e-01 1.03187835e+00 4.40332413e-01 -1.43147326e+00 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-9.46764886e-01 7.95019150e-01 4.28275585e-01 -7.50465155e-01 -1.90707639e-01 -1.22848593e-01]	[13.833788871765137, 3.1314306259155273]
2416754b-1115-46f2-9540-18df29b65b24	deep-speech-denoising-with-vector-space	1804.10669	null	http://arxiv.org/abs/1804.10669v1	http://arxiv.org/pdf/1804.10669v1.pdf	Deep Speech Denoising with Vector Space Projections	We propose an algorithm to denoise speakers from a single microphone in the presence of non-stationary and dynamic noise. Our approach is inspired by the recent success of neural network models separating speakers from other speakers and singers from instrumental accompaniment. Unlike prior art, we leverage embedding spaces produced with source-contrastive estimation, a technique derived from negative sampling techniques in natural language processing, while simultaneously obtaining a continuous inference mask. Our embedding space directly optimizes for the discrimination of speaker and noise by jointly modeling their characteristics. This space is generalizable in that it is not speaker or noise specific and is capable of denoising speech even if the model has not seen the speaker in the training set. Parameters are trained with dual objectives: one that promotes a selective bandpass filter that eliminates noise at time-frequency positions that exceed signal power, and another that proportionally splits time-frequency content between signal and noise. We compare to state of the art algorithms as well as traditional sparse non-negative matrix factorization solutions. The resulting algorithm avoids severe computational burden by providing a more intuitive and easily optimized approach, while achieving competitive accuracy.	['Karl Ni', 'Paul Gamble', 'Maria Barrios', 'Jeff Hetherly', 'Cory Stephenson']	2018-04-27	null	null	null	null	['speech-denoising']	['speech']	[ 4.00307417e-01 1.16840079e-01 3.10801771e-02 -2.21044794e-01 -1.10696089e+00 -7.89349735e-01 5.78790367e-01 -1.92021132e-01 -4.88817424e-01 4.97448295e-01 7.16598988e-01 -8.71769339e-02 -2.07871333e-01 -4.85274374e-01 -3.73374969e-01 -8.59985769e-01 4.88816835e-02 9.70264301e-02 -2.62483716e-01 -2.36184910e-01 -2.32246727e-01 3.44770998e-01 -1.60974562e+00 6.12567328e-02 6.82479680e-01 7.95397937e-01 1.40256971e-01 9.38457966e-01 1.01447321e-01 6.41340017e-01 -8.74606848e-01 -2.12123111e-01 2.11740807e-01 -6.98720634e-01 -2.09754154e-01 3.40166129e-02 5.88367164e-01 2.86239028e-01 -3.34914953e-01 1.15746427e+00 7.62674212e-01 4.58554298e-01 5.70620060e-01 -8.94422412e-01 -5.69248378e-01 6.22124493e-01 -3.30010623e-01 3.96878034e-01 3.02617222e-01 -1.18876301e-01 1.02746379e+00 -9.80392277e-01 2.34423786e-01 1.35168564e+00 1.00672936e+00 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718c5de8-83ec-4188-a22e-a6740b841b1e	voice-cloning-a-multi-speaker-text-to-speech	2102.05630	null	https://arxiv.org/abs/2102.05630v1	https://arxiv.org/pdf/2102.05630v1.pdf	Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning	Deep learning models are becoming predominant in many fields of machine learning. Text-to-Speech (TTS), the process of synthesizing artificial speech from text, is no exception. To this end, a deep neural network is usually trained using a corpus of several hours of recorded speech from a single speaker. Trying to produce the voice of a speaker other than the one learned is expensive and requires large effort since it is necessary to record a new dataset and retrain the model. This is the main reason why the TTS models are usually single speaker. The proposed approach has the goal to overcome these limitations trying to obtain a system which is able to model a multi-speaker acoustic space. This allows the generation of speech audio similar to the voice of different target speakers, even if they were not observed during the training phase.	['Vincent Pollet', 'Luigi di Caro', 'Enrico Zovato', 'Giuseppe Ruggiero']	2021-02-10	null	null	null	null	['voice-cloning']	['speech']	[ 1.96395606e-01 2.71944940e-01 4.72145587e-01 -3.91311467e-01 -5.93949854e-01 -4.07880992e-01 7.12795138e-01 1.61773980e-01 -2.92360812e-01 6.11851931e-01 -3.09416149e-02 -2.89465576e-01 2.46201813e-01 -4.85031486e-01 -7.99394786e-01 -7.94354677e-01 2.63079077e-01 8.56783748e-01 9.35021415e-02 -6.56758398e-02 -6.62619546e-02 6.62145376e-01 -1.82336557e+00 2.21031666e-01 4.23287213e-01 5.96573353e-01 7.46929646e-01 9.12879884e-01 -4.95090157e-01 4.96834040e-01 -1.02381170e+00 -1.26231164e-01 4.60661240e-02 -8.40712845e-01 -6.89663291e-01 3.17218691e-01 3.74446183e-01 -1.75540388e-01 -3.17027941e-02 1.03323174e+00 4.09538627e-01 3.06464881e-01 6.16951108e-01 -7.88529992e-01 -6.49303868e-02 9.61446047e-01 1.14595376e-01 -7.40133971e-02 1.80606455e-01 -1.72525808e-01 7.48347402e-01 -6.10282123e-01 3.96516085e-01 1.16572118e+00 2.18743563e-01 8.59972000e-01 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e61e562c-f3d4-4788-9c73-4343e5eb6319	blockchain-based-federated-learning-for-2	2306.17186	null	https://arxiv.org/abs/2306.17186v1	https://arxiv.org/pdf/2306.17186v1.pdf	Blockchain-based Federated Learning for Decentralized Energy Management Systems	The Internet of Energy (IoE) is a distributed paradigm that leverages smart networks and distributed system technologies to enable decentralized energy systems. In contrast to the traditional centralized energy systems, distributed Energy Internet systems comprise multiple components and communication requirements that demand innovative technologies for decentralization, reliability, efficiency, and security. Recent advances in blockchain architectures, smart contracts, and distributed federated learning technologies have opened up new opportunities for realizing decentralized Energy Internet services. In this paper, we present a comprehensive analysis and classification of state-of-the-art solutions that employ blockchain, smart contracts, and federated learning for the IoE domains. Specifically, we identify four representative system models and discuss their key aspects. These models demonstrate the diverse ways in which blockchain, smart contracts, and federated learning can be integrated to support the main domains of IoE, namely distributed energy trading and sharing, smart microgrid energy networks, and electric and connected vehicle management. Furthermore, we provide a detailed comparison of the different levels of decentralization, the advantages of federated learning, and the benefits of using blockchain for the IoE systems. Additionally, we identify open issues and areas for future research for integrating federated learning and blockchain in the Internet of Energy domains.	['Öznur Özkasap', 'Abdulrezzak Zekiye']	2023-06-23	null	null	null	null	['management', 'energy-management']	['miscellaneous', 'time-series']	[-8.95073056e-01 -5.28457128e-02 -8.02151740e-01 -1.70203656e-01 -2.96325713e-01 -1.29095411e+00 1.09029019e+00 -1.38879880e-01 3.16846400e-01 1.01481783e+00 3.54056716e-01 -6.64346755e-01 -1.88586838e-03 -9.71835256e-01 -5.24970233e-01 -1.10017383e+00 -9.50290710e-02 3.78409624e-01 -1.01152249e-01 -2.11021543e-01 -1.03322215e-01 3.36125165e-01 -1.21265042e+00 -2.01107681e-01 8.08318615e-01 1.06591940e+00 -6.35494411e-01 1.51260987e-01 1.64624467e-01 9.22108054e-01 -2.95358360e-01 -5.50447516e-02 5.83553255e-01 -5.77294171e-01 -5.48731387e-01 -8.24818671e-01 -4.38999861e-01 -7.59827852e-01 -3.18852782e-01 1.25147331e+00 2.42497057e-01 -5.83560228e-01 1.91167325e-01 -2.45188355e+00 -4.41963315e-01 1.28474724e+00 -1.75870985e-01 -4.44681734e-01 -5.43609560e-02 7.75809824e-01 8.57618213e-01 -2.34161228e-01 6.21816337e-01 6.22654796e-01 4.99864429e-01 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-1.06622291e+00 -2.95412421e-01 -8.17970574e-01 -4.11822610e-02 4.81334955e-01 1.58189788e-01 -7.85092831e-01 -9.25352052e-02]	[5.876958847045898, 6.422941207885742]
a6b3e783-031c-4ed1-b951-7c551dcd441d	lt-net-label-transfer-by-learning-reversible	2003.07072	null	https://arxiv.org/abs/2003.07072v3	https://arxiv.org/pdf/2003.07072v3.pdf	LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation	We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-based segmentation problem, where voxel-wise correspondence from the atlas to the unlabelled data is learned. Subsequently, segmentation label of the atlas can be transferred to the unlabelled data with the learned correspondence. However, since ground truth correspondence between images is usually unavailable, the learning system must be well-supervised to avoid mode collapse and convergence failure. To overcome this difficulty, we resort to the forward-backward consistency, which is widely used in correspondence problems, and additionally learn the backward correspondences from the warped atlases back to the original atlas. This cycle-correspondence learning design enables a variety of extra, cycle-consistency-based supervision signals to make the training process stable, while also boost the performance. We demonstrate the superiority of our method over both deep learning-based one-shot segmentation methods and a classical multi-atlas segmentation method via thorough experiments.	['Renzhen Wang', 'Yefeng Zheng', 'Dong Wei', 'Shuxin Wang', 'Shilei Cao', 'Liansheng Wang', 'Kai Ma', 'Deyu Meng']	2020-03-16	lt-net-label-transfer-by-learning-reversible-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.pdf	cvpr-2020-6	['one-shot-segmentation']	['computer-vision']	[ 1.96742192e-01 3.79792511e-01 -2.52774030e-01 -6.38165772e-01 -1.17072809e+00 -3.67636591e-01 3.05425555e-01 -3.79057191e-02 -3.66188258e-01 5.24638295e-01 -7.67388269e-02 5.19606769e-02 -1.06692594e-02 -6.47914231e-01 -5.96675754e-01 -9.59334373e-01 2.83462793e-01 7.45837450e-01 6.86412752e-01 -5.09739555e-02 -1.55959219e-01 2.36686960e-01 -1.00536978e+00 -2.32094452e-01 8.82297575e-01 8.29384267e-01 3.61180723e-01 1.62733361e-01 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391055b1-14e1-410b-9a7b-c954d6f66446	reconciling-a-centroid-hypothesis-conflict-in	2212.03795	null	https://arxiv.org/abs/2212.03795v1	https://arxiv.org/pdf/2212.03795v1.pdf	Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation	Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation. Existing approaches for SFDA focus on self-training usually including well-established entropy minimization techniques. One of the main challenges in SFDA is to reduce accumulation of errors caused by domain misalignment. A recent strategy successfully managed to reduce error accumulation by pseudo-labeling the target samples based on class-wise prototypes (centroids) generated by their clustering in the representation space. However, this strategy also creates cases for which the cross-entropy of a pseudo-label and the minimum entropy have a conflict in their objectives. We call this conflict the centroid-hypothesis conflict. We propose to reconcile this conflict by aligning the entropy minimization objective with that of the pseudo labels' cross entropy. We demonstrate the effectiveness of aligning the two loss objectives on three domain adaptation datasets. In addition, we provide state-of-the-art results using up-to-date architectures also showing the consistency of our method across these architectures.	['Arnon Netzer', 'Hai Victor Habi', 'Oranit Dror', 'Roy H. Jennings', 'Idit Diamant']	2022-12-07	null	null	null	null	['source-free-domain-adaptation']	['computer-vision']	[ 3.56716365e-01 2.88548261e-01 -1.18224978e-01 -6.88687801e-01 -9.42647815e-01 -5.51867127e-01 6.94800794e-01 1.24575227e-01 -4.83767062e-01 1.08893180e+00 4.42554653e-02 1.85224816e-01 -1.27784848e-01 -4.85070109e-01 -7.18523204e-01 -7.55825162e-01 1.31822243e-01 8.27430606e-01 1.10777304e-01 -3.56441624e-02 5.70068173e-02 2.08791554e-01 -1.46243119e+00 2.34127119e-01 1.07138431e+00 9.70992744e-01 6.77454695e-02 2.27390647e-01 -2.06860632e-01 3.60378742e-01 -7.84948051e-01 -4.66782838e-01 5.59412360e-01 -7.69141316e-01 -9.76724088e-01 1.02643773e-01 4.67209756e-01 1.50104556e-02 2.74973065e-01 1.18865764e+00 4.05923814e-01 2.87242651e-01 1.08386707e+00 -1.36293721e+00 -6.48094058e-01 3.63473594e-01 -3.34105581e-01 -6.21477049e-03 1.26553560e-02 -2.67988294e-01 8.34072649e-01 -6.47746444e-01 8.63832355e-01 1.02138674e+00 6.81211591e-01 8.41709912e-01 -1.39414883e+00 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-4.98879939e-01 7.68527448e-01 4.54678357e-01 -8.91554177e-01 -4.87152576e-01 3.10870916e-01]	[10.277645111083984, 3.1751914024353027]
