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train · 44.6k rows

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dfc28557-eb9e-4cdb-99bf-549d87ab7ef8	d-2conv3d-dynamic-dilated-convolutions-for	2111.07774	null	https://arxiv.org/abs/2111.07774v1	https://arxiv.org/pdf/2111.07774v1.pdf	D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos	Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the efficacy of dilated and deformable convolutions for various image-level segmentation tasks. This gives reason to believe that 3D extensions of such convolutions should also yield performance improvements for video-level segmentation tasks. However, this aspect has not yet been explored thoroughly in existing literature. In this paper, we propose Dynamic Dilated Convolutions (D^2Conv3D): a novel type of convolution which draws inspiration from dilated and deformable convolutions and extends them to the 3D (spatio-temporal) domain. We experimentally show that D^2Conv3D can be used to improve the performance of multiple 3D CNN architectures across multiple video segmentation related benchmarks by simply employing D^2Conv3D as a drop-in replacement for standard convolutions. We further show that D^2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D. Lastly, we set a new state-of-the-art on the DAVIS 2016 Unsupervised Video Object Segmentation benchmark. Code is made publicly available at https://github.com/Schmiddo/d2conv3d .	['Bastian Leibe', 'Sabarinath Mahadevan', 'Ali Athar', 'Christian Schmidt']	2021-11-15	null	null	null	null	['unsupervised-video-object-segmentation']	['computer-vision']	[ 1.05711870e-01 -1.23140611e-01 -1.58157974e-01 -4.34994489e-01 -2.50868678e-01 -9.14967060e-01 4.98293966e-01 -4.23091888e-01 -5.79001606e-01 3.95402551e-01 -2.22579818e-02 -6.61474526e-01 2.70882010e-01 -5.38888872e-01 -9.24982965e-01 -4.46611792e-01 -1.91242427e-01 -5.79323806e-03 8.22544575e-01 -1.60933826e-02 -3.18456702e-02 7.63632298e-01 -1.43285739e+00 1.88302681e-01 5.90996981e-01 9.51463044e-01 -1.24492310e-02 8.45511854e-01 -3.67385782e-02 3.30708385e-01 -2.75190324e-01 -4.00214911e-01 8.14411402e-01 -2.22796544e-01 -1.23443472e+00 2.24625215e-01 9.21861410e-01 -6.93878174e-01 -4.30735648e-01 1.04902864e+00 3.52686822e-01 2.15759486e-01 4.47426736e-01 -1.23617053e+00 -7.08326519e-01 4.01744843e-01 -5.67823112e-01 6.98949516e-01 -8.43844004e-03 3.57982814e-01 5.42492628e-01 -7.22904682e-01 7.41250217e-01 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86913aea-bafd-4f6c-aaed-b3ac6ab4f2d1	harvesting-detecting-and-characterizing-liver	2006.15691	null	https://arxiv.org/abs/2006.15691v2	https://arxiv.org/pdf/2006.15691v2.pdf	Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning	Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subjects, with the hope of reducing the need for invasive surgical biopsies of liver cancer patients, which can cause many harmful side-effects. To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT). Our system comprises four sequential processes of tumor proposal detection, tumor harvesting, primary tumor site selection, and deep texture-based tumor characterization. Our main contributions are that, (1) we propose a 3D non-isotropic anchor-free detection method for liver lesions; (2) we present and validate spatially adaptivedeep texture (SaDT) learning, which allows for more precise characterization of liver lesions; (3) using a semi-automatic process, we bootstrap off of 200 gold standard annotations to curate another 1001 patients. Experimental evaluations demonstrate that our new data curation strategy, combined with the SaDT deep dynamic texture analysis, can effectively improve the mean F1 scores by >8.6% compared with baselines, in differentiating four major liver lesion types. Our F1 score of (hepatocellular carcinoma versus remaining subclasses) is 0.763, which is higher than reported human observer performance using dynamic CT and comparable to an advanced magnetic resonance imagery protocol. Apart from demonstrating the benefits of our data curation approach and physician-inspired workflow, these results also indicate that analyzing texture features, instead of standard object-based analysis, is a promising strategy for lesion differentiation.	['Chien-Hung Liao', 'Chi-Tung Cheng', 'Ke Yan', 'Yuankai Huo', 'Le Lu', 'Jinzheng Cai', 'Jing Xiao', 'Bennett A. Landman', 'Ashwin Raju', 'Adam P. Harrison']	2020-06-28	null	null	null	null	['texture-classification']	['computer-vision']	[ 4.62347968e-03 -1.63425714e-01 -2.96192974e-01 -2.69365553e-02 -1.16157353e+00 -6.42133713e-01 6.91382587e-01 5.17624915e-01 -3.37226599e-01 3.26112479e-01 4.22269136e-01 -4.20417994e-01 -2.94148088e-01 -6.05967224e-01 -6.21117130e-02 -1.33158779e+00 -4.26037699e-01 1.03687632e+00 3.39661300e-01 3.10447931e-01 -1.54928476e-01 9.46227968e-01 -5.66205144e-01 1.74902976e-01 9.16938603e-01 1.06850743e+00 4.91747260e-01 6.62674606e-01 2.36203745e-01 5.65458238e-01 4.57554087e-02 2.57680833e-01 1.09099627e-01 -5.04691958e-01 -9.52228129e-01 3.02787393e-01 1.86518535e-01 -3.20950091e-01 -3.70447710e-02 7.45255709e-01 7.35860884e-01 -2.89257705e-01 1.02465415e+00 -2.40352154e-01 -1.57383189e-01 5.54227531e-01 -5.07128775e-01 3.82037640e-01 -8.66994858e-02 4.94561434e-01 2.69597769e-01 -8.87431443e-01 4.67702806e-01 3.05314422e-01 8.21061313e-01 4.94032532e-01 -1.37757146e+00 -2.27110833e-01 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f8467f1c-c3fe-48bd-883e-13420432adc8	controlling-for-unknown-confounders-in	2006.13135	null	https://arxiv.org/abs/2006.13135v4	https://arxiv.org/pdf/2006.13135v4.pdf	Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum	Studying the relationship between neuroanatomy and cognitive decline due to Alzheimer's has been a major research focus in the last decade. However, to infer cause-effect relationships rather than simple associations from observational data, we need to (i) express the causal relationships leading to cognitive decline in a graphical model, and (ii) ensure the causal effect of interest is identifiable from the collected data. We derive a causal graph from the current clinical knowledge on cause and effect in the Alzheimer's disease continuum, and show that identifiability of the causal effect requires all confounders to be known and measured. However, in complex neuroimaging studies, we neither know all potential confounders nor do we have data on them. To alleviate this requirement, we leverage the dependencies among multiple causes by deriving a substitute confounder via a probabilistic latent factor model. In our theoretical analysis, we prove that using the substitute confounder enables identifiability of the causal effect of neuroanatomy on cognition. We quantitatively evaluate the effectiveness of our approach on semi-synthetic data, where we know the true causal effects, and illustrate its use on real data on the Alzheimer's disease continuum, where it reveals important causes that otherwise would have been missed.	['Sebastian Pölsterl', 'Christian Wachinger']	2020-06-23	null	null	null	null	['clinical-knowledge']	['miscellaneous']	[ 2.40971714e-01 2.87214637e-01 -1.72107667e-01 -4.05237228e-01 -3.55481505e-01 -4.56291914e-01 4.77572531e-01 2.25154281e-01 -3.99736136e-01 1.09959269e+00 6.15604758e-01 -6.41831458e-01 -7.34330416e-01 -8.27725768e-01 -8.04766595e-01 -4.35496688e-01 -7.52532065e-01 4.37022030e-01 -4.71459404e-02 2.35778525e-01 -1.48887455e-01 2.04461813e-01 -8.74597669e-01 -1.52064025e-01 1.11656845e+00 4.96115625e-01 2.61357967e-02 3.37152630e-01 1.17512189e-01 5.59481025e-01 -3.90276521e-01 -3.19287479e-01 2.14607250e-02 -3.72682154e-01 -5.83132207e-01 -1.04101807e-01 3.55609506e-01 -5.25504470e-01 -2.89835393e-01 1.08777297e+00 3.04527193e-01 -5.53252816e-01 1.03609455e+00 -1.30074251e+00 -5.79658687e-01 6.60308599e-01 -6.85859323e-01 3.52146447e-01 2.43872032e-02 -6.57735616e-02 9.95313287e-01 -4.19120610e-01 6.20477259e-01 1.47744405e+00 5.97305000e-01 2.13127017e-01 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debad490-d015-4b43-b845-51d3f2f0d79a	sacha-soft-actor-critic-with-heuristic-based	2307.02691	null	https://arxiv.org/abs/2307.02691v1	https://arxiv.org/pdf/2307.02691v1.pdf	SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding	Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning a decentralized single-agent policy in a centralized fashion based on each agent's partial observation. However, existing learning-based methods are ineffective in achieving complex multi-agent cooperation, especially in congested environments, due to the non-stationarity of this setting. To tackle this challenge, we propose a multi-agent actor-critic method called Soft Actor-Critic with Heuristic-Based Attention (SACHA), which employs novel heuristic-based attention mechanisms for both the actors and critics to encourage cooperation among agents. SACHA learns a neural network for each agent to selectively pay attention to the shortest path heuristic guidance from multiple agents within its field of view, thereby allowing for more scalable learning of cooperation. SACHA also extends the existing multi-agent actor-critic framework by introducing a novel critic centered on each agent to approximate $Q$-values. Compared to existing methods that use a fully observable critic, our agent-centered multi-agent actor-critic method results in more impartial credit assignment and better generalizability of the learned policy to MAPF instances with varying numbers of agents and types of environments. We also implement SACHA(C), which embeds a communication module in the agent's policy network to enable information exchange among agents. We evaluate both SACHA and SACHA(C) on a variety of MAPF instances and demonstrate decent improvements over several state-of-the-art learning-based MAPF methods with respect to success rate and solution quality.	['Hang Ma', 'Qiushi Lin']	2023-07-05	null	null	null	null	['multi-agent-reinforcement-learning', 'multi-agent-path-finding']	['methodology', 'playing-games']	[-0.5041956 0.3980175 -0.24580055 0.06391977 -0.8258258 -0.41844457 0.47724497 0.28878573 -0.7333846 1.1056882 0.01850952 -0.03428832 -0.5012357 -0.76752126 -0.82618946 -0.81757903 -0.5226821 1.0764201 0.44393188 -0.4718582 0.14384456 0.42179772 -1.1735337 -0.3706989 0.9643475 0.51718867 0.4983032 0.8043183 0.3585535 1.1067203 -0.6783014 0.04903348 0.47797236 -0.24065737 -0.8193948 0.07694843 -0.21367939 -0.59958804 -0.48659152 0.8201195 0.5560202 0.4192212 0.531461 -1.8653506 -0.43138766 0.775628 -0.81511533 0.04075037 0.27742907 0.6333634 1.0518252 -0.186506 0.46683925 1.5665225 0.22697227 0.68296933 -0.8828692 -0.38331425 0.68907803 0.41067725 -0.9822703 0.01862964 0.65503055 0.08038587 1.3748326 -0.28169626 0.7514558 0.789465 0.33419156 1.0732247 0.92274255 -0.23194088 0.54165 -0.20701331 -0.43363866 0.9181738 0.12879167 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7fa1ea59-27ef-4e2c-8e2d-d6c10358aece	peking-opera-synthesis-via-duration-informed	2008.03029	null	https://arxiv.org/abs/2008.03029v1	https://arxiv.org/pdf/2008.03029v1.pdf	Peking Opera Synthesis via Duration Informed Attention Network	Peking Opera has been the most dominant form of Chinese performing art since around 200 years ago. A Peking Opera singer usually exhibits a very strong personal style via introducing improvisation and expressiveness on stage which leads the actual rhythm and pitch contour to deviate significantly from the original music score. This inconsistency poses a great challenge in Peking Opera singing voice synthesis from a music score. In this work, we propose to deal with this issue and synthesize expressive Peking Opera singing from the music score based on the Duration Informed Attention Network (DurIAN) framework. To tackle the rhythm mismatch, Lagrange multiplier is used to find the optimal output phoneme duration sequence with the constraint of the given note duration from music score. As for the pitch contour mismatch, instead of directly inferring from music score, we adopt a pseudo music score generated from the real singing and feed it as input during training. The experiments demonstrate that with the proposed system we can synthesize Peking Opera singing voice with high-quality timbre, pitch and expressiveness.	['Heng Lu', 'Shengchen Li', 'Yusong Wu', 'Chao Weng', 'Liqiang Zhang', 'Chengzhu Yu', 'Dong Yu']	2020-08-07	null	null	null	null	['singing-voice-synthesis']	['speech']	[ 3.04957330e-01 -6.43714843e-03 -2.70433314e-02 2.94953529e-02 -7.86375165e-01 -6.30446315e-01 4.93622944e-02 -6.62592590e-01 -1.67225450e-01 6.26921356e-01 2.39511728e-01 6.72950223e-02 -3.78958851e-01 -3.03489923e-01 -3.73195767e-01 -7.85778761e-01 3.28748405e-01 1.35537565e-01 -2.63955206e-01 -3.01672578e-01 2.62778163e-01 1.56734988e-01 -1.62862551e+00 1.97122589e-01 8.26857030e-01 9.13711607e-01 5.06978214e-01 9.87457275e-01 4.32603881e-02 4.41023499e-01 -1.10906267e+00 -1.92213744e-01 4.71796304e-01 -1.14360285e+00 -4.29866076e-01 5.00506200e-02 1.19549878e-01 -2.10126743e-01 -7.65212774e-02 1.14974272e+00 6.86640382e-01 2.75811046e-01 5.04336119e-01 -8.18963647e-01 -4.19150501e-01 1.16897452e+00 -2.44948417e-01 4.89478521e-02 2.09147915e-01 1.93925589e-01 1.15535927e+00 -6.26907945e-01 3.01335216e-01 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b08abd7a-18ad-473e-8196-ac45f53f56ba	simultaneous-speech-extraction-for-multiple	2206.08525	null	https://arxiv.org/abs/2206.08525v1	https://arxiv.org/pdf/2206.08525v1.pdf	Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios(V1)	Recently, the target speech separation or extraction techniques under the meeting scenario have become a hot research trend. We propose a speaker diarization aware multiple target speech separation system (SD-MTSS) to simultaneously extract the voice of each speaker from the mixed speech, rather than requiring a succession of independent processes as presented in previous solutions. SD-MTSS consists of a speaker diarization (SD) module and a multiple target speech separation (MTSS) module. The former one infers the target speaker voice activity detection (TSVAD) states of the mixture, as well as gets different speakers' single-talker audio segments as the reference speech. The latter one employs both the mixed audio and reference speech as inputs, and then it generates an estimated mask. By exploiting the TSVAD decision and the estimated mask, our SD-MTSS model can extract the speech of each speaker concurrently in a conversion recording without additional enrollment audio in advance.Experimental results show that our MTSS model outperforms our baselines with a large margin, achieving 1.38dB SDR, 1.34dB SI-SNR, and 0.13 PESQ improvements over the state-of-the-art SpEx+ baseline on the WSJ0-2mix-extr dataset, respectively. The SD-MTSS system makes a significant improvement than the baseline on the Alimeeting dataset as well.	['Ming Li', 'Yuanyuan Bao', 'Weiqing Wang', 'Bang Zeng']	2022-06-17	null	null	null	null	['activity-detection', 'speech-separation', 'speech-extraction']	['computer-vision', 'speech', 'speech']	[ 3.04950267e-01 -1.40844092e-01 -9.20188278e-02 -1.26713887e-01 -1.52853906e+00 -5.20790279e-01 3.73503029e-01 -2.51260847e-01 -2.50032067e-01 2.95352906e-01 3.20380181e-01 -3.84212077e-01 2.49021813e-01 1.54627129e-01 -3.52611840e-01 -1.12911344e+00 1.45922169e-01 2.82858789e-01 3.48545909e-01 3.01209069e-03 -9.59830433e-02 2.01799333e-01 -1.37151802e+00 1.81511194e-01 9.30356622e-01 1.10065544e+00 6.15488708e-01 1.04107773e+00 -1.35987893e-01 4.13270503e-01 -1.09342718e+00 3.75840813e-02 2.06322476e-01 -6.32711232e-01 -1.14423901e-01 8.97820219e-02 3.62121642e-01 -1.30882502e-01 -3.61396313e-01 1.01497138e+00 1.03322172e+00 4.03164513e-02 6.01153016e-01 -1.07417178e+00 2.64478344e-02 1.03728378e+00 -9.63311613e-01 4.87285644e-01 6.90695047e-02 -2.14889869e-02 8.30689847e-01 -1.11648154e+00 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51790190-1d3f-4b88-b4ad-d339a0a5cc09	alisnet-accurate-and-lightweight-human	2304.07533	null	https://arxiv.org/abs/2304.07533v1	https://arxiv.org/pdf/2304.07533v1.pdf	ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce	Accurately estimating human body shape from photos can enable innovative applications in fashion, from mass customization, to size and fit recommendations and virtual try-on. Body silhouettes calculated from user pictures are effective representations of the body shape for downstream tasks. Smartphones provide a convenient way for users to capture images of their body, and on-device image processing allows predicting body segmentation while protecting users privacy. Existing off-the-shelf methods for human segmentation are closed source and cannot be specialized for our application of body shape and measurement estimation. Therefore, we create a new segmentation model by simplifying Semantic FPN with PointRend, an existing accurate model. We finetune this model on a high-quality dataset of humans in a restricted set of poses relevant for our application. We obtain our final model, ALiSNet, with a size of 4MB and 97.6$\pm$1.0$\%$ mIoU, compared to Apple Person Segmentation, which has an accuracy of 94.4$\pm$5.7$\%$ mIoU on our dataset.	['Reza Shirvany', 'Anna Volokitin', 'Malte Alf', 'Alessandro Canopoli', 'Timon Künzle', 'Koen Vernooij', 'Amrollah Seifoddini']	2023-04-15	null	null	null	null	['virtual-try-on']	['computer-vision']	[ 1.19901203e-01 2.87370950e-01 -3.46045136e-01 -3.90768051e-01 -2.89219409e-01 -3.64516288e-01 -1.64847717e-01 -9.40500572e-02 -2.60017097e-01 3.11824858e-01 -1.59928367e-01 -1.96248572e-03 3.38839501e-01 -9.69955802e-01 -7.32328653e-01 -2.62510609e-02 -4.75403443e-02 6.60729587e-01 2.96688229e-01 -7.80382007e-02 -7.02852830e-02 1.54697090e-01 -1.21054053e+00 2.01706979e-02 7.12473810e-01 1.37693119e+00 -4.64884460e-01 6.99938953e-01 2.95400023e-01 -2.38246784e-01 -6.43949211e-01 -1.43218219e+00 8.08627486e-01 -1.90271661e-01 -3.12556654e-01 3.06746542e-01 6.63181007e-01 -5.65235436e-01 -5.96153699e-02 8.00116122e-01 7.56196618e-01 -6.80637360e-02 3.37585866e-01 -1.31925321e+00 -2.96419591e-01 3.85328472e-01 -9.65174675e-01 -2.77348042e-01 6.40098929e-01 1.86580837e-01 5.62588692e-01 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62a491e1-a18b-4a87-a33b-12b7f5a8764a	detecting-and-correcting-learner-korean	null	null	https://aclanthology.org/I13-1199	https://aclanthology.org/I13-1199.pdf	Detecting and Correcting Learner Korean Particle Omission Errors	null	['Sun-Hee Lee', 'Markus Dickinson', 'Ross Israel']	2013-10-01	detecting-and-correcting-learner-korean-1	https://aclanthology.org/I13-1199	https://aclanthology.org/I13-1199.pdf	ijcnlp-2013-10	['grammatical-error-detection']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 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ad28b87d-13a2-4c93-bf4e-c3c11b3d64ab	benchmarking-graph-neural-networks-on-dynamic	null	null	https://openreview.net/forum?id=I2KAe7x67JU	https://openreview.net/pdf?id=I2KAe7x67JU	Benchmarking Graph Neural Networks on Dynamic Link Prediction	Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data. Link prediction is a near-universal benchmark for new GNN models. Many advanced models such as Dynamic graph neural networks(DGNNs) specifically target dynamic link prediction. However, these models, particularly DGNNs, are rarely compared to each other or existing heuristics. Different works evaluate their models in different ways, thus one cannot compare evaluation metrics directly. Motivated by this, we perform a comprehensive comparison study. We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on dynamic link prediction. We find that simple link prediction heuristics often perform better than GNNs and DGNNs, different sliding window sizes greatly affect performance, and of all examined graph neural networks, that DGNNs consistently outperform static GNNs.	['Katarzyna Musial-Gabrys', 'Bogdan Gabrys', 'Matthew Hellmich', 'Joakim Skarding']	2021-09-29	null	null	null	null	['dynamic-link-prediction']	['graphs']	[-3.38937372e-01 1.90644354e-01 -9.77376401e-01 8.69789533e-03 3.25063080e-01 -4.07936633e-01 6.00203395e-01 4.99284387e-01 5.67076690e-02 9.84720051e-01 -1.09197713e-01 -7.99925864e-01 -7.22200751e-01 -1.31144428e+00 -5.55291295e-01 -2.07275286e-01 -8.51583958e-01 7.31669724e-01 8.09382975e-01 -4.95483398e-01 -1.05490901e-01 6.39771461e-01 -1.05153823e+00 -3.04158062e-01 9.27126169e-01 7.06286848e-01 -1.78688198e-01 8.06902945e-01 -3.04248959e-01 9.07498956e-01 -3.83539110e-01 -8.47756803e-01 4.60978061e-01 -1.84326351e-01 -7.32330918e-01 -7.78877735e-01 3.56988490e-01 -4.04359177e-02 -1.09495676e+00 1.02549458e+00 5.82617164e-01 2.34810144e-01 4.13860261e-01 -1.86201894e+00 -7.96902597e-01 1.32377672e+00 -3.30834240e-01 3.35046589e-01 3.89292449e-01 2.63669640e-02 1.33049858e+00 -5.76327220e-02 1.06390238e+00 1.26240766e+00 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1.20190606e-01 -7.61644840e-01 -5.13979018e-01 1.34308254e-02 7.75280595e-03 -5.86245358e-01 -3.48331392e-01 -7.10244358e-01]	[7.062472820281982, 6.158669948577881]