a442695f-0408-48d3-989d-6e6bd67941bb	fast-and-incremental-loop-closure-detection	1911.10752	null	https://arxiv.org/abs/1911.10752v1	https://arxiv.org/pdf/1911.10752v1.pdf	Fast and Incremental Loop Closure Detection Using Proximity Graphs	Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bag-of-words (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory costs for mobile robot applications is not well satisfied. In this paper, we propose a novel loop closure detection framework titled `FILD' (Fast and Incremental Loop closure Detection), which focuses on an on-line and incremental graph vocabulary construction for fast loop closure detection. The global and local features of frames are extracted using the Convolutional Neural Networks (CNN) and SURF on the GPU, which guarantee extremely fast extraction speeds. The graph vocabulary construction is based on one type of proximity graph, named Hierarchical Navigable Small World (HNSW) graphs, which is modified to adapt to this specific application. In addition, this process is coupled with a novel strategy for real-time geometrical verification, which only keeps binary hash codes and significantly saves on memory usage. Extensive experiments on several publicly available datasets show that the proposed approach can achieve fairly good recall at 100\% precision compared to other state-of-the-art methods. The source code can be downloaded at https://github.com/AnshanTJU/FILD for further studies.	['Xianglong Liu', 'Guangfu Che', 'Yu Chen', 'Shan An', 'Fangru Zhou', 'Xin Ma']	2019-11-25	null	null	null	null	['loop-closure-detection']	['computer-vision']	[-1.32106140e-01 -2.85518497e-01 -3.02619845e-01 -1.53861642e-01 -6.91129625e-01 -2.45683253e-01 5.94677329e-01 6.38446987e-01 -7.20324457e-01 3.93489867e-01 -2.55875647e-01 -4.32642579e-01 -6.87866211e-02 -1.07273650e+00 -7.86750436e-01 -4.59172755e-01 -2.26190597e-01 4.17241752e-01 6.77152097e-01 -3.71621728e-01 5.36935031e-01 7.10755467e-01 -1.75883675e+00 -3.04107279e-01 7.29297757e-01 1.13439333e+00 6.26056910e-01 4.75071549e-01 -1.08238138e-01 4.79959935e-01 -1.18763193e-01 -1.02537148e-01 2.42444977e-01 9.53739583e-02 -3.93055201e-01 -3.20341915e-01 4.82634395e-01 -3.42748314e-01 -5.73815942e-01 1.15684128e+00 4.45355535e-01 2.13922918e-01 4.23245758e-01 -1.22404110e+00 -3.07758361e-01 -9.04684013e-04 -7.10445940e-01 6.08094707e-02 4.82841313e-01 -1.87645018e-01 7.99605489e-01 -1.09649730e+00 6.72703385e-01 1.07009387e+00 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a5ea7ba9-90ac-4251-824f-f893aa965215	tcn-aa-a-wi-fi-based-temporal-convolution	2305.18211	null	https://arxiv.org/abs/2305.18211v1	https://arxiv.org/pdf/2305.18211v1.pdf	TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention	The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.	['Timothy K. Shih', 'Chih-Yang Lin', 'Yu-Tso Liu', 'Chia-Yu Lin']	2023-05-21	null	null	null	null	['human-interaction-recognition', 'human-activity-recognition', 'human-activity-recognition']	['computer-vision', 'computer-vision', 'time-series']	[ 5.14231265e-01 -3.17844659e-01 -2.20692948e-01 -2.01688975e-01 -6.37834311e-01 -1.65863603e-01 2.64504373e-01 -1.65263101e-01 -4.92058009e-01 8.73752475e-01 3.15520614e-01 -1.38085827e-01 1.83258597e-02 -6.87143624e-01 -4.90962207e-01 -4.85082150e-01 -1.26063138e-01 -2.53922820e-01 3.35998207e-01 -5.62571920e-03 -1.36062458e-01 1.26107037e-01 -1.41209674e+00 1.73109874e-01 6.81211114e-01 1.45451164e+00 -4.64094803e-02 6.67480350e-01 4.78756316e-02 1.10693657e+00 -6.30632162e-01 -4.00637910e-02 1.26669053e-02 -5.59954643e-01 -5.46962917e-01 -1.39048934e-01 2.11246595e-01 -6.02253795e-01 -6.91491425e-01 5.07151008e-01 7.06721961e-01 2.60783762e-01 8.02768543e-02 -1.14044142e+00 -3.03411663e-01 1.24766171e-01 -3.66783142e-01 3.72413278e-01 6.76153898e-01 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8909705d-2a4a-4674-abd9-f0e5af7d42d3	topology-preserving-segmentation-network-a	2202.13331	null	https://arxiv.org/abs/2202.13331v1	https://arxiv.org/pdf/2202.13331v1.pdf	Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component	Medical image segmentation, which aims to automatically extract anatomical or pathological structures, plays a key role in computer-aided diagnosis and disease analysis. Despite the problem has been widely studied, existing methods are prone to topological errors. In medical imaging, the topology of the structure, such as the kidney or lung, is usually known. Preserving the topology of the structure in the segmentation process is of utmost importance for accurate image analysis. In this work, a novel learning-based segmentation model is proposed. A {\it topology-preserving segmentation network (TPSN)} is trained to give an accurate segmentation result of an input image that preserves the prescribed topology. TPSN is a deformation-based model that yields a deformation map through a UNet, which takes the medical image and a template mask as inputs. The main idea is to deform a template mask describing the prescribed topology by a diffeomorphism to segment the object in the image. The topology of the shape in the template mask is well preserved under the diffeomorphic map. The diffeomorphic property of the map is controlled by introducing a regularization term related to the Jacobian in the loss function. As such, a topology-preserving segmentation result can be guaranteed. Furthermore, a multi-scale TPSN is developed in this paper that incorporates multi-level information of images to produce more precise segmentation results. To evaluate our method, we applied the 2D TPSN on Ham10000 and 3D TPSN on KiTS21. Experimental results illustrate our method outperforms the baseline UNet segmentation model with/without connected-component analysis (CCA) by both the dice score and IoU score. Besides, results show that our method can produce reliable results even in challenging cases, where pixel-wise segmentation models by UNet and CCA fail to obtain accurate results.	['Lok Ming Lui', 'Han Zhang']	2022-02-27	null	null	null	null	['unet-segmentation']	['computer-vision']	[ 2.63381988e-01 2.35481501e-01 1.76401976e-02 -3.40633780e-01 -3.77729803e-01 -4.46268290e-01 2.32314765e-01 1.57252029e-02 -3.00958842e-01 5.59343576e-01 -1.90363973e-01 -5.41785024e-02 -2.61802852e-01 -9.35380697e-01 -5.41939735e-01 -8.94233882e-01 3.30238119e-02 5.76807499e-01 4.65087980e-01 1.87681019e-02 3.16810086e-02 7.97120512e-01 -8.29336405e-01 -2.90371686e-01 1.39786935e+00 9.14133728e-01 4.23416048e-01 2.51329333e-01 -1.80216253e-01 2.14226916e-01 -9.10075605e-02 -3.04590493e-01 6.30852282e-01 -7.66524732e-01 -1.16499805e+00 3.14543486e-01 3.08135062e-01 -9.28825140e-02 -8.45292211e-02 1.43570077e+00 3.13595712e-01 -5.72531186e-02 7.58013904e-01 -9.21477079e-01 -2.52383262e-01 2.52287894e-01 -4.66103196e-01 -8.05384070e-02 -2.59318769e-01 4.14102487e-02 3.90069336e-01 -5.83201826e-01 8.39742124e-01 9.18500245e-01 6.47554457e-01 3.78976703e-01 -1.34940732e+00 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e840f4ec-ee6c-4621-adb6-bce21261b263	bongard-hoi-benchmarking-few-shot-visual	2205.13803	null	https://arxiv.org/abs/2205.13803v2	https://arxiv.org/pdf/2205.13803v2.pdf	Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions	A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object interactions (HOIs) from natural images. It is inspired by two desirable characteristics from the classical Bongard problems (BPs): 1) few-shot concept learning, and 2) context-dependent reasoning. We carefully curate the few-shot instances with hard negatives, where positive and negative images only disagree on action labels, making mere recognition of object categories insufficient to complete our benchmarks. We also design multiple test sets to systematically study the generalization of visual learning models, where we vary the overlap of the HOI concepts between the training and test sets of few-shot instances, from partial to no overlaps. Bongard-HOI presents a substantial challenge to today's visual recognition models. The state-of-the-art HOI detection model achieves only 62% accuracy on few-shot binary prediction while even amateur human testers on MTurk have 91% accuracy. With the Bongard-HOI benchmark, we hope to further advance research efforts in visual reasoning, especially in holistic perception-reasoning systems and better representation learning.	['Song-Chun Zhu', 'Anima Anandkumar', 'Yuke Zhu', 'Zhiding Yu', 'Weili Nie', 'Xiaojian Ma', 'Huaizu Jiang']	2022-05-27	null	http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.pdf	cvpr-2022-1	['few-shot-image-classification', 'novel-concepts']	['computer-vision', 'reasoning']	[ 3.36352319e-01 1.83451682e-01 -1.41860709e-01 -3.13555032e-01 -3.91341716e-01 -3.28708023e-01 7.91980684e-01 4.76854183e-02 -9.33184996e-02 4.04256374e-01 1.25444695e-01 -4.17214334e-01 -3.32101405e-01 -5.81921756e-01 -7.03548133e-01 -3.15732002e-01 6.94052279e-02 5.56219280e-01 6.51213229e-01 -3.69580239e-01 2.95978069e-01 1.27397671e-01 -2.12627840e+00 1.05609298e+00 4.63131875e-01 1.06681561e+00 -1.46583110e-01 1.03911567e+00 4.68793437e-02 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c5396760-3563-4378-8869-ef1f1b3b8441	metaassist-robust-dialogue-state-tracking	2210.12397	null	https://arxiv.org/abs/2210.12397v1	https://arxiv.org/pdf/2210.12397v1.pdf	MetaASSIST: Robust Dialogue State Tracking with Meta Learning	Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an auxiliary model to generate pseudo labels for the noisy training set. These pseudo labels are combined with vanilla labels by a common fixed weighting parameter to train the primary DST model. Notwithstanding the improvements of ASSIST on DST, tuning the weighting parameter is challenging. Moreover, a single parameter shared by all slots and all instances may be suboptimal. To overcome these limitations, we propose a meta learning-based framework MetaASSIST to adaptively learn the weighting parameter. Specifically, we propose three schemes with varying degrees of flexibility, ranging from slot-wise to both slot-wise and instance-wise, to convert the weighting parameter into learnable functions. These functions are trained in a meta-learning manner by taking the validation set as meta data. Experimental results demonstrate that all three schemes can achieve competitive performance. Most impressively, we achieve a state-of-the-art joint goal accuracy of 80.10% on MultiWOZ 2.4.	['Emine Yilmaz', 'Samuel Stern', 'Shenghui Li', 'Jie Huang', 'Xi Wang', 'Fanghua Ye']	2022-10-22	null	null	null	null	['dialogue-state-tracking']	['natural-language-processing']	[ 1.78995475e-01 4.95582938e-01 -5.33819497e-01 -6.35570049e-01 -1.13521159e+00 -6.28511012e-01 8.75366986e-01 2.27941647e-02 -4.68709022e-01 9.61596131e-01 2.99069166e-01 -2.29724109e-01 2.11639658e-01 -3.97863984e-01 -3.11402231e-01 -5.22592545e-01 2.12860629e-01 6.13993764e-01 4.21447903e-01 -7.19763517e-01 8.68705064e-02 -3.97676706e-01 -1.15047169e+00 5.58378816e-01 1.16514516e+00 7.26701796e-01 1.36914656e-01 5.66911697e-01 -4.34627682e-01 8.34166706e-01 -1.05477619e+00 -4.63967741e-01 1.38730690e-01 -4.86978471e-01 -1.08778250e+00 1.18633978e-01 3.27579975e-02 -4.21865761e-01 -3.76069635e-01 8.56725514e-01 5.19921541e-01 4.34298486e-01 3.53588670e-01 -1.30031252e+00 -2.11002454e-01 1.14242375e+00 -2.92853326e-01 -1.96214914e-01 4.07361835e-01 2.36686125e-01 1.01942253e+00 -6.58903718e-01 5.94160259e-01 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3bc80990-d067-404e-89c7-ea3b50d11d39	cosea-convolutional-code-search-with-layer	2010.09520	null	https://arxiv.org/abs/2010.09520v1	https://arxiv.org/pdf/2010.09520v1.pdf	COSEA: Convolutional Code Search with Layer-wise Attention	Semantic code search, which aims to retrieve code snippets relevant to a given natural language query, has attracted many research efforts with the purpose of accelerating software development. The huge amount of online publicly available code repositories has prompted the employment of deep learning techniques to build state-of-the-art code search models. Particularly, they leverage deep neural networks to embed codes and queries into a unified semantic vector space and then use the similarity between code's and query's vectors to approximate the semantic correlation between code and the query. However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes. In this paper, we propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the valuable code's intrinsic structural logic. To further increase the learning efficiency of COSEA, we propose a variant of contrastive loss for training the code search model, where the ground-truth code should be distinguished from the most similar negative sample. We have implemented a prototype of COSEA. Extensive experiments over existing public datasets of Python and SQL have demonstrated that COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.	['Tie-Yan Liu', 'Chao Zhang', 'Jiang Bian', 'Yingce Xia', 'Jia Zhang', 'Hao Wang']	2020-10-19	null	null	null	null	['code-search', 'code-search']	['computer-code', 'computer-vision']	[-2.97154963e-01 -4.62554127e-01 -5.21493495e-01 -3.75141382e-01 -7.02933371e-01 -5.17839551e-01 2.93044180e-01 3.47857356e-01 -8.96096528e-02 -1.22600757e-01 1.71035856e-01 -4.67752188e-01 -9.22468826e-02 -7.72748113e-01 -6.79987788e-01 -1.11898705e-01 1.71558291e-01 9.81558338e-02 3.04634124e-01 -2.56830335e-01 5.94410717e-01 -1.16366722e-01 -1.60968947e+00 4.38513696e-01 1.13956070e+00 1.19976521e+00 4.65317309e-01 2.15147302e-01 -6.90528512e-01 1.20546889e+00 -4.09675151e-01 -5.95574558e-01 -1.38553856e-02 -2.32724860e-01 -1.15688121e+00 -6.18123829e-01 2.25020975e-01 -1.70060992e-01 -4.83100414e-01 1.51481187e+00 2.58924872e-01 -2.26313546e-01 2.30434954e-01 -1.28036559e+00 -1.24113286e+00 6.49498761e-01 -4.51401353e-01 2.18690827e-01 5.71038425e-01 1.18583724e-01 1.57826114e+00 -9.42130029e-01 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69848df8-d548-493c-8b79-90cae961d7ba	blind-image-deblurring-a-review	2201.10522	null	https://arxiv.org/abs/2201.10522v1	https://arxiv.org/pdf/2201.10522v1.pdf	Blind Image Deblurring: a Review	This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind deblurring. Finally, we introduce some typical optimization-based methods and learning-based methods.	['Zhengrong Xue']	2022-01-22	null	null	null	null	['blind-image-deblurring']	['computer-vision']	[ 1.77882373e-01 -4.04538959e-01 -3.20750296e-01 9.03774947e-02 -3.93377036e-01 -4.88026828e-01 4.38377529e-01 -5.58220804e-01 -5.50227225e-01 6.59796596e-01 8.82114172e-01 -3.44705701e-01 -6.83110207e-03 1.98561341e-01 -3.08377028e-01 -6.88661695e-01 1.61198214e-01 -3.65647376e-01 -3.48630399e-01 1.57782927e-01 9.30545330e-01 1.63298309e-01 -1.42556441e+00 -9.44910273e-02 1.17887676e+00 5.21851420e-01 3.23124141e-01 1.06202996e+00 3.45537096e-01 8.27381313e-01 -6.93042338e-01 -5.60714304e-01 1.17524885e-01 -8.35254610e-01 -1.11892664e+00 2.40141153e-01 9.51790810e-01 -8.76322210e-01 -7.62141466e-01 1.66372240e+00 9.17534173e-01 6.43172935e-02 8.58015895e-01 -8.42725039e-01 -1.79320562e+00 1.55269563e-01 -4.62079644e-01 8.57485652e-01 5.93542278e-01 9.87061709e-02 5.74223638e-01 -1.00981402e+00 2.11853266e-01 1.21691585e+00 5.63609540e-01 8.34784210e-01 -7.62995362e-01 -4.50097442e-01 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-7.40599334e-01 -1.38918549e-01 1.14958155e+00 -4.93016183e-01 -1.04470050e+00 4.64306265e-01]	[11.654312133789062, -2.781005382537842]