68568195-19b5-4d30-91d0-1aec65530231	synopses-of-movie-narratives-a-video-language	null	null	https://openreview.net/forum?id=ZLRckoIm-EH	https://openreview.net/pdf?id=ZLRckoIm-EH	Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding	Despite recent advances of AI, story understanding remains an open and under-investigated problem. We collect, preprocess, and publicly release a video-language story dataset, Synopses of Movie Narratives(SyMoN), containing 5,193 video summaries of popular movies and TV series. SyMoN captures naturalistic storytelling videos for human audience made by human creators, and has higher story coverage and more frequent mental-state references than similar video-language story datasets. Differing from most existing video-text datasets, SyMoN features large semantic gaps between the visual and the textual modalities due to the prevalence of reporting bias and mental state descriptions. We establish benchmarks on video-text retrieval and zero-shot alignment on movie summary videos. With SyMoN, we hope to lay the groundwork for progress in multimodal story understanding.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['video-text-retrieval']	['computer-vision']	[ 3.52708668e-01 -2.94566005e-01 -7.31062353e-01 -1.20895319e-01 -9.38936710e-01 -9.45572257e-01 1.07184887e+00 3.94375950e-01 -8.89244527e-02 5.16576946e-01 1.26483035e+00 3.47094297e-01 -4.23253281e-03 -2.82510310e-01 -5.41374207e-01 -2.16892928e-01 -4.88442462e-03 1.14033282e-01 1.89381316e-01 -6.74970746e-02 5.16156495e-01 -2.72694826e-01 -1.58543503e+00 1.11246169e+00 -1.44604608e-01 7.64480948e-01 2.13523477e-01 9.09004688e-01 3.32116783e-02 1.65580463e+00 -2.98059106e-01 -7.04131007e-01 -2.89674968e-01 -6.25590980e-01 -7.97225058e-01 4.18027073e-01 8.20309877e-01 -4.28960443e-01 -1.13232255e+00 8.15492153e-01 3.90691310e-01 2.45360687e-01 6.20185196e-01 -1.59999084e+00 -7.09082901e-01 8.99787545e-01 -2.84014374e-01 6.29811585e-01 1.18114686e+00 1.08660400e-01 1.17210746e+00 -6.84464335e-01 1.55653167e+00 1.15369010e+00 6.88165367e-01 4.49693412e-01 -1.01695192e+00 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14888e7e-13e3-4253-87aa-301f4ad6b246	190503711	1905.03711	null	https://arxiv.org/abs/1905.03711v2	https://arxiv.org/pdf/1905.03711v2.pdf	Processing Megapixel Images with Deep Attention-Sampling Models	Existing deep architectures cannot operate on very large signals such as megapixel images due to computational and memory constraints. To tackle this limitation, we propose a fully differentiable end-to-end trainable model that samples and processes only a fraction of the full resolution input image. The locations to process are sampled from an attention distribution computed from a low resolution view of the input. We refer to our method as attention sampling and it can process images of several megapixels with a standard single GPU setup. We show that sampling from the attention distribution results in an unbiased estimator of the full model with minimal variance, and we derive an unbiased estimator of the gradient that we use to train our model end-to-end with a normal SGD procedure. This new method is evaluated on three classification tasks, where we show that it allows to reduce computation and memory footprint by an order of magnitude for the same accuracy as classical architectures. We also show the consistency of the sampling that indeed focuses on informative parts of the input images.	['François Fleuret', 'Angelos Katharopoulos']	2019-05-03	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[ 4.33982402e-01 3.82072926e-01 3.42108220e-01 -4.10491645e-01 -1.31661355e+00 -4.40519124e-01 6.93732798e-01 -2.38560572e-01 -8.08414578e-01 7.91658759e-01 -1.66144054e-02 8.86359215e-02 2.13124171e-01 -6.80933893e-01 -1.31747830e+00 -7.30882943e-01 3.75698060e-01 7.50835001e-01 1.30305961e-01 4.63766605e-01 2.83370197e-01 3.72795880e-01 -1.53668177e+00 3.73513520e-01 6.12132013e-01 1.09705245e+00 5.63525736e-01 1.16354716e+00 1.16130680e-01 7.00108171e-01 -5.39887667e-01 -1.52635112e-01 4.67891961e-01 -4.60632920e-01 -7.75687933e-01 2.09203586e-01 1.02189147e+00 -6.95729196e-01 -2.68229060e-02 1.20486391e+00 5.29984593e-01 1.40615553e-01 6.32797480e-01 -7.17838824e-01 -4.96157706e-01 4.89957571e-01 -7.03560233e-01 1.76309571e-01 -1.78268477e-01 1.75326571e-01 8.44150841e-01 -1.07195950e+00 8.00595582e-01 1.19851327e+00 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-1.49529731e+00 -9.03550625e-01 -9.75614130e-01 -3.83195698e-01 2.25138053e-01 4.07655627e-01 -1.33835852e-01 -4.67302740e-01 2.83512831e-01]	[11.20679759979248, -0.5238173604011536]
43094d9e-262f-4737-a552-5db0e86673dd	what-and-how-well-you-performed-a-multitask	1904.04346	null	https://arxiv.org/abs/1904.04346v2	https://arxiv.org/pdf/1904.04346v2.pdf	What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment	Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features that explain three related tasks - fine-grained action recognition, commentary generation, and estimating the AQA score. A new multitask-AQA dataset, the largest to date, comprising of 1412 diving samples was collected to evaluate our approach (https://github.com/ParitoshParmar/MTL-AQA). We show that our MTL approach outperforms STL approach using two different kinds of architectures: C3D-AVG and MSCADC. The C3D-AVG-MTL approach achieves the new state-of-the-art performance with a rank correlation of 90.44%. Detailed experiments were performed to show that MTL offers better generalization than STL, and representations from action recognition models are not sufficient for the AQA task and instead should be learned.	['Brendan Tran Morris', 'Paritosh Parmar']	2019-04-08	what-and-how-well-you-performed-a-multitask-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Parmar_What_and_How_Well_You_Performed_A_Multitask_Learning_Approach_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Parmar_What_and_How_Well_You_Performed_A_Multitask_Learning_Approach_CVPR_2019_paper.pdf	cvpr-2019-6	['action-quality-assessment', 'skills-assessment', 'fine-grained-action-recognition']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.19854636e-01 -2.00126976e-01 -3.07077199e-01 -4.36290205e-01 -1.43075264e+00 -3.65380138e-01 7.21743107e-01 4.64584306e-02 -5.25319695e-01 6.64289176e-01 7.20329821e-01 1.86724111e-01 -5.16691148e-01 -5.90251029e-01 -6.03816271e-01 -5.27568400e-01 -1.21225290e-01 3.60421389e-01 4.69043016e-01 -2.22268283e-01 3.94016296e-01 2.52254069e-01 -1.80577314e+00 1.02297246e+00 8.87742341e-01 1.48325157e+00 -1.95796475e-01 7.95515835e-01 9.68883783e-02 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71bfac69-1699-4b20-b690-7eb6d3142f34	open-set-relation-extraction-via-unknown	2306.04950	null	https://arxiv.org/abs/2306.04950v1	https://arxiv.org/pdf/2306.04950v1.pdf	Open Set Relation Extraction via Unknown-Aware Training	The existing supervised relation extraction methods have achieved impressive performance in a closed-set setting, where the relations during both training and testing remain the same. In a more realistic open-set setting, unknown relations may appear in the test set. Due to the lack of supervision signals from unknown relations, a well-performing closed-set relation extractor can still confidently misclassify them into known relations. In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances. To facilitate a compact decision boundary, ``difficult'' negative instances are necessary. Inspired by text adversarial attacks, we adaptively apply small but critical perturbations to original training instances and thus synthesizing negative instances that are more likely to be mistaken by the model as known relations. Experimental results show that this method achieves SOTA unknown relation detection without compromising the classification of known relations.	['Xuanjing Huang', 'Xiang Gao', 'Yunwen Chen', 'Zhongyu Wei', 'Tao Gui', 'Qi Zhang', 'WenYu Zhan', 'Xin Zhao', 'Jun Zhao']	2023-06-08	null	null	null	null	['relation-extraction']	['natural-language-processing']	[ 7.13849127e-01 9.52419221e-01 -5.15976369e-01 -4.84680861e-01 -7.26039648e-01 -8.31720829e-01 4.03150618e-01 3.47877562e-01 -7.20662475e-02 1.08658206e+00 -5.15048385e-01 -5.82681537e-01 -1.57820851e-01 -1.14634860e+00 -9.10698473e-01 -5.89627147e-01 -1.73152819e-01 9.09374952e-01 1.59967333e-01 -2.14023247e-01 -3.55526417e-01 4.61531281e-01 -1.16364610e+00 3.27337116e-01 8.72107327e-01 7.27888227e-01 -6.49597347e-01 4.32371825e-01 2.58530974e-01 7.84798443e-01 -1.15900946e+00 -9.45760429e-01 5.38557470e-01 -5.29129386e-01 -1.04553223e+00 9.79514495e-02 3.27297837e-01 -4.59240451e-02 -4.52467352e-01 1.37934065e+00 1.95082724e-02 1.72374070e-01 6.63411379e-01 -1.48116541e+00 -6.12811208e-01 1.01645863e+00 -2.74031848e-01 1.48176789e-01 4.15623397e-01 -1.43506765e-01 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63f44822-f79f-4e45-9d55-e09693856dfb	forward-looking-sonar-patch-matching-modern	2108.01066	null	https://arxiv.org/abs/2108.01066v1	https://arxiv.org/pdf/2108.01066v1.pdf	Forward-Looking Sonar Patch Matching: Modern CNNs, Ensembling, and Uncertainty	Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task. An important issue in sonar perception is matching image patches, which can enable other techniques like localization, change detection, and mapping. There is a rich literature for this problem in color images, but for acoustic images, it is lacking, due to the physics that produce these images. In this paper we improve on our previous results for this problem (Valdenegro-Toro et al, 2017), instead of modeling features manually, a Convolutional Neural Network (CNN) learns a similarity function and predicts if two input sonar images are similar or not. With the objective of improving the sonar image matching problem further, three state of the art CNN architectures are evaluated on the Marine Debris dataset, namely DenseNet, and VGG, with a siamese or two-channel architecture, and contrastive loss. To ensure a fair evaluation of each network, thorough hyper-parameter optimization is executed. We find that the best performing models are DenseNet Two-Channel network with 0.955 AUC, VGG-Siamese with contrastive loss at 0.949 AUC and DenseNet Siamese with 0.921 AUC. By ensembling the top performing DenseNet two-channel and DenseNet-Siamese models overall highest prediction accuracy obtained is 0.978 AUC, showing a large improvement over the 0.91 AUC in the state of the art.	['Matias Valdenegro-Toro', 'Paul Plöger', 'Arka Mallick']	2021-08-02	null	null	null	null	['patch-matching']	['computer-vision']	[-7.51535594e-02 -2.94594821e-02 7.00611115e-01 -1.99891299e-01 -4.58745122e-01 -5.06828547e-01 3.85059148e-01 1.30648613e-01 -1.11356926e+00 5.14892995e-01 -9.89938900e-02 4.76782322e-02 -3.22310776e-01 -9.41106021e-01 -9.47774649e-01 -7.89320290e-01 -7.95125842e-01 2.24746734e-01 4.88421619e-01 -5.94171762e-01 1.66371405e-01 3.60567808e-01 -1.65569794e+00 -1.53028384e-01 6.98352933e-01 1.16617072e+00 2.19029918e-01 1.01421523e+00 1.82761967e-01 3.01919401e-01 -4.51503962e-01 -5.94373167e-01 5.74155867e-01 -2.61256903e-01 -2.79388070e-01 -7.68172026e-01 8.72542262e-01 -2.26380572e-01 -2.61130691e-01 1.12083972e+00 8.18336070e-01 2.81210989e-01 5.08085966e-01 -7.37067044e-01 -1.92826405e-01 6.29436851e-01 -2.20332563e-01 2.28255913e-01 4.27920185e-02 2.34778747e-02 1.10234511e+00 -8.56586576e-01 3.77485961e-01 1.22803700e+00 1.15830219e+00 5.29056489e-01 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38cefbbc-fb30-43f2-9b4b-b4529cd6535a	see-few-seed-expand-and-entail-for-few-shot	2210.05632	null	https://arxiv.org/abs/2210.05632v1	https://arxiv.org/pdf/2210.05632v1.pdf	SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition	Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used. To tackle training-from-scratch setting, it is crucial to make full use of the annotation information (the boundaries and entity types). Therefore, in this paper, we propose a novel multi-task (Seed, Expand and Entail) learning framework, SEE-Few, for Few-shot NER without using source domain data. The seeding and expanding modules are responsible for providing as accurate candidate spans as possible for the entailing module. The entailing module reformulates span classification as a textual entailment task, leveraging both the contextual clues and entity type information. All the three modules share the same text encoder and are jointly learned. Experimental results on four benchmark datasets under the training-from-scratch setting show that the proposed method outperformed state-of-the-art few-shot NER methods with a large margin. Our code is available at \url{https://github.com/unveiled-the-red-hat/SEE-Few}.	['Deyu Zhou', 'Linhai Zhang', 'Zeng Yang']	2022-10-11	null	https://aclanthology.org/2022.coling-1.224	https://aclanthology.org/2022.coling-1.224.pdf	coling-2022-10	['few-shot-ner', 'low-resource-named-entity-recognition']	['natural-language-processing', 'natural-language-processing']	[-2.04545204e-02 8.63753036e-02 -2.13512331e-01 -3.78022820e-01 -1.24118996e+00 -5.11047423e-01 3.44599843e-01 1.81601539e-01 -7.69777834e-01 9.25981641e-01 2.76771635e-01 -2.03964971e-02 1.35824993e-01 -7.60285914e-01 -5.96705019e-01 -4.37631845e-01 2.36351714e-01 2.25556716e-01 2.61491209e-01 -2.10080042e-01 -1.74483545e-02 -9.22643468e-02 -1.18135011e+00 2.19475463e-01 9.85866666e-01 7.74426877e-01 3.42276901e-01 4.99014080e-01 -4.28547800e-01 8.99246693e-01 -3.86665791e-01 -7.89136648e-01 1.24503136e-01 -5.00664532e-01 -8.49398077e-01 -2.19613940e-01 1.99186295e-01 -1.08753912e-01 -4.05036420e-01 9.34954345e-01 9.13100898e-01 5.46269357e-01 3.94826651e-01 -8.41934085e-01 -9.50618207e-01 7.89005578e-01 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9278a623-2a21-4162-95e3-89e11a0fba0d	dimensionality-reduction-using-similarity	1706.05692	null	http://arxiv.org/abs/1706.05692v3	http://arxiv.org/pdf/1706.05692v3.pdf	Dimensionality Reduction using Similarity-induced Embeddings	The vast majority of Dimensionality Reduction (DR) techniques rely on second-order statistics to define their optimization objective. Even though this provides adequate results in most cases, it comes with several shortcomings. The methods require carefully designed regularizers and they are usually prone to outliers. In this work, a new DR framework, that can directly model the target distribution using the notion of similarity instead of distance, is introduced. The proposed framework, called Similarity Embedding Framework, can overcome the aforementioned limitations and provides a conceptually simpler way to express optimization targets similar to existing DR techniques. Deriving a new DR technique using the Similarity Embedding Framework becomes simply a matter of choosing an appropriate target similarity matrix. A variety of classical tasks, such as performing supervised dimensionality reduction and providing out-of-of-sample extensions, as well as, new novel techniques, such as providing fast linear embeddings for complex techniques, are demonstrated in this paper using the proposed framework. Six datasets from a diverse range of domains are used to evaluate the proposed method and it is demonstrated that it can outperform many existing DR techniques.	['Anastasios Tefas', 'Nikolaos Passalis']	2017-06-18	null	null	null	null	['supervised-dimensionality-reduction']	['computer-vision']	[ 1.03303371e-02 -3.80851746e-01 -1.76474705e-01 -4.61626709e-01 -6.92524552e-01 -4.94093210e-01 7.48769045e-01 2.75883526e-01 -3.80314767e-01 4.12484467e-01 2.15321735e-01 5.68565093e-02 -6.31415248e-01 -6.36074722e-01 -9.64780152e-02 -7.97428310e-01 -4.16785441e-02 3.18033248e-01 2.90753320e-02 -1.59030885e-01 6.53166056e-01 6.69547379e-01 -1.77721715e+00 -3.16517264e-01 1.10480058e+00 1.07535887e+00 6.62189052e-02 2.73057520e-01 -2.57980645e-01 3.35345119e-01 -5.73363900e-01 -2.49258310e-01 4.97952908e-01 -1.92938045e-01 -1.47457838e-01 -1.80728582e-03 3.32759529e-01 -6.32478148e-02 -2.40905419e-01 1.08316481e+00 6.36445642e-01 5.06350875e-01 9.83107388e-01 -1.16980398e+00 -1.04774487e+00 2.53071129e-01 -5.12368441e-01 2.08463475e-01 3.99300963e-01 -2.32360750e-01 1.11999714e+00 -1.15654695e+00 3.34726423e-01 1.30424893e+00 6.85949445e-01 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688237dc-4452-4800-b198-445199ddcf29	unsupervised-4d-lidar-moving-object	2212.14750	null	https://arxiv.org/abs/2212.14750v2	https://arxiv.org/pdf/2212.14750v2.pdf	Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series	In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary setting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of unsupervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self-supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering algorithm into moving or stationary. Experiments on stationary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is comparable to that of supervised state-of-the-art approaches.	['Alejandro Sanchez Guinea', 'Max Mühlhäuser', 'Thomas Kreutz']	2022-12-30	null	null	null	null	['time-series-clustering']	['time-series']	[ 5.16018271e-01 -3.00561517e-01 -2.51234353e-01 -6.06067300e-01 -8.21336746e-01 -3.00907165e-01 3.17490518e-01 2.84596920e-01 -6.17558658e-01 5.64745545e-01 -3.04293394e-01 -1.08610436e-01 -9.49312001e-02 -9.75416243e-01 -9.93232071e-01 -7.35880017e-01 -2.73278147e-01 9.79163766e-01 5.24238288e-01 3.89330208e-01 3.44248153e-02 5.80855072e-01 -1.94560301e+00 -9.82969776e-02 6.88841820e-01 1.09423041e+00 2.38761902e-01 5.27324080e-01 -4.39959288e-01 4.03107196e-01 -3.40930641e-01 5.00194669e-01 2.86611825e-01 -5.03318831e-02 -6.23899877e-01 3.66903037e-01 6.30882919e-01 -9.19577852e-02 -3.08572739e-01 7.85320461e-01 2.78368980e-01 1.69686347e-01 8.84014070e-01 -1.27749097e+00 -6.20505475e-02 4.89141613e-01 -8.19127858e-01 4.04027253e-01 -3.04497272e-01 -2.10030749e-02 7.81550229e-01 -7.20512450e-01 5.09140670e-01 1.19844103e+00 6.68607831e-01 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381607d7-ffc1-49cf-aee5-f74589635068	an-empirical-study-on-robustness-to-spurious	2007.06778	null	https://arxiv.org/abs/2007.06778v3	https://arxiv.org/pdf/2007.06778v3.pdf	An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models	Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.	['Lifu Tu', 'Garima Lalwani', 'Spandana Gella', 'He He']	2020-07-14	null	null	null	null	['paraphrase-identification']	['natural-language-processing']	[ 7.59015083e-02 -1.99008301e-01 -6.50758445e-01 -3.15572053e-01 -1.19059384e+00 -5.76693594e-01 6.74478412e-01 5.73669933e-02 -4.55952257e-01 1.00334215e+00 3.98384154e-01 -3.94047797e-01 -2.79184997e-01 -3.69459629e-01 -9.41395283e-01 -4.83864784e-01 6.55689538e-02 4.34606045e-01 6.19121753e-02 -3.04497480e-01 2.19082594e-01 1.10094525e-01 -1.33009040e+00 6.22383416e-01 1.13858104e+00 4.44676936e-01 3.79009871e-03 3.91337425e-01 -1.98883578e-01 9.01100039e-01 -6.63295925e-01 -5.30441046e-01 2.56193161e-01 -5.32065690e-01 -7.13048160e-01 -1.88457564e-01 7.16246367e-01 -1.99954331e-01 -1.35350779e-01 9.03986037e-01 5.50960124e-01 5.69522195e-02 7.39253223e-01 -1.30949116e+00 -6.53467417e-01 9.21931267e-01 -5.94376802e-01 4.12686050e-01 1.75775290e-01 2.19248280e-01 1.45695734e+00 -1.19020081e+00 3.66849929e-01 1.30025446e+00 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f4c4fd6c-2a96-44bd-9b11-87c01402129a	predicting-phoneme-level-prosody-latents	2211.01327	null	https://arxiv.org/abs/2211.01327v1	https://arxiv.org/pdf/2211.01327v1.pdf	Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis	A large part of the expressive speech synthesis literature focuses on learning prosodic representations of the speech signal which are then modeled by a prior distribution during inference. In this paper, we compare different prior architectures at the task of predicting phoneme level prosodic representations extracted with an unsupervised FVAE model. We use both subjective and objective metrics to show that normalizing flow based prior networks can result in more expressive speech at the cost of a slight drop in quality. Furthermore, we show that the synthesized speech has higher variability, for a given text, due to the nature of normalizing flows. We also propose a Dynamical VAE model, that can generate higher quality speech although with decreased expressiveness and variability compared to the flow based models.	['Pirros Tsiakoulis', 'Aimilios Chalamandaris', 'Spyros Raptis', 'Inchul Hwang', 'June Sig Sung', 'Nikolaos Ellinas', 'Karolos Nikitaras', 'Konstantinos Klapsas']	2022-11-02	null	null	null	null	['expressive-speech-synthesis']	['speech']	[ 6.85756505e-02 7.63162017e-01 -1.50524303e-01 -3.50488693e-01 -5.11312127e-01 -4.79451269e-01 6.22712553e-01 -2.12575346e-01 6.02891967e-02 8.50365937e-01 1.01546049e+00 1.57495454e-01 1.52946021e-02 -7.22761512e-01 -4.94184375e-01 -5.25506914e-01 3.55066568e-01 4.41594958e-01 6.82929680e-02 -3.96546543e-01 -1.83778033e-01 5.28405190e-01 -1.31575263e+00 2.75436103e-01 5.23376167e-01 8.55015635e-01 6.73328191e-02 8.76276314e-01 -1.23026296e-01 9.11687911e-01 -1.12853324e+00 -3.07983935e-01 6.91887066e-02 -7.51896143e-01 -7.98212111e-01 1.49445325e-01 1.50500745e-01 -3.05274963e-01 -3.48147690e-01 9.51773584e-01 4.03410971e-01 5.22200286e-01 1.03157353e+00 -1.01430070e+00 -4.19516921e-01 1.22428858e+00 4.66303676e-02 4.61185873e-02 1.69917315e-01 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82aea945-f188-4b54-9a6c-4812129d113d	near-optimal-decentralized-momentum-method	2304.10902	null	https://arxiv.org/abs/2304.10902v1	https://arxiv.org/pdf/2304.10902v1.pdf	Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems	Minimax optimization plays an important role in many machine learning tasks such as generative adversarial networks (GANs) and adversarial training. Although recently a wide variety of optimization methods have been proposed to solve the minimax problems, most of them ignore the distributed setting where the data is distributed on multiple workers. Meanwhile, the existing decentralized minimax optimization methods rely on the strictly assumptions such as (strongly) concavity and variational inequality conditions. In the paper, thus, we propose an efficient decentralized momentum-based gradient descent ascent (DM-GDA) method for the distributed nonconvex-PL minimax optimization, which is nonconvex in primal variable and is nonconcave in dual variable and satisfies the Polyak-Lojasiewicz (PL) condition. In particular, our DM-GDA method simultaneously uses the momentum-based techniques to update variables and estimate the stochastic gradients. Moreover, we provide a solid convergence analysis for our DM-GDA method, and prove that it obtains a near-optimal gradient complexity of $O(\epsilon^{-3})$ for finding an $\epsilon$-stationary solution of the nonconvex-PL stochastic minimax problems, which reaches the lower bound of nonconvex stochastic optimization. To the best of our knowledge, we first study the decentralized algorithm for Nonconvex-PL stochastic minimax optimization over a network.	['Songcan Chen', 'Feihu Huang']	2023-04-21	null	null	null	null	['stochastic-optimization']	['methodology']	[-2.67692655e-01 2.67639663e-02 -3.35521698e-01 -1.65041506e-01 -1.21694267e+00 -5.43687046e-01 -8.15875903e-02 -3.10607143e-02 -4.15125251e-01 1.20402122e+00 -1.49811804e-01 -4.78806019e-01 -4.38228011e-01 -7.06993401e-01 -9.36762273e-01 -1.05884182e+00 1.30137756e-01 4.67731684e-01 -4.32627261e-01 -2.69325882e-01 -1.29035236e-02 2.41338223e-01 -5.37517071e-01 -4.61208045e-01 1.06643641e+00 1.15706933e+00 4.06317636e-02 3.98417890e-01 1.78944871e-01 6.38404250e-01 -4.32863057e-01 -4.84475613e-01 5.25445580e-01 -4.99759614e-01 -5.01639485e-01 -3.01824678e-02 2.58795023e-01 -1.45210803e-01 -2.45666236e-01 1.53473723e+00 6.43992782e-01 2.94761360e-01 4.80153054e-01 -1.38695621e+00 -6.48059309e-01 5.42876422e-01 -1.02302957e+00 2.05951005e-01 -3.52602452e-01 1.40508011e-01 9.30684507e-01 -6.17749393e-01 4.24082190e-01 1.04138160e+00 7.70741463e-01 5.81219137e-01 -9.52498555e-01 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6791504f-97f3-480a-9328-b8dded08197f	variable-decision-frequency-option-critic	2212.04407	null	https://arxiv.org/abs/2212.04407v3	https://arxiv.org/pdf/2212.04407v3.pdf	Variable Decision-Frequency Option Critic	In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as setting it too short may increase the difficulty of the problem by requiring the agent to make numerous decisions to achieve its goal, while setting it too long can result in the agent losing control over the system. However, physical systems do not necessarily require a constant control frequency, and for learning agents, it is often preferable to operate with a low frequency when possible and a high frequency when necessary. We propose a framework called Continuous-Time Continuous-Options (CTCO), where the agent chooses options as sub-policies of variable durations. These options are time-continuous and can interact with the system at any desired frequency providing a smooth change of actions. We demonstrate the effectiveness of CTCO by comparing its performance to classical RL and temporal-abstraction RL methods on simulated continuous control tasks with various action-cycle times. We show that our algorithm's performance is not affected by choice of environment interaction frequency. Furthermore, we demonstrate the efficacy of CTCO in facilitating exploration in a real-world visual reaching task for a 7 DOF robotic arm with sparse rewards.	['Samuele Tosatto', 'Martin Jagersand', 'A. Rupam Mahmood', 'Jun Luo', 'Jun Jin', 'Amirmohammad Karimi']	2022-12-06	null	null	null	null	['continuous-control']	['playing-games']	[ 4.99982350e-02 9.69661400e-03 -3.25866103e-01 2.29453683e-01 -3.91534835e-01 -7.52934158e-01 6.92348123e-01 1.36890173e-01 -8.33990455e-01 1.26559985e+00 -2.86563575e-01 -4.33698446e-01 -4.29392844e-01 -6.37740433e-01 -4.47082400e-01 -8.65015507e-01 -5.01184046e-01 5.44137359e-01 2.31857583e-01 -2.95754820e-01 2.70483702e-01 5.72766662e-01 -1.59444129e+00 -4.55745876e-01 5.75382292e-01 6.88166261e-01 5.11106431e-01 7.03827441e-01 2.39912823e-01 7.07908452e-01 -8.52912784e-01 6.65813267e-01 4.96414721e-01 -5.73492467e-01 -5.58069587e-01 1.67821303e-01 -5.25136888e-01 -3.62483829e-01 -6.44642189e-02 6.80588782e-01 4.22243983e-01 7.11440921e-01 5.41130304e-01 -1.44864714e+00 -1.98384132e-02 5.95498860e-01 -3.93571287e-01 -3.79741490e-02 4.34272707e-01 6.24904096e-01 6.95566654e-01 -2.12815143e-02 6.05013669e-01 1.20005226e+00 1.48446381e-01 7.32034564e-01 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-7.87743330e-01 -5.83683670e-01 -2.61650197e-02 7.97146738e-01 -1.18097413e+00 -3.41655195e-01 -3.62834483e-01]	[4.275467872619629, 1.8837440013885498]