16455db5-1ac4-4441-b7fc-7fc02aac4d0b	time-contrastive-learning-based-dnn	1704.02373	null	https://arxiv.org/abs/1704.02373v3	https://arxiv.org/pdf/1704.02373v3.pdf	Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification	In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN)feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered generic. The method is evaluated on the RedDots Challenge 2016 database. Experimental results show that TCL-BN is superior to the existing speaker and pass-phrase discriminant BN features and the Mel-frequency cepstral coefficient feature for text-dependent speaker verification.	['Zheng-Hua Tan', 'Achintya Kr. Sarkar']	2017-04-06	null	null	null	null	['text-dependent-speaker-verification']	['speech']	[ 1.15139969e-01 -4.85034198e-01 -3.40365797e-01 -6.50564730e-01 -9.80368376e-01 -5.85089087e-01 6.10423505e-01 1.50558382e-01 -6.91767097e-01 5.40717125e-01 3.23393673e-01 -7.54546225e-01 -3.74291353e-02 -1.74645334e-01 -1.74022660e-01 -1.05561185e+00 -3.81033570e-01 1.47402152e-01 -6.34759367e-02 -2.01648429e-01 -9.68112275e-02 7.91468263e-01 -1.31024063e+00 1.73336715e-01 3.05928856e-01 1.14577508e+00 -3.43544409e-02 7.61844933e-01 -4.63565858e-03 5.07500827e-01 -9.63790357e-01 -6.35761619e-02 -7.26786777e-02 -6.47371948e-01 -6.61562383e-01 -4.22569811e-01 1.90316349e-01 -2.35820517e-01 -3.53931844e-01 7.91719854e-01 9.08445597e-01 3.40642184e-01 5.48040926e-01 -9.86113787e-01 -1.67294383e-01 8.47675741e-01 -6.14034571e-02 8.56556475e-01 2.48594329e-01 3.68918255e-02 9.35468674e-01 -1.07025552e+00 2.77348816e-01 1.20735514e+00 8.07128608e-01 8.44154000e-01 -1.30886865e+00 -8.52777004e-01 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1.58680409e-01 1.27140462e+00 -3.97019982e-01 -8.27314615e-01 2.22543955e-01]	[14.406624794006348, 6.110065937042236]
29e2059a-ea14-4fb3-9e40-e6a4995f41e6	improving-the-generalizability-and-robustness	2306.01925	null	https://arxiv.org/abs/2306.01925v2	https://arxiv.org/pdf/2306.01925v2.pdf	Improving the generalizability and robustness of large-scale traffic signal control	A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows.	['Laurent Charlin', 'Denis Larocque', 'Francois-Xavier Devailly', 'Tianyu Shi']	2023-06-02	null	null	null	null	['distributional-reinforcement-learning', 'multi-agent-reinforcement-learning']	['methodology', 'methodology']	[-1.19626805e-01 8.86580572e-02 -4.20370042e-01 -8.34798589e-02 -7.57956862e-01 -3.82194132e-01 6.07495308e-01 -1.49235100e-01 -2.49269038e-01 1.25355113e+00 6.71148673e-02 -6.98768675e-01 -6.74512625e-01 -1.30932367e+00 -9.97719467e-01 -5.90619922e-01 -4.33326930e-01 6.88348889e-01 5.27154922e-01 -8.34093511e-01 -5.06253168e-02 8.96060467e-01 -1.37625206e+00 -3.10551435e-01 7.52543986e-01 8.74220431e-01 -3.18121403e-01 7.09375083e-01 3.54171060e-02 9.72871840e-01 -5.58406770e-01 -2.47963533e-01 5.39100528e-01 5.02168722e-02 -3.72395784e-01 5.87177463e-03 3.11007738e-01 -4.26912844e-01 -9.30980265e-01 4.87762064e-01 6.29509926e-01 2.59460986e-01 5.54791212e-01 -2.02287889e+00 -2.95238882e-01 4.70494479e-01 -5.42699695e-01 8.15491080e-02 -1.07230492e-01 7.72273898e-01 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6.03477776e-01 6.76384449e-01 9.54493225e-01 -5.76383710e-01 -7.53998756e-01 -8.00656140e-01 -6.95072591e-01 2.12746009e-01 8.10841501e-01 -1.03429997e+00 -2.83447891e-01 -6.39445662e-01]	[5.298422336578369, 1.4953187704086304]
2c4542f9-737d-40eb-823f-bc46914c32aa	hltsuda-at-semeval-2019-task-1-ucca-graph	1903.04153	null	http://arxiv.org/abs/1903.04153v2	http://arxiv.org/pdf/1903.04153v2.pdf	HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing	This paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery. In this way, we can make use of existing syntactic parsing techniques. Based on the data statistics, we recover discontinuous nodes directly according to the output labels of the constituent parser and use a biaffine classification model to recover the more complex remote edges. The classification model and the constituent parser are simultaneously trained under the multi-task learning framework. We use the multilingual BERT as extra features in the open tracks. Our system ranks the first place in the six English/German closed/open tracks among seven participating systems. For the seventh cross-lingual track, where there is little training data for French, we propose a language embedding approach to utilize English and German training data, and our result ranks the second place.	['Wei Jiang', 'Zhenghua Li', 'Min Zhang', 'Yu Zhang']	2019-03-11	null	null	null	null	['ucca-parsing']	['natural-language-processing']	[-7.09655136e-02 6.06640577e-01 -4.56022322e-01 -4.18398112e-01 -1.20067620e+00 -8.41700554e-01 4.99731958e-01 2.02905223e-01 -5.38647652e-01 4.63597417e-01 2.94753551e-01 -4.93465871e-01 1.44522220e-01 -8.97919595e-01 -7.49107897e-01 -5.50414741e-01 1.10742196e-01 5.81562221e-01 2.79619455e-01 -2.19849944e-01 -2.07636371e-01 7.82695860e-02 -9.97521698e-01 3.58663350e-01 7.69985557e-01 9.39810693e-01 2.14254975e-01 7.44435906e-01 -5.56027055e-01 6.78285778e-01 -3.51163745e-01 -7.40685284e-01 2.59145081e-01 -3.84548485e-01 -1.00167418e+00 -1.67759836e-01 4.60553318e-01 8.00081119e-02 -2.80454934e-01 1.15415096e+00 1.60340980e-01 -1.68552576e-03 3.59620184e-01 -9.68313992e-01 -7.19887316e-01 1.00888491e+00 -4.75018889e-01 -1.19972430e-01 2.96029449e-01 -5.78781605e-01 1.59819758e+00 -8.92059565e-01 1.01038480e+00 1.38364720e+00 6.88649774e-01 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737f529c-9265-4178-b1ed-f7de84433e93	neural-sign-reenactor-deep-photorealistic	2209.01470	null	https://arxiv.org/abs/2209.01470v2	https://arxiv.org/pdf/2209.01470v2.pdf	Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting	In this paper, we introduce a neural rendering pipeline for transferring the facial expressions, head pose, and body movements of one person in a source video to another in a target video. We apply our method to the challenging case of Sign Language videos: given a source video of a sign language user, we can faithfully transfer the performed manual (e.g., handshape, palm orientation, movement, location) and non-manual (e.g., eye gaze, facial expressions, mouth patterns, head, and body movements) signs to a target video in a photo-realistic manner. Our method can be used for Sign Language Anonymization, Sign Language Production (synthesis module), as well as for reenacting other types of full body activities (dancing, acting performance, exercising, etc.). We conduct detailed qualitative and quantitative evaluations and comparisons, which demonstrate the particularly promising and realistic results that we obtain and the advantages of our method over existing approaches.	['Petros Maragos', 'Anastasios Roussos', 'Athanasia-Lida Dimou', 'Panagiotis P. Filntisis', 'Christina O. Tze']	2022-09-03	null	null	null	null	['sign-language-production']	['natural-language-processing']	[ 3.09312463e-01 -3.31782456e-03 1.97873726e-01 -5.62036037e-01 -3.77483815e-01 -8.49784791e-01 6.97041631e-01 -8.79674196e-01 -2.88395852e-01 5.36208928e-01 5.38733423e-01 1.88754544e-01 3.05948645e-01 -3.54932956e-02 -6.27625287e-01 -5.99741399e-01 8.73151273e-02 2.81746499e-02 6.91663614e-03 -8.94414112e-02 1.25336125e-01 1.00383615e+00 -1.81216156e+00 1.25307992e-01 1.92611888e-01 6.47279382e-01 -6.19565725e-01 1.04377568e+00 3.87150794e-01 9.37443674e-01 -7.52125382e-01 -5.31425297e-01 2.11230084e-01 -6.69530869e-01 -6.17939591e-01 3.48009288e-01 8.97212803e-01 -8.14489305e-01 -4.63795632e-01 7.82439172e-01 8.44672561e-01 2.05287263e-01 5.50264120e-01 -1.56358099e+00 -1.94900781e-01 -7.06897825e-02 -5.47691405e-01 -5.42779505e-01 9.68914926e-01 4.73410040e-01 3.86800855e-01 -4.17055279e-01 1.23359907e+00 1.26149631e+00 6.03848398e-01 1.13251042e+00 -7.73748159e-01 -9.68906760e-01 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9.32579339e-01 4.65129167e-01 -5.21709979e-01 -3.36061180e-01 6.49022520e-01]	[13.06446647644043, -0.45164573192596436]
15e415a0-6fe1-4278-8f07-081fa9b90403	a-greedy-graph-search-algorithm-based-on	2102.03538	null	https://arxiv.org/abs/2102.03538v1	https://arxiv.org/pdf/2102.03538v1.pdf	A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection	The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal; thereby, the R-peak detection task can be relaxed from any preprocessing step. In this novel approach, prior biological knowledge about the expected sequence of changes is incorporated into the model using the constraint graph, which can be defined manually or automatically. First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme. Finally, we compare the manually defined graphs and learned graphs in terms of graph structure and detection accuracy. We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.	['Fatemeh Afghah', 'Toby Hocking', 'Atiyeh Fotoohinasab']	2021-02-06	null	null	null	null	['qrs-complex-detection']	['medical']	[ 2.79494166e-01 4.69705835e-02 -2.02912822e-01 -8.39842558e-02 -5.10695040e-01 -7.04826593e-01 -1.23853631e-01 5.40115595e-01 -2.12599173e-01 6.00455999e-01 -4.61705387e-01 -4.31878328e-01 -3.05439383e-01 -4.76913124e-01 -2.23910257e-01 -7.36036062e-01 -5.07049143e-01 2.54078329e-01 1.41259730e-01 3.40234905e-01 1.40722707e-01 7.17423916e-01 -7.40831673e-01 -1.75844640e-01 9.72440898e-01 9.12112057e-01 6.44190889e-03 7.95611262e-01 4.84833479e-01 1.36798769e-01 -7.28788435e-01 6.33487180e-02 1.66214660e-01 -8.16723287e-01 -3.54825526e-01 1.83002964e-01 -9.08070356e-02 1.59076422e-01 1.01695687e-01 1.05538833e+00 7.66802013e-01 -9.58344266e-02 6.22266293e-01 -1.08760273e+00 1.95092052e-01 3.81064475e-01 -8.62371862e-01 4.59457457e-01 8.30752626e-02 -1.50752261e-01 7.28354037e-01 -7.23609805e-01 5.45647562e-01 4.74504292e-01 8.65925014e-01 1.85491711e-01 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0f7ab870-70fb-4549-a9ec-f34f97496b31	unified-language-model-pre-training-for	1905.03197	null	https://arxiv.org/abs/1905.03197v3	https://arxiv.org/pdf/1905.03197v3.pdf	Unified Language Model Pre-training for Natural Language Understanding and Generation	This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm.	['Hsiao-Wuen Hon', 'Nan Yang', 'Furu Wei', 'Yu Wang', 'Xiaodong Liu', 'Ming Zhou', 'Jianfeng Gao', 'Wenhui Wang', 'Li Dong']	2019-05-08	unified-language-model-pre-training-for-1	http://papers.nips.cc/paper/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation	http://papers.nips.cc/paper/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation.pdf	neurips-2019-12	['generative-question-answering']	['natural-language-processing']	[ 1.38031691e-01 3.27187628e-01 7.41914138e-02 -3.45732629e-01 -1.58711660e+00 -8.44906449e-01 1.03072846e+00 -3.06799877e-02 -5.01877964e-01 1.09725571e+00 7.13643014e-01 -5.37459970e-01 3.62859815e-01 -7.73369849e-01 -5.70353448e-01 -4.12110150e-01 4.72916037e-01 9.58103776e-01 8.92073754e-03 -7.34454453e-01 2.58576930e-01 -8.38192850e-02 -8.00810397e-01 6.99556649e-01 1.17723322e+00 8.50215793e-01 1.30716890e-01 1.44770348e+00 -1.68338642e-01 1.04588628e+00 -9.94978607e-01 -7.37117052e-01 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45eb7dd5-a5b3-4599-9e56-f904d397bf45	contrastive-audio-visual-masked-autoencoder	2210.07839	null	https://arxiv.org/abs/2210.07839v4	https://arxiv.org/pdf/2210.07839v4.pdf	Contrastive Audio-Visual Masked Autoencoder	In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks, to learn a joint and coordinated audio-visual representation. Our experiments show that the contrastive audio-visual correspondence learning objective not only enables the model to perform audio-visual retrieval tasks, but also helps the model learn a better joint representation. As a result, our fully self-supervised pretrained CAV-MAE achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the previous best supervised pretrained model on AudioSet in the audio-visual event classification task. Code and pretrained models are at https://github.com/yuangongnd/cav-mae.	['James Glass', 'Hilde Kuehne', 'Leonid Karlinsky', 'David Harwath', 'Alexander H. Liu', 'Andrew Rouditchenko', 'Yuan Gong']	2022-10-02	null	null	null	null	['audio-tagging', 'multi-modal-classification']	['audio', 'miscellaneous']	[ 1.11062244e-01 -2.02082232e-01 -2.23907053e-01 -3.82041752e-01 -1.54947162e+00 -2.17784211e-01 5.83835602e-01 -3.66196670e-02 -1.55294627e-01 4.21323270e-01 3.79579425e-01 3.04895520e-01 2.62631565e-01 -3.99933040e-01 -9.52742696e-01 -4.83780146e-01 -1.49112076e-01 1.91962183e-01 -7.08853304e-02 1.98827267e-01 -3.01874906e-01 -6.44895658e-02 -1.96300232e+00 1.00853419e+00 3.40403289e-01 1.29708803e+00 2.21111238e-01 9.44971919e-01 2.27521509e-01 1.09003711e+00 -2.67445326e-01 -1.28699183e-01 -2.38430977e-01 -5.47352135e-01 -6.55755460e-01 1.56206951e-01 8.12025428e-01 -3.97322565e-01 -6.48280561e-01 6.73367679e-01 6.70090318e-01 5.37904445e-03 7.36114204e-01 -1.65882862e+00 -9.36917961e-01 8.77322793e-01 -5.83934546e-01 4.08285677e-01 3.16956431e-01 7.06068724e-02 1.48557913e+00 -1.48649216e+00 3.81953418e-01 1.21119678e+00 3.71634394e-01 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81e65d71-23f7-4dc6-aec9-9a66be98dc21	omnilayout-room-layout-reconstruction-from	2104.09403	null	https://arxiv.org/abs/2104.09403v1	https://arxiv.org/pdf/2104.09403v1.pdf	OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas	Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout. However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance. Instead, we propose to use spherical convolutions. The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular distortions. Using a new evaluation metric, we show that our network reduces the error in the heavily distorted regions (near the poles) by approx 25 % when compared to standard convolutional networks. Experimental results show that OmniLayout outperforms the state-of-the-art by approx 4% on two different benchmark datasets (PanoContext and Stanford 2D-3D). Code is available at https://github.com/rshivansh/OmniLayout.	