acb03d76-5c86-4e00-b67d-95f9f896ea32	facial-expression-recognition-with-swin	2203.13472	null	https://arxiv.org/abs/2203.13472v1	https://arxiv.org/pdf/2203.13472v1.pdf	Facial Expression Recognition with Swin Transformer	The task of recognizing human facial expressions plays a vital role in various human-related systems, including health care and medical fields. With the recent success of deep learning and the accessibility of a large amount of annotated data, facial expression recognition research has been mature enough to be utilized in real-world scenarios with audio-visual datasets. In this paper, we introduce Swin transformer-based facial expression approach for an in-the-wild audio-visual dataset of the Aff-Wild2 Expression dataset. Specifically, we employ a three-stream network (i.e., Visual stream, Temporal stream, and Audio stream) for the audio-visual videos to fuse the multi-modal information into facial expression recognition. Experimental results on the Aff-Wild2 dataset show the effectiveness of our proposed multi-modal approaches.	['Chee Sun Won', 'NamHo Kim', 'Jun-Hwa Kim']	2022-03-25	null	null	null	null	['facial-expression-recognition']	['computer-vision']	[ 1.04554191e-01 -4.59106952e-01 -1.28566504e-01 -7.14013100e-01 -7.75637627e-01 -1.08732678e-01 3.84445041e-01 -2.19975755e-01 -2.46572778e-01 4.34779704e-01 2.15648741e-01 2.95453101e-01 9.31538343e-02 -3.88671279e-01 -2.18425006e-01 -9.88413155e-01 -1.15303434e-01 -1.05562918e-01 -2.23572701e-01 -4.28475082e-01 -4.66111541e-01 6.56082511e-01 -1.88837039e+00 6.15465820e-01 -3.93577330e-02 1.82005572e+00 -4.13702279e-01 5.20505190e-01 -2.15631306e-01 1.25271797e+00 -3.88932377e-01 -5.59521377e-01 7.81668443e-03 -4.05694544e-01 -5.35299540e-01 1.92563668e-01 1.66844875e-01 -2.30956033e-01 -3.02359611e-01 9.18779671e-01 8.51340890e-01 -4.68519032e-02 2.63217479e-01 -1.85795820e+00 -1.14249043e-01 1.52973402e-02 -9.91338432e-01 4.98256553e-03 4.99325156e-01 -1.84438527e-02 6.78565860e-01 -9.46821153e-01 5.56585908e-01 1.24631226e+00 5.31169057e-01 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bd0e99b0-18ab-4252-8243-3c0c5d4ee93f	dualmix-unleashing-the-potential-of-data	2303.07864	null	https://arxiv.org/abs/2303.07864v1	https://arxiv.org/pdf/2303.07864v1.pdf	DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning	Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literature have applied the data augmentation (DA) to alleviate the model forgetting, while the role of DA in OCI has not been well understood so far. In this paper, we theoretically show that augmented samples with lower correlation to the original data are more effective in preventing forgetting. However, aggressive augmentation may also reduce the consistency between data and corresponding labels, which motivates us to exploit proper DA to boost the OCI performance and prevent the CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the augmented samples and their labels simultaneously, which is shown to enhance the sample diversity while maintaining strong consistency with corresponding labels. Further, to solve the class imbalance problem, we design an Adaptive Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples from both old and new classes and dynamically adjusting the label mixing ratio. Our approach is demonstrated to be effective on several benchmark datasets through extensive experiments, and it is shown to be compatible with other replay-based techniques.	['Song Guo', 'Junxiao Wang', 'Jiaqi Zhu', 'Haozhao Wang', 'Wenchao Xu', 'Yunfeng Fan']	2023-03-14	null	null	null	null	['class-incremental-learning']	['computer-vision']	[ 3.29544514e-01 -1.66691095e-01 -4.53287870e-01 -4.76025343e-01 -4.30959076e-01 -4.43541795e-01 3.54005873e-01 4.61498648e-01 -2.13164791e-01 9.70139861e-01 3.77574861e-02 -5.46286814e-02 -1.44426271e-01 -6.24759853e-01 -6.93795979e-01 -9.41787839e-01 1.15681313e-01 4.90236789e-01 3.47371280e-01 4.46849875e-02 2.60130644e-01 3.05230141e-01 -2.02970600e+00 4.15531576e-01 1.28061783e+00 8.59451115e-01 -2.29834676e-01 3.99250627e-01 -3.90847802e-01 8.31794262e-01 -1.11506367e+00 -1.37027889e-01 3.47262204e-01 -5.57821214e-01 -4.88165796e-01 1.88614726e-01 3.14382285e-01 -2.97790647e-01 -1.67332888e-01 7.75006473e-01 5.26625872e-01 9.51484218e-02 3.65054816e-01 -1.73807466e+00 -5.93288898e-01 7.48900890e-01 -7.77782679e-01 2.75763512e-01 -1.33680835e-01 -1.41845807e-01 6.60486937e-01 -1.00446260e+00 3.55695456e-01 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2bda5617-1ca3-496a-a627-82095b0d38e4	clip-reident-contrastive-training-for-player	2303.11855	null	https://arxiv.org/abs/2303.11855v1	https://arxiv.org/pdf/2303.11855v1.pdf	CLIP-ReIdent: Contrastive Training for Player Re-Identification	Sports analytics benefits from recent advances in machine learning providing a competitive advantage for teams or individuals. One important task in this context is the performance measurement of individual players to provide reports and log files for subsequent analysis. During sport events like basketball, this involves the re-identification of players during a match either from multiple camera viewpoints or from a single camera viewpoint at different times. In this work, we investigate whether it is possible to transfer the out-standing zero-shot performance of pre-trained CLIP models to the domain of player re-identification. For this purpose we reformulate the contrastive language-to-image pre-training approach from CLIP to a contrastive image-to-image training approach using the InfoNCE loss as training objective. Unlike previous work, our approach is entirely class-agnostic and benefits from large-scale pre-training. With a fine-tuned CLIP ViT-L/14 model we achieve 98.44 % mAP on the MMSports 2022 Player Re-Identification challenge. Furthermore we show that the CLIP Vision Transformers have already strong OCR capabilities to identify useful player features like shirt numbers in a zero-shot manner without any fine-tuning on the dataset. By applying the Score-CAM algorithm we visualise the most important image regions that our fine-tuned model identifies when calculating the similarity score between two images of a player.	['Norbert Oswald', 'Fabian Deuser', 'Konrad Habel']	2023-03-21	null	null	null	null	['optical-character-recognition', 'sports-analytics']	['computer-vision', 'computer-vision']	[ 4.17116016e-01 -3.20669860e-01 2.66864654e-02 -2.73105383e-01 -1.32536030e+00 -8.66836309e-01 4.47765499e-01 4.19057161e-01 -8.72898281e-01 1.62881762e-01 1.13458773e-02 3.43091488e-01 -7.96391070e-02 -5.82688928e-01 -8.49240720e-01 -2.77411312e-01 6.98016137e-02 7.02192307e-01 5.84724844e-01 -5.07857382e-01 5.01730680e-01 4.30760115e-01 -1.98043013e+00 8.31704915e-01 6.84059635e-02 7.85604119e-01 4.88561057e-02 1.29423141e+00 2.63490796e-01 7.28842199e-01 -8.32791030e-01 -1.00661767e+00 7.61720061e-01 -3.25356841e-01 -6.48967922e-01 1.47083998e-01 8.92342746e-01 -9.56463721e-03 -2.62674481e-01 9.25336897e-01 6.88049257e-01 2.91060418e-01 2.75704980e-01 -1.12344253e+00 -7.36976191e-02 6.13612592e-01 -7.86090493e-01 5.67185104e-01 6.52163267e-01 3.45396578e-01 9.02591825e-01 -6.24413371e-01 6.29193783e-01 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e35060c9-ad2e-46f1-a070-60424e95c621	pixel-level-intra-domain-adaptation-for	null	null	https://dl.acm.org/doi/10.1145/3474085.3475174	https://dl.acm.org/doi/pdf/10.1145/3474085.3475174	Pixel-level Intra-domain Adaptation for Semantic Segmentation	Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks. Despite such progress, existing works mainly focus on bridging the inter-domain gaps between the source and target domain, while only few of them noticed the intra-domain gaps within the target data. In this work, we propose a pixel-level intra-domain adaptation approach to reduce the intra-domain gaps within the target data. Compared with image-level methods, ours treats each pixel as an instance, which adapts the segmentation model at a more fine-grained level. Specifically, we first conduct the inter-domain adaptation between the source and target domain; Then, we separate the pixels in target images into the easy and hard subdomains; Finally, we propose a pixel-level adversarial training strategy to adapt a segmentation network from the easy to the hard subdomain. Moreover, we show that the segmentation accuracy can be further improved by incorporating a continuous indexing technique in the adversarial training. Experimental results show the effectiveness of our method against existing state-of-the-art approaches.	['Shuguang Cui', 'Xiaoguang Han', 'Yushuang Wu', 'Yipeng Qin', 'Xianggang Yu', 'Zizheng Yan']	2021-10-17	null	null	null	acm-international-conference-on-multimedia-2	['synthetic-to-real-translation']	['computer-vision']	[ 6.43320084e-01 2.94368804e-01 -2.81150818e-01 -4.32031482e-01 -9.16255116e-01 -6.52982295e-01 3.14656228e-01 -9.16870236e-02 -3.30457687e-01 5.45147300e-01 -1.06580444e-01 4.53540571e-02 1.57543242e-01 -1.00109184e+00 -7.80276537e-01 -7.02015042e-01 6.22313023e-01 6.18280649e-01 8.71424496e-01 -1.09334655e-01 8.19478780e-02 2.04476446e-01 -1.10747361e+00 2.00991109e-01 1.32332623e+00 1.09414411e+00 2.46303365e-01 2.52907008e-01 -3.99843007e-01 4.46141541e-01 -6.65541530e-01 -1.19048439e-01 3.52479845e-01 -5.14634848e-01 -1.01909137e+00 4.47601855e-01 4.59229648e-01 -2.19309479e-01 -2.74551153e-01 1.40862978e+00 3.72269779e-01 6.31948560e-02 6.04847372e-01 -1.15614808e+00 -8.16163182e-01 3.30738902e-01 -8.99977803e-01 -9.24887210e-02 -1.49237499e-01 -1.28838811e-02 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4e151b23-3f90-4018-a2b5-5d4ca4755a71	a-comparative-study-of-hyper-parameter	2201.06433	null	https://arxiv.org/abs/2201.06433v1	https://arxiv.org/pdf/2201.06433v1.pdf	A Comparative study of Hyper-Parameter Optimization Tools	Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right hyper-parameter combination. Hyper-parameter optimization (HPO) is a systematic process that helps in finding the right values for them. The conventional methods for this purpose are grid search and random search and both methods create issues in industrial-scale applications. Hence a set of strategies have been recently proposed based on Bayesian optimization and evolutionary algorithm principles that help in runtime issues in a production environment and robust performance. In this paper, we compare the performance of four python libraries, namely Optuna, Hyper-opt, Optunity, and sequential model-based algorithm configuration (SMAC) that has been proposed for hyper-parameter optimization. The performance of these tools is tested using two benchmarks. The first one is to solve a combined algorithm selection and hyper-parameter optimization (CASH) problem The second one is the NeurIPS black-box optimization challenge in which a multilayer perception (MLP) architecture has to be chosen from a set of related architecture constraints and hyper-parameters. The benchmarking is done with six real-world datasets. From the experiments, we found that Optuna has better performance for CASH problem and HyperOpt for MLP problem.	['Asif Salim', 'Adesh Bansode', 'Shashank Shekhar']	2022-01-17	null	null	null	null	['smac-1', 'smac']	['playing-games', 'playing-games']	[-3.50982286e-02 -3.10937613e-01 2.50805467e-01 -3.52331549e-01 -2.10063815e-01 -3.09219271e-01 4.60832030e-01 1.58855274e-01 -7.58701146e-01 7.05344021e-01 -2.83195198e-01 -1.30303591e-01 -7.42997050e-01 -6.41292632e-01 -2.97618836e-01 -9.76113915e-01 4.34541665e-02 9.11308169e-01 4.55175221e-01 -3.30966026e-01 9.47366774e-01 4.04298484e-01 -2.00005412e+00 1.93536922e-01 7.30470181e-01 8.65400851e-01 7.04833865e-01 7.93999970e-01 -1.88974142e-01 -2.06763316e-02 -4.72990006e-01 6.91925064e-02 3.87282670e-01 -1.85055092e-01 -6.89995885e-01 -3.47948581e-01 -2.78274804e-01 5.54815710e-01 7.63088226e-01 9.17812824e-01 8.96868646e-01 1.73194364e-01 7.20028162e-01 -1.42213905e+00 3.32214862e-01 4.82037127e-01 -3.41690809e-01 1.53715789e-01 -1.83370188e-02 2.75940210e-01 4.03589964e-01 -3.86704147e-01 3.83601338e-01 1.31003046e+00 5.73653579e-01 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6b15d2ff-15ae-4cbd-8e8f-c6bc778bb7ae	abaw-facial-expression-recognition-in-the	2303.09785	null	https://arxiv.org/abs/2303.09785v1	https://arxiv.org/pdf/2303.09785v1.pdf	ABAW : Facial Expression Recognition in the wild	The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge. In this paper we have dealt only expression classification challenge using multiple approaches such as fully supervised, semi-supervised and noisy label approach. Our approach using noise aware model has performed better than baseline model by 10.46% and semi supervised model has performed better than baseline model by 9.38% and the fully supervised model has performed better than the baseline by 9.34%	['S Balasubramanian', 'Bobbili Veerendra Raj Kumar', 'Badveeti Naveen Siva Kumar', 'Darshan Gera']	2023-03-17	null	null	null	null	['action-unit-detection']	['computer-vision']	[ 8.09219573e-03 1.46774366e-01 6.90902174e-02 -8.95986319e-01 -9.82567728e-01 -5.94957232e-01 3.04727077e-01 3.47814173e-01 -7.62966692e-01 1.14730752e+00 4.01246607e-01 7.91399360e-01 5.60026407e-01 -2.92624161e-03 3.33678350e-02 -4.55413908e-01 -2.34547228e-01 1.01010583e-01 -2.09941402e-01 -5.62619507e-01 -1.08983830e-01 8.69500488e-02 -1.31076217e+00 8.10266376e-01 3.56891789e-02 1.47425961e+00 -8.73534262e-01 9.53033328e-01 2.10626811e-01 1.36121905e+00 -7.62851059e-01 -1.42182648e-01 6.75390810e-02 -4.01786864e-01 -1.02483606e+00 -2.52577037e-01 1.25264049e-01 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5f6ed23d-497c-4ace-970e-0dd88114f8de	local-contrastive-loss-with-pseudo-label	2112.09645	null	https://arxiv.org/abs/2112.09645v1	https://arxiv.org/pdf/2112.09645v1.pdf	Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation	Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled data along with limited annotated data. Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images and achieve high performance in classification tasks on popular natural image datasets like ImageNet. In pixel-level prediction tasks such as segmentation, it is crucial to also learn good local level representations along with global representations to achieve better accuracy. However, the impact of the existing local contrastive loss-based methods remains limited for learning good local representations because similar and dissimilar local regions are defined based on random augmentations and spatial proximity; not based on the semantic label of local regions due to lack of large-scale expert annotations in the semi/self-supervised setting. In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images. In particular, we define the proposed loss to encourage similar representations for the pixels that have the same pseudo-label/ label while being dissimilar to the representation of pixels with different pseudo-label/label in the dataset. We perform pseudo-label based self-training and train the network by jointly optimizing the proposed contrastive loss on both labeled and unlabeled sets and segmentation loss on only the limited labeled set. We evaluated on three public cardiac and prostate datasets, and obtain high segmentation performance.	['Ender Konukoglu', 'Neerav Karani', 'Ertunc Erdil', 'Krishna Chaitanya']	2021-12-17	null	null	null	null	['semi-supervised-medical-image-segmentation']	['computer-vision']	[ 5.29385626e-01 5.92485309e-01 -7.38866508e-01 -8.79428625e-01 -1.20069599e+00 -4.14295912e-01 2.22265735e-01 6.06240988e-01 -6.67304456e-01 8.22077036e-01 1.19913265e-01 7.98431411e-02 8.36098790e-02 -6.95289612e-01 -8.08108509e-01 -8.76650989e-01 6.08080067e-02 6.03947401e-01 3.06433588e-01 1.84752107e-01 -1.39522299e-01 3.77846956e-01 -1.07525432e+00 2.43043005e-01 9.97802079e-01 1.10280752e+00 3.16240907e-01 1.23486817e-01 -2.26379871e-01 8.41695309e-01 -1.92569539e-01 8.10694844e-02 5.22280455e-01 -6.89600527e-01 -9.70251977e-01 3.07710767e-01 5.57420075e-01 -9.23234299e-02 -2.13876739e-02 1.20443523e+00 4.50909704e-01 -1.74075644e-02 7.99035728e-01 -7.29349911e-01 -3.85192573e-01 4.51622397e-01 -7.81000972e-01 2.10553631e-01 -2.84295946e-01 1.71514094e-01 1.00409544e+00 -3.62143070e-01 7.08518147e-01 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ea000ac7-cdb5-42a7-8ece-c2f37ba090e1	wildgait-learning-of-gait-representations	2105.05528	null	https://arxiv.org/abs/2105.05528v5	https://arxiv.org/pdf/2105.05528v5.pdf	WildGait: Learning Gait Representations from Raw Surveillance Streams	The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera. We aim to address the challenges of real-world scenarios in which camera feeds capture multiple people, who in most cases pass in front of the camera only once. We address privacy concerns by using only motion information of walking individuals, with no identifiable appearance-based information. As such, we propose a novel weakly supervised learning framework, WildGait, which consists of training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton sequences obtained from raw, real-world, surveillance streams to learn useful gait signatures. We collected the training data and compiled the largest dataset of walking skeletons called Uncooperative Wild Gait, containing over 38k tracklets of anonymized walking 2D skeletons. We release the dataset for public use. Our results show that, with fine-tuning, we surpass the current state-of-the-art pose-based gait recognition solutions. Our proposed method is reliable in training gait recognition methods in unconstrained environments, especially in settings with scarce amounts of annotated data.	['Emilian Radoi', 'Adrian Cosma']	2021-05-12	null	null	null	null	['person-identification']	['computer-vision']	[ 1.91608459e-01 -1.97194442e-01 -4.43555228e-02 -2.13705957e-01 -5.54323673e-01 -6.56197965e-01 2.40738869e-01 -1.94155738e-01 -6.95099533e-01 5.53983331e-01 2.52478272e-01 1.59365490e-01 2.77144492e-01 -5.99141002e-01 -5.34874618e-01 -6.07179105e-01 -3.33975226e-01 5.55405259e-01 2.07949758e-01 1.38122901e-01 -2.70922333e-01 3.81265074e-01 -1.38464463e+00 -9.81085375e-02 3.47968489e-01 8.61552417e-01 -4.97285157e-01 7.27758527e-01 8.26674044e-01 2.52694786e-01 -4.25005645e-01 -1.01171494e+00 5.86701691e-01 -1.26336932e-01 -4.29555178e-01 3.14403653e-01 7.26919830e-01 -7.08816528e-01 -7.73270547e-01 7.33531833e-01 6.79945230e-01 -2.67762970e-02 3.65674794e-01 -1.43578589e+00 -3.11632425e-01 -9.26118419e-02 -7.09459722e-01 5.84463067e-02 7.27962017e-01 6.49407268e-01 7.60429919e-01 -3.33977133e-01 4.75039601e-01 9.53281045e-01 1.30145621e+00 7.71334052e-01 -1.04650593e+00 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6.82569563e-01 4.08272892e-01 -8.48106980e-01 -4.61674146e-02 4.84660774e-01]	[14.254825592041016, 1.4012690782546997]
6ecef900-8cd7-4f4b-8675-120956504b25	artificial-neural-networks-for-cloud-masking	null	null	https://www.tandfonline.com/doi/abs/10.1080/01431161.2020.1714776	http://users.ntua.gr/vkristoll/assets/manuscript_cloud_masking_revised_technical_V2preprint.pdf	Artificial neural networks for cloud masking of Sentinel-2 ocean images with noise and sunglint	Cloudy regions in optical satellite images prevent the extraction of valuable information by image processing techniques. Several threshold, multi-temporal and machine learning approaches have been developed for the separation of clouds in land and ocean applications, but this task still remains a challenge. Concerning deep water marine applications, the main difficulties are imposed in regions with high noise levels and sunglint. In this study, artificial neural networks (ANNs) with different configurations are evaluated for the detection of clouds in Sentinel-2 images depicting deep water regions with several noise levels. The ANNs are trained on a manual public dataset and on a manual dataset created for the needs of this study, which authors intend to make publicly available. Results are compared with the cloud masks produced by three state-of-the-art algorithms: Fmask, MAJA, and Sen2Cor. It was shown that the ANNs trained on the second dataset perform very favourably, in contrast to the ANNs trained on the first dataset that fails to adequately represent the spectra of the noisy Sentinel-2 images. This study further reinforces the value of the ‘cirrus’ band and indicates the bands that mitigate the influence of noisy spectra, by defining and examining an index that characterizes the importance of the bands according to the weights produced by the ANNs. Finally, the possibility of improving results by making predictions using the feature scaling parameters of the test set instead of those of the training set is also investigated in cases where the test set cannot be adequately represented by the training set.	['Vassilia Karathanassi', 'Viktoria Kristollari']	2020-01-26	null	null	null	null	['cloud-detection']	['computer-vision']	[ 2.13298738e-01 -3.97260070e-01 5.11032939e-01 -1.27384171e-01 -4.12142903e-01 -5.78899205e-01 6.23832822e-01 1.54999793e-01 -7.36397088e-01 5.82627416e-01 -3.04232568e-01 -3.74110430e-01 -6.13838792e-01 -7.86098421e-01 -9.91168842e-02 -1.09329057e+00 -2.09681511e-01 1.88366547e-01 1.92468405e-01 -4.09482419e-01 3.23443785e-02 8.12155247e-01 -2.10180712e+00 2.73245633e-01 1.04267335e+00 9.44422543e-01 4.14349347e-01 5.75964153e-01 -1.53409779e-01 1.81984946e-01 -7.21460342e-01 2.16204613e-01 6.19894147e-01 -5.07913888e-01 -9.66896191e-02 2.27023423e-01 5.73239803e-01 3.44545916e-02 4.73282397e-01 1.15569556e+00 3.71615082e-01 -1.84639156e-01 5.69865048e-01 -8.09639454e-01 -2.87866290e-03 -1.20923743e-01 -1.28759980e-01 2.43945986e-01 -1.28160655e-01 2.58991241e-01 5.11320531e-01 -7.45121479e-01 4.80911911e-01 6.07383370e-01 6.32581294e-01 -8.72002840e-02 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fcef9ff2-81a4-4c34-be92-fe1789084e18	hierarchical-open-vocabulary-universal-image	2307.00764	null	https://arxiv.org/abs/2307.00764v1	https://arxiv.org/pdf/2307.00764v1.pdf	Hierarchical Open-vocabulary Universal Image Segmentation	Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple levels of granularity, introducing inherent segmentation ambiguity. Unlike existing methods that typically sidestep this ambiguity and treat it as an external factor, our approach actively incorporates a hierarchical representation encompassing different semantic-levels into the learning process. We propose a decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff".1 Additionally, we systematically examine the differences that exist in the textual and visual features between these types of categories. Our resulting model, named HIPIE, tackles HIerarchical, oPen-vocabulary, and unIvErsal segmentation tasks within a unified framework. Benchmarked on over 40 datasets, e.g., ADE20K, COCO, Pascal-VOC Part, RefCOCO/RefCOCOg, ODinW and SeginW, HIPIE achieves the state-of-the-art results at various levels of image comprehension, including semantic-level (e.g., semantic segmentation), instance-level (e.g., panoptic/referring segmentation and object detection), as well as part-level (e.g., part/subpart segmentation) tasks. Our code is released at https://github.com/berkeley-hipie/HIPIE.	['Trevor Darrell', 'Kazuki Kozuka', 'Yusuke Kato', 'Konstantinos Kallidromitis', 'Shufan Li', 'Xudong Wang']	2023-07-03	null	null	null	null	['image-comprehension']	['computer-vision']	[ 3.12070042e-01 2.00224414e-01 -1.87563702e-01 -4.10365134e-01 -8.48412335e-01 -8.62382770e-01 5.40765941e-01 2.75641263e-01 -3.81030053e-01 2.39945710e-01 1.53635312e-02 -2.31536388e-01 1.40438586e-01 -8.12113643e-01 -8.10044765e-01 -5.18693209e-01 6.44603968e-01 3.86335403e-01 4.67373312e-01 -1.08804114e-01 1.42130479e-01 1.26318708e-01 -1.74454331e+00 4.87483382e-01 9.40103531e-01 1.17875910e+00 3.08207482e-01 5.14051616e-01 -5.01976192e-01 6.04880512e-01 -4.42290485e-01 -6.51920617e-01 1.84123635e-01 -2.12107450e-01 -1.30347538e+00 7.13677764e-01 7.23313808e-01 6.17245622e-02 -2.01764889e-02 1.35641634e+00 2.99447253e-02 -2.71446351e-02 6.54784143e-01 -1.30950367e+00 -6.17651165e-01 4.76294041e-01 -7.37573206e-01 -1.42711416e-01 2.27435768e-01 2.83286661e-01 1.26436949e+00 -7.54916251e-01 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a8be424e-351f-421f-aec9-c800d4225ac7	boundary-loss-for-highly-unbalanced	1812.07032	null	https://arxiv.org/abs/1812.07032v4	https://arxiv.org/pdf/1812.07032v4.pdf	Boundary loss for highly unbalanced segmentation	Widely used loss functions for CNN segmentation, e.g., Dice or cross-entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that differ by several orders of magnitude across classes, which affects training performance and stability. We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions. This can mitigate the difficulties of highly unbalanced problems because it uses integrals over the interface between regions instead of unbalanced integrals over the regions. Furthermore, a boundary loss complements regional information. Inspired by graph-based optimization techniques for computing active-contour flows, we express a non-symmetric $L_2$ distance on the space of contours as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation. We report comprehensive evaluations and comparisons on different unbalanced problems, showing that our boundary loss can yield significant increases in performances while improving training stability. Our code is publicly available: https://github.com/LIVIAETS/surface-loss .	['Eric Granger', 'Hoel Kervadec', 'Christian Desrosiers', 'Jihene Bouchtiba', 'Jose Dolz', 'Ismail Ben Ayed']	2018-12-17	null	null	null	null	['unbalanced-segmentation', 'ischemic-stroke-lesion-segmentation', 'brain-lesion-segmentation-from-mri']	['computer-vision', 'medical', 'medical']	[-1.21043742e-01 2.40571097e-01 -6.89068139e-02 -5.85593820e-01 -7.87459314e-01 -7.73834527e-01 5.38965128e-02 3.02650005e-01 -6.59784436e-01 5.68757057e-01 -3.75366151e-01 -2.35983089e-01 3.09982777e-01 -8.73107374e-01 -6.62235975e-01 -7.56060064e-01 -5.63462963e-03 1.87033247e-02 5.46589971e-01 -4.06698771e-02 2.03247428e-01 8.32942724e-01 -1.07920933e+00 -2.15133846e-01 1.13532245e+00 1.35213590e+00 -2.08110347e-01 4.03546751e-01 -3.66223544e-01 3.45368326e-01 -7.35379219e-01 -5.96652508e-01 4.32004452e-01 -3.13967645e-01 -7.86264122e-01 -1.59071639e-01 6.05895162e-01 -2.84337044e-01 -6.51746318e-02 1.26597393e+00 4.67535108e-01 2.66062528e-01 7.59484053e-01 -1.05220926e+00 -4.04142559e-01 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eb3a4c9c-c5f5-4c6f-bcf8-3601cd26404c	vision-transformer-based-covid-19-detection	2110.04458	null	https://arxiv.org/abs/2110.04458v1	https://arxiv.org/pdf/2110.04458v1.pdf	Vision Transformer based COVID-19 Detection using Chest X-rays	COVID-19 is a global pandemic, and detecting them is a momentous task for medical professionals today due to its rapid mutations. Current methods of examining chest X-rays and CT scan requires profound knowledge and are time consuming, which suggests that it shrinks the precious time of medical practitioners when people's lives are at stake. This study tries to assist this process by achieving state-of-the-art performance in classifying chest X-rays by fine-tuning Vision Transformer(ViT). The proposed approach uses pretrained models, fine-tuned for detecting the presence of COVID-19 disease on chest X-rays. This approach achieves an accuracy score of 97.61%, precision score of 95.34%, recall score of 93.84% and, f1-score of 94.58%. This result signifies the performance of transformer-based models on chest X-ray.	['Karthik Sivarama Krishnan', 'Koushik Sivarama Krishnan']	2021-10-09	null	null	null	null	['covid-19-detection', 'covid-19-modelling']	['medical', 'time-series']	[ 2.37496242e-01 -2.62800485e-01 -1.92694858e-01 4.06183153e-02 -6.90699935e-01 -4.20029253e-01 3.37648809e-01 2.44852573e-01 -4.49828684e-01 6.65670097e-01 2.61523500e-02 -6.16513014e-01 -2.14954212e-01 -7.51609862e-01 -3.12049419e-01 -6.52258933e-01 3.82527784e-02 7.23798215e-01 2.90572673e-01 -5.94313182e-02 9.28730890e-02 1.81256935e-01 -1.02301812e+00 3.86396676e-01 5.51268518e-01 9.57839310e-01 2.69908130e-01 1.13930726e+00 1.08532146e-01 8.76644194e-01 -5.42828202e-01 -5.05466342e-01 -4.87031639e-02 -3.92915368e-01 -7.94070601e-01 -3.01027715e-01 -2.91224159e-02 -3.89614135e-01 -6.31483272e-02 7.95426726e-01 3.77809018e-01 -2.26046443e-01 9.48793411e-01 -7.91265011e-01 -6.82851672e-01 1.15781188e-01 -8.26424718e-01 8.64775896e-01 2.41223291e-01 1.36118308e-01 5.26710153e-01 -6.67315483e-01 4.31707799e-01 7.48696446e-01 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-5.17344296e-01 -7.64565110e-01 -1.80764407e-01 2.04758957e-01 5.00822425e-01 -5.42026818e-01 1.55951813e-01 1.64366230e-01]	[15.56101131439209, -1.7030218839645386]
a3dc2806-2faa-4d28-a3a0-42590290f25d	attention-based-multi-input-deep-learning	1906.05168	null	https://arxiv.org/abs/1906.05168v3	https://arxiv.org/pdf/1906.05168v3.pdf	Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors	Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical properties by separated model. In this study, we proposed an architecture training simultaneously both type of data in order to improve the overall performance. Given the molecular structure in the form of SMILES notation and their label, we generated the SMILES-based feature matrix and molecular descriptors. These data were trained on a deep learning model which was also integrated with the Attention mechanism to facilitate training and interpreting. Experiments showed that our model could raise the performance of prediction comparing to the reference. With the maximum MCC 0.58 and AUC 90% by cross-validation on EGFR inhibitors dataset, our architecture was outperforming the referring model. We also successfully integrated Attention mechanism into our model, which helped to interpret the contribution of chemical structures on bioactivity.	['Trung Hoang Le', 'Huy Ngoc Pham']	2019-06-12	null	null	null	null	['activity-prediction', 'activity-prediction']	['computer-vision', 'time-series']	[ 2.60546178e-01 5.09119704e-02 -3.83470714e-01 -4.56032306e-01 -5.42289257e-01 -4.08079982e-01 5.22764862e-01 3.41513425e-01 -2.38380060e-01 1.11524737e+00 1.21661551e-01 -6.52224362e-01 2.31206734e-02 -6.56574607e-01 -7.85178125e-01 -7.51939714e-01 -5.30206636e-02 1.77484557e-01 -2.26335645e-01 2.42573638e-02 4.76735085e-01 3.73892695e-01 -1.15422463e+00 4.55558449e-01 1.31553984e+00 1.05697405e+00 2.15164572e-01 2.12639481e-01 -1.65265098e-01 1.05914271e+00 -5.30603886e-01 -2.61307925e-01 -3.39497216e-02 -2.97397912e-01 -6.49292111e-01 -4.77910429e-01 1.58659980e-01 -1.78862303e-01 -1.46082938e-01 7.61276841e-01 7.95579135e-01 2.19513848e-02 7.71333098e-01 -7.37278461e-01 -1.05837345e+00 3.99053693e-01 -5.17184615e-01 3.37728150e-02 4.65818852e-01 2.09785104e-01 9.28856313e-01 -1.04799688e+00 2.92579323e-01 1.04735398e+00 6.05338454e-01 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45533852-9955-4e95-9e49-b546cfc32bee	skeleton-free-pose-transfer-for-stylized-3d	2208.00790	null	https://arxiv.org/abs/2208.00790v1	https://arxiv.org/pdf/2208.00790v1.pdf	Skeleton-free Pose Transfer for Stylized 3D Characters	We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focuses on a novel scenario to handle skeleton-free characters with diverse shapes, topologies, and mesh connectivities. The key idea of our method is to represent the characters in a unified articulation model so that the pose can be transferred through the correspondent parts. To achieve this, we propose a novel pose transfer network that predicts the character skinning weights and deformation transformations jointly to articulate the target character to match the desired pose. Our method is trained in a semi-supervised manner absorbing all existing character data with paired/unpaired poses and stylized shapes. It generalizes well to unseen stylized characters and inanimate objects. We conduct extensive experiments and demonstrate the effectiveness of our method on this novel task.	['Yang Zhou', 'Gerard Pons-Moll', 'Jun Saito', 'Jimei Yang', 'Zhouyingcheng Liao']	2022-07-28	null	null	null	null	['pose-transfer']	['computer-vision']	[ 2.12154120e-01 2.55709916e-01 -8.88445750e-02 -2.02147007e-01 -5.06685913e-01 -9.27725196e-01 6.96203411e-01 -4.78073299e-01 6.22705445e-02 4.51045305e-01 1.62706494e-01 8.81923661e-02 1.68631598e-01 -7.29586720e-01 -8.79231095e-01 -2.96633959e-01 4.21283036e-01 1.05075026e+00 3.69094849e-01 -3.06414545e-01 1.20558523e-01 9.69475389e-01 -1.07041168e+00 -1.04585484e-01 8.93288255e-01 4.64693993e-01 -6.78794906e-02 5.85217118e-01 -8.71531889e-02 6.31579757e-02 -4.92149115e-01 -8.94553661e-01 5.75937271e-01 -4.08679932e-01 -8.44031215e-01 5.35982966e-01 8.63661051e-01 -2.46696591e-01 -4.42900866e-01 8.57793689e-01 4.99862641e-01 -1.31146088e-02 1.04363036e+00 -9.61612523e-01 -8.41732740e-01 4.95510936e-01 -6.91962481e-01 -5.66242099e-01 4.25817996e-01 5.25320210e-02 9.77953613e-01 -8.21972072e-01 8.54582846e-01 1.53921711e+00 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ffbba140-ed59-469d-a290-a0b370765fa6	sam-fails-to-segment-anything-sam-adapter	2304.09148	null	https://arxiv.org/abs/2304.09148v3	https://arxiv.org/pdf/2304.09148v3.pdf	SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More	The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. By integrating task-specific knowledge with general knowledge learnt by the large model, SAM-Adapter can significantly elevate the performance of SAM in challenging tasks as shown in extensive experiments. We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection, shadow detection. We also tested polyp segmentation (medical image segmentation) and achieves better results. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more.	['Ying Zang', 'Papa Mao', 'Lingyun Sun', 'Zejian Li', 'Yan Wang', 'Runlong Cao', 'Chaotao Ding', 'Lanyun Zhu', 'Tianrun Chen']	2023-04-18	null	null	null	null	['shadow-detection', 'general-knowledge']	['computer-vision', 'miscellaneous']	[ 8.56283188e-01 3.10896397e-01 -8.49424675e-02 -1.51170403e-01 -4.10383493e-01 -6.35635614e-01 1.02127418e-01 -2.96649367e-01 -2.94948131e-01 3.45781952e-01 -3.14022213e-01 -7.65827894e-01 1.88741848e-01 -7.06806362e-01 -8.19830835e-01 -7.05033481e-01 4.06402424e-02 2.80534327e-01 6.76368475e-01 -9.88716707e-02 1.15339443e-01 4.36233401e-01 -1.29795110e+00 4.10717070e-01 1.09624076e+00 8.46029878e-01 4.65353936e-01 7.90948927e-01 1.65825579e-02 3.66284370e-01 -7.92182863e-01 -1.84310526e-01 3.79706830e-01 -2.22796530e-01 -7.70592868e-01 3.20525229e-01 4.48014528e-01 -4.06526923e-01 3.09524685e-01 9.68914509e-01 3.78075987e-01 -9.68245938e-02 4.43308771e-01 -1.16438866e+00 -7.58579969e-01 6.55170381e-01 -8.14805865e-01 3.01863346e-02 -1.76046435e-02 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c76c36d3-e0c0-464e-8902-49dd108d5fd1	medai-at-semeval-2021-task-5-start-to-end	null	null	https://aclanthology.org/2021.semeval-1.30	https://aclanthology.org/2021.semeval-1.30.pdf	MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection	This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection. The task concerns evaluating systems that detect the spans that make a text toxic when detecting such spans are possible. To address the possibly multi-span detection problem, we develop a start-to-end tagging framework on top of RoBERTa based language model. Besides, we design a custom loss function that takes distance into account. In comparison to other participating teams, our system has achieved 69.03{\%} F1 score, which is slightly lower (-1.8 and -1.73) than the top 1(70.83{\%}) and top 2 (70.77{\%}), respectively.	['Junfei Liu', 'Hongjie Fan', 'Zhen Wang']	2021-08-01	null	null	null	semeval-2021	['toxic-spans-detection']	['natural-language-processing']	[-1.86676055e-01 1.72158718e-01 -8.69268253e-02 -4.11104262e-02 -1.25989628e+00 -8.67527902e-01 4.36091930e-01 4.63382065e-01 -5.37062705e-01 8.41487229e-01 1.40879601e-01 -3.23076755e-01 -1.94104668e-03 -5.74919999e-01 -6.33354783e-01 -1.13841161e-01 -3.04429710e-01 2.78127402e-01 4.56495166e-01 -1.04297869e-01 3.13793391e-01 2.74545789e-01 -1.05630004e+00 5.48753381e-01 7.87588000e-01 9.10771668e-01 -1.62433028e-01 7.55162120e-01 -9.85629112e-03 9.03285444e-01 -1.09015477e+00 -7.28022635e-01 -3.52141485e-02 -1.69420719e-01 -1.14007962e+00 -2.72278368e-01 8.34168553e-01 1.96937785e-01 -3.02152038e-01 9.94081736e-01 4.79531914e-01 1.07018434e-01 7.86967874e-01 -1.12364078e+00 -6.95975602e-01 1.15174830e+00 -6.28446102e-01 5.05199313e-01 5.11005580e-01 -8.46484005e-02 1.32338750e+00 -8.04894626e-01 5.74630320e-01 9.83691633e-01 9.60534275e-01 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8b3b2bb1-e777-4be3-b7d2-94a1ff9bdbbf	a-distributional-view-on-multi-objective-1	null	null	https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf	https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf	A distributional view on multi objective policy optimization	Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units with different scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We propose to learn a target local policy for each objective, and we use supervised learning to fit a parametric policy to a combination of these distributions. We demonstrate the effectiveness of our approach on challenging high-dimensional real and simulated robotics tasks, and show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.	['Sandy Huang', 'Martin Riedmiller', 'Abbas Abdolmaleki', 'Nicolas Heess', 'Francis Song', 'Raia Hadsell', 'Murilo Martins', 'Michael Neunert', 'Martina Zambelli', 'Leonard Hasenclever']	null	null	https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf	https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf	icml-2020-1	['multi-objective-reinforcement-learning']	['methodology']	[ 8.48504007e-02 -1.87547132e-01 -2.78369725e-01 -2.10879534e-01 -9.18127656e-01 -8.32592785e-01 2.99531549e-01 1.88042931e-02 -8.43809783e-01 1.10501313e+00 6.17776625e-02 -6.55350313e-02 -6.87044561e-01 -5.47977269e-01 -6.27498150e-01 -7.72916019e-01 -1.84650630e-01 9.21282351e-01 1.00126259e-01 -1.93286687e-01 5.88735282e-01 5.03715336e-01 -1.37007248e+00 -1.10704921e-01 1.13849235e+00 7.86342263e-01 4.77938473e-01 8.20732832e-01 1.97415665e-01 2.41770759e-01 -8.04752469e-01 1.67640448e-02 6.64191663e-01 -1.20190866e-01 -6.71447277e-01 1.60334468e-01 1.90198541e-01 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38ef76ec-3fc1-4713-a316-96d35a3b4c1e	semsegdepth-a-combined-model-for-semantic	2209.00381	null	https://arxiv.org/abs/2209.00381v1	https://arxiv.org/pdf/2209.00381v1.pdf	SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion	Holistic scene understanding is pivotal for the performance of autonomous machines. In this paper we propose a new end-to-end model for performing semantic segmentation and depth completion jointly. The vast majority of recent approaches have developed semantic segmentation and depth completion as independent tasks. Our approach relies on RGB and sparse depth as inputs to our model and produces a dense depth map and the corresponding semantic segmentation image. It consists of a feature extractor, a depth completion branch, a semantic segmentation branch and a joint branch which further processes semantic and depth information altogether. The experiments done on Virtual KITTI 2 dataset, demonstrate and provide further evidence, that combining both tasks, semantic segmentation and depth completion, in a multi-task network can effectively improve the performance of each task. Code is available at https://github.com/juanb09111/semantic depth.	['Esa Rahtu', 'Juan Pablo Lagos']	2022-09-01	null	null	null	null	['depth-completion']	['computer-vision']	[ 3.00267369e-01 3.89594644e-01 -1.13236777e-01 -7.91976690e-01 -8.37356329e-01 -4.33873951e-01 5.64068496e-01 -8.59571397e-02 -5.67228436e-01 3.31646413e-01 8.14173669e-02 -7.70302936e-02 3.30726594e-01 -7.32485175e-01 -7.95467198e-01 -5.34137368e-01 3.47332895e-01 6.51237965e-01 4.56959933e-01 1.28221944e-01 3.32062334e-01 1.91404447e-01 -1.49144709e+00 3.78239006e-01 7.40637660e-01 1.30603230e+00 6.31427407e-01 9.18530524e-01 -3.21083128e-01 8.53614330e-01 -2.06207171e-01 -1.52188865e-02 4.91803735e-01 -7.53907189e-02 -1.10621762e+00 3.04399818e-01 3.34830165e-01 -6.98100686e-01 -3.20613772e-01 7.86890268e-01 3.15093517e-01 2.56205767e-01 3.16764593e-01 -1.23729944e+00 -8.45860988e-02 4.16892439e-01 -7.05448151e-01 -9.91503894e-02 1.86989844e-01 2.31118172e-01 8.93725276e-01 -7.20688879e-01 3.85349661e-01 1.26069522e+00 4.13294971e-01 6.79489553e-01 -8.68284702e-01 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3.18192244e-01 2.46723279e-01 -5.01082182e-01 -2.78098822e-01 3.09364833e-02]	[8.512372970581055, -2.4439613819122314]