['Ankur Mali', 'Lee Giles', 'Daniel Kifer', 'Vikas Kumar', 'Shivansh Rao']	2021-04-19	null	null	null	null	['3d-room-layouts-from-a-single-rgb-panorama']	['computer-vision']	[ 2.08479896e-01 1.22177251e-01 5.87678730e-01 -3.95793289e-01 -2.73928910e-01 -6.03588045e-01 7.32000053e-01 -1.87506855e-01 -2.68317580e-01 4.02277589e-01 3.57567251e-01 -3.35988522e-01 -1.48931220e-01 -9.71388578e-01 -1.12360787e+00 -5.60540497e-01 3.69416438e-02 1.85882255e-01 -1.78177282e-01 -1.76919624e-02 1.08314134e-01 7.00759709e-01 -1.50715983e+00 1.58435509e-01 7.86173284e-01 1.08172548e+00 7.68254846e-02 8.32780480e-01 2.88764566e-01 2.97753066e-01 -3.56342435e-01 -2.32310474e-01 4.46551472e-01 -1.09804891e-01 -5.39468408e-01 4.07684892e-02 8.68760705e-01 -3.96477550e-01 -6.11858785e-01 8.17972660e-01 5.13820827e-01 1.25439137e-01 4.94732916e-01 -9.43113625e-01 -6.02019787e-01 8.53286311e-02 -3.94419312e-01 -3.45461249e-01 4.39035565e-01 -1.40133098e-01 7.58513629e-01 -1.10190248e+00 4.66509700e-01 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44efb708-5a05-4fda-9e23-973b4950f5aa	open-vocabulary-affordance-detection-in-3d	2303.02401	null	https://arxiv.org/abs/2303.02401v2	https://arxiv.org/pdf/2303.02401v2.pdf	Open-Vocabulary Affordance Detection in 3D Point Clouds	Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds. By simultaneously learning the affordance text and the point feature, OpenAD successfully exploits the semantic relationships between affordances. Therefore, our proposed method enables zero-shot detection and can be able to detect previously unseen affordances without a single annotation example. Intensive experimental results show that OpenAD works effectively on a wide range of affordance detection setups and outperforms other baselines by a large margin. Additionally, we demonstrate the practicality of the proposed OpenAD in real-world robotic applications with a fast inference speed (~100ms). Our project is available at https://openad2023.github.io.	['Toan Nguyen', 'Anh Nguyen', 'Ngan Le', 'Thieu Vo', 'Dzung Nguyen', 'An Vuong', 'Minh Nhat Vu']	2023-03-04	null	null	null	null	['affordance-detection']	['computer-vision']	[-1.16319515e-01 -1.57121301e-01 -2.12008640e-01 -6.86273873e-02 -3.70983928e-01 -7.47780442e-01 5.54483712e-01 5.16974293e-02 -4.16758597e-01 2.35241383e-01 -2.98357010e-02 -1.68032214e-01 -1.35022506e-01 -3.94834250e-01 -6.22433364e-01 -3.95883501e-01 -3.37417096e-01 3.79267365e-01 7.31740713e-01 -2.75156170e-01 2.70890981e-01 6.58296704e-01 -1.88622308e+00 -3.43716979e-01 7.72219121e-01 9.02921319e-01 7.75503755e-01 5.59044182e-01 4.29048330e-01 2.66739547e-01 -3.35457593e-01 2.65314519e-01 4.60683882e-01 3.97745252e-01 -7.12606132e-01 -3.26445624e-02 6.21528327e-01 -7.93436944e-01 -5.72468102e-01 1.02654696e+00 4.31930423e-01 4.66351420e-01 4.98046726e-01 -1.62424290e+00 -6.80153668e-01 1.73364252e-01 -2.83948421e-01 2.78719187e-01 8.07482481e-01 2.84985840e-01 1.33352590e+00 -1.02570462e+00 5.96663654e-01 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1ff331c0-fa24-4c24-a5b8-66b7bfe5b854	data-augmentation-for-sign-language-gloss	2105.07476	null	https://arxiv.org/abs/2105.07476v1	https://arxiv.org/pdf/2105.07476v1.pdf	Data Augmentation for Sign Language Gloss Translation	Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss-to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed. We focus here on gloss-to-text translation, which we treat as a low-resource neural machine translation (NMT) problem. However, unlike traditional low-resource NMT, gloss-to-text translation differs because gloss-text pairs often have a higher lexical overlap and lower syntactic overlap than pairs of spoken languages. We exploit this lexical overlap and handle syntactic divergence by proposing two rule-based heuristics that generate pseudo-parallel gloss-text pairs from monolingual spoken language text. By pre-training on the thus obtained synthetic data, we improve translation from American Sign Language (ASL) to English and German Sign Language (DGS) to German by up to 3.14 and 2.20 BLEU, respectively.	['Yoav Goldberg', 'Graham Neubig', 'Kayo Yin', 'Amit Moryossef']	2021-05-16	null	https://aclanthology.org/2021.mtsummit-at4ssl.1	https://aclanthology.org/2021.mtsummit-at4ssl.1.pdf	mtsummit-2021-8	['sign-language-translation', 'low-resource-neural-machine-translation']	['computer-vision', 'natural-language-processing']	[ 7.42178440e-01 6.86528906e-02 -1.34638622e-01 -7.66070068e-01 -1.36688221e+00 -7.26134300e-01 7.54268289e-01 -5.18411517e-01 -5.98073244e-01 1.03206158e+00 5.66737473e-01 -3.00495207e-01 3.48700613e-01 -4.27196473e-01 -8.89705420e-01 -5.57901740e-01 3.86447698e-01 9.81265426e-01 -1.55151725e-01 -1.66243955e-01 -7.03943297e-02 1.35454804e-01 -1.50677431e+00 6.28386676e-01 8.34764242e-01 4.59352702e-01 8.71500671e-02 8.25092196e-01 -4.11865652e-01 4.16697174e-01 -5.28536022e-01 -5.44913113e-01 3.11963797e-01 -1.17185354e+00 -7.35701442e-01 6.57943962e-03 8.91971707e-01 -3.65525693e-01 1.19873852e-01 8.94687355e-01 6.13775551e-01 -1.10989250e-01 8.98564637e-01 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20951556-b454-48d2-b1e9-47d66d4dcaf0	detecting-offensive-language-in-tweets-using	1801.04433	null	http://arxiv.org/abs/1801.04433v1	http://arxiv.org/pdf/1801.04433v1.pdf	Detecting Offensive Language in Tweets Using Deep Learning	This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.	['Helge Langseth', 'Georgios K. Pitsilis', 'Heri Ramampiaro']	2018-01-13	null	null	null	null	['abuse-detection']	['natural-language-processing']	[ 1.79630890e-01 -2.06065401e-01 -5.60091019e-01 -2.37259522e-01 -2.97012419e-01 -2.17549235e-01 9.04909849e-01 3.84146214e-01 -5.90927303e-01 4.70396727e-01 6.63937509e-01 -3.84835452e-01 1.74910963e-01 -6.98622227e-01 9.09235477e-02 -6.04421318e-01 -9.99588296e-02 -4.34250720e-02 -3.38931590e-01 -7.05931127e-01 8.26141715e-01 4.45386797e-01 -1.67847168e+00 3.92980099e-01 9.36945677e-01 7.76574135e-01 -5.40257752e-01 5.85131049e-01 -1.67967930e-01 1.50738931e+00 -9.40297067e-01 -5.34874678e-01 -3.83355796e-01 -4.17250633e-01 -1.04418814e+00 -3.21383744e-01 4.70405132e-01 -3.95989031e-01 -6.27625942e-01 1.30232477e+00 3.84740919e-01 1.70980841e-01 7.55133033e-01 -1.04584694e+00 -9.75424588e-01 7.10697472e-01 -5.44612229e-01 6.62919223e-01 2.58893788e-01 -3.20497036e-01 9.87856925e-01 -7.72800505e-01 5.81364334e-01 1.40862930e+00 7.25434363e-01 6.09080613e-01 -8.20892930e-01 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9.13335979e-01 6.90480471e-01 -6.21501386e-01 -6.02635443e-01 -3.95707548e-01]	[8.77604866027832, 10.55078125]
7a2a6e81-9122-43d5-9520-d9535fc02364	orthographic-features-for-bilingual-lexicon	null	null	https://aclanthology.org/P18-2062	https://aclanthology.org/P18-2062.pdf	Orthographic Features for Bilingual Lexicon Induction	Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages. This work extends embedding-based methods to incorporate these features, resulting in significant accuracy gains for related languages.	['Parker Riley', 'Daniel Gildea']	2018-07-01	null	null	null	acl-2018-7	['multilingual-word-embeddings', 'unsupervised-machine-translation']	['methodology', 'natural-language-processing']	[-6.39975369e-01 -3.34944814e-01 -6.22566938e-01 -3.43771070e-01 -5.05878687e-01 -7.32296705e-01 5.47715664e-01 5.90992868e-01 -8.35678220e-01 6.87175393e-01 5.39865077e-01 -4.35841233e-01 1.40876949e-01 -9.23299909e-01 -2.49971107e-01 -2.85654932e-01 -3.27446252e-01 5.54932654e-01 1.07996970e-01 -7.12677419e-01 1.74773395e-01 4.33040708e-01 -1.17380285e+00 2.57058769e-01 8.14925790e-01 8.77310410e-02 -2.84258444e-02 1.94959208e-01 -4.22996223e-01 2.01801881e-01 -2.50181556e-01 -7.29263425e-01 2.32885286e-01 -6.43775821e-01 -6.44578993e-01 -4.70807254e-01 1.56773612e-01 -1.54298872e-01 -5.59522331e-01 1.15415394e+00 5.44906378e-01 -7.54323676e-02 7.31222749e-01 -1.00154901e+00 -8.97473276e-01 7.36385345e-01 -4.62481350e-01 4.68337029e-01 6.94158018e-01 -1.49814948e-01 1.44840753e+00 -1.02533758e+00 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73426e07-36f9-4cd8-ae39-3f08094b5f42	190501964	1905.01964	null	http://arxiv.org/abs/1905.01964v1	http://arxiv.org/pdf/1905.01964v1.pdf	Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation	Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly context-dependent. In addition, Chinese texts lack delimiters to separate words, making it difficult to identify the boundary of entities. Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming. In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER. Second, we propose a unified framework to jointly train CNER and word segmentation models in order to enhance the ability of CNER model in identifying entity boundaries. Third, we introduce an automatic method to generate pseudo labeled samples from existing labeled data which can enrich the training data. Experiments on two benchmark datasets show that our approach can effectively improve the performance of Chinese named entity recognition, especially when training data is insufficient.	['Xing Xie', 'Yongfeng Huang', 'Junxin Liu', 'Fangzhao Wu', 'Chuhan Wu']	2019-04-26	null	null	null	null	['chinese-named-entity-recognition']	['natural-language-processing']	[-1.06489815e-01 -2.91618913e-01 -1.49718627e-01 -4.90109682e-01 -6.35029137e-01 -5.58847904e-01 8.96898881e-02 -4.21753479e-03 -9.59864914e-01 8.55665743e-01 2.43664339e-01 -3.38058889e-01 5.62661171e-01 -9.19541121e-01 -4.17412996e-01 -4.16686505e-01 4.63734478e-01 3.18543732e-01 2.72638112e-01 1.14545174e-01 1.21808179e-01 4.01828647e-01 -6.49384022e-01 1.14475206e-01 1.51834786e+00 6.75018311e-01 6.47787213e-01 1.78993553e-01 -7.96797335e-01 5.35529077e-01 -8.76548767e-01 -3.01681906e-01 -1.94880486e-01 -5.24688065e-01 -1.06144631e+00 1.00892752e-01 -2.46026218e-01 -1.63902253e-01 -9.23194885e-02 1.19133663e+00 3.63468379e-01 2.21117422e-01 3.62796366e-01 -7.15119243e-01 -9.64208007e-01 1.06731224e+00 -3.47513765e-01 5.97997159e-02 -1.23551570e-01 -2.16860756e-01 8.50642800e-01 -8.67987871e-01 7.28578508e-01 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77c44fe8-2e6e-4598-b171-9856f25c3101	extending-multi-object-tracking-systems-to	1912.11651	null	https://arxiv.org/abs/1912.11651v1	https://arxiv.org/pdf/1912.11651v1.pdf	Extending Multi-Object Tracking systems to better exploit appearance and 3D information	Tracking multiple objects in real time is essential for a variety of real-world applications, with self-driving industry being at the foremost. This work involves exploiting temporally varying appearance and motion information for tracking. Siamese networks have recently become highly successful at appearance based single object tracking and Recurrent Neural Networks have started dominating both motion and appearance based tracking. Our work focuses on combining Siamese networks and RNNs to exploit appearance and motion information respectively to build a joint system capable of real time multi-object tracking. We further explore heuristics based constraints for tracking in the Birds Eye View Space for efficiently exploiting 3D information as a constrained optimization problem for track prediction.	['Sahan Liyanaarachchi', 'Mayuka Jayawardhana', 'Kanchana Ranasinghe', 'Harsha Ranasinghe']	2019-12-25	null	null	null	null	['real-time-multi-object-tracking']	['computer-vision']	[-2.73882389e-01 -7.31085360e-01 -3.59860510e-01 2.87570879e-02 -2.57920653e-01 -6.50846064e-01 4.26057577e-01 -4.51586276e-01 -5.43785691e-01 4.55514193e-01 -1.43906608e-01 -7.73186143e-03 -2.40989700e-01 -1.52096376e-01 -3.52188051e-01 -5.48754156e-01 -1.30066901e-01 5.80001712e-01 6.58656955e-01 -7.32637644e-02 -8.34115073e-02 1.19614375e+00 -1.59893513e+00 -1.90006867e-01 1.15732379e-01 7.15197444e-01 2.75990725e-01 1.26346016e+00 5.77487387e-02 8.95707011e-01 -3.01382691e-01 -8.53682384e-02 5.22148490e-01 -1.50786027e-01 -2.00920299e-01 2.92992622e-01 1.03292358e+00 -5.68176173e-02 -5.14324784e-01 1.00649369e+00 4.96477067e-01 5.64033628e-01 2.36335248e-01 -1.58635652e+00 -4.93617326e-01 9.41922218e-02 -6.89192474e-01 6.31455839e-01 -6.12213388e-02 3.19202006e-01 7.13259876e-01 -4.41797823e-01 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-1.06814384e+00 -3.26700985e-01 -5.91138065e-01 -4.53553855e-01 1.47433251e-01 6.25138283e-01 -9.14143980e-01 1.17069654e-01 1.46591559e-01]	[6.299977779388428, -2.0391201972961426]
9ecaa0c9-bc13-4e8b-9af9-15d44b497aec	zero-shot-visual-reasoning-through	2209.15087	null	https://arxiv.org/abs/2209.15087v1	https://arxiv.org/pdf/2209.15087v1.pdf	Zero-shot visual reasoning through probabilistic analogical mapping	Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a model of visual reasoning that synthesizes these two approaches. VisiPAM employs learned representations derived directly from naturalistic visual inputs, coupled with a similarity-based mapping operation derived from cognitive theories of human reasoning. We show that without any direct training, visiPAM outperforms a state-of-the-art deep learning model on an analogical mapping task. In addition, visiPAM closely matches the pattern of human performance on a novel task involving mapping of 3D objects across disparate categories.	['Hongjing Lu', 'Keith J. Holyoak', 'Trevor Bihl', 'Shuhao Fu', 'Taylor W. Webb']	2022-09-29	null	null	null	null	['visual-reasoning', 'visual-reasoning']	['computer-vision', 'reasoning']	[ 1.57051787e-01 1.10513397e-01 1.34344071e-01 -4.11995113e-01 -1.40793800e-01 -7.31842458e-01 1.05135739e+00 5.19939601e-01 -4.20260727e-01 2.30175838e-01 2.29026407e-01 -7.02061713e-01 -4.20397997e-01 -7.36967146e-01 -6.98378980e-01 -1.09980494e-01 2.45615810e-01 8.61118972e-01 2.60935456e-01 -3.58141631e-01 8.45471203e-01 6.15002930e-01 -1.37269783e+00 7.21319735e-01 7.74944782e-01 6.17089331e-01 2.35555634e-01 4.85704452e-01 -4.25234318e-01 1.15132046e+00 -2.98966408e-01 -6.19229674e-01 2.47940317e-01 -7.34758556e-01 -1.15201116e+00 -4.88820672e-01 8.15245688e-01 -2.36016005e-01 -5.31886935e-01 1.01064897e+00 1.30108789e-01 3.53522241e-01 1.13446629e+00 -1.18403304e+00 -1.77010584e+00 3.62133116e-01 -4.44888949e-01 7.46243834e-01 6.83945000e-01 2.74007529e-01 1.00656068e+00 -9.67240512e-01 3.67599130e-01 1.56594419e+00 8.50952029e-01 4.65170026e-01 -1.69298601e+00 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6.47718191e-01 5.19902408e-02 -9.47418928e-01 -8.21714848e-02 -3.05982549e-02]	[10.580645561218262, 2.2955355644226074]