7ba05c0d-9434-4b16-bd00-653c3b410871	4d-unsupervised-object-discovery	2210.04801	null	https://arxiv.org/abs/2210.04801v1	https://arxiv.org/pdf/2210.04801v1.pdf	4D Unsupervised Object Discovery	Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.	['Zhaoxiang Zhang', 'Yuntao Chen', 'Yuqi Wang']	2022-10-10	null	null	null	null	['3d-instance-segmentation-1', 'object-discovery']	['computer-vision', 'computer-vision']	[ 5.81962802e-03 3.71899083e-03 -3.62846822e-01 -4.23408449e-01 -6.42948866e-01 -5.82401991e-01 3.66699606e-01 2.00770289e-01 -3.56870979e-01 1.54293686e-01 -5.76546907e-01 -1.08916596e-01 -1.60279021e-01 -3.91184628e-01 -7.50968039e-01 -7.12942481e-01 -1.61245897e-01 8.63396108e-01 7.83101976e-01 2.30054960e-01 2.52948076e-01 7.57868767e-01 -1.78423893e+00 -1.41269088e-01 7.73131132e-01 1.08757961e+00 5.04660189e-01 2.65737891e-01 -4.36221242e-01 3.07522744e-01 -5.24476469e-02 1.78893767e-02 5.13127148e-01 -6.16081133e-02 -7.88610399e-01 5.46754122e-01 4.48990762e-01 -1.48071229e-01 -1.27405897e-01 1.09026194e+00 3.88758361e-01 1.33087620e-01 5.01998663e-01 -1.35760856e+00 -3.43842387e-01 2.07297578e-01 -7.78299868e-01 2.28833348e-01 -4.96523678e-02 2.46272162e-01 1.02418578e+00 -9.93754089e-01 5.69118798e-01 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64afe43a-97e3-44b8-8044-892137af8809	lila-a-unified-benchmark-for-mathematical	2210.17517	null	https://arxiv.org/abs/2210.17517v2	https://arxiv.org/pdf/2210.17517v2.pdf	Lila: A Unified Benchmark for Mathematical Reasoning	Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose LILA, a unified mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions: (i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., question-answering, fill-in-the-blanks (iii) language diversity e.g., no language, simple language (iv) external knowledge e.g., commonsense, physics. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs, thereby obtaining explainable solutions in addition to the correct answer. We additionally introduce two evaluation datasets to measure out-of-distribution performance and robustness to language perturbation. Finally, we introduce BHASKARA, a general-purpose mathematical reasoning model trained on LILA. Importantly, we find that multi-tasking leads to significant improvements (average relative improvement of 21.83% F1 score vs. single-task models), while the best performing model only obtains 60.40%, indicating the room for improvement in general mathematical reasoning and understanding.	['Ashwin Kalyan', 'Peter Clark', 'Ashish Sabharwal', 'Oyvind Tafjord', 'Tanmay Rajpurohit', 'Chitta Baral', 'Sean Welleck', 'Leonard Tang', 'Pan Lu', 'Matthew Finlayson', 'Swaroop Mishra']	2022-10-31	null	null	null	null	['mathematical-reasoning']	['natural-language-processing']	[-1.12464130e-01 -2.27468424e-02 2.33050391e-01 -2.89922804e-01 -8.25212061e-01 -9.25068736e-01 5.34589231e-01 2.69976318e-01 -4.36708927e-01 6.94644690e-01 1.05425984e-01 -8.04595947e-01 -4.28297669e-01 -1.00486887e+00 -9.72556889e-01 -3.15225542e-01 1.70288727e-01 5.71014643e-01 -1.72725379e-01 -5.01349986e-01 4.69243824e-01 1.63229585e-01 -1.30612206e+00 3.33588809e-01 1.61693490e+00 6.96451187e-01 9.28778499e-02 8.81526232e-01 -4.17869449e-01 1.39074481e+00 -7.58862674e-01 -8.28812957e-01 -6.78910837e-02 -5.92120439e-02 -1.02528691e+00 -5.73974907e-01 7.50374138e-01 -2.98600823e-01 -3.17311078e-01 9.03372049e-01 2.25505039e-01 2.99020618e-01 7.19305873e-01 -1.29495049e+00 -1.22541845e+00 9.77987051e-01 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3388c5f1-dc50-4f17-9f98-515a981ae29a	learning-to-generate-piano-music-with-sustain	2111.01216	null	https://arxiv.org/abs/2111.01216v1	https://arxiv.org/pdf/2111.01216v1.pdf	Learning To Generate Piano Music With Sustain Pedals	Recent years have witnessed a growing interest in research related to the detection of piano pedals from audio signals in the music information retrieval community. However, to our best knowledge, recent generative models for symbolic music have rarely taken piano pedals into account. In this work, we employ the transcription model proposed by Kong et al. to get pedal information from the audio recordings of piano performance in the AILabs1k7 dataset, and then modify the Compound Word Transformer proposed by Hsiao et al. to build a Transformer decoder that generates pedal-related tokens along with other musical tokens. While the work is done by using inferred sustain pedal information as training data, the result shows hope for further improvement and the importance of the involvement of sustain pedal in tasks of piano performance generations.	['Yi-Hsuan Yang', 'Joann Ching']	2021-11-01	null	null	null	null	['music-information-retrieval']	['music']	[ 4.14524913e-01 1.06309004e-01 4.24735211e-02 7.91696385e-02 -1.01949775e+00 -7.19263911e-01 6.39835119e-01 -7.31023848e-02 -3.31838685e-03 3.73332679e-01 5.94698668e-01 5.83359338e-02 -3.36016119e-01 -5.75219631e-01 -5.89436293e-01 -6.28980935e-01 1.07791178e-01 4.91381496e-01 2.09861174e-01 -2.66933292e-01 4.30170625e-01 9.65797007e-02 -1.69061255e+00 4.35230851e-01 2.35953867e-01 7.04520285e-01 1.16121724e-01 1.05942976e+00 4.59201247e-01 8.17231894e-01 -8.90832126e-01 -4.74492669e-01 1.98534727e-01 -8.82360339e-01 -8.34908068e-01 -2.70811081e-01 4.63303864e-01 -7.08097368e-02 -2.83720613e-01 6.95869982e-01 8.83766711e-01 1.24627441e-01 7.54947960e-01 -1.04094303e+00 -5.03632367e-01 1.63746345e+00 -2.96303183e-01 2.21342787e-01 2.27119356e-01 -3.32923532e-02 1.39708781e+00 -6.47230208e-01 5.20052493e-01 9.23002660e-01 8.65684509e-01 1.48836896e-01 -1.29751217e+00 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c041246d-2be4-4ee6-bfc2-6922c565333e	one-shot-weakly-supervised-segmentation-in	2111.10773	null	https://arxiv.org/abs/2111.10773v1	https://arxiv.org/pdf/2111.10773v1.pdf	One-shot Weakly-Supervised Segmentation in Medical Images	Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and utilizing coarse labels instead, respectively. Previous works usually fail to leverage the anatomical structure and suffer from class imbalance and low contrast problems. Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings. Firstly a propagation-reconstruction network is proposed to project scribbles from annotated volume to unlabeled 3D images based on the assumption that anatomical patterns in different human bodies are similar. Then a dual-level feature denoising module is designed to refine the scribbles based on anatomical- and pixel-level features. After expanding the scribbles to pseudo masks, we could train a segmentation model for the new class with the noisy label training strategy. Experiments on one abdomen and one head-and-neck CT dataset show the proposed method obtains significant improvement over the state-of-the-art methods and performs robustly even under severe class imbalance and low contrast.	['Shaoting Zhang', 'Xiaofan Zhang', 'Guotai Wang', 'Xinglong Liu', 'Na Wang', 'Ran Gu', 'Qi Su', 'Wenhui Lei']	2021-11-21	null	null	null	null	['one-shot-segmentation']	['computer-vision']	[ 5.05658090e-01 4.37614202e-01 -3.86625081e-01 -7.68518329e-01 -9.75623965e-01 -2.62025714e-01 1.23195335e-01 3.86227906e-01 -4.71481591e-01 4.76222217e-01 -2.07073927e-01 -9.99470204e-02 1.30246878e-01 -5.29201806e-01 -5.76302469e-01 -8.66092682e-01 1.41119361e-01 6.65775597e-01 4.92164671e-01 2.47032225e-01 -1.73630327e-01 2.64570951e-01 -1.11842430e+00 1.38221845e-01 1.02417374e+00 9.45347548e-01 3.36744815e-01 3.24213386e-01 -3.30309540e-01 8.27612281e-01 -2.66543984e-01 -1.20480567e-01 4.55691785e-01 -4.96070594e-01 -9.75179672e-01 6.56770706e-01 1.94783792e-01 -4.15072560e-01 8.26031789e-02 1.34669113e+00 6.18943036e-01 2.85616517e-02 6.92771614e-01 -6.96266592e-01 -1.75494716e-01 4.66588348e-01 -9.37111735e-01 1.37321219e-01 -1.82117179e-01 7.45834112e-02 5.05876541e-01 -7.87907243e-01 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5f472be6-cdb9-43f2-87b1-0c1f478afcf9	introducing-the-hearthstone-ai-competition	1906.04238	null	https://arxiv.org/abs/1906.04238v1	https://arxiv.org/pdf/1906.04238v1.pdf	Introducing the Hearthstone-AI Competition	The Hearthstone AI framework and competition motivates the development of artificial intelligence agents that can play collectible card games. A special feature of those games is the high variety of cards, which can be chosen by the players to create their own decks. In contrast to simpler card games, the value of many cards is determined by their possible synergies. The vast amount of possible decks, the randomness of the game, as well as the restricted information during the player's turn offer quite a hard challenge for the development of game-playing agents. This short paper introduces the competition framework and goes into more detail on the problems and challenges that need to be faced during the development process.	['Sanaz Mostaghim', 'Alexander Dockhorn']	2019-05-06	null	null	null	null	['card-games']	['playing-games']	[-3.91660005e-01 -1.20816184e-02 -1.58198085e-02 1.76090240e-01 1.36763945e-01 -9.85260963e-01 5.75249612e-01 -3.69584858e-01 -3.97936821e-01 9.83176351e-01 -9.87154394e-02 -1.99478522e-01 -5.39374113e-01 -1.02757764e+00 -7.40203336e-02 -3.87183130e-01 -3.31141055e-02 9.88445759e-01 3.36664021e-01 -8.64743292e-01 3.72420400e-01 2.33401850e-01 -1.76420283e+00 1.63336828e-01 6.71966374e-01 6.49068415e-01 5.13131320e-01 6.59776032e-01 -2.32389465e-01 1.16043198e+00 -7.87906408e-01 -6.21851921e-01 5.22415400e-01 -7.45933831e-01 -5.00753105e-01 -2.16271847e-01 -6.05807364e-01 -4.16023701e-01 -1.44313738e-01 8.73478055e-01 2.20464915e-01 8.60681534e-02 4.18392628e-01 -1.21755445e+00 -1.18185081e-01 8.72611761e-01 2.07374617e-02 4.36035916e-02 7.33307958e-01 1.88984722e-01 1.10692751e+00 -2.93214083e-01 6.72649741e-01 8.24875116e-01 1.68501779e-01 5.20626187e-01 -6.67584896e-01 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65aa92a0-1508-43e1-ba28-b5357b588d7a	fluid-mitigating-stragglers-in-federated	2307.02623	null	https://arxiv.org/abs/2307.02623v2	https://arxiv.org/pdf/2307.02623v2.pdf	FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout	Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a result, straggler devices with lower performance often dictate the overall training time in FL. In this work, we aim to alleviate this performance bottleneck due to stragglers by dynamically balancing the training load across the system. We introduce Invariant Dropout, a method that extracts a sub-model based on the weight update threshold, thereby minimizing potential impacts on accuracy. Building on this dropout technique, we develop an adaptive training framework, Federated Learning using Invariant Dropout (FLuID). FLuID offers a lightweight sub-model extraction to regulate computational intensity, thereby reducing the load on straggler devices without affecting model quality. Our method leverages neuron updates from non-straggler devices to construct a tailored sub-model for each straggler based on client performance profiling. Furthermore, FLuID can dynamically adapt to changes in stragglers as runtime conditions shift. We evaluate FLuID using five real-world mobile clients. The evaluations show that Invariant Dropout maintains baseline model efficiency while alleviating the performance bottleneck of stragglers through a dynamic, runtime approach.	['Divya Mahajan', 'Prashant J. Nair', 'Irene Wang']	2023-07-05	null	null	null	null	['model-extraction', 'federated-learning', 'model-extraction']	['adversarial', 'methodology', 'methodology']	[ 2.70352699e-02 -2.68343359e-01 -5.58665156e-01 -5.44451475e-01 -9.38123226e-01 -7.11429715e-01 2.24773616e-01 -3.87562424e-01 -5.19569695e-01 6.13711715e-01 -3.11075933e-02 -3.93146247e-01 3.34050157e-03 -5.58490574e-01 -1.08339918e+00 -7.33667016e-01 1.93286076e-01 8.16881582e-02 2.10692734e-01 2.89709061e-01 2.67875008e-02 2.62104452e-01 -1.41932678e+00 6.93486333e-01 8.79596710e-01 8.87170851e-01 -2.28365231e-02 6.18519664e-01 1.75536424e-02 7.20226228e-01 -7.97068000e-01 -4.37269837e-01 3.36578548e-01 -6.15502335e-02 -3.88320446e-01 -1.64955586e-01 5.59790969e-01 -7.16194570e-01 -3.61655712e-01 7.24547863e-01 5.50984740e-01 -1.30951228e-02 1.89075798e-01 -1.56719387e+00 -1.44125775e-01 8.34226906e-01 -9.78946164e-02 1.27327651e-01 -5.69580793e-02 3.76200557e-01 6.62343562e-01 -2.79310167e-01 3.33281428e-01 7.33943462e-01 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44145dbb-77fe-45ae-92ab-ba95b9ac73ee	graph-embedding-on-biomedical-networks	1906.05017	null	https://arxiv.org/abs/1906.05017v3	https://arxiv.org/pdf/1906.05017v3.pdf	Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations	Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks.	['Wen Zhang', 'Yungui Huang', 'Srinivasan Parthasarathy', 'Ping Zhang', 'Soheil Moosavinasab', 'Xiang Yue', 'Simon M. Lin', 'Jingong Huang', 'Zhen Wang', 'Huan Sun']	2019-06-12	null	null	null	null	['protein-function-prediction']	['medical']	[ 9.17133093e-02 5.66439867e-01 -5.04960120e-01 -8.26551765e-02 -5.22502325e-02 -1.27506062e-01 5.34727693e-01 7.91171074e-01 -2.15002880e-01 6.15513921e-01 1.62235424e-01 -5.99447548e-01 -4.17824715e-01 -8.23408186e-01 -1.82968840e-01 -9.60268915e-01 -5.30421436e-01 6.06991708e-01 1.70359984e-01 -5.92099428e-02 -1.87356934e-01 4.99683648e-01 -9.83817220e-01 -1.31866187e-01 8.04946125e-01 5.44554651e-01 -2.02917770e-01 3.90862316e-01 -2.04934612e-01 4.01903719e-01 -4.24293339e-01 -5.83660483e-01 -3.61803770e-01 -4.34650928e-01 -8.66416216e-01 -4.80834305e-01 -1.91148400e-01 2.76730865e-01 -8.05372238e-01 1.04835474e+00 8.93813133e-01 -1.61609635e-01 9.51470017e-01 -1.41358995e+00 -1.13934112e+00 3.84575814e-01 -4.52606738e-01 2.63422340e-01 5.26085734e-01 -1.23182051e-01 1.31163096e+00 -7.53773153e-01 9.21057105e-01 1.28370154e+00 8.65830898e-01 6.87763512e-01 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a16c3d08-7cf9-4281-9cc3-607a5a579cba	robust-authorship-verification-with-transfer	null	null	https://openreview.net/forum?id=BkgdPnjQ84	https://openreview.net/pdf?id=BkgdPnjQ84	Robust Authorship Verification with Transfer Learning	We address the problem of open-set authorship verification, a classification task that consists of attributing texts of unknown authorship to a given author when the unknown documents in the test set are excluded from the training set. We present an end-to-end model-building process that is universally applicable to a wide variety of corpora with little to no modification or fine-tuning. It relies on transfer learning of a deep language model and uses a generative adversarial network and a number of text augmentation techniques to improve the model's generalization ability. The language model encodes documents of known and unknown authorship into a domain-invariant space, aligning document pairs as input to the classifier, while keeping them separate. The resulting embeddings are used to train to an ensemble of recurrent and quasi-recurrent neural networks. The entire pipeline is bidirectional; forward and backward pass results are averaged. We perform experiments on four traditional authorship verification datasets, a collection of machine learning papers mined from the web, and a large Amazon-Reviews dataset. Experimental results surpass baseline and current state-of-the-art techniques, validating the proposed approach.	['Anonymous']	2019-02-27	null	null	null	null	['text-augmentation', 'authorship-verification']	['natural-language-processing', 'natural-language-processing']	[ 4.04822797e-01 2.41973296e-01 -1.61981374e-01 -4.29317683e-01 -8.75637472e-01 -9.71289158e-01 1.02010024e+00 3.71983089e-02 -4.54019576e-01 5.07852495e-01 1.38000185e-02 -4.32305455e-01 2.50432968e-01 -3.99042010e-01 -5.78368127e-01 -3.76477450e-01 4.83851731e-01 9.86739933e-01 -1.41655549e-01 -9.48449038e-03 6.58713937e-01 4.61326629e-01 -1.33329642e+00 3.63261312e-01 5.83617091e-01 7.10198283e-01 -3.96322221e-01 9.28944349e-01 -1.75049439e-01 7.72015810e-01 -9.13669646e-01 -1.02669442e+00 3.25783193e-01 -5.37930548e-01 -9.20277596e-01 -4.69333194e-02 7.78308392e-01 -1.38076141e-01 -4.16325420e-01 6.30010366e-01 3.80208433e-01 -1.98316097e-01 9.20022786e-01 -1.26727200e+00 -1.40556836e+00 7.78784096e-01 -3.18434596e-01 1.04572192e-01 1.18016541e-01 -7.35108778e-02 1.20151281e+00 -9.93060052e-01 8.23325038e-01 1.16852772e+00 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febb2714-9f6f-434c-81cd-4da774a32117	task-driven-graph-attention-for-hierarchical	2306.13760	null	https://arxiv.org/abs/2306.13760v1	https://arxiv.org/pdf/2306.13760v1.pdf	Task-Driven Graph Attention for Hierarchical Relational Object Navigation	Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relational object navigation (HRON), where the goal is to find objects specified by logical predicates organized in a hierarchical structure - objects related to furniture and then to rooms - such as finding an apple on top of a table in the kitchen. Solving such a task requires an efficient representation to reason about object relations and correlate the relations in the environment and in the task goal. HRON in large scenes (e.g. homes) is particularly challenging due to its partial observability and long horizon, which invites solutions that can compactly store the past information while effectively exploring the scene. We demonstrate experimentally that scene graphs are the best-suited representation compared to conventional representations such as images or 2D maps. We propose a solution that uses scene graphs as part of its input and integrates graph neural networks as its backbone, with an integrated task-driven attention mechanism, and demonstrate its better scalability and learning efficiency than state-of-the-art baselines.	['Jiajun Wu', 'Li Fei-Fei', 'Ruohan Zhang', 'Roberto Martín-Martín', 'Alan Lou', 'Andrey Kurenkov', 'Minjune Hwang', 'Chengshu Li', 'Michael Lingelbach']	2023-06-23	null	null	null	null	['graph-attention', 'navigate']	['graphs', 'reasoning']	[ 1.50002629e-01 3.57876539e-01 6.30590245e-02 -2.48929083e-01 -1.99314691e-02 -4.81720507e-01 5.57248652e-01 6.19946837e-01 -4.55419928e-01 4.75350946e-01 4.68301386e-01 -2.38445103e-01 -5.86443901e-01 -1.07999897e+00 -1.00465536e+00 -4.21950787e-01 -4.99459594e-01 8.93720090e-01 3.26238185e-01 -3.14107686e-01 1.20663896e-01 6.62667751e-01 -1.67922366e+00 2.73179710e-01 5.57327986e-01 1.08730912e+00 8.24741542e-01 5.68922460e-01 -1.03872761e-01 1.56671488e+00 -2.83707529e-01 -7.69861713e-02 1.04953550e-01 -2.26740792e-01 -1.23615897e+00 6.84214234e-02 4.24282521e-01 -3.74587685e-01 -7.62134016e-01 9.61438000e-01 -5.78776598e-02 7.55656481e-01 5.59321225e-01 -1.40801656e+00 -1.11604071e+00 7.38650560e-01 -4.75740246e-02 2.03357413e-01 6.07991338e-01 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0c04971f-d8ef-46d6-b77b-c1341cbf3e72	a-cross-corpus-study-on-speech-emotion	2207.02104	null	https://arxiv.org/abs/2207.02104v1	https://arxiv.org/pdf/2207.02104v1.pdf	A cross-corpus study on speech emotion recognition	For speech emotion datasets, it has been difficult to acquire large quantities of reliable data and acted emotions may be over the top compared to less expressive emotions displayed in everyday life. Lately, larger datasets with natural emotions have been created. Instead of ignoring smaller, acted datasets, this study investigates whether information learnt from acted emotions is useful for detecting natural emotions. Cross-corpus research has mostly considered cross-lingual and even cross-age datasets, and difficulties arise from different methods of annotating emotions causing a drop in performance. To be consistent, four adult English datasets covering acted, elicited and natural emotions are considered. A state-of-the-art model is proposed to accurately investigate the degradation of performance. The system involves a bi-directional LSTM with an attention mechanism to classify emotions across datasets. Experiments study the effects of training models in a cross-corpus and multi-domain fashion and results show the transfer of information is not successful. Out-of-domain models, followed by adapting to the missing dataset, and domain adversarial training (DAT) are shown to be more suitable to generalising to emotions across datasets. This shows positive information transfer from acted datasets to those with more natural emotions and the benefits from training on different corpora.	['Thomas Hain', 'Raymond W. M. Ng', 'Md Asif Jalal', 'Rosanna Milner']	2022-07-05	null	null	null	null	['cross-corpus']	['computer-vision']	[ 2.64825702e-01 3.72906029e-01 2.48784527e-01 -8.26255858e-01 -6.52470946e-01 -3.82627457e-01 6.75778031e-01 -1.15140490e-01 -6.79780126e-01 8.38197649e-01 3.87749970e-01 1.71984911e-01 2.44621411e-01 -3.41306418e-01 -6.35253310e-01 -4.26987231e-01 1.97411962e-02 4.21768606e-01 -2.67472863e-01 -4.91463691e-01 -2.36873001e-01 3.31148863e-01 -1.61394536e+00 8.41624498e-01 4.50324327e-01 8.62179816e-01 -2.16300234e-01 6.32299900e-01 -3.47416312e-01 9.57653940e-01 -1.17645299e+00 -7.83376753e-01 1.38825282e-01 -6.59439564e-01 -9.64087129e-01 1.93260890e-02 2.00566724e-01 -7.85208493e-02 1.01026937e-01 7.30424047e-01 1.02234745e+00 7.63895437e-02 6.90843403e-01 -1.42951703e+00 -9.27747071e-01 6.85736239e-01 -6.30232245e-02 -8.45309272e-02 6.05735481e-01 -7.95030817e-02 6.06016815e-01 -5.44096589e-01 7.64182806e-01 1.50177467e+00 7.35654950e-01 1.13527751e+00 -1.10009933e+00 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-4.76479322e-01 1.07605267e+00 6.02485947e-02 -5.12873769e-01 -6.51283979e-01 2.18346566e-01]	[13.482054710388184, 5.876943111419678]
55c0e805-9153-4624-861c-368b283003e8	deepsource-point-source-detection-using-deep	1807.02701	null	http://arxiv.org/abs/1807.02701v1	http://arxiv.org/pdf/1807.02701v1.pdf	DeepSource: Point Source Detection using Deep Learning	Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - a deep learning solution - that uses convolutional neural networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio (SNR) of the original map and then uses dynamic blob detection to detect sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT images with a total of 300,000 sources, DeepSource is essentially perfect in both purity and completeness down to SNR = 4 and outperforms PyBDSF in all metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For natural-weighting we find a smaller improvement of ~40% in the PC score at SNR = 3. If instead we ask where either of the purity or completeness first drop to 90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the 4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration. Our results show that deep learning is a promising approach to point source detection in astronomical images.	['A. Vafaei Sadr', 'Zafiirah Hosenie', 'Michelle Lochner', 'Etienne. E. Vos', 'N. Oozeer', 'Bruce A. Bassett']	2018-07-07	null	null	null	null	['radio-interferometry']	['miscellaneous']	[ 3.07868011e-02 -1.96697667e-01 1.53269663e-01 1.64826423e-01 -1.02778602e+00 -6.50586128e-01 6.94646120e-01 -2.53522366e-01 -5.19478500e-01 4.54631150e-01 1.32954612e-01 -5.33730507e-01 -5.24981320e-01 -6.86714053e-01 -3.78024250e-01 -1.13009977e+00 -5.25037825e-01 2.35318169e-01 4.10798490e-01 -5.60382679e-02 7.11647496e-02 9.22774792e-01 -1.05959535e+00 -1.99790895e-01 2.66029090e-01 1.08191323e+00 2.70008296e-01 1.01238573e+00 2.11778596e-01 9.01929021e-01 -6.33418560e-01 2.95220483e-02 7.54924834e-01 -4.88496691e-01 -5.06066740e-01 -3.73513460e-01 6.06383324e-01 -2.49315262e-01 -5.97260654e-01 1.11710787e+00 9.13436651e-01 -1.51131362e-01 6.28140569e-01 -7.61784434e-01 -1.16203420e-01 1.74115926e-01 -8.03892255e-01 8.82755876e-01 -1.50205463e-01 5.21633506e-01 8.47597301e-01 -7.44698286e-01 2.94106901e-01 8.61314833e-01 7.81674325e-01 -9.55978036e-02 -1.18171644e+00 -5.35580039e-01 -7.90332496e-01 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90164a67-f71b-4c3c-8f1e-fe5a463d34dd	evaluating-deep-neural-networks-for-image	2106.15286	null	https://arxiv.org/abs/2106.15286v1	https://arxiv.org/pdf/2106.15286v1.pdf	Evaluating Deep Neural Networks for Image Document Enhancement	This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively and quantitatively using Image Quality Assessment (IQA) metrics, and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth.	['Ricardo Ribani', 'Ricardo Piccoli', 'Lucas N. Kirsten']	2021-06-11	null	null	null	null	['document-enhancement']	['computer-vision']	[ 4.01853055e-01 -2.22804695e-01 3.33093315e-01 -3.25342357e-01 -3.70971322e-01 -3.24387431e-01 9.94826257e-01 1.51630938e-01 -7.01747179e-01 4.33561057e-01 2.59609967e-01 -1.43759713e-01 -2.85994709e-01 -8.61806214e-01 -4.56636131e-01 -8.87086451e-01 8.81559029e-02 1.61239415e-01 3.96586545e-02 -3.12344968e-01 4.86797720e-01 9.08135235e-01 -1.48441994e+00 6.77829444e-01 5.24014533e-01 9.70519185e-01 2.20761895e-01 1.05869937e+00 -4.67336066e-02 1.04728794e+00 -1.17762947e+00 -5.10079265e-01 1.70617744e-01 -2.63860822e-01 -7.90717840e-01 3.04698408e-01 8.33207726e-01 -8.06436062e-01 -4.55435693e-01 1.24722326e+00 7.61794090e-01 3.58828127e-01 7.37277985e-01 -9.30082023e-01 -1.22583854e+00 3.41631740e-01 -4.53765601e-01 5.05428970e-01 1.25090241e-01 -1.83506962e-02 4.33621436e-01 -8.78036320e-01 7.42138386e-01 1.21375477e+00 8.53773177e-01 4.18189913e-01 -9.37403083e-01 -2.53532529e-01 -2.74223506e-01 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6c51240c-5cea-4f54-abce-653218f9364c	egovsr-towards-high-quality-egocentric-video	2305.14708	null	https://arxiv.org/abs/2305.14708v1	https://arxiv.org/pdf/2305.14708v1.pdf	EgoVSR: Towards High-Quality Egocentric Video Super-Resolution	Due to the limitations of capture devices and scenarios, egocentric videos frequently have low visual quality, mainly caused by high compression and severe motion blur. With the increasing application of egocentric videos, there is an urgent need to enhance the quality of these videos through super-resolution. However, existing Video Super-Resolution (VSR) works, focusing on third-person view videos, are actually unsuitable for handling blurring artifacts caused by rapid ego-motion and object motion in egocentric videos. To this end, we propose EgoVSR, a VSR framework specifically designed for egocentric videos. We explicitly tackle motion blurs in egocentric videos using a Dual Branch Deblur Network (DB$^2$Net) in the VSR framework. Meanwhile, a blurring mask is introduced to guide the DB$^2$Net learning, and can be used to localize blurred areas in video frames. We also design a MaskNet to predict the mask, as well as a mask loss to optimize the mask estimation. Additionally, an online motion blur synthesis model for common VSR training data is proposed to simulate motion blurs as in egocentric videos. In order to validate the effectiveness of our proposed method, we introduce an EgoVSR dataset containing a large amount of fast-motion egocentric video sequences. Extensive experiments demonstrate that our EgoVSR model can efficiently super-resolve low-quality egocentric videos and outperform strong comparison baselines. Our code, pre-trained models, and data will be released.	['Yapeng Tian', 'Wenming Yang', 'Jiamiao Zhang', 'Junhao Gu', 'Yichen Chi']	2023-05-24	null	null	null	null	['video-super-resolution']	['computer-vision']	[-1.83927696e-02 -4.55988348e-01 -9.90091413e-02 -1.56366423e-01 -4.61317241e-01 -4.19908851e-01 3.50081861e-01 -9.92550731e-01 1.99572928e-02 6.43495619e-01 8.14876199e-01 1.13940984e-01 -3.91832702e-02 -3.20115387e-01 -8.45023990e-01 -4.77749944e-01 3.04759573e-02 -4.11152840e-01 1.15428790e-01 -1.41064823e-03 2.12081581e-01 2.73922443e-01 -1.26054668e+00 3.80470514e-01 9.85752940e-01 7.12084472e-01 7.53051758e-01 7.57208884e-01 4.43289250e-01 1.08207166e+00 -4.58288133e-01 -2.15121791e-01 3.50874513e-01 -3.77677381e-01 -5.29902458e-01 2.14058995e-01 6.82738662e-01 -1.07532811e+00 -1.19834697e+00 1.18933618e+00 5.83857596e-01 3.43978852e-01 2.87751079e-01 -8.82455051e-01 -9.99774754e-01 5.30896425e-01 -7.90459156e-01 6.11351311e-01 5.42184114e-01 5.10101616e-01 5.79526305e-01 -1.04820168e+00 6.14333034e-01 1.41577756e+00 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06dde386-2f10-45ad-807c-ce87a3d4563a	asymmetric-proxy-loss-for-multi-view-acoustic	2203.16080	null	https://arxiv.org/abs/2203.16080v2	https://arxiv.org/pdf/2203.16080v2.pdf	Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings	Acoustic word embeddings (AWEs) are discriminative representations of speech segments, and learned embedding space reflects the phonetic similarity between words. With multi-view learning, where text labels are considered as supplementary input, AWEs are jointly trained with acoustically grounded word embeddings (AGWEs). In this paper, we expand the multi-view approach into a proxy-based framework for deep metric learning by equating AGWEs with proxies. A simple modification in computing the similarity matrix allows the general pair weighting to formulate the data-to-proxy relationship. Under the systematized framework, we propose an asymmetric-proxy loss that combines different parts of loss functions asymmetrically while keeping their merits. It follows the assumptions that the optimal function for anchor-positive pairs may differ from one for anchor-negative pairs, and a proxy may have a different impact when it substitutes for different positions in the triplet. We present comparative experiments with various proxy-based losses including our asymmetric-proxy loss, and evaluate AWEs and AGWEs for word discrimination tasks on WSJ corpus. The results demonstrate the effectiveness of the proposed method.	['Hoirin Kim', 'Myunghun Jung']	2022-03-30	null	null	null	null	['multi-view-learning']	['computer-vision']	[-2.59367581e-02 -1.02701798e-01 -6.78192675e-02 -5.36583364e-01 -1.06553423e+00 -4.91865188e-01 3.46990258e-01 -7.81130567e-02 -6.53225780e-01 3.80844176e-01 4.92641121e-01 -7.16125816e-02 -1.16377376e-01 -6.78623915e-01 -4.07983214e-01 -9.89201844e-01 2.41734922e-01 3.08763981e-01 3.16497862e-01 -2.99542814e-01 1.27381563e-01 2.80098528e-01 -1.33581138e+00 6.21965080e-02 5.45770228e-01 9.64990139e-01 3.55581105e-01 5.96466780e-01 -2.15079740e-01 1.04773149e-01 -7.41533220e-01 -6.89733982e-01 3.69529814e-01 -2.97411889e-01 -4.63117182e-01 -2.91345805e-01 2.13255495e-01 -2.50333637e-01 -3.60685050e-01 1.12339163e+00 9.97158349e-01 2.20796093e-01 8.68846774e-01 -1.37453771e+00 -8.95281434e-01 4.22364116e-01 -5.46751022e-01 1.68177173e-01 1.25361010e-01 -2.72677362e-01 1.76050746e+00 -1.15224707e+00 1.05224952e-01 1.21960533e+00 6.64992511e-01 4.88019407e-01 -1.14131331e+00 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5.22050738e-01 6.50290728e-01 -9.41077054e-01 -6.21766746e-01 -3.42475384e-01]	[10.983842849731445, 8.516276359558105]
d21452ea-31db-4da2-b7c5-3bc526c92513	deep-occlusion-reasoning-for-multi-camera	1704.05775	null	http://arxiv.org/abs/1704.05775v2	http://arxiv.org/pdf/1704.05775v2.pdf	Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection	People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly model those ambiguities. One of its key ingredients are high-order CRF terms that model potential occlusions and give our approach its robustness even when many people are present. Our model is trained end-to-end and we show that it outperforms several state-of-art algorithms on challenging scenes.	['François Fleuret', 'Pierre Baqué', 'Pascal Fua']	2017-04-19	deep-occlusion-reasoning-for-multi-camera-1	http://openaccess.thecvf.com/content_iccv_2017/html/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.pdf	iccv-2017-10	['multiview-detection']	['computer-vision']	[-3.44861984e-01 -4.96092677e-01 1.08921394e-01 -4.56285238e-01 -4.45062816e-01 -5.09692073e-01 7.50100434e-01 5.89169152e-02 -9.94533956e-01 7.28654206e-01 2.38181859e-01 1.62944824e-01 2.68179089e-01 -5.53433418e-01 -4.33043182e-01 -2.52826184e-01 -1.32902548e-01 9.70711768e-01 5.94040155e-01 1.02177542e-03 -2.66702026e-01 5.03304601e-01 -1.42793882e+00 -1.64294526e-01 6.30451322e-01 3.99278343e-01 2.18328089e-03 9.85029757e-01 1.40228942e-01 7.25465477e-01 -4.88721132e-01 -6.87143028e-01 3.79287302e-01 2.38583967e-01 -5.10450959e-01 2.52188355e-01 1.02910042e+00 -6.19131207e-01 -7.11832821e-01 1.06865668e+00 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4a6806d3-8bf0-4f63-b9c6-dd259d257092	probabilistic-lexicase-selection	2305.11681	null	https://arxiv.org/abs/2305.11681v1	https://arxiv.org/pdf/2305.11681v1.pdf	Probabilistic Lexicase Selection	Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexicase selection has been proven to be an NP-hard problem, which discourages deeper theoretical understanding and practical improvements to the algorithm. In this work, we introduce probabilistic lexicase selection (plexicase selection), a novel parent selection algorithm that efficiently approximates the probability distribution of lexicase selection. Our method not only demonstrates superior problem-solving capabilities as a semantic-aware selection method, but also benefits from having a probabilistic representation of the selection process for enhanced efficiency and flexibility. Experiments are conducted in two prevalent domains in genetic programming: program synthesis and symbolic regression, using standard benchmarks including PSB and SRBench. The empirical results show that plexicase selection achieves state-of-the-art problem-solving performance that is competitive to the lexicase selection, and significantly outperforms lexicase selection in computation efficiency.	['Lee Spector', 'Edward Pantridge', 'Li Ding']	2023-05-19	null	null	null	null	['program-synthesis']	['computer-code']	[ 2.40578115e-01 -1.06015213e-01 -8.15197289e-01 -4.17597771e-01 -7.11501181e-01 -3.97210091e-01 2.06491172e-01 2.22722709e-01 8.06046054e-02 9.90427136e-01 -3.62640411e-01 -4.14129555e-01 -4.52482045e-01 -1.07531285e+00 -7.82082379e-01 -6.68175519e-01 -2.28622213e-01 8.40746522e-01 4.43719715e-01 -9.27763581e-02 6.25284553e-01 2.67609805e-01 -2.16728020e+00 2.50501364e-01 1.28871131e+00 7.53608763e-01 1.32503733e-01 4.60126132e-01 -2.05148160e-01 3.59182328e-01 -6.94590867e-01 -1.70718417e-01 -9.54240710e-02 -6.17112458e-01 -7.70511985e-01 -5.51256835e-01 1.46863200e-02 3.99915576e-01 2.80910522e-01 1.17789733e+00 3.97286057e-01 -5.44357784e-02 3.65781128e-01 -1.74769652e+00 -2.58623898e-01 1.31054711e+00 -9.92862821e-01 -1.07842594e-01 5.96358418e-01 -1.47624686e-01 1.20012975e+00 -4.94050950e-01 5.13119996e-01 1.46057355e+00 5.13933420e-01 3.98710966e-01 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a79205c8-66aa-42ea-86e5-1b439b09ac83	applying-bert-and-chatgpt-for-sentiment	2302.06474	null	https://arxiv.org/abs/2302.06474v1	https://arxiv.org/pdf/2302.06474v1.pdf	Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature	This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pre-trained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of using emerging state-of-the-art large language models like ChatGPT. This serves as a useful resource for researchers and practitioners interested in using NLP techniques for sentiment analysis in the medical domain.	['Teo Susnjak']	2023-02-07	null	null	null	null	['interpretable-machine-learning']	['methodology']	[ 5.09716749e-01 2.47657970e-01 -1.74243689e-01 -7.68401206e-01 -6.48590267e-01 -4.30652887e-01 5.62251568e-01 9.78558064e-01 -6.65661812e-01 7.82132804e-01 5.20397782e-01 -7.05534220e-01 -2.12215036e-02 -2.59014547e-01 -2.07549319e-01 -5.42293310e-01 -5.13215601e-01 7.35889673e-01 -3.42835069e-01 -5.92850387e-01 5.55441976e-01 2.77318060e-01 -9.92173672e-01 9.12243783e-01 3.36789608e-01 3.57034683e-01 -3.61444242e-02 6.96672618e-01 -5.10810792e-01 1.12563121e+00 -1.01954579e+00 -3.25178355e-01 -4.70948786e-01 -6.86068907e-02 -1.01060879e+00 -2.27627963e-01 -1.45666718e-01 5.00721969e-02 6.79227829e-01 5.95736921e-01 4.23021257e-01 -4.67547059e-01 4.67542529e-01 -9.48889613e-01 1.54694334e-01 5.03033638e-01 -3.41915756e-01 4.65216041e-01 9.19637918e-01 -4.85847611e-03 7.82434583e-01 -2.89291859e-01 1.24575222e+00 1.50979924e+00 5.68914771e-01 5.83399773e-01 -9.14604127e-01 -6.04193151e-01 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-3.84506345e-01 2.76363015e-01 2.89360791e-01 -6.43748231e-03 -5.92725873e-01 4.34744447e-01]	[8.47828197479248, 8.838959693908691]
e2de1f35-826c-410c-aaf8-482d88469a1e	multimodal-machine-learning-based-knee	1904.06236	null	https://arxiv.org/abs/1904.06236v2	https://arxiv.org/pdf/1904.06236v2.pdf	Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data	Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans.	['Jérôme Thevenot', 'Sita M. A. Bierma-Zeinstra', 'Stefan Klein', 'Esa Rahtu', 'Edwin H. G. Oei', 'Joyce van Meurs', 'Simo Saarakkala', 'Aleksei Tiulpin']	2019-04-12	null	null	null	null	['knee-osteoarthritis-prediction']	['medical']	[-1.28005490e-01 -3.01956296e-01 -7.03659236e-01 -2.83830259e-02 -1.16289961e+00 -1.57111958e-01 1.35695010e-01 4.92025405e-01 -4.29198444e-01 1.07793784e+00 2.32264146e-01 -1.56097516e-01 -4.57397014e-01 -4.74872321e-01 -1.99725375e-01 -4.99429107e-01 -5.25758922e-01 7.62475908e-01 4.76587594e-01 1.84795350e-01 1.67193756e-01 3.10886145e-01 -1.29299378e+00 1.76360071e-01 1.13999045e+00 1.17783260e+00 1.57446787e-01 4.38462228e-01 3.20675611e-01 4.72207099e-01 -4.95237619e-01 -4.16278839e-02 4.36614081e-03 -2.50391901e-01 -3.71826172e-01 -3.37792039e-02 5.34982979e-02 -2.82191128e-01 -2.18524873e-01 4.77590561e-01 6.18548870e-01 -2.67852724e-01 8.93769026e-01 -6.55476153e-01 -5.75543582e-01 -1.33539051e-01 -5.97771227e-01 9.98131111e-02 7.30419010e-02 1.48985133e-01 6.63453937e-01 -8.85476053e-01 7.17925131e-01 5.86506724e-01 4.49824125e-01 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8267fd39-3fe9-4485-a356-1da582b9ec68	pushing-the-performance-limit-of-scene-text	2204.07714	null	https://arxiv.org/abs/2204.07714v2	https://arxiv.org/pdf/2204.07714v2.pdf	Pushing the Performance Limit of Scene Text Recognizer without Human Annotation	Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a fully supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-tosynthetic domain gap that restricts model performance. In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly. A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images. A character-level consistency regularization is designed to mitigate the misalignment between characters in sequence recognition. Extensive experiments on standard text recognition benchmarks demonstrate the effectiveness of the proposed method. It can steadily improve existing STR models, and boost an STR model to achieve new state-of-the-art results. To our best knowledge, this is the first consistency regularization based framework that applies successfully to STR.	['Peng Wang', 'Jae-Joon Han', 'Seungju Han', 'Seon-Min Rhee', 'Hui Li', 'Caiyuan Zheng']	2022-04-16	null	http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.pdf	cvpr-2022-1	['scene-text-recognition']	['computer-vision']	[ 4.52427179e-01 -3.49501818e-01 -3.12265754e-01 -4.94267672e-01 -9.68731701e-01 -3.88170272e-01 6.99672639e-01 -4.31946695e-01 -2.70382285e-01 6.19318604e-01 -4.63489778e-02 -1.58781186e-02 2.73346841e-01 -3.16979468e-01 -7.59721160e-01 -6.98436856e-01 7.02269197e-01 4.54371035e-01 3.72312963e-01 -8.64950493e-02 3.48579377e-01 1.50125295e-01 -1.20071578e+00 4.50400442e-01 1.21537769e+00 9.12211955e-01 3.61418784e-01 1.01444563e-02 -3.34118366e-01 1.02140617e+00 -4.08502042e-01 -3.06320369e-01 2.56675869e-01 -4.71283287e-01 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2e8aa110-a01c-4667-b4e0-0853e2b7bb98	radar-artifact-labeling-framework-ralf-method	2012.01993	null	https://arxiv.org/abs/2012.01993v1	https://arxiv.org/pdf/2012.01993v1.pdf	Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets	Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is challenging. For a dataset generation, we propose the cross sensor Radar Artifact Labeling Framework (RALF). Automatically generated labels for automotive radar data help to cure radar shortcomings like artifacts for the application of artificial intelligence. RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets. The optical evaluation backbone consists of a generalized monocular depth image estimation of surround view cameras plus LiDAR scans. Modern car sensor sets of cameras and LiDAR allow to calibrate image-based relative depth information in overlapping sensing areas. K-Nearest Neighbors matching relates the optical perception point cloud with raw radar detections. In parallel, a temporal tracking evaluation part considers the radar detections' transient behavior. Based on the distance between matches, respecting both sensor and model uncertainties, we propose a plausibility rating of every radar detection. We validate the results by evaluating error metrics on semi-manually labeled ground truth dataset of $3.28\cdot10^6$ points. Besides generating plausible radar detections, the framework enables further labeled low-level radar signal datasets for applications of perception and Autonomous Driving learning tasks.	['J. Marius Zoellner', 'Sascha Saralajew', 'Fabian E. Klein', 'Marcel P. Schilling', 'Simon T. Isele']	2020-12-03	null	null	null	null	['depth-image-estimation']	['computer-vision']	[ 4.05821145e-01 3.97758419e-03 4.43206467e-02 -8.49798858e-01 -1.19135189e+00 -6.66332066e-01 7.23178983e-01 1.40350536e-02 -2.75398403e-01 6.33836746e-01 -2.04988778e-01 -1.83453321e-01 -3.59136909e-01 -9.52627182e-01 -7.44640291e-01 -3.60765576e-01 8.81637633e-02 5.80112100e-01 3.42644721e-01 4.57761213e-02 2.67246664e-01 9.98531342e-01 -1.95459628e+00 2.69974712e-02 6.99420750e-01 1.29482067e+00 2.50846982e-01 4.69302595e-01 -2.66577266e-02 6.74666941e-01 -5.09654999e-01 -3.26190531e-01 6.81800663e-01 2.62996823e-01 4.19964105e-01 1.96622819e-01 1.17549562e+00 -4.38891739e-01 -7.62279332e-02 1.28311443e+00 9.42854062e-02 -2.14986771e-01 7.04124629e-01 -1.68013906e+00 -4.97150034e-01 1.84106335e-01 -4.25105691e-01 3.61385457e-02 6.13941848e-02 1.93229124e-01 6.86566174e-01 -1.18464088e+00 4.37830716e-01 9.95937109e-01 6.81910574e-01 2.88119584e-01 -8.98332715e-01 -9.74544764e-01 1.10715978e-01 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ac482ef2-349f-47b5-b27f-a41b3fd0f1a2	diverse-probabilistic-trajectory-forecasting	2302.03462	null	https://arxiv.org/abs/2302.03462v1	https://arxiv.org/pdf/2302.03462v1.pdf	Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints	Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents. However, the generative models used for multiple-trajectory forecasting suffer from a lack of diversity in their proposals. To avoid this form of collapse, we propose a novel method for structured prediction of diverse trajectories. To this end, we complement an underlying pretrained generative model with a diversity component, based on a determinantal point process (DPP). We balance and structure this diversity with the inclusion of knowledge-based quality constraints, independent from the underlying generative model. We combine these two novel components with a gating operation, ensuring that the predictions are both diverse and within the drivable area. We demonstrate on the nuScenes driving dataset the relevance of our compound approach, which yields significant improvements in the diversity and the quality of the generated trajectories.	['Nicolas Thome', 'Patrick Pérez', 'Hedi Ben-Younes', 'Laura Calem']	2023-02-07	null	null	null	null	['trajectory-forecasting', 'motion-planning']	['computer-vision', 'robots']	[-7.96391256e-03 3.28417957e-01 -1.12861276e-01 -4.51485068e-01 -6.18871689e-01 -7.91134000e-01 1.02120948e+00 -1.07720278e-01 -2.99400333e-02 8.55212867e-01 4.86358762e-01 -2.67744124e-01 -2.03829557e-01 -9.58048463e-01 -8.03610623e-01 -9.22785103e-01 1.61968648e-01 7.89565980e-01 6.10462487e-01 -5.71787655e-01 2.20221460e-01 6.37284935e-01 -1.84051669e+00 1.16632618e-01 1.13150275e+00 4.28654045e-01 1.32987708e-01 6.66934669e-01 1.87266067e-01 6.75025880e-01 8.52748565e-03 -4.76467907e-01 2.04268202e-01 -4.72292244e-01 -3.95358413e-01 2.35830620e-01 2.66551286e-01 -1.72038496e-01 -4.95069206e-01 6.49395406e-01 2.57135779e-01 4.08285737e-01 8.82527471e-01 -1.34734023e+00 -5.36077619e-02 2.72470802e-01 -1.33266553e-01 4.64074314e-02 7.25181475e-02 3.77122074e-01 1.08803511e+00 -6.63151622e-01 9.52286363e-01 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0b3b0db8-5b0f-4073-b95b-2e46c8db5c6f	hybrid-rule-neural-coreference-resolution	2212.10087	null	https://arxiv.org/abs/2212.10087v1	https://arxiv.org/pdf/2212.10087v1.pdf	Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning	A coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to tackle two main tasks: one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention. In this paper, we propose a hybrid rule-neural coreference resolution system based on actor-critic learning, such that it can achieve better coreference performance by leveraging the advantages from both the heuristic rules and a neural conference model. This end-to-end system can also perform both mention detection and resolution by leveraging a joint training algorithm. We experiment on the BERT model to generate input span representations. Our model with the BERT span representation achieves the state-of-the-art performance among the models on the CoNLL-2012 Shared Task English Test Set.	['Hongxia Jin', 'Yu Wang']	2022-12-20	null	null	null	null	['coreference-resolution']	['natural-language-processing']	[ 5.88747822e-02 6.33428991e-01 -4.24996078e-01 -3.68053466e-01 -1.37096739e+00 -2.95344442e-01 4.54917312e-01 1.62307292e-01 -4.61297035e-01 8.32049429e-01 6.15027010e-01 -9.03154314e-02 -2.76119947e-01 -5.87700903e-01 -6.55016601e-01 -4.92080003e-01 -3.83298695e-02 1.14619803e+00 2.98812360e-01 -3.88066381e-01 -2.50852723e-02 3.20608705e-01 -1.25364649e+00 4.34243888e-01 1.03970301e+00 6.51199698e-01 1.89188600e-01 2.92981446e-01 -1.89753011e-01 8.87667775e-01 -3.77452463e-01 -7.16774225e-01 -1.48652479e-01 -1.38771787e-01 -1.29628611e+00 -8.00951838e-01 4.01159257e-01 1.78819999e-01 -3.57602209e-01 1.02753603e+00 6.33322358e-01 4.98435289e-01 4.30846751e-01 -1.06948256e+00 -4.34800684e-01 1.59137106e+00 -6.08285189e-01 3.02464306e-01 5.20441771e-01 -2.91831076e-01 1.45669520e+00 -4.50379401e-01 6.71762764e-01 1.60341311e+00 4.23361480e-01 7.09247351e-01 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c0295d28-d45b-4e4b-8a60-b4253a3fa938	discriminant-analysis-in-contrasting	2201.03029	null	https://arxiv.org/abs/2201.03029v1	https://arxiv.org/pdf/2201.03029v1.pdf	Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication	A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensionality Reduction method can give us a new edge to tackle this problem. In this paper we present Discriminant Analysis in different dimensions with Linear and Quadratic form for binary classification along with metrics. We were able to achieve good accuracy and less variation with Discriminant Analysis as compared to many commonly used classification algorithms with training accuracy reaching 97.37% and testing accuracy of 95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of data with visualizations for deeper understanding of problem.	