b83ecfe7-940f-4fcb-982f-915346ad36d2	learning-from-synthetic-human-group	2306.16772	null	https://arxiv.org/abs/2306.16772v1	https://arxiv.org/pdf/2306.16772v1.pdf	Learning from Synthetic Human Group Activities	The understanding of complex human interactions and group activities has garnered attention in human-centric computer vision. However, the advancement of the related tasks is hindered due to the difficulty of obtaining large-scale labeled real-world datasets. To mitigate the issue, we propose M3Act, a multi-view multi-group multi-person human atomic action and group activity data generator. Powered by the Unity engine, M3Act contains simulation-ready 3D scenes and human assets, configurable lighting and camera systems, highly parameterized modular group activities, and a large degree of domain randomization during the data generation process. Our data generator is capable of generating large-scale datasets of human activities with multiple viewpoints, modalities (RGB images, 2D poses, 3D motions), and high-quality annotations for individual persons and multi-person groups (2D bounding boxes, instance segmentation masks, individual actions and group activity categories). Using M3Act, we perform synthetic data pre-training for 2D skeleton-based group activity recognition and RGB-based multi-person pose tracking. The results indicate that learning from our synthetic datasets largely improves the model performances on real-world datasets, with the highest gain of 5.59% and 7.32% respectively in group and person recognition accuracy on CAD2, as well as an improvement of 6.63 in MOTP on HiEve. Pre-training with our synthetic data also leads to faster model convergence on downstream tasks (up to 6.8% faster). Moreover, M3Act opens new research problems for 3D group activity generation. We release M3Act3D, an 87.6-hour 3D motion dataset of human activities with larger group sizes and higher complexity of inter-person interactions than previous multi-person datasets. We define multiple metrics and propose a competitive baseline for the novel task.	['Mubbasir Kapadia', 'Vladimir Pavlovic', 'Sejong Yoon', 'Samuel S. Sohn', 'Seonghyeon Moon', 'Aditya Bhat', 'Parth Goel', 'Honglu Zhou', 'Che-Jui Chang']	2023-06-29	null	null	null	null	['pose-tracking', 'activity-recognition', 'person-recognition', 'unity', 'group-activity-recognition', 'instance-segmentation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 2.00848207e-01 -1.60929263e-02 2.29759485e-01 -3.16730738e-01 -8.28648031e-01 -5.50625443e-01 7.27707505e-01 -5.28047979e-01 -3.43287140e-01 5.15588403e-01 3.31973135e-01 3.43155265e-01 2.42768899e-01 -4.98310447e-01 -7.06661940e-01 -5.39188445e-01 -8.27402622e-03 9.94802296e-01 2.84457147e-01 -6.69272915e-02 -3.31649870e-01 4.76893187e-01 -1.70334804e+00 3.03028733e-01 4.98989522e-01 8.31836343e-01 -1.75087154e-01 1.00487220e+00 5.48792839e-01 4.35437262e-01 -7.87697971e-01 -2.98227042e-01 7.38840103e-01 -5.25966406e-01 -4.40634280e-01 8.06206167e-01 7.06144035e-01 -4.36260641e-01 -2.06962034e-01 2.98809558e-01 8.95276785e-01 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a2c2c0d8-8341-44a0-813f-7047890cca8d	rethinking-vision-transformer-and-masked	2302.05744	null	https://arxiv.org/abs/2302.05744v1	https://arxiv.org/pdf/2302.05744v1.pdf	Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing	Recently, vision transformer (ViT) based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no works to explore the fundamental natures (\textit{e.g.}, modality-aware inputs, suitable multimodal pre-training, and efficient finetuning) in vanilla ViT for multimodal FAS. In this paper, we investigate three key factors (i.e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth. First, in terms of the ViT inputs, we find that leveraging local feature descriptors benefits the ViT on IR modality but not RGB or Depth modalities. Second, in observation of the inefficiency on direct finetuning the whole or partial ViT, we design an adaptive multimodal adapter (AMA), which can efficiently aggregate local multimodal features while freezing majority of ViT parameters. Finally, in consideration of the task (FAS vs. generic object classification) and modality (multimodal vs. unimodal) gaps, ImageNet pre-trained models might be sub-optimal for the multimodal FAS task. To bridge these gaps, we propose the modality-asymmetric masked autoencoder (M$^{2}$A$^{2}$E) for multimodal FAS self-supervised pre-training without costly annotated labels. Compared with the previous modality-symmetric autoencoder, the proposed M$^{2}$A$^{2}$E is able to learn more intrinsic task-aware representation and compatible with modality-agnostic (e.g., unimodal, bimodal, and trimodal) downstream settings. Extensive experiments with both unimodal (RGB, Depth, IR) and multimodal (RGB+Depth, RGB+IR, Depth+IR, RGB+Depth+IR) settings conducted on multimodal FAS benchmarks demonstrate the superior performance of the proposed methods. We hope these findings and solutions can facilitate the future research for ViT-based multimodal FAS.	['Alex Kot', 'Yongjian Hu', 'Xin Liu', 'Yawen Cui', 'Rizhao Cai', 'Zitong Yu']	2023-02-11	null	null	null	null	['face-anti-spoofing']	['computer-vision']	[ 4.16408837e-01 -1.20301038e-01 -2.27612123e-01 -2.61531413e-01 -6.44085526e-01 -7.39260614e-01 6.66597962e-01 -3.63719642e-01 -2.05287904e-01 5.89999974e-01 1.26714513e-01 -4.73130554e-01 -3.08852941e-01 -7.53479719e-01 -7.43540108e-01 -1.19646788e+00 2.74976701e-01 -4.62672040e-02 -5.59844226e-02 -4.69496220e-01 3.70212831e-02 5.60431898e-01 -1.85228586e+00 5.15290022e-01 6.38462663e-01 1.48111069e+00 -1.39033161e-02 6.44224226e-01 -1.02466695e-01 5.51697314e-01 -4.78941560e-01 -8.80249083e-01 2.38543406e-01 -4.94748414e-01 -8.27294886e-01 3.34171988e-02 5.21305799e-01 -3.16988498e-01 -4.63531286e-01 8.99595320e-01 5.92530787e-01 6.86475891e-04 8.85093033e-01 -1.62606049e+00 -8.10289264e-01 4.09573495e-01 -7.68025339e-01 -5.67384176e-02 4.41141665e-01 4.98562336e-01 7.46062040e-01 -8.11322570e-01 2.24951163e-01 1.36389685e+00 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8d347808-15a9-4cbd-b220-50617b2d9e42	interactive-query-clarification-and	2205.15918	null	https://arxiv.org/abs/2205.15918v1	https://arxiv.org/pdf/2205.15918v1.pdf	Interactive Query Clarification and Refinement via User Simulation	When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve documents aligned with users' intents. While some work focus on query disambiguation using users' browsing history, a recent line of work proposes to interact with users by asking clarification questions or/and proposing clarification panels. However, these approaches count either a limited number (i.e., 1) of interactions with user or log-based interactions. In this paper, we propose and evaluate a fully simulated query clarification framework allowing multi-turn interactions between IR systems and user agents.	['Laure Soulier', 'Ludovic Denoyer', 'Pierre Erbacher']	2022-05-31	null	null	null	null	['user-simulation', 'document-ranking']	['natural-language-processing', 'natural-language-processing']	[ 1.59776121e-01 1.50129393e-01 -3.82581919e-01 -4.00025785e-01 -6.97274148e-01 -9.77338433e-01 9.07128930e-01 5.27725577e-01 -6.62309408e-01 7.07089603e-01 3.69399011e-01 -4.40803319e-01 -7.35555470e-01 -4.26795483e-01 2.54417568e-01 -7.09846094e-02 3.03493619e-01 9.33992863e-01 5.53957582e-01 -6.23064339e-01 7.91436434e-01 5.22418976e-01 -1.69468796e+00 3.95087987e-01 9.25624728e-01 1.69204026e-01 4.70477283e-01 8.74431074e-01 -5.59746683e-01 5.36005974e-01 -8.87241066e-01 1.19926900e-01 1.19132236e-01 -3.18388194e-01 -1.19160593e+00 -2.51954585e-01 3.63525391e-01 -7.49445081e-01 -7.78618157e-02 7.41155982e-01 6.88992202e-01 4.93339658e-01 3.15923214e-01 -1.07884955e+00 -5.28523147e-01 6.23466969e-01 -1.95091471e-01 4.01819944e-01 1.03742480e+00 -3.37324739e-01 1.00155020e+00 -4.98992145e-01 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cd8cf347-136d-4a06-8ddb-8df2c9362d20	federated-learning-based-energy-demand	2210.15850	null	https://arxiv.org/abs/2210.15850v1	https://arxiv.org/pdf/2210.15850v1.pdf	Federated Learning based Energy Demand Prediction with Clustered Aggregation	To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. Energy usage information collected by the clients' smart homes can be used to train a deep neural network to predict the future energy demand. Collecting data from a large number of distributed clients for centralized model training is expensive in terms of communication resources. To take advantage of distributed data in edge systems, centralized training can be replaced by federated learning where each client only needs to upload model updates produced by training on its local data. These model updates are aggregated into a single global model by the server. But since different clients can have different attributes, model updates can have diverse weights and as a result, it can take a long time for the aggregated global model to converge. To speed up the convergence process, we can apply clustering to group clients based on their properties and aggregate model updates from the same cluster together to produce a cluster specific global model. In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.	['Choong Seon Hong', 'Chu Myaet Thwal', 'Kyi Thar', 'Ye Lin Tun']	2022-10-28	null	null	null	null	['energy-management']	['time-series']	[-5.97027063e-01 -2.79712230e-01 -2.48774767e-01 -6.72064602e-01 -4.03132677e-01 -2.34797210e-01 -6.64784759e-03 1.15530849e-01 -5.19212596e-02 6.17346823e-01 7.03212842e-02 9.14308950e-02 -1.00149423e-01 -1.31433105e+00 -3.84866953e-01 -1.07073593e+00 2.41121203e-01 7.67830551e-01 -7.47841522e-02 2.95019835e-01 -2.82377511e-01 5.54789901e-01 -1.31095707e+00 2.37540916e-01 8.91176641e-01 1.41658759e+00 5.25325954e-01 3.33584785e-01 -3.87240916e-01 9.47887838e-01 -4.88312751e-01 1.88800707e-01 7.37736151e-02 -3.89603853e-01 -4.28044289e-01 -2.49965310e-01 -6.20693147e-01 -5.93508005e-01 -1.81878775e-01 1.03482783e+00 6.06766701e-01 4.00520474e-01 3.87180448e-01 -1.59400213e+00 -3.34475577e-01 8.95713985e-01 -1.38280138e-01 -1.60198629e-01 -4.46217358e-01 -1.74009353e-01 7.38733292e-01 -3.05646122e-01 1.25893474e-01 5.66092551e-01 5.34682274e-01 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-9.76294935e-01 -7.16419518e-01 2.67552286e-01 1.32178664e-01 -4.47218977e-02 -3.98992479e-01 8.73122830e-03]	[5.911602973937988, 2.7267258167266846]
9e44e32f-b733-4ff5-9d08-425c3cadb4c5	happydb-a-corpus-of-100000-crowdsourced-happy	1801.07746	null	http://arxiv.org/abs/1801.07746v2	http://arxiv.org/pdf/1801.07746v2.pdf	HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments	The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the science of happiness into users' daily lives by steering them towards behaviors that increase happiness. With the goal of building technology that can understand how people express their happy moments in text, we crowd-sourced HappyDB, a corpus of 100,000 happy moments that we make publicly available. This paper describes HappyDB and its properties, and outlines several important NLP problems that can be studied with the help of the corpus. We also apply several state-of-the-art analysis techniques to analyze HappyDB. Our results demonstrate the need for deeper NLP techniques to be developed which makes HappyDB an exciting resource for follow-on research.	['Wang-Chiew Tan', 'Yinzhan Xu', 'Vivian Li', 'Sara Evensen', 'Daniela Stepanov', 'Alon Halevy', 'Yoshihiko Suhara', 'Behzad Golshan', 'Andrei Lopatenko', 'Akari Asai']	2018-01-23	happydb-a-corpus-of-100000-crowdsourced-happy-1	https://aclanthology.org/L18-1103	https://aclanthology.org/L18-1103.pdf	lrec-2018-5	['art-analysis']	['computer-vision']	[-4.08067346e-01 3.50209713e-01 -7.96598613e-01 -9.49855983e-01 -4.88348961e-01 -2.26874560e-01 4.96078938e-01 4.73761559e-01 -3.33795816e-01 7.09595025e-01 1.01064754e+00 1.92767277e-01 3.05075794e-01 -8.02145720e-01 -1.54157802e-01 -2.00076386e-01 2.22366646e-01 9.52863991e-02 -6.80864215e-01 -7.76190519e-01 4.81618315e-01 1.89308375e-01 -1.56122220e+00 2.99093902e-01 6.51935041e-01 3.30861479e-01 -2.43778884e-01 2.14335650e-01 -3.68936837e-01 8.28398049e-01 -1.51615188e-01 -1.07269537e+00 -2.92645097e-01 -1.93023771e-01 -9.71613586e-01 -2.25838333e-01 -7.81228021e-02 4.60476018e-02 1.77060992e-01 7.72018552e-01 5.26714683e-01 4.13192213e-01 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649554dc-d811-4cfa-b5df-022aa6200798	lvit-language-meets-vision-transformer-in	2206.14718	null	https://arxiv.org/abs/2206.14718v4	https://arxiv.org/pdf/2206.14718v4.pdf	LViT: Language meets Vision Transformer in Medical Image Segmentation	Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.	['Dazhou Guo', 'Puyang Wang', 'Qingqi Hong', 'Dakai Jin', 'Le Lu', 'You Zhang', 'Qingde Li', 'Yunxiang Li', 'Zihan Li']	2022-06-29	null	null	null	null	['text-annotation']	['natural-language-processing']	[ 5.94706714e-01 4.25582260e-01 -4.80910242e-01 -7.40278244e-01 -1.20371616e+00 -3.19431573e-01 2.04090685e-01 8.77315253e-02 -6.77002132e-01 6.05686307e-01 7.02843741e-02 -2.72289932e-01 2.46911496e-01 -4.70114648e-01 -7.26534486e-01 -7.41024137e-01 6.52517200e-01 6.14347637e-01 1.63863465e-01 2.42032036e-01 -6.90864474e-02 7.21319839e-02 -7.44258761e-01 3.12257200e-01 1.20476758e+00 9.18966889e-01 6.25190973e-01 5.58190882e-01 -2.27070481e-01 1.05997276e+00 -6.43363819e-02 -3.25259805e-01 2.09349558e-01 -7.67258346e-01 -1.08991170e+00 5.58577955e-01 2.61272997e-01 -6.01302743e-01 -2.11186379e-01 1.25593948e+00 5.92647552e-01 -7.36898929e-02 6.11655593e-01 -1.09861028e+00 -6.26650989e-01 6.56186402e-01 -7.97514498e-01 1.45027414e-01 -1.03433497e-01 2.50846624e-01 7.85125136e-01 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d89a50e5-971e-453e-ba81-447b9f12feaa	zero-shot-framework-for-satellite-image	2306.02921	null	https://arxiv.org/abs/2306.02921v1	https://arxiv.org/pdf/2306.02921v1.pdf	Zero shot framework for satellite image restoration	Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availability of paired datasets is a strong assumption. Consequently, many unsupervised algorithms have been proposed to address this problem. These methods synthetically generate a large dataset of degraded images using image formation models. A neural network is then trained with an adversarial loss to discriminate between images from distorted and clean domains. However, these methods yield suboptimal performance when tested on real images that do not necessarily conform to the generation mechanism. Also, they require a large amount of training data and are rendered unsuitable when only a few images are available. We propose a distortion disentanglement and knowledge distillation framework for satellite image restoration to address these important issues. Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics. Specifically, we first propose a mechanism to disentangle distortion. This enables us to generate images with varying degrees of distortion using the disentangled distortion and the reference image. We then propose the use of knowledge distillation to train a restoration network using the generated image pairs. As a final step, the distorted image is passed through the restoration network to get the final output. Ablation studies show that our proposed mechanism successfully disentangles distortion.	['A. N. Rajagopalan', 'Praveen Kandula']	2023-06-05	null	null	null	null	['image-restoration', 'disentanglement']	['computer-vision', 'methodology']	[ 6.37714744e-01 -1.01904362e-01 1.86803922e-01 -4.13964301e-01 -6.55915022e-01 -8.50771308e-01 8.43061924e-01 -4.32875723e-01 -2.79142916e-01 8.84121716e-01 1.06468514e-01 -1.76155895e-01 -4.42640670e-02 -1.03183246e+00 -8.69712591e-01 -9.79290485e-01 2.54135996e-01 5.16832545e-02 -1.84337422e-01 -1.91863164e-01 -3.32604572e-02 5.30405641e-01 -1.58984983e+00 -3.61589231e-02 1.30058348e+00 6.66790485e-01 3.77683640e-01 6.64247513e-01 2.94215262e-01 3.82735133e-01 -7.60022223e-01 -2.45306388e-01 7.58295059e-01 -5.57911754e-01 -5.46185434e-01 4.58003610e-01 3.73581558e-01 -6.85171068e-01 -6.07953072e-01 1.38211286e+00 5.54320514e-01 2.74685659e-02 5.25406063e-01 -1.19811428e+00 -9.45838213e-01 4.53508645e-01 -3.32982391e-01 1.95433386e-02 9.50110555e-02 4.03851718e-01 6.82633162e-01 -7.04225123e-01 3.73354077e-01 1.06881320e+00 1.85109586e-01 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a118c60f-e5e4-4be6-88a4-752b1ac1ffa1	capacity-bandwidth-and-compositionality-in	1910.11424	null	https://arxiv.org/abs/1910.11424v3	https://arxiv.org/pdf/1910.11424v3.pdf	Capacity, Bandwidth, and Compositionality in Emergent Language Learning	Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is not the only contributing factor. In this paper, we investigate the learning biases that affect the efficacy and compositionality of emergent languages. Our foremost contribution is to explore how capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.	['Cinjon Resnick', 'Kyunghyun Cho', 'Jakob Foerster', 'Abhinav Gupta', 'Andrew M. Dai']	2019-10-24	null	null	null	null	['systematic-generalization']	['reasoning']	[ 1.27623454e-01 2.99019337e-01 -2.93328285e-01 -6.28086105e-02 -1.69228628e-01 -6.12265885e-01 7.60929465e-01 1.89705223e-01 -3.87925655e-01 7.26236582e-01 4.40436512e-01 -7.13049293e-01 -3.33920181e-01 -6.40578449e-01 -9.66296732e-01 -7.56958783e-01 -1.87161312e-01 7.06627369e-02 1.69944137e-01 -3.43057245e-01 2.57688701e-01 4.10284579e-01 -1.54486120e+00 1.34855984e-02 9.61214423e-01 5.83486617e-01 1.36603400e-01 5.11095107e-01 -2.66758725e-02 8.94002974e-01 -5.03395379e-01 -9.91478860e-02 2.81653464e-01 -7.64927745e-01 -8.61648738e-01 -1.14792734e-01 2.84648329e-01 -7.17481300e-02 -8.32425132e-02 8.95990551e-01 4.43397850e-01 -2.46222705e-01 7.87238002e-01 -8.76811624e-01 -5.09613872e-01 1.07852912e+00 -2.61816561e-01 4.32509989e-01 -7.79035836e-02 3.99341047e-01 1.29354799e+00 -3.50899518e-01 6.08349800e-01 1.10087109e+00 2.78259367e-01 4.56507385e-01 -1.33048630e+00 -7.29302347e-01 3.35704744e-01 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449112bd-0beb-4575-9cbd-43ba55f14aea	findvehicle-and-vehiclefinder-a-ner-dataset	2304.10893	null	https://arxiv.org/abs/2304.10893v1	https://arxiv.org/pdf/2304.10893v1.pdf	FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system	Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM.	['Yutao Yue', 'Eng Gee Lim', 'Jeremy Smith', 'Xiaohui Zhu', 'Rongsheng Hu', 'Shanliang Yao', 'Feifan Chen', 'Ka Lok Man', 'Runwei Guan']	2023-04-21	null	null	null	null	['named-entity-recognition-ner']	['natural-language-processing']	[-2.99053282e-01 -4.91667360e-01 -5.22207141e-01 -4.96244758e-01 -1.07158947e+00 -5.83878636e-01 7.53755867e-01 -4.25074808e-03 -5.60252368e-01 5.08605421e-01 -1.85244754e-02 -5.08006394e-01 4.72646020e-02 -1.09858394e+00 -9.19070303e-01 -4.97356027e-01 1.95987776e-01 4.79970336e-01 4.06178534e-01 -8.14784542e-02 1.53304696e-01 6.12272918e-01 -1.85049987e+00 1.23954989e-01 8.30965638e-01 1.10954154e+00 6.29385352e-01 4.62492943e-01 -4.77552444e-01 6.90665007e-01 -3.00717652e-01 -4.48360056e-01 1.83304876e-01 1.76343977e-01 -5.82796156e-01 -3.05332571e-01 3.62326205e-01 -4.05714929e-01 -7.10047901e-01 1.15617704e+00 4.56207305e-01 1.77032098e-01 6.65843189e-01 -1.62289333e+00 -4.57099080e-01 5.08487038e-02 -1.00051105e-01 1.27118513e-01 1.52848899e-01 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dbdf73de-92a5-484a-9322-52a7b4af6c59	training-dynamics-for-curriculum-learning-a-1	2210.12499	null	https://arxiv.org/abs/2210.12499v2	https://arxiv.org/pdf/2210.12499v2.pdf	Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU	Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability. Current approaches for Natural Language Understanding (NLU) tasks use CL to improve in-distribution data performance often via heuristic-oriented or task-agnostic difficulties. In this work, instead, we employ CL for NLU by taking advantage of training dynamics as difficulty metrics, i.e., statistics that measure the behavior of the model at hand on specific task-data instances during training and propose modifications of existing CL schedulers based on these statistics. Differently from existing works, we focus on evaluating models on in-distribution (ID), out-of-distribution (OOD) as well as zero-shot (ZS) cross-lingual transfer datasets. We show across several NLU tasks that CL with training dynamics can result in better performance mostly on zero-shot cross-lingual transfer and OOD settings with improvements up by 8.5% in certain cases. Overall, experiments indicate that training dynamics can lead to better performing models with smoother training compared to other difficulty metrics while being 20% faster on average. In addition, through analysis we shed light on the correlations of task-specific versus task-agnostic metrics.	['Ignacio Iacobacci', 'Gerasimos Lampouras', 'Fenia Christopoulou']	2022-10-22	null	null	null	null	['zero-shot-cross-lingual-transfer']	['natural-language-processing']	[-1.08156249e-01 -5.42458110e-02 -3.19883078e-01 -3.36510032e-01 -1.09201646e+00 -7.99711168e-01 7.27993488e-01 3.66672516e-01 -8.32767069e-01 7.14892864e-01 1.15365833e-01 -5.60246050e-01 -3.76059383e-01 -3.77313912e-01 -7.71383166e-01 -4.30636287e-01 -1.15518920e-01 8.65080535e-01 2.76174396e-01 -3.40494156e-01 1.67937785e-01 -1.08135527e-03 -1.55167127e+00 3.35712314e-01 1.16983163e+00 6.03364885e-01 5.64284980e-01 7.86179006e-01 -4.07760710e-01 5.73650420e-01 -6.37301683e-01 -1.61843389e-01 3.87277752e-02 -9.74481553e-02 -9.25867677e-01 -3.33784610e-01 6.86683297e-01 8.46823025e-03 8.15412179e-02 7.29104340e-01 7.16019928e-01 2.95467436e-01 8.36687088e-01 -1.08486211e+00 -5.25072813e-01 6.83287621e-01 -3.22760910e-01 5.31276822e-01 1.95505723e-01 1.93006873e-01 9.74707365e-01 -6.73107684e-01 7.05211163e-01 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3bf3cb76-6735-44ff-9ba9-c10468e10443	investigation-of-ensemble-methods-for-the	2304.07395	null	https://arxiv.org/abs/2304.07395v1	https://arxiv.org/pdf/2304.07395v1.pdf	Investigation of ensemble methods for the detection of deepfake face manipulations	The recent wave of AI research has enabled a new brand of synthetic media, called deepfakes. Deepfakes have impressive photorealism, which has generated exciting new use cases but also raised serious threats to our increasingly digital world. To mitigate these threats, researchers have tried to come up with new methods for deepfake detection that are more effective than traditional forensics and heavily rely on deep AI technology. In this paper, following up on encouraging prior work for deepfake detection with attribution and ensemble techniques, we explore and compare multiple designs for ensemble detectors. The goal is to achieve robustness and good generalization ability by leveraging ensembles of models that specialize in different manipulation categories. Our results corroborate that ensembles can achieve higher accuracy than individual models when properly tuned, while the generalization ability relies on access to a large number of training data for a diverse set of known manipulations.	['Ioannis Kompatsiaris', 'Symeon Papadopoulos', 'Nikolaos Giatsoglou']	2023-04-14	null	null	null	null	['face-swapping']	['computer-vision']	[ 2.45689601e-01 -2.59646207e-01 -1.18002348e-01 1.47286475e-01 -2.65376478e-01 -1.04353130e+00 9.45058405e-01 -5.30224182e-02 -3.52889419e-01 5.48599958e-01 1.79186374e-01 -3.09218854e-01 -2.11962387e-01 -7.27997422e-01 -5.24355352e-01 -4.16364253e-01 -9.64270905e-02 1.06424227e-01 7.55294785e-02 -2.75000960e-01 7.10440874e-01 6.71839654e-01 -1.63728893e+00 3.72540623e-01 6.99587643e-01 6.76739573e-01 -3.16062838e-01 9.20034528e-01 4.04586703e-01 9.06884849e-01 -1.06744456e+00 -1.01241529e+00 5.56933641e-01 -2.01635838e-01 -4.81394231e-01 -2.44050205e-01 8.21055710e-01 -8.23720038e-01 -5.82803428e-01 1.04710960e+00 7.55296350e-01 2.43478566e-02 5.90154052e-01 -1.52034497e+00 -9.24246609e-01 5.16860306e-01 -7.88244784e-01 5.51150799e-01 2.50732511e-01 8.33852887e-01 7.12890089e-01 -4.23842311e-01 6.16930604e-01 1.43474531e+00 1.01300669e+00 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20b7818a-04d0-4c50-aafb-d5cd2d8bd33a	cad-based-design-optimization-of-four-bar	2201.01590	null	https://arxiv.org/abs/2201.01590v1	https://arxiv.org/pdf/2201.01590v1.pdf	CAD Based Design Optimization of Four-bar Mechanisms: a coronaventilator case study	Design optimization of mechanisms is a promising research area as it results in more energy-efficient machines without compromising performance. However, machine builders do not actually use the design methods described in the literature as these algorithms require too much theoretical analysis. Moreover, the design synthesis approaches in the literature predominantly utilize heuristic optimizers leading to suboptimal local minima. This research introduces a convenient optimization workflow allowing wide industrial adoption, while guaranteeing to reveal the global optimum. To guarantee that we find the global optimum, a mathematical expression of the constraints describing the feasible region of possible designs is of great importance. Therefore, kinematic analysis of the point-to-point (PTP) planar four-bar mechanism is discussed to obtain the static and dynamic constraints. Within the feasible region, objective value samples are generated through CAD multi-body software. These motion simulations determine the required torque to fulfill the movement for a certain combination of design parameters. Sparse interpolation techniques allow minimizing the required amount of samples and thus CAD simulations. Moreover, this interpolation of simulation results enables the representation of the objective in a mathematical description without in-depth analytical design analysis by the machine designer. Subsequently, the mathematical expression of the objective allows global optimizers to find a global optimal design within the feasible design space. In a case study of a coronaventilator mechanism with three design parameters (DP's), 1870 CAD motion simulations from which only 618 are used to build a model allowed to reduce the RMS torque of the mechanism by 67%.	['Stijn Derammelaere', 'Annie Cuyt', 'Bart Vanwalleghem', 'Jan Herregodts', 'Stijn Herregodts', 'Simon Houwen', 'Ferre Knaepkens', 'Nick Van Oosterwyck', 'Abdelmajid Ben Yahya']	2022-01-05	null	null	null	null	['design-synthesis']	['adversarial']	[-2.39110053e-01 5.95651194e-02 -5.72640181e-01 1.51288137e-01 -1.67480946e-01 -4.40598905e-01 2.88298484e-02 8.57863352e-02 -9.78506505e-02 7.34786391e-01 -3.90716761e-01 -2.90963709e-01 -9.74935532e-01 -8.18163812e-01 -5.06682932e-01 -6.75829291e-01 3.52730118e-02 4.38381255e-01 -1.94028303e-01 -2.12009057e-01 5.72970152e-01 9.22819316e-01 -1.46110117e+00 -3.26313078e-01 7.88948596e-01 8.57526541e-01 5.71919441e-01 1.67969257e-01 7.09136426e-02 7.77428523e-02 -5.80525696e-01 -5.03646769e-02 4.36732434e-02 -4.22782838e-01 -6.08519614e-01 9.91861746e-02 -3.94993395e-01 -1.89900026e-01 3.05730015e-01 5.40398061e-01 4.92360026e-01 6.23855293e-01 8.05758536e-01 -9.77522254e-01 -3.29386890e-01 2.51970112e-01 -4.97884870e-01 -4.18610632e-01 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a32f74c5-a344-43e4-af63-b69d7724cf2b	video-in-10-bits-few-bit-videoqa-for	2210.08391	null	https://arxiv.org/abs/2210.08391v2	https://arxiv.org/pdf/2210.08391v2.pdf	Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy	In Video Question Answering (VideoQA), answering general questions about a video requires its visual information. Yet, video often contains redundant information irrelevant to the VideoQA task. For example, if the task is only to answer questions similar to "Is someone laughing in the video?", then all other information can be discarded. This paper investigates how many bits are really needed from the video in order to do VideoQA by introducing a novel Few-Bit VideoQA problem, where the goal is to accomplish VideoQA with few bits of video information (e.g., 10 bits). We propose a simple yet effective task-specific feature compression approach to solve this problem. Specifically, we insert a lightweight Feature Compression Module (FeatComp) into a VideoQA model which learns to extract task-specific tiny features as little as 10 bits, which are optimal for answering certain types of questions. We demonstrate more than 100,000-fold storage efficiency over MPEG4-encoded videos and 1,000-fold over regular floating point features, with just 2.0-6.6% absolute loss in accuracy, which is a surprising and novel finding. Finally, we analyze what the learned tiny features capture and demonstrate that they have eliminated most of the non-task-specific information, and introduce a Bit Activation Map to visualize what information is being stored. This decreases the privacy risk of data by providing k-anonymity and robustness to feature-inversion techniques, which can influence the machine learning community, allowing us to store data with privacy guarantees while still performing the task effectively.	