['Ronald Melwin Laban', 'Raunak Joshi', 'Himanshu Soni', 'Abhishek Gupta']	2022-01-09	null	null	null	null	['supervised-dimensionality-reduction']	['computer-vision']	[-9.52180475e-02 5.70448264e-02 -2.28604466e-01 -6.52791381e-01 4.09968793e-02 -4.61902678e-01 3.17019016e-01 6.67790115e-01 1.77322868e-02 7.47232854e-01 -1.61414444e-01 -1.91181034e-01 -9.16243672e-01 -5.57703972e-01 2.36426204e-01 -5.60730517e-01 -5.39116681e-01 8.42917860e-01 -4.37896073e-01 -1.65280327e-01 4.91980046e-01 7.58905947e-01 -1.45958424e+00 -9.49973762e-02 1.17843938e+00 6.43482447e-01 -2.86292791e-01 8.38576496e-01 -1.01549357e-01 -2.05959901e-01 -3.74319404e-01 -1.67693451e-01 3.91250908e-01 -5.78710973e-01 -3.01243305e-01 -1.10396907e-01 2.57440388e-01 4.01194483e-01 2.19142258e-01 7.71631539e-01 7.34920561e-01 -2.74146438e-01 1.36563241e+00 -1.55556822e+00 -5.04689753e-01 2.15566084e-01 -9.82720554e-01 3.92961204e-01 1.61120772e-01 -2.33930483e-01 1.67133600e-01 -6.28790855e-01 5.82861722e-01 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ab6d7dbb-da6d-45b1-b0be-69f5ecab4dc1	a-graph-kernel-based-on-context-vectors-for	null	null	https://www.sciencedirect.com/science/article/pii/S1532046416300053	https://www.sciencedirect.com/science/article/pii/S1532046416300053	A graph kernel based on context vectors for extracting drug–drug interactions	The clinical recognition of drug–drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of texts, these features can’t yet adequately express long and complicated sentences. In this paper, we present an effective graph kernel which makes full use of different types of contexts to identify DDIs from biomedical literature. In our approach, the relations among long-range words, in addition to close-range words, are obtained by the graph representation of a parsed sentence. Context vectors of a vertex, an iterative vectorial representation of all labeled nodes adjacent and nonadjacent to it, adequately capture the direct and indirect substructures’ information. Furthermore, the graph kernel considering the distance between context vectors is used to detect DDIs. Experimental results on the DDIExtraction 2013 corpus show that our system achieves the best detection and classification performance (F-score) of DDIs (81.8 and 68.4, respectively). Especially for the Medline-2013 dataset, our system outperforms the top-ranking DDIs systems by F-scores of 10.7 and 12.2 in detection and classification, respectively.	['Jian Wang', 'Zhihao Yang', 'Yijia Zhang', 'Bo Xu', 'Zhehuan Zhao', 'Hongfei Lin', 'Wei Zheng']	2016-03-21	null	null	null	journal-of-biomedical-informatics-2016-3	['drug-drug-interaction-extraction']	['natural-language-processing']	[ 1.82012916e-01 -1.07934847e-01 -4.80586141e-01 -2.12545887e-01 -3.98135632e-01 -4.82717007e-01 4.77925956e-01 1.03216028e+00 -3.23856562e-01 8.05366755e-01 5.57013229e-02 -6.35434270e-01 -6.40637696e-01 -7.24753261e-01 -3.50393444e-01 -7.67945528e-01 -4.02591437e-01 4.96872574e-01 3.24334688e-02 -1.31436978e-02 3.14053655e-01 7.90546894e-01 -9.49330628e-01 3.40137511e-01 1.30451989e+00 5.41698575e-01 2.52413571e-01 4.36220497e-01 -5.59244096e-01 4.62960273e-01 -6.49367988e-01 -3.98458004e-01 -3.45879793e-01 -5.94673932e-01 -5.83059609e-01 -2.47315422e-01 1.36376798e-01 1.16698705e-01 -3.68434578e-01 1.18633437e+00 5.35894930e-01 -3.66824359e-01 1.03461790e+00 -7.98917174e-01 -4.47065771e-01 3.17604542e-01 -7.25254118e-01 4.98541057e-01 6.29104853e-01 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dd7d272e-3d0a-468a-ae4c-63cfa2448786	strumming-to-the-beat-audio-conditioned	2104.02687	null	https://arxiv.org/abs/2104.02687v1	https://arxiv.org/pdf/2104.02687v1.pdf	Strumming to the Beat: Audio-Conditioned Contrastive Video Textures	We introduce a non-parametric approach for infinite video texture synthesis using a representation learned via contrastive learning. We take inspiration from Video Textures, which showed that plausible new videos could be generated from a single one by stitching its frames together in a novel yet consistent order. This classic work, however, was constrained by its use of hand-designed distance metrics, limiting its use to simple, repetitive videos. We draw on recent techniques from self-supervised learning to learn this distance metric, allowing us to compare frames in a manner that scales to more challenging dynamics, and to condition on other data, such as audio. We learn representations for video frames and frame-to-frame transition probabilities by fitting a video-specific model trained using contrastive learning. To synthesize a texture, we randomly sample frames with high transition probabilities to generate diverse temporally smooth videos with novel sequences and transitions. The model naturally extends to an audio-conditioned setting without requiring any finetuning. Our model outperforms baselines on human perceptual scores, can handle a diverse range of input videos, and can combine semantic and audio-visual cues in order to synthesize videos that synchronize well with an audio signal.	['Trevor Darrell', 'Alexei A. Efros', 'Andrew Owens', 'Shiry Ginosar', 'Medhini Narasimhan']	2021-04-06	null	null	null	null	['texture-synthesis']	['computer-vision']	[ 6.03845417e-01 -3.37020308e-02 -2.30582446e-01 -2.54077613e-01 -1.15478659e+00 -7.58570850e-01 1.01366282e+00 -3.41614068e-01 -3.41103151e-02 6.75494611e-01 6.00850940e-01 1.16370119e-01 1.24474086e-01 -4.85069633e-01 -1.26927805e+00 -7.02439904e-01 -3.32295209e-01 2.13812754e-01 3.40353549e-01 -1.67947784e-01 1.77315310e-01 3.34350020e-01 -1.99007142e+00 7.71884084e-01 2.16326043e-01 8.28849196e-01 3.84099782e-01 1.05524361e+00 2.26017743e-01 8.91479313e-01 -4.65712726e-01 3.51000614e-02 3.51524651e-01 -7.85736203e-01 -6.17573440e-01 3.62513900e-01 9.40809309e-01 -4.20159459e-01 -4.63742673e-01 5.56328952e-01 2.96148270e-01 5.19815624e-01 8.01976860e-01 -1.14847565e+00 -5.91180980e-01 4.99311477e-01 -3.58596861e-01 1.58045545e-01 8.92445266e-01 4.66713578e-01 1.00195146e+00 -7.72525966e-01 1.12820947e+00 1.57811475e+00 6.49761319e-01 6.80618465e-01 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-3.12556744e-01 7.32877910e-01 6.89929903e-01 -6.83621585e-01 -4.86874551e-01 7.71450281e-01]	[10.87969970703125, -0.670634925365448]
a76da5a6-c05d-4ebe-b0f6-b88c1ef0a9d4	high-resolution-breast-cancer-screening-with	1703.07047	null	http://arxiv.org/abs/1703.07047v3	http://arxiv.org/pdf/1703.07047v3.pdf	High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks	Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differences between these two types of images. Specifically, fine details are necessary for detection in medical images, unlike in natural images where coarse structures matter most. This difference makes it inadequate to use the existing network architectures developed for natural images, because they work on heavily downscaled images to reduce the memory requirements. This hides details necessary to make accurate predictions. Additionally, a single exam in medical imaging often comes with a set of views which must be fused in order to reach a correct conclusion. In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images. We evaluate it on large-scale mammography-based breast cancer screening (BI-RADS prediction) using 886,000 images. We focus on investigating the impact of the training set size and image size on the prediction accuracy. Our results highlight that performance increases with the size of training set, and that the best performance can only be achieved using the original resolution. In the reader study, performed on a random subset of the test set, we confirmed the efficacy of our model, which achieved performance comparable to a committee of radiologists when presented with the same data.	['Ujas Parikh', 'Nan Wu', 'Laura Heacock', 'Kyunghyun Cho', 'Krzysztof J. Geras', 'Stacey Wolfson', 'Linda Moy', 'Eric Kim', 'S. Gene Kim', 'Yiqiu Shen']	2017-03-21	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 5.14117002e-01 3.69507611e-01 8.41726065e-02 -5.48503399e-01 -8.68486404e-01 -2.57236481e-01 3.64450127e-01 3.52536321e-01 -7.07537234e-01 3.74496788e-01 2.41680279e-01 -5.68491161e-01 -1.59292549e-01 -9.86666739e-01 -7.87159443e-01 -5.20762503e-01 -8.63240585e-02 4.56321359e-01 4.88945127e-01 -2.09329352e-01 -1.53929710e-01 6.17122710e-01 -1.32521176e+00 8.10804367e-01 4.07356888e-01 8.81123483e-01 4.73788887e-01 8.85429025e-01 1.87559590e-01 8.22177470e-01 -4.05853540e-01 -5.37027180e-01 2.23858327e-01 -3.25980306e-01 -8.54728043e-01 2.03444675e-01 5.88090420e-01 -7.36468077e-01 -2.16328502e-01 7.88387775e-01 6.46373749e-01 -1.90078169e-01 5.77184975e-01 -2.81427056e-01 -2.49704197e-01 4.27070737e-01 -4.93469775e-01 5.40576577e-01 4.27111611e-02 -1.07123882e-01 5.42620897e-01 -5.31134427e-01 6.69386864e-01 9.43920493e-01 9.94070113e-01 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-1.03940725e+00 -4.59424615e-01 4.54704076e-01 4.31301922e-01 -3.24554086e-01 2.63737142e-02 1.06515482e-01]	[15.018254280090332, -2.4602878093719482]
58735911-0782-4b15-ab81-1736360e0f92	end-to-end-dense-video-captioning-with-masked	1804.00819	null	http://arxiv.org/abs/1804.00819v1	http://arxiv.org/pdf/1804.00819v1.pdf	End-to-End Dense Video Captioning with Masked Transformer	Dense video captioning aims to generate text descriptions for all events in an untrimmed video. This involves both detecting and describing events. Therefore, all previous methods on dense video captioning tackle this problem by building two models, i.e. an event proposal and a captioning model, for these two sub-problems. The models are either trained separately or in alternation. This prevents direct influence of the language description to the event proposal, which is important for generating accurate descriptions. To address this problem, we propose an end-to-end transformer model for dense video captioning. The encoder encodes the video into appropriate representations. The proposal decoder decodes from the encoding with different anchors to form video event proposals. The captioning decoder employs a masking network to restrict its attention to the proposal event over the encoding feature. This masking network converts the event proposal to a differentiable mask, which ensures the consistency between the proposal and captioning during training. In addition, our model employs a self-attention mechanism, which enables the use of efficient non-recurrent structure during encoding and leads to performance improvements. We demonstrate the effectiveness of this end-to-end model on ActivityNet Captions and YouCookII datasets, where we achieved 10.12 and 6.58 METEOR score, respectively.	['Richard Socher', 'Yingbo Zhou', 'Luowei Zhou', 'Caiming Xiong', 'Jason J. Corso']	2018-04-03	end-to-end-dense-video-captioning-with-masked-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_End-to-End_Dense_Video_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_End-to-End_Dense_Video_CVPR_2018_paper.pdf	cvpr-2018-6	['dense-video-captioning']	['computer-vision']	[ 3.81437957e-01 3.48908931e-01 -1.82941213e-01 -3.49043697e-01 -9.61345851e-01 -3.03878725e-01 7.85239816e-01 -1.74574241e-01 -2.63268858e-01 7.02310681e-01 8.56150627e-01 1.13394998e-01 5.53153992e-01 -5.36789834e-01 -1.05111241e+00 -5.95133305e-01 1.96494851e-02 3.47481400e-01 1.61973551e-01 1.81814075e-01 -2.91650221e-02 -1.60867378e-01 -1.39675093e+00 7.88061917e-01 6.14511967e-01 1.08378851e+00 6.35399878e-01 6.91703200e-01 -1.81396529e-01 1.17316973e+00 -6.19250357e-01 -3.48404169e-01 -4.42161635e-02 -8.46726060e-01 -5.89049160e-01 1.70362040e-01 2.93432474e-01 -5.35203874e-01 -8.39974046e-01 8.89741957e-01 3.90020609e-01 6.89674839e-02 5.25379360e-01 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139320fe-3fd9-44c6-82f4-13a8dbbfa66a	online-multi-object-tracking-via-structural	null	null	http://openaccess.thecvf.com/content_cvpr_2016/html/Yoon_Online_Multi-Object_Tracking_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Yoon_Online_Multi-Object_Tracking_CVPR_2016_paper.pdf	Online Multi-Object Tracking via Structural Constraint Event Aggregation	Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera movements and thus not always smooth or predictable. To deal with such unexpected camera motion for online 2D MOT, a structural motion constraint between objects has been utilized thanks to its robustness to camera motion. In this paper, we propose a new data association method that effectively exploits structural motion constraints in the presence of large camera motion. In addition, to further improve the robustness of data association against mis-detections and clutters, a novel event aggregation approach is developed to integrate structural constraints in assignment costs for online MOT. Experimental results on a large number of datasets demonstrate the effectiveness of the proposed algorithm for online 2D MOT.	['Kuk-Jin Yoon', 'Ming-Hsuan Yang', 'Chang-Ryeol Lee', 'Ju Hong Yoon']	2016-06-01	null	null	null	cvpr-2016-6	['online-multi-object-tracking']	['computer-vision']	[ 1.84348449e-01 -6.66271031e-01 1.96842961e-02 -2.01780722e-02 -4.12185341e-01 -5.18053770e-01 3.02714914e-01 3.56249332e-01 -4.09916610e-01 7.59168327e-01 -3.81753623e-01 2.86261618e-01 -4.52028394e-01 -4.43716347e-01 -6.38497829e-01 -8.98507535e-01 -1.06759198e-01 3.15078825e-01 8.34154069e-01 3.62608582e-01 8.65072757e-02 5.56077600e-01 -1.65008414e+00 -9.69389603e-02 6.22325122e-01 1.00305068e+00 6.87395811e-01 7.33570695e-01 2.34001689e-02 6.71695650e-01 -5.96401155e-01 2.11090385e-03 5.63520908e-01 -1.94225609e-01 -1.35985032e-01 6.86954856e-01 5.09671152e-01 -4.21253800e-01 -1.54849604e-01 1.02365601e+00 3.52028131e-01 4.45810288e-01 3.56685728e-01 -1.43358886e+00 3.97005230e-02 1.85766459e-01 -1.00978661e+00 6.97675467e-01 2.82012105e-01 2.18850300e-01 7.90719628e-01 -7.09122062e-01 4.98016626e-01 1.27005649e+00 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-1.84081674e-01 -6.29993081e-01 -7.09933400e-01 3.43187809e-01 4.94245857e-01 -1.05337358e+00 -3.53612036e-01 1.53539672e-01]	[6.4921698570251465, -2.0279595851898193]
e03bc3e9-ebdd-4134-9a07-5607745d385b	review-dual-benefits-compositions-recommended	1905.12405	null	https://arxiv.org/abs/1905.12405v1	https://arxiv.org/pdf/1905.12405v1.pdf	Review: dual benefits, compositions, recommended storage, and intake duration of mother's milk	Breastfeeding benefits both infants and mothers. Nutrients in mother's milk help protect infants from multiple diseases including infections, cancers, diabetes, gastrointestinal and respiratory diseases. We performed literature mining on 31,496 mother's-milk-related abstracts from PubMed and the results suggest the need for individualized mother's milk fortification and proper maternal supplementations (e.g. probiotics, vitamin D), because mother's milk compositions (e.g. fatty acids) vary according to maternal diet and responses to infection in mothers and/or infants. We review at details the variability observed in mother's milk compositions and its possible health effects in infants. We also review the effects of storage practices on mother's milk nutrients, recommended durations for mother's milk intake and the associated health benefits.	['Suryani Lukman', 'Ammu Prasanna Kumar']	2019-05-28	null	null	null	null	['literature-mining']	['natural-language-processing']	[ 2.16944277e-01 6.50160164e-02 -1.05837488e+00 -4.78363782e-01 1.09787874e-01 -5.57414830e-01 -2.41255462e-01 1.26493990e+00 -1.71693102e-01 4.76843894e-01 6.24540567e-01 -7.88295388e-01 7.44783506e-02 -8.27406168e-01 -1.19425869e+00 -7.44167089e-01 -2.29702946e-02 -1.27084866e-01 -5.69062047e-02 2.28523910e-01 1.80295080e-01 2.97838986e-01 -1.94193208e+00 5.58522224e-01 8.53072584e-01 3.01686317e-01 5.04860938e-01 3.82880390e-01 -7.53697395e-01 5.99404037e-01 -7.69398138e-02 -5.86398579e-02 -5.45410454e-01 -8.48224521e-01 1.70245409e-01 -6.39240742e-01 -9.32380408e-02 -7.37858832e-01 7.74244010e-01 8.83365989e-01 4.56795752e-01 -5.82233727e-01 7.49359846e-01 -8.21772158e-01 -9.35267031e-01 7.09826469e-01 -6.98829174e-01 1.90915614e-01 2.69312829e-01 -1.45097628e-01 -1.45372301e-01 -6.85880542e-01 2.48093441e-01 1.41890895e+00 5.88093758e-01 1.03526449e+00 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-1.00161541e+00 -4.97410089e-01 -5.21839917e-01 8.20593476e-01 -7.04054415e-01 -3.25082541e-01 -4.91484255e-02]	[13.846563339233398, 3.0104565620422363]
1bc0946b-c06d-4580-9afb-dba8c137d0c7	is-chatgpt-equipped-with-emotional-dialogue	2304.09582	null	https://arxiv.org/abs/2304.09582v1	https://arxiv.org/pdf/2304.09582v1.pdf	Is ChatGPT Equipped with Emotional Dialogue Capabilities?	This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI. The study evaluates the performance of ChatGPT on emotional dialogue understanding and generation through a series of experiments on several downstream tasks. Our findings indicate that while ChatGPT's performance on emotional dialogue understanding may still lag behind that of supervised models, it exhibits promising results in generating emotional responses. Furthermore, the study suggests potential avenues for future research directions.	['Bing Qin', 'Yanpeng Tong', 'Shilong Wang', 'Xin Lu', 'Yanyan Zhao', 'Weixiang Zhao']	2023-04-19	null	null	null	null	['dialogue-understanding']	['natural-language-processing']	[-0.4401266 1.021031 -0.04135167 -0.49719507 -0.5560196 -0.4087794 0.6535852 0.00867274 0.12772016 1.1058378 0.6501693 -0.25456086 0.41581476 -0.49323484 0.20083073 -0.22240563 -0.29381955 0.5722504 -0.5073857 -0.89335036 0.23562938 -0.05700759 -0.845816 0.64031154 0.85751545 0.3792336 -0.28817677 1.002553 -0.26920366 1.563155 -0.98197836 -0.68590134 -0.35778424 -0.83130383 -1.5378296 -0.23453832 -0.5805204 -0.31630778 -0.02183366 0.4975939 0.7167127 0.6146023 0.46903464 -1.3729663 -0.84152657 0.9386135 0.13598576 0.15075192 1.0345683 0.33621866 1.1561195 -0.6611495 0.9388796 1.3178793 0.81871426 1.1103255 -0.9845357 -0.55431885 -0.0323325 -0.30082 -0.7474535 -0.5733567 0.43891189 -0.21686082 1.719634 0.32473934 0.74696064 1.4063156 0.5415384 0.7786803 1.3529526 -0.36812308 0.20325692 0.65090024 0.37875885 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e4527cbf-012f-4bea-8f53-ca22e1a23209	toward-3d-object-reconstruction-from-stereo	1910.08223	null	https://arxiv.org/abs/1910.08223v2	https://arxiv.org/pdf/1910.08223v2.pdf	Toward 3D Object Reconstruction from Stereo Images	Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem. These methods suffer from poor generalization and may lead to low-quality reconstructions for unseen objects. Nowadays, stereo cameras are pervasive in emerging devices such as dual-lens smartphones and robots, which enables the use of the two-view nature of stereo images to explore the 3D structure and thus improve the reconstruction performance. In this paper, we propose a new deep learning framework for reconstructing the 3D shape of an object from a pair of stereo images, which reasons about the 3D structure of the object by taking bidirectional disparities and feature correspondences between the two views into account. Besides, we present a large-scale synthetic benchmarking dataset, namely StereoShapeNet, containing 1,052,976 pairs of stereo images rendered from ShapeNet along with the corresponding bidirectional depth and disparity maps. Experimental results on the StereoShapeNet benchmark demonstrate that the proposed framework outperforms the state-of-the-art methods.	['Shangchen Zhou', 'Xiaoshuai Sun', 'Wenxiu Sun', 'Hongxun Yao', 'Haozhe Xie', 'Shengping Zhang']	2019-10-18	null	null	null	null	['3d-object-reconstruction']	['computer-vision']	[ 9.24324710e-03 -1.60276145e-01 4.77283448e-01 -4.48691666e-01 -4.85445023e-01 -6.18531764e-01 5.01289070e-01 -4.52901959e-01 -1.16598204e-01 4.10556614e-01 3.38981226e-02 -4.70397174e-02 7.56145716e-02 -8.35297227e-01 -1.01544559e+00 -7.52970397e-01 5.43893099e-01 7.93070614e-01 3.19748938e-01 9.15873330e-03 2.16248959e-01 6.33737504e-01 -1.89578640e+00 3.64393383e-01 5.19602597e-01 1.41614556e+00 6.10315204e-01 7.99514055e-02 -1.69101253e-01 2.44442463e-01 -1.02426961e-01 -3.92500103e-01 5.91546297e-01 7.76848122e-02 -5.80056310e-01 5.31933248e-01 5.58455467e-01 -7.40207851e-01 -5.81832290e-01 1.07732689e+00 4.33846772e-01 -2.13862419e-01 3.27712595e-01 -1.04579830e+00 -3.91414672e-01 -2.96387225e-01 -5.38975716e-01 -2.70479530e-01 6.79796219e-01 2.30273947e-01 7.09587932e-01 -1.05693591e+00 7.07019866e-01 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b9ced89a-8695-48b9-a9dc-20b308b1c6d3	bayesian-imbalanced-regression-debiasing	null	null	https://openreview.net/forum?id=IeYEepOLsFT	https://openreview.net/pdf?id=IeYEepOLsFT	Bayesian Imbalanced Regression Debiasing	Imbalanced regression, where the training data has an uneven distribution on its range, is widely encountered in the real world, e.g., age estimation (uni-dimensional regression) and pose estimation (multi-dimensional regression). Compared to imbalanced and long-tailed classification, imbalanced regression has its unique challenges as the regression label space can be continuous, boundless, and high-dimensional. In this work, we present a principled framework, Bayesian Posterior Debiasing (Bayesian-PD), for re-balancing the regression among frequent and rare observations. Our key insight is that a balanced posterior can be obtained by debiasing the conditional probability with a regression label space prior. Importantly, through a normalization reparameterization technique, we derive a general debiasing function between the empirical posterior and the balanced posterior without relying on task-specific assumptions. We show that the Bayesian-PD framework has multiple instantiations in both training and testing time, with either closed-form or numerical implementations. We further uncover that several existing methods in imbalanced classification/regression serve as special cases of our Bayesian-PD framework. Extensive experiments on both uni- and multi-dimensional regression benchmarks demonstrate the effectiveness of the Bayesian-PD framework on various real-world tasks. Notably, Bayesian-PD exhibits strong robustness to different skewness of the training distributions.	['Ziwei Liu', 'Cunjun Yu', 'Mingyuan Zhang', 'Jiawei Ren']	2021-09-29	null	null	null	null	['age-estimation', 'age-estimation']	['computer-vision', 'miscellaneous']	[ 9.36704725e-02 -2.60318667e-01 -5.44449210e-01 -4.93590534e-01 -1.00126565e+00 -2.75419414e-01 4.02966827e-01 1.47109643e-01 -2.56341964e-01 1.08218896e+00 -1.61716625e-01 -2.21885875e-01 -3.80177736e-01 -5.77783287e-01 -6.35677934e-01 -1.00084674e+00 1.38014227e-01 7.74666667e-01 -1.30663872e-01 6.61903322e-02 2.06021041e-01 1.65386453e-01 -1.39886081e+00 -3.64275813e-01 9.99215662e-01 9.82437253e-01 -6.55235350e-01 6.11203015e-01 3.04256976e-01 2.75083452e-01 -9.58157182e-01 -5.49567521e-01 2.86008000e-01 -3.47755244e-03 -3.56590599e-01 -9.48096514e-02 5.72305977e-01 -1.51961938e-01 -1.94958046e-01 9.43779707e-01 5.91969788e-01 1.26736775e-01 1.16759145e+00 -1.82961321e+00 -3.24413836e-01 1.55840933e-01 -1.49343622e+00 2.46190175e-01 1.41083762e-01 -3.16342026e-01 7.12925136e-01 -6.79690480e-01 1.02874316e-01 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5eabcab0-9dd3-4a28-9f50-f264ddf94766	an-analysis-of-abusive-language-data	null	null	https://aclanthology.org/2022.games-1.1	https://aclanthology.org/2022.games-1.1.pdf	An Analysis of Abusive Language Data Collected through a Game with a Purpose	In this work we present an analysis of abusive language annotations collected through a 3D video game. With this approach, we are able to involve in the annotation teenagers, i.e. typical targets of cyberbullying, whose data are usually not available for research purposes. Using the game in the framework of educational activities to empower teenagers against online abuse we are able to obtain insights into how teenagers communicate, and what kind of messages they consider more offensive. While players produced interesting annotations and the distributions of classes between players and experts are similar, we obtained a significant number of mismatching judgements between experts and players.	