['Gunnar A. Sigurdsson', 'Shih-Fu Chang', 'Robinson Piramuthu', 'Shiyuan Huang']	2022-10-15	null	null	null	null	['feature-compression', 'video-question-answering']	['computer-vision', 'computer-vision']	[ 3.73126298e-01 -4.80821840e-02 3.46598811e-02 -4.56852406e-01 -9.12917256e-01 -7.78146446e-01 3.68186422e-02 1.33912116e-01 -5.28314054e-01 5.65088689e-01 2.70340025e-01 -3.22495997e-01 5.31602278e-02 -6.51773155e-01 -1.12966549e+00 -6.53759778e-01 -1.77998632e-01 -2.57936209e-01 1.09340258e-01 -9.41631198e-02 2.10965902e-01 8.02645553e-03 -1.75174236e+00 6.64830506e-01 5.80930531e-01 1.47271419e+00 -8.27740878e-02 7.41486192e-01 2.55891234e-02 8.98112357e-01 -6.80210114e-01 -7.67980635e-01 5.91203153e-01 -2.01044604e-01 -1.03416359e+00 -2.61454936e-02 7.79071569e-01 -8.15913379e-01 -6.42303646e-01 1.07390845e+00 1.43108606e-01 2.62136348e-02 1.47013783e-01 -1.43092215e+00 -3.80638629e-01 1.79922745e-01 -2.40849331e-01 1.54151127e-01 6.95522130e-01 3.22507173e-01 1.05585992e+00 -3.98787975e-01 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7.36327231e-01 -1.74767002e-01 -5.90567708e-01 -1.19332559e-02 -3.68060768e-01 3.38624388e-01 5.19988537e-01 -2.04648942e-01 -6.30279183e-01 2.24890321e-01]	[5.865006923675537, 6.700124740600586]
2c551d92-6d47-466f-864e-3e413604cfba	the-value-equivalence-principle-for-model	2011.03506	null	https://arxiv.org/abs/2011.03506v1	https://arxiv.org/pdf/2011.03506v1.pdf	The Value Equivalence Principle for Model-Based Reinforcement Learning	Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use, by constructing a model of the environment's dynamics that correctly predicts the observed state transitions. In this paper we argue that the limited representational resources of model-based RL agents are better used to build models that are directly useful for value-based planning. As our main contribution, we introduce the principle of value equivalence: two models are value equivalent with respect to a set of functions and policies if they yield the same Bellman updates. We propose a formulation of the model learning problem based on the value equivalence principle and analyze how the set of feasible solutions is impacted by the choice of policies and functions. Specifically, we show that, as we augment the set of policies and functions considered, the class of value equivalent models shrinks, until eventually collapsing to a single point corresponding to a model that perfectly describes the environment. In many problems, directly modelling state-to-state transitions may be both difficult and unnecessary. By leveraging the value-equivalence principle one may find simpler models without compromising performance, saving computation and memory. We illustrate the benefits of value-equivalent model learning with experiments comparing it against more traditional counterparts like maximum likelihood estimation. More generally, we argue that the principle of value equivalence underlies a number of recent empirical successes in RL, such as Value Iteration Networks, the Predictron, Value Prediction Networks, TreeQN, and MuZero, and provides a first theoretical underpinning of those results.	['David Silver', 'Satinder Singh', 'André Barreto', 'Christopher Grimm']	2020-11-06	null	http://proceedings.neurips.cc/paper/2020/hash/3bb585ea00014b0e3ebe4c6dd165a358-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/3bb585ea00014b0e3ebe4c6dd165a358-Paper.pdf	neurips-2020-12	['value-prediction']	['computer-code']	[ 1.05060957e-01 3.80081773e-01 -6.69126630e-01 -2.56218106e-01 -3.63366008e-01 -7.28648365e-01 7.33472288e-01 1.43968895e-01 -6.54683411e-01 1.11458635e+00 6.61193803e-02 -4.73924518e-01 -5.44035852e-01 -8.76635909e-01 -6.19211018e-01 -6.19218588e-01 -2.39508316e-01 7.20769882e-01 5.04246950e-02 -3.30512285e-01 2.47800335e-01 5.05702257e-01 -1.52378881e+00 -1.45360708e-01 5.40745556e-01 9.38938200e-01 2.33581349e-01 6.96890295e-01 -1.38892978e-02 1.09965682e+00 -2.95603812e-01 1.67189650e-02 2.89012849e-01 -6.12072766e-01 -1.02908885e+00 -1.04787096e-01 -4.60747153e-01 -3.47117305e-01 -1.77413464e-01 8.91983569e-01 1.18220121e-01 4.91634309e-01 5.53053498e-01 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c60359c1-c553-4d70-8bf1-04bfdc514139	fast-learning-of-dynamic-hand-gesture	2212.08363	null	https://arxiv.org/abs/2212.08363v1	https://arxiv.org/pdf/2212.08363v1.pdf	Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models	We develop Few-Shot Learning models trained to recognize five or ten different dynamic hand gestures, respectively, which are arbitrarily interchangeable by providing the model with one, two, or five examples per hand gesture. All models were built in the Few-Shot Learning architecture of the Relation Network (RN), in which Long-Short-Term Memory cells form the backbone. The models use hand reference points extracted from RGB-video sequences of the Jester dataset which was modified to contain 190 different types of hand gestures. Result show accuracy of up to 88.8% for recognition of five and up to 81.2% for ten dynamic hand gestures. The research also sheds light on the potential effort savings of using a Few-Shot Learning approach instead of a traditional Deep Learning approach to detect dynamic hand gestures. Savings were defined as the number of additional observations required when a Deep Learning model is trained on new hand gestures instead of a Few Shot Learning model. The difference with respect to the total number of observations required to achieve approximately the same accuracy indicates potential savings of up to 630 observations for five and up to 1260 observations for ten hand gestures to be recognized. Since labeling video recordings of hand gestures implies significant effort, these savings can be considered substantial.	['Michael Bücker', 'Niels Schlüsener']	2022-12-16	null	null	null	null	['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.88064426e-01 -2.31217891e-02 -1.54873505e-01 -3.82289380e-01 -6.51324928e-01 -4.37269419e-01 6.51886702e-01 -6.06456995e-01 -6.43610895e-01 2.34270304e-01 2.95221299e-01 9.24355071e-03 -2.54771203e-01 -5.20045042e-01 -3.77243102e-01 -7.31673241e-01 -1.51826560e-01 4.42571312e-01 2.53985971e-01 1.52014166e-01 1.60028607e-01 8.16742957e-01 -1.61465943e+00 2.03145877e-01 -1.32292479e-01 7.71242023e-01 1.44261405e-01 1.06720412e+00 -2.31245473e-01 9.78261292e-01 -7.72089779e-01 -1.36126772e-01 5.26288331e-01 -1.59855917e-01 -8.24133039e-01 3.78063694e-02 5.22018790e-01 -1.11625803e+00 -8.82960677e-01 4.17778075e-01 8.33553255e-01 7.45987236e-01 5.98324358e-01 -1.06096578e+00 -4.41274613e-01 4.69668478e-01 -3.89364213e-01 3.15811783e-01 4.26115632e-01 4.96129662e-01 9.22875047e-01 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25375f7e-6411-4d00-abdb-3c514c91dc3c	from-nerflix-to-nerflix-a-general-nerf	2306.06388	null	https://arxiv.org/abs/2306.06388v2	https://arxiv.org/pdf/2306.06388v2.pdf	From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm	Neural radiance fields (NeRF) have shown great success in novel view synthesis. However, recovering high-quality details from real-world scenes is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality training frames, the synthetic novel views produced by NeRF models still suffer from notable rendering artifacts, such as noise and blur. To address this, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer. Specially, we design a NeRF-style degradation modeling approach and construct large-scale training data, enabling the possibility of effectively removing NeRF-native rendering artifacts for deep neural networks. Moreover, beyond the degradation removal, we propose an inter-viewpoint aggregation framework that fuses highly related high-quality training images, pushing the performance of cutting-edge NeRF models to entirely new levels and producing highly photo-realistic synthetic views. Based on this paradigm, we further present NeRFLiX++ with a stronger two-stage NeRF degradation simulator and a faster inter-viewpoint mixer, achieving superior performance with significantly improved computational efficiency. Notably, NeRFLiX++ is capable of restoring photo-realistic ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views. Extensive experiments demonstrate the excellent restoration ability of NeRFLiX++ on various novel view synthesis benchmarks.	['Jiangbo Lu', 'Xiaoguang Han', 'Nianjuan Jiang', 'Wenbo Li', 'Kun Zhou']	2023-06-10	null	null	null	null	['novel-view-synthesis']	['computer-vision']	[ 3.29810739e-01 -3.11649919e-01 3.53335202e-01 -2.43672401e-01 -8.85365188e-01 -3.92828226e-01 4.64488119e-01 -4.72880423e-01 3.11146319e-01 6.59124792e-01 6.44622386e-01 5.40547073e-02 -1.40396640e-01 -9.07911003e-01 -1.01462913e+00 -7.23592699e-01 4.25892979e-01 -1.31012321e-01 -1.58210322e-02 -5.38148105e-01 -8.30373317e-02 6.29755795e-01 -1.75182021e+00 5.56297541e-01 1.08513081e+00 9.76967096e-01 3.17418039e-01 7.70153463e-01 3.68565887e-01 9.74681616e-01 -5.66133380e-01 -4.66304958e-01 5.53727090e-01 -2.68970370e-01 -2.71331012e-01 1.40579045e-01 1.04625618e+00 -9.59974349e-01 -7.55657732e-01 9.41675901e-01 5.67875266e-01 1.98759601e-01 2.09888563e-01 -8.56209040e-01 -1.05729640e+00 -6.98843449e-02 -5.29287219e-01 -1.15802184e-01 3.86953175e-01 4.98432696e-01 6.54091239e-01 -9.77591693e-01 6.45149648e-01 1.41427219e+00 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a48fb4c1-acbd-47dc-9185-6013723230b9	cct-code-cross-consistency-training-for	2305.11626	null	https://arxiv.org/abs/2305.11626v1	https://arxiv.org/pdf/2305.11626v1.pdf	CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search	We consider the clone detection and information retrieval problems for source code, well-known tasks important for any programming language. Although it is also an important and interesting problem to find code snippets that operate identically but are written in different programming languages, to the best of our knowledge multilingual clone detection has not been studied in literature. In this work, we formulate the multilingual clone detection problem and present XCD, a new benchmark dataset produced from the CodeForces submissions dataset. Moreover, we present a novel training procedure, called cross-consistency training (CCT), that we apply to train language models on source code in different programming languages. The resulting CCT-LM model, initialized with GraphCodeBERT and fine-tuned with CCT, achieves new state of the art, outperforming existing approaches on the POJ-104 clone detection benchmark with 95.67\% MAP and AdvTest code search benchmark with 47.18\% MRR; it also shows the best results on the newly created multilingual clone detection benchmark XCD across all programming languages.	['Valentin Malykh', 'Sergey Nikolenko', 'Dmitry Abulkhanov', 'Nikita Sorokin']	2023-05-19	null	null	null	null	['code-search', 'code-search']	['computer-code', 'computer-vision']	[-1.79647833e-01 -5.17146230e-01 -5.52461326e-01 2.18446419e-01 -1.08822906e+00 -7.84933388e-01 4.41831589e-01 5.28951228e-01 -1.02037393e-01 2.66525447e-01 -2.47648954e-01 -6.72819972e-01 6.43932447e-02 -2.74440914e-01 -8.33992958e-01 -1.40606642e-01 -1.92984641e-01 2.51087308e-01 4.69003648e-01 6.03435338e-02 7.54280388e-01 -6.72068670e-02 -1.69620049e+00 4.18027580e-01 1.21034110e+00 2.59185642e-01 7.50924468e-01 7.07465589e-01 -2.76274353e-01 1.02122176e+00 -6.94553316e-01 -7.25860000e-01 -3.67976562e-03 -4.32616264e-01 -1.00250542e+00 -4.78286356e-01 6.52466953e-01 3.30612123e-01 1.60693660e-01 1.38981652e+00 3.40524077e-01 -5.62678397e-01 4.97317433e-01 -1.22319901e+00 -8.24955940e-01 9.26834464e-01 -9.02129769e-01 2.89690375e-01 5.34891784e-01 -3.24477792e-01 1.04135871e+00 -7.60713935e-01 1.20752418e+00 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fa084714-3f81-4322-a54d-dd51dc27f935	multi-cpr-a-multi-domain-chinese-dataset-for	2203.03367	null	https://arxiv.org/abs/2203.03367v2	https://arxiv.org/pdf/2203.03367v2.pdf	Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval	Passage retrieval is a fundamental task in information retrieval (IR) research, which has drawn much attention recently. In the English field, the availability of large-scale annotated dataset (e.g, MS MARCO) and the emergence of deep pre-trained language models (e.g, BERT) has resulted in a substantial improvement of existing passage retrieval systems. However, in the Chinese field, especially for specific domains, passage retrieval systems are still immature due to quality-annotated dataset being limited by scale. Therefore, in this paper, we present a novel multi-domain Chinese dataset for passage retrieval (Multi-CPR). The dataset is collected from three different domains, including E-commerce, Entertainment video and Medical. Each dataset contains millions of passages and a certain amount of human annotated query-passage related pairs. We implement various representative passage retrieval methods as baselines. We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain. Nevertheless, a passage retrieval system built on in-domain annotated dataset can achieve significant improvement, which indeed demonstrates the necessity of domain labeled data for further optimization. We hope the release of the Multi-CPR dataset could benchmark Chinese passage retrieval task in specific domain and also make advances for future studies.	['Ping Yang', 'Luxi Xing', 'Guanjun Jiang', 'Jian Xu', 'Ruijie Guo', 'Pengjun Xie', 'Guangwei Xu', 'Kuan Zou', 'Qiong Gao', 'Dingkun Long']	2022-03-07	null	null	null	null	['passage-retrieval']	['natural-language-processing']	[-3.01593274e-01 -7.92688608e-01 -2.60614008e-01 -1.01127438e-01 -1.61018109e+00 -8.27351332e-01 6.11129105e-01 3.43094110e-01 -8.17342758e-01 8.52556884e-01 4.24800366e-01 -7.11605698e-02 -2.69439608e-01 -6.73283637e-01 -4.21321541e-01 -3.32493871e-01 1.46519557e-01 5.50381958e-01 5.03064454e-01 -8.10133100e-01 4.68460232e-01 1.47679552e-01 -9.69393611e-01 3.94676030e-01 8.89870226e-01 7.65565395e-01 3.20550412e-01 6.23229444e-01 -2.01364025e-01 6.00677967e-01 -8.59492898e-01 -3.35813105e-01 1.95441052e-01 -4.31871414e-01 -1.23063612e+00 -5.85053086e-01 9.43198428e-02 -5.09943008e-01 -6.18721485e-01 6.76994145e-01 1.13806617e+00 3.62698466e-01 7.35868275e-01 -7.87898183e-01 -1.30989945e+00 4.96503592e-01 -3.44926953e-01 4.35231924e-01 6.14251435e-01 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b3f498fe-c6fa-47e2-910e-5dc9e11d6b7d	infrared-and-visible-image-fusion-via	2203.15337	null	https://arxiv.org/abs/2203.15337v1	https://arxiv.org/pdf/2203.15337v1.pdf	Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning	The existing generative adversarial fusion methods generally concatenate source images and extract local features through convolution operation, without considering their global characteristics, which tends to produce an unbalanced result and is biased towards the infrared image or visible image. Toward this end, we propose a novel end-to-end mode based on generative adversarial training to achieve better fusion balance, termed as \textit{interactive compensatory attention fusion network} (ICAFusion). In particular, in the generator, we construct a multi-level encoder-decoder network with a triple path, and adopt infrared and visible paths to provide additional intensity and gradient information. Moreover, we develop interactive and compensatory attention modules to communicate their pathwise information, and model their long-range dependencies to generate attention maps, which can more focus on infrared target perception and visible detail characterization, and further increase the representation power for feature extraction and feature reconstruction. In addition, dual discriminators are designed to identify the similar distribution between fused result and source images, and the generator is optimized to produce a more balanced result. Extensive experiments illustrate that our ICAFusion obtains superior fusion performance and better generalization ability, which precedes other advanced methods in the subjective visual description and objective metric evaluation. Our codes will be public at \url{https://github.com/Zhishe-Wang/ICAFusion}	['Xiaoqin Zhang', 'Jiawei Xu', 'Yanlin Chen', 'Wenyu Shao', 'Zhishe Wang']	2022-03-29	null	null	null	null	['infrared-and-visible-image-fusion']	['computer-vision']	[ 9.61396843e-02 -3.38794082e-01 8.49816874e-02 -2.94281393e-01 -7.33488202e-01 -4.17901307e-01 4.73386317e-01 -4.77360010e-01 -1.56717692e-02 5.25834560e-01 4.29774553e-01 -1.23939849e-01 1.57129839e-02 -1.07293701e+00 -5.19719005e-01 -1.01383114e+00 2.69782305e-01 -4.13938880e-01 -1.30567372e-01 -3.25726509e-01 -1.54574335e-01 3.25437158e-01 -1.04444861e+00 2.42571890e-01 1.32227421e+00 1.16121745e+00 3.62711608e-01 4.98293370e-01 4.64227162e-02 7.25586593e-01 -7.04014599e-01 -5.50223947e-01 3.38418722e-01 -7.74209857e-01 -2.81236827e-01 4.76117432e-02 1.00632720e-01 -4.93064761e-01 -6.83000445e-01 1.36077523e+00 9.07194734e-01 6.27589673e-02 6.10953867e-01 -1.23806798e+00 -1.38718355e+00 4.73394722e-01 -7.15847015e-01 1.99133724e-01 2.87992477e-01 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bd78bb69-626f-4e82-b6cf-d126cfc92a84	conceptual-design-generation-using-large	2306.01779	null	https://arxiv.org/abs/2306.01779v1	https://arxiv.org/pdf/2306.01779v1.pdf	Conceptual Design Generation Using Large Language Models	Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty. We experiment with prompt engineering and find that leveraging few-shot learning can lead to the generation of solutions that are more similar to the crowdsourced solutions. These findings provide insight into the quality of design solutions generated with LLMs and begins to evaluate prompt engineering techniques that could be leveraged by practitioners to generate higher-quality design solutions synergistically with LLMs.	['Kosa Goucher-Lambert', 'Christopher McComb', 'Daniele Grandi', 'Kevin Ma']	2023-05-30	null	null	null	null	['prompt-engineering']	['natural-language-processing']	[ 1.36401564e-01 2.89473802e-01 3.99556290e-03 -1.65365994e-01 -9.14648652e-01 -9.03307557e-01 7.04404533e-01 1.89698175e-01 5.01410775e-02 5.00296950e-01 7.64472902e-01 -2.78316081e-01 -1.75170079e-01 -8.39118242e-01 -3.52419764e-01 1.59812495e-01 5.54138541e-01 5.31876028e-01 -9.49224234e-02 -4.03726548e-01 5.83754718e-01 2.00905427e-01 -1.70025384e+00 8.13115895e-01 8.95393312e-01 5.88166952e-01 2.57904172e-01 5.94638288e-01 -8.10125291e-01 1.02198553e+00 -8.96848142e-01 -3.82871211e-01 2.70259827e-01 -4.12828118e-01 -8.03134084e-01 2.30857506e-01 2.36301064e-01 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3d62d309-84dc-438c-ab97-ac003c9a5b3f	the-asnr-miccai-brain-tumor-segmentation	2305.07642	null	https://arxiv.org/abs/2305.07642v1	https://arxiv.org/pdf/2305.07642v1.pdf	The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma	Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.	['Evan Calabrese', 'Benedikt Wiestler', 'Javier Villanueva-Meyer', 'Nourel Hoda Tahon', 'Jeff Rudie', 'Andreas M Rauschecker', 'Ayman Nada', 'Bjoern Menze', 'Marius George Linguraru', 'Goldey Khanna', 'Anastasia Janas', 'Adam Flanders', 'Spyridon Bakas', 'Udunna Anazodo', 'Jake Albrecht', 'Mariam Aboian', 'Walter Wiggins', 'Pranav Warman', 'Chunhao Wang', 'Yury Velichko', 'Russell Takeshi Shinohara', 'Zachary Reitman', 'Arif Rashid', 'Marie Piraud', 'Maxence Pajot', 'Pierre Nedelec', 'John Mongan', 'Ahmed W Moawad', 'Zeke Meier', 'Ryan McLean', 'Shan McBurney-Lin', 'Aria Mahtabfar', 'Xinyang Liu', 'Hongwei Bran Li', 'Koen van Leemput', 'Florian Kofler', 'John Kirkpatrick', 'Collin Kent', 'Anahita Fathi Kazerooni', 'Elaine Johanson', 'Zhifan Jiang', 'Juan Eugenio Iglesias', 'Keyvan Farahani', 'Ariana Familiar', 'Fathi Hilal', 'Devon Godfrey', 'Ivan Ezhov', 'James Eddy', 'Farouk Dako', 'Gian-Marco Conte', 'Verena Chung', 'Sully Chen', 'Radhika Bhalerao', 'Timothy Bergquist', 'Ujjwal Baid', 'Syed Muhammad Anwar', 'Talissa Altes', 'Michelle Alonso-Basanta', 'Maruf Adewole', 'Dominic LaBella']	2023-05-12	null	null	null	null	['tumor-segmentation', 'brain-image-segmentation', 'brain-tumor-segmentation']	['computer-vision', 'medical', 'medical']	[ 2.15401486e-01 3.39679360e-01 7.70889595e-02 -3.71657282e-01 -1.20792687e+00 -5.88374853e-01 6.98093176e-01 5.69522440e-01 -7.47820020e-01 5.34854949e-01 3.72238219e-01 -4.61564630e-01 -3.04033637e-01 -3.24688315e-01 9.60394666e-02 -6.69481397e-01 -5.22116184e-01 1.17880833e+00 5.13609290e-01 5.66800907e-02 1.23097569e-01 7.94803441e-01 -6.88335598e-01 4.19182956e-01 9.00540292e-01 9.79260266e-01 5.85144937e-01 6.40538692e-01 -1.04196243e-01 9.85641420e-01 -2.92684436e-01 5.61831780e-02 1.81547657e-01 1.51011482e-01 -1.05355966e+00 -3.71457748e-02 4.78699684e-01 -1.24759518e-01 -4.04999793e-01 9.10204470e-01 3.60089034e-01 2.76368763e-02 1.06915379e+00 -7.46808589e-01 -2.45804399e-01 5.83831310e-01 -5.65059423e-01 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8dd014ca-7d09-42bd-879b-3e5881e2b9a9	on-the-information-bottleneck-theory-of-deep	null	null	https://openreview.net/forum?id=ry_WPG-A-	https://openreview.net/pdf?id=ry_WPG-A-	On the Information Bottleneck Theory of Deep Learning	The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior. In this work, we study the information bottleneck (IB) theory of deep learning, which makes three specific claims: first, that deep networks undergo two distinct phases consisting of an initial fitting phase and a subsequent compression phase; second, that the compression phase is causally related to the excellent generalization performance of deep networks; and third, that the compression phase occurs due to the diffusion-like behavior of stochastic gradient descent. Here we show that none of these claims hold true in the general case. Through a combination of analytical results and simulation, we demonstrate that the information plane trajectory is predominantly a function of the neural nonlinearity employed: double-sided saturating nonlinearities like tanh yield a compression phase as neural activations enter the saturation regime, but linear activation functions and single-sided saturating nonlinearities like the widely used ReLU in fact do not. Moreover, we find that there is no evident causal connection between compression and generalization: networks that do not compress are still capable of generalization, and vice versa. Next, we show that the compression phase, when it exists, does not arise from stochasticity in training by demonstrating that we can replicate the IB findings using full batch gradient descent rather than stochastic gradient descent. Finally, we show that when an input domain consists of a subset of task-relevant and task-irrelevant information, hidden representations do compress the task-irrelevant information, although the overall information about the input may monotonically increase with training time, and that this compression happens concurrently with the fitting process rather than during a subsequent compression period.	['Artemy Kolchinsky', 'Joel Dapello', 'David Daniel Cox', 'Brendan Daniel Tracey', 'Yamini Bansal', 'Madhu Advani', 'Andrew Michael Saxe']	2018-01-01	null	null	null	iclr-2018-1	['information-plane']	['methodology']	[ 3.07772249e-01 3.97812501e-02 -9.48947445e-02 -1.00210905e-01 -5.72504364e-02 -4.32904243e-01 7.15208888e-01 2.69480616e-01 -8.17399800e-01 6.75590634e-01 1.79005444e-01 -5.93485892e-01 -5.42775095e-01 -5.50449431e-01 -8.89314651e-01 -1.01513326e+00 -1.27062812e-01 2.99428552e-01 4.34811145e-01 -4.29335624e-01 3.68322939e-01 6.23105049e-01 -1.44708729e+00 1.02511555e-01 5.65938771e-01 8.35020483e-01 1.66166559e-01 7.25126266e-01 -1.77982584e-01 9.53841805e-01 -4.85525489e-01 -1.32200032e-01 4.19893116e-01 -7.73272038e-01 -8.13634098e-01 -2.44915590e-01 1.66612908e-01 -3.08223367e-01 -5.74373126e-01 8.83022726e-01 3.95925641e-01 2.49578640e-01 7.50306547e-01 -8.79938304e-01 -4.80156600e-01 6.22205853e-01 -2.98090339e-01 6.47198856e-01 -3.25272888e-01 1.78248480e-01 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46fd77d2-0c04-4c9b-853a-8c5678e6804c	smoothnet-a-plug-and-play-network-for	2112.13715	null	https://arxiv.org/abs/2112.13715v2	https://arxiv.org/pdf/2112.13715v2.pdf	SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos	When analyzing human motion videos, the output jitters from existing pose estimators are highly-unbalanced with varied estimation errors across frames. Most frames in a video are relatively easy to estimate and only suffer from slight jitters. In contrast, for rarely seen or occluded actions, the estimated positions of multiple joints largely deviate from the ground truth values for a consecutive sequence of frames, rendering significant jitters on them. To tackle this problem, we propose to attach a dedicated temporal-only refinement network to existing pose estimators for jitter mitigation, named SmoothNet. Unlike existing learning-based solutions that employ spatio-temporal models to co-optimize per-frame precision and temporal smoothness at all the joints, SmoothNet models the natural smoothness characteristics in body movements by learning the long-range temporal relations of every joint without considering the noisy correlations among joints. With a simple yet effective motion-aware fully-connected network, SmoothNet improves the temporal smoothness of existing pose estimators significantly and enhances the estimation accuracy of those challenging frames as a side-effect. Moreover, as a temporal-only model, a unique advantage of SmoothNet is its strong transferability across various types of estimators and datasets. Comprehensive experiments on five datasets with eleven popular backbone networks across 2D and 3D pose estimation and body recovery tasks demonstrate the efficacy of the proposed solution. Code is available at https://github.com/cure-lab/SmoothNet.	['Qiang Xu', 'Jianyi Wang', 'Jiefeng Li', 'Xuan Ju', 'Lei Yang', 'Ailing Zeng']	2021-12-27	null	null	null	null	['3d-pose-estimation', '3d-human-reconstruction', '2d-human-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision']	[-2.71134943e-01 3.99508812e-02 -4.36588645e-01 -2.69195080e-01 -4.85232800e-01 -2.60106236e-01 2.24364415e-01 -4.00334328e-01 -2.72666305e-01 6.78138375e-01 2.45135263e-01 5.90560377e-01 -2.26018488e-01 -3.18422079e-01 -9.00717854e-01 -6.20231628e-01 -2.79220670e-01 2.47233093e-01 5.70767343e-01 -1.65453777e-01 -3.38230789e-01 2.95626968e-01 -1.05501842e+00 -2.16965944e-01 6.99979722e-01 8.99473786e-01 2.11323127e-02 5.32759190e-01 4.28472251e-01 7.34336317e-01 -5.67693830e-01 -3.09112400e-01 1.42118141e-01 -2.70150542e-01 -4.89917636e-01 1.88412160e-01 6.59550190e-01 -5.49917042e-01 -7.03448117e-01 8.67618799e-01 7.25416660e-01 2.57130146e-01 9.24255177e-02 -1.24783051e+00 -4.74292785e-02 4.19541001e-01 -7.86561906e-01 3.27809602e-02 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