['Sara Tonelli', 'Federico Bonetti']	null	null	null	null	games-lrec-2022-6	['abusive-language']	['natural-language-processing']	[-2.26387918e-01 5.68140149e-01 -7.04996660e-02 -2.92642355e-01 -5.21717966e-01 -8.64026845e-01 5.02286434e-01 6.93848610e-01 -5.98648667e-01 5.40055275e-01 3.00987899e-01 -1.29185095e-01 6.64383993e-02 -7.66104341e-01 -2.46703073e-01 -4.40093160e-01 -3.61301042e-02 4.48999435e-01 5.81876040e-01 -3.84409577e-01 3.93618494e-01 1.79172963e-01 -1.50925529e+00 4.19776499e-01 5.98940015e-01 6.13207221e-01 -4.98204380e-01 5.64087749e-01 -2.34587669e-01 1.12717164e+00 -9.42803264e-01 -1.19982648e+00 -3.70125100e-02 -1.62580550e-01 -8.85808587e-01 -1.04461417e-01 5.86835146e-01 -4.94063020e-01 -7.36767054e-02 1.31556261e+00 3.73282492e-01 8.89555067e-02 6.15418077e-01 -1.06532574e+00 1.62147939e-01 1.01585734e+00 -4.28384930e-01 6.44804299e-01 9.52740431e-01 -1.12392828e-01 8.22558403e-01 -5.25015414e-01 9.67369437e-01 1.44239211e+00 6.49938464e-01 5.80227673e-01 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a368732b-a99f-47d0-8570-903d6a32bbb5	spatio-temporal-dynamic-inference-network-for	2108.11743	null	https://arxiv.org/abs/2108.11743v1	https://arxiv.org/pdf/2108.11743v1.pdf	Spatio-Temporal Dynamic Inference Network for Group Activity Recognition	Group activity recognition aims to understand the activity performed by a group of people. In order to solve it, modeling complex spatio-temporal interactions is the key. Previous methods are limited in reasoning on a predefined graph, which ignores the inherent person-specific interaction context. Moreover, they adopt inference schemes that are computationally expensive and easily result in the over-smoothing problem. In this paper, we manage to achieve spatio-temporal person-specific inferences by proposing Dynamic Inference Network (DIN), which composes of Dynamic Relation (DR) module and Dynamic Walk (DW) module. We firstly propose to initialize interaction fields on a primary spatio-temporal graph. Within each interaction field, we apply DR to predict the relation matrix and DW to predict the dynamic walk offsets in a joint-processing manner, thus forming a person-specific interaction graph. By updating features on the specific graph, a person can possess a global-level interaction field with a local initialization. Experiments indicate both modules' effectiveness. Moreover, DIN achieves significant improvement compared to previous state-of-the-art methods on two popular datasets under the same setting, while costing much less computation overhead of the reasoning module.	['Mang Wang', 'Dong Ni', 'Hangjie Yuan']	2021-08-26	null	http://openaccess.thecvf.com//content/ICCV2021/html/Yuan_Spatio-Temporal_Dynamic_Inference_Network_for_Group_Activity_Recognition_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Yuan_Spatio-Temporal_Dynamic_Inference_Network_for_Group_Activity_Recognition_ICCV_2021_paper.pdf	iccv-2021-1	['group-activity-recognition']	['computer-vision']	[ 1.36796266e-01 -1.83974504e-02 -8.49953666e-02 -3.61782670e-01 1.53340930e-02 -9.39433724e-02 7.30629623e-01 2.27130756e-01 -3.63157243e-01 4.97526169e-01 3.72322828e-01 -2.44903490e-02 -6.07817292e-01 -1.21610105e+00 -2.98072904e-01 -5.47170460e-01 -1.33785605e-01 5.48789680e-01 3.67136389e-01 -2.91840043e-02 -6.55627102e-02 2.84176618e-01 -1.35194755e+00 1.98623300e-01 9.76682901e-01 7.01187432e-01 9.14995000e-03 5.21683097e-01 1.29341811e-01 1.18319738e+00 -5.40763259e-01 -5.06211758e-01 -7.07449466e-02 -5.83981872e-01 -6.71015799e-01 3.74218017e-01 2.03796580e-01 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d9e27c6e-e609-46fa-83bb-8a57122c0eb2	2d-human-pose-estimation-new-benchmark-and	null	null	http://openaccess.thecvf.com/content_cvpr_2014/html/Andriluka_2D_Human_Pose_2014_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2014/papers/Andriluka_2D_Human_Pose_2014_CVPR_paper.pdf	2D Human Pose Estimation: New Benchmark and State of the Art Analysis	Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.	['Peter Gehler', 'Mykhaylo Andriluka', 'Leonid Pishchulin', 'Bernt Schiele']	2014-06-01	null	null	null	cvpr-2014-6	['2d-human-pose-estimation', 'art-analysis']	['computer-vision', 'computer-vision']	[ 7.00947270e-02 -9.48066115e-02 -3.37315708e-01 -3.36395383e-01 -5.79902589e-01 -5.61145186e-01 7.03908801e-01 -2.23275155e-01 -3.42715979e-01 7.23095357e-01 7.36079454e-01 4.97310162e-01 8.94784331e-02 -9.02390182e-02 -3.46179456e-01 -4.22989190e-01 -4.03410137e-01 7.94621527e-01 2.31772885e-01 -3.77630174e-01 -7.20999017e-02 4.84880030e-01 -1.45993483e+00 9.76284668e-02 -6.07146285e-02 6.23288453e-01 -3.04049522e-01 6.32441640e-01 6.80201352e-01 4.60864723e-01 -7.10066497e-01 -6.18181765e-01 2.27385238e-01 -3.62106502e-01 -9.73092139e-01 3.96434367e-01 7.07788408e-01 -4.37384635e-01 -4.28541660e-01 2.94950277e-01 9.47237909e-01 3.52532953e-01 5.18351138e-01 -1.44714785e+00 3.01248133e-01 2.07606629e-02 -5.22749305e-01 1.50460407e-01 1.12411118e+00 1.34816170e-01 8.35041106e-01 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88424a2b-df07-414a-9cd5-88a806a5dda9	in-neural-machine-translation-what-does	null	null	https://aclanthology.org/2020.acl-main.688	https://aclanthology.org/2020.acl-main.688.pdf	In Neural Machine Translation, What Does Transfer Learning Transfer?	Transfer learning improves quality for low-resource machine translation, but it is unclear what exactly it transfers. We perform several ablation studies that limit information transfer, then measure the quality impact across three language pairs to gain a black-box understanding of transfer learning. Word embeddings play an important role in transfer learning, particularly if they are properly aligned. Although transfer learning can be performed without embeddings, results are sub-optimal. In contrast, transferring only the embeddings but nothing else yields catastrophic results. We then investigate diagonal alignments with auto-encoders over real languages and randomly generated sequences, finding even randomly generated sequences as parents yield noticeable but smaller gains. Finally, transfer learning can eliminate the need for a warm-up phase when training transformer models in high resource language pairs.	['Kenneth Heafield', 'Nikolay Bogoychev', 'Alham Fikri Aji', 'Rico Sennrich']	2020-07-01	null	null	null	acl-2020-6	['learning-word-embeddings']	['methodology']	[ 2.13254988e-01 1.00668557e-01 -3.94250959e-01 -1.93654448e-01 -1.31108832e+00 -9.10236239e-01 6.05898619e-01 -6.51274547e-02 -6.01294041e-01 1.11493015e+00 6.40355527e-01 -8.49682570e-01 3.45740527e-01 -8.45124960e-01 -1.02876329e+00 -3.34977001e-01 4.75616418e-02 6.82325423e-01 -6.24031499e-02 -5.72202682e-01 2.33827392e-03 9.37529206e-02 -9.23498034e-01 4.04762059e-01 1.04724431e+00 1.67847518e-02 1.98882014e-01 4.97269481e-01 -1.47983432e-01 3.43832165e-01 -5.78197002e-01 -8.13627243e-01 4.75825638e-01 -7.40809441e-01 -1.08529711e+00 -2.88585126e-01 4.50962991e-01 -4.52063859e-01 -1.93187758e-01 8.80529881e-01 7.50267208e-01 -3.23622197e-01 7.99735785e-01 -1.07599926e+00 -1.24163592e+00 7.55218625e-01 -4.59072649e-01 4.19283390e-01 3.27284634e-01 4.80820388e-01 1.37812066e+00 -1.01038754e+00 7.66469240e-01 1.24472487e+00 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6.57462001e-01 -3.80290717e-01 -8.03769767e-01 -1.23549891e+00 2.34819025e-01 4.05785710e-01 2.33010933e-01 -2.76501596e-01 -1.56216338e-01 5.13349436e-02]	[11.582469940185547, 10.232970237731934]
fbd81dac-65cf-459c-9b28-d551ce1cbc4d	the-autofeat-python-library-for-automatic	1901.07329	null	https://arxiv.org/abs/1901.07329v4	https://arxiv.org/pdf/1901.07329v4.pdf	The autofeat Python Library for Automated Feature Engineering and Selection	This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, they generally provide lower prediction accuracies. Our library provides a multi-step feature engineering and selection process, where first a large pool of non-linear features is generated, from which then a small and robust set of meaningful features is selected, which improve the prediction accuracy of a linear model while retaining its interpretability.	['Robert Pack', 'Franziska Horn', 'Michael Rieger']	2019-01-22	null	null	null	null	['automated-feature-engineering']	['methodology']	[-1.99242294e-01 -2.25130841e-01 -2.63154179e-01 -7.32181370e-01 -5.47606111e-01 -6.21111214e-01 2.97781914e-01 4.07669306e-01 -2.71728393e-02 8.70372653e-01 -1.35277689e-01 -6.93014681e-01 -4.81581807e-01 -7.64864326e-01 -7.12447047e-01 -5.69383323e-01 -1.96652576e-01 4.40507561e-01 -1.35972798e-01 7.61587080e-03 3.54524523e-01 5.41901469e-01 -1.41607141e+00 1.65037915e-01 9.83944833e-01 1.04569149e+00 -1.83676377e-01 4.69896287e-01 -5.12990095e-02 7.78476238e-01 5.56818442e-04 -1.54504046e-01 2.65133590e-01 -2.59827644e-01 -4.76286680e-01 -5.43222129e-01 1.56606078e-01 -1.94468901e-01 -2.46867668e-02 3.73914778e-01 2.58334965e-01 5.14387079e-02 8.02418470e-01 -1.46529090e+00 -7.11772680e-01 5.11107802e-01 -2.65477180e-01 -8.94651413e-02 4.12972212e-01 5.65136671e-01 1.19882071e+00 -1.05143738e+00 1.74141854e-01 1.21447027e+00 1.11876690e+00 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4f14df83-df82-44ba-9203-50bd39a3de1b	cross-region-domain-adaptation-for-class	2109.06422	null	https://arxiv.org/abs/2109.06422v2	https://arxiv.org/pdf/2109.06422v2.pdf	Cross-Region Domain Adaptation for Class-level Alignment	Semantic segmentation requires a lot of training data, which necessitates costly annotation. There have been many studies on unsupervised domain adaptation (UDA) from one domain to another, e.g., from computer graphics to real images. However, there is still a gap in accuracy between UDA and supervised training on native domain data. It is arguably attributable to class-level misalignment between the source and target domain data. To cope with this, we propose a method that applies adversarial training to align two feature distributions in the target domain. It uses a self-training framework to split the image into two regions (i.e., trusted and untrusted), which form two distributions to align in the feature space. We term this approach cross-region adaptation (CRA) to distinguish from the previous methods of aligning different domain distributions, which we call cross-domain adaptation (CDA). CRA can be applied after any CDA method. Experimental results show that this always improves the accuracy of the combined CDA method, having updated the state-of-the-art.	['Takayuki Okatani', 'Masanori Suganuma', 'Xing Liu', 'Zhijie Wang']	2021-09-14	null	null	null	null	['synthetic-to-real-translation']	['computer-vision']	[ 5.43661654e-01 1.72136694e-01 1.94626246e-02 -4.97885853e-01 -8.59182239e-01 -9.88196313e-01 6.64370179e-01 -3.03777575e-01 -3.16198707e-01 7.99737573e-01 -3.41651887e-01 -2.21094534e-01 2.94216186e-01 -7.12091148e-01 -8.74728322e-01 -8.90593112e-01 7.00362563e-01 5.62508225e-01 6.81271672e-01 -5.62047176e-02 9.65994373e-02 1.72467083e-01 -1.26128983e+00 2.02511132e-01 1.17506707e+00 7.52487659e-01 5.21176942e-02 2.86698401e-01 -4.23834234e-01 2.81976402e-01 -9.03503299e-01 -6.17624819e-01 3.14184070e-01 -5.12648761e-01 -9.84012008e-01 1.81752801e-01 3.08984965e-01 -2.27433905e-01 1.62890151e-01 1.40978992e+00 4.06029612e-01 -5.67417592e-02 9.21171784e-01 -1.55580699e+00 -6.88956976e-01 2.18665138e-01 -8.23093355e-01 -2.74829447e-01 2.35500291e-01 -1.49143800e-01 2.28174299e-01 -5.18021762e-01 9.02165830e-01 1.19569337e+00 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18f3dccd-3346-43a8-a9bc-11d4d97e22a0	exploring-zero-and-few-shot-techniques-for	2305.07157	null	https://arxiv.org/abs/2305.07157v1	https://arxiv.org/pdf/2305.07157v1.pdf	Exploring Zero and Few-shot Techniques for Intent Classification	Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore four different zero and few-shot intent classification approaches with this low-resource constraint: 1) domain adaptation, 2) data augmentation, 3) zero-shot intent classification using descriptions large language models (LLMs), and 4) parameter-efficient fine-tuning of instruction-finetuned language models. Our results show that all these approaches are effective to different degrees in low-resource settings. Parameter-efficient fine-tuning using T-few recipe (Liu et al., 2022) on Flan-T5 (Chang et al., 2022) yields the best performance even with just one sample per intent. We also show that the zero-shot method of prompting LLMs using intent descriptions	['Mitul Tiwari', 'Prashil Tumbade', 'Quaizar Vohra', 'Soham Parikh']	2023-05-11	null	null	null	null	['intent-classification']	['natural-language-processing']	[-2.09323496e-01 -2.61164367e-01 -5.05668163e-01 -7.85578549e-01 -1.09224963e+00 -4.87804383e-01 6.90490723e-01 1.97801031e-02 -4.92194116e-01 6.21276796e-01 5.17342389e-01 -5.66722691e-01 2.21799716e-01 -4.31895614e-01 -1.32817179e-01 -2.42747396e-01 3.76880914e-02 1.14310932e+00 -6.84724525e-02 -4.91772443e-01 5.05471170e-01 1.93192348e-01 -1.41884303e+00 4.01008517e-01 1.03963554e+00 8.13775182e-01 2.79251367e-01 9.94857371e-01 -8.48607302e-01 8.82855117e-01 -4.37097698e-01 -2.52721310e-01 2.53107816e-01 -5.18795736e-02 -8.16390514e-01 -2.17079639e-01 3.76859754e-01 -7.32527971e-01 -2.99092196e-02 4.68897074e-01 3.56542051e-01 6.97995365e-01 4.44429308e-01 -1.39561856e+00 -7.21807420e-01 6.16520405e-01 -2.12772667e-01 4.32832986e-01 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b2744136-fdc2-4f5a-8d9f-54c1e9474ce5	joint-face-hallucination-and-deblurring-via	1811.09019	null	http://arxiv.org/abs/1811.09019v1	http://arxiv.org/pdf/1811.09019v1.pdf	Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement	We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.	['Ming-Hsuan Yang', 'Lijun Gong', 'Linchao Bao', 'Jinshan Pan', 'Yibing Song', 'Shengfeng He', 'Qingxiong Yang', 'Jiawei Zhang']	2018-11-22	null	null	null	null	['face-hallucination']	['computer-vision']	[ 4.70084995e-01 -2.86780387e-01 2.66893476e-01 -2.50716746e-01 -6.98554814e-01 -1.59324303e-01 3.23989719e-01 -8.90324295e-01 -1.10994399e-01 8.33090425e-01 4.49482828e-01 3.02889526e-01 -1.73875704e-01 -7.08787382e-01 -6.95750117e-01 -8.44239175e-01 4.47283119e-01 -1.23968750e-01 -2.47017026e-01 -2.94907123e-01 2.19640806e-01 6.90094233e-01 -1.78469193e+00 2.77115792e-01 9.07376766e-01 9.26624238e-01 2.30576918e-01 4.56440717e-01 1.53953746e-01 5.79613626e-01 -3.71493548e-01 -1.27873570e-01 3.31032813e-01 -6.54775858e-01 -7.42498517e-01 3.65007579e-01 9.39847052e-01 -9.15064573e-01 -3.21183413e-01 1.47377074e+00 5.69339514e-01 8.53881612e-02 5.13413012e-01 -7.80584931e-01 -1.17560601e+00 3.43806343e-03 -9.85913336e-01 3.84883463e-01 3.12576652e-01 -1.61392286e-01 2.55820751e-01 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c3ff6734-e9c7-4068-927f-52b8275a35dd	alignment-enhancement-network-for-fine	null	null	https://dl.acm.org/doi/abs/10.1145/3446208	https://dl.acm.org/doi/abs/10.1145/3446208	Alignment Enhancement Network for Fine-grained Visual Categorization	Fine-grained visual categorization (FGVC) aims to automatically recognize objects from different sub-ordinate categories. Despite attracting considerable attention from both academia and industry, it remains a challenging task due to subtle visual differences among different classes. Cross-layer feature aggregation and crossimage pairwise learning become prevailing in improving the performance of FGVC by extracting discriminative class-specific features. However, they are still inefficient to fully use the cross-layer information based on the simple aggregation strategy, while existing pairwise learning methods also fail to explore long-range interactions between different images. To address these problems, we propose a novel Alignment Enhancement Network (AENet), including two-level alignments, Cross-layer Alignment (CLA) and Cross-image Alignment (CIA). The CLA module exploits the cross-layer relationship between low-level spatial information and highlevel semantic information, which contributes to cross-layer feature aggregation to improve the capacity of feature representation for input images. The new CIA module is further introduced to produce the aligned feature map, which can enhance the relevant information as well as suppress the irrelevant information across the whole spatial region. Our method is based on an underlying assumption that the aligned feature map should be closer to the inputs of CIA when they belong to the same category. Accordingly, we establish Semantic Affinity Loss to supervise the feature alignment within each CIA block. Experimental results on four challenging datasets show that the proposed AENet achieves the state-of-the-art results over prior arts.	['Yutao Hu']	2021-03-01	null	null	null	null	['fine-grained-visual-categorization']	['computer-vision']	[ 3.20005924e-01 -5.51124871e-01 -5.06704524e-02 -5.33376515e-01 -4.21341538e-01 -4.85759526e-01 6.42115891e-01 1.53465956e-01 -4.26013142e-01 3.49750757e-01 2.40409672e-01 1.80615321e-01 -4.89556462e-01 -6.85233176e-01 -6.08308494e-01 -9.23167408e-01 2.02840224e-01 -1.29417226e-01 5.83991826e-01 -6.56967983e-02 4.23899263e-01 5.76162398e-01 -1.80304480e+00 7.82338917e-01 1.04906154e+00 1.37941384e+00 4.47316378e-01 -7.63206705e-02 -4.70287621e-01 5.67478538e-01 -3.89281869e-01 6.34405240e-02 1.86704814e-01 -3.00124258e-01 -6.90275371e-01 7.53530338e-02 6.90917552e-01 2.54753530e-02 -6.65485486e-02 1.27804363e+00 2.60465980e-01 1.14452399e-01 7.02379823e-01 -1.38389325e+00 -6.81787908e-01 2.34474778e-01 -8.48993599e-01 2.54557222e-01 -1.47238851e-01 1.79506210e-03 1.08132589e+00 -1.16856086e+00 3.16429347e-01 1.27807510e+00 4.62741286e-01 2.20658049e-01 -1.12342918e+00 -9.15851176e-01 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d92d91d5-0474-4d13-ad55-fd92c4a9f508	on-the-optimization-landscape-of-burer	2302.10963	null	https://arxiv.org/abs/2302.10963v1	https://arxiv.org/pdf/2302.10963v1.pdf	On the Optimization Landscape of Burer-Monteiro Factorization: When do Global Solutions Correspond to Ground Truth?	In low-rank matrix recovery, the goal is to recover a low-rank matrix, given a limited number of linear and possibly noisy measurements. Low-rank matrix recovery is typically solved via a nonconvex method called Burer-Monteiro factorization (BM). If the rank of the ground truth is known, BM is free of sub-optimal local solutions, and its true solutions coincide with the global solutions -- that is, the true solutions are identifiable. When the rank of the ground truth is unknown, it must be over-estimated, giving rise to an over-parameterized BM. In the noiseless regime, it is recently shown that over-estimation of the rank leads to progressively fewer sub-optimal local solutions while preserving the identifiability of the true solutions. In this work, we show that with noisy measurements, the global solutions of the over-parameterized BM no longer correspond to the true solutions, essentially transmuting over-parameterization from blessing to curse. In particular, we study two classes of low-rank matrix recovery, namely matrix completion and matrix sensing. For matrix completion, we show that even if the rank is only slightly over-estimated and with very mild assumptions on the noise, none of the true solutions are local or global solutions. For matrix sensing, we show that to guarantee the correspondence between global and true solutions, it is necessary and sufficient for the number of samples to scale linearly with the over-estimated rank, which can be drastically larger than its optimal sample complexity that only scales with the true rank.	['Salar Fattahi', 'Jianhao Ma']	2023-02-21	null	null	null	null	['matrix-completion']	['methodology']	[ 2.32881546e-01 4.27545726e-01 -1.25314286e-02 3.94752294e-01 -1.26472366e+00 -9.30181742e-01 6.49843663e-02 -1.99488923e-01 -4.12887940e-03 7.89175868e-01 4.14163113e-01 -5.81360422e-02 -5.59781373e-01 -4.33186680e-01 -1.00251377e+00 -1.07140517e+00 -2.22354800e-01 5.06944835e-01 -3.35163683e-01 -1.97292417e-01 -1.22710645e-01 2.29871273e-01 -9.05763865e-01 -4.93105799e-02 8.21725547e-01 7.74965405e-01 1.30656183e-01 8.70106876e-01 4.05107766e-01 5.00873327e-01 -2.17785284e-01 1.23643212e-01 7.64481783e-01 -4.06427532e-01 -5.62808216e-01 4.70297277e-01 4.67799962e-01 -1.10515460e-01 -5.52517951e-01 1.61914897e+00 1.49911776e-01 5.40201850e-02 3.12592149e-01 -9.29200172e-01 -3.97362083e-01 5.50015211e-01 -6.73221588e-01 -2.68740177e-01 4.59115744e-01 -3.66498560e-01 1.15324509e+00 -1.00105762e+00 5.85760236e-01 9.75765347e-01 9.19660389e-01 2.55663604e-01 -1.59536767e+00 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3bde5d6f-adac-48d3-94c5-5b4833ab94ab	generating-faithful-synthetic-data-with-large	2305.15041	null	https://arxiv.org/abs/2305.15041v1	https://arxiv.org/pdf/2305.15041v1.pdf	Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science	Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a pervasive problem in synthetic data generation: its generative distribution often differs from the distribution of real-world data researchers care about (in other words, it is unfaithful). In a case study on sarcasm detection, we study three strategies to increase the faithfulness of synthetic data: grounding, filtering, and taxonomy-based generation. We evaluate these strategies using the performance of classifiers trained with generated synthetic data on real-world data. While all three strategies improve the performance of classifiers, we find that grounding works best for the task at hand. As synthetic data generation plays an ever-increasing role in NLP research, we expect this work to be a stepping stone in improving its utility. We conclude this paper with some recommendations on how to generate high(er)-fidelity synthetic data for specific tasks.	['Robert West', 'Ashton Anderson', 'Martin Josifoski', 'Akhil Arora', 'Manoel Horta Ribeiro', 'Veniamin Veselovsky']	2023-05-24	null	null	null	null	['synthetic-data-generation', 'synthetic-data-generation', 'sarcasm-detection']	['medical', 'miscellaneous', 'natural-language-processing']	[ 1.77178398e-01 5.47156155e-01 -2.39885315e-01 -1.94362313e-01 -8.64818692e-01 -7.45906293e-01 9.64939594e-01 2.77413070e-01 -5.04067004e-01 9.06341612e-01 8.40158343e-01 -2.87165642e-01 2.06298843e-01 -8.59221458e-01 -4.89296436e-01 -2.93364614e-01 5.87047219e-01 6.07597470e-01 -2.48317868e-01 -4.51530933e-01 3.99162650e-01 1.63678437e-01 -1.50740242e+00 5.63778698e-01 1.21479928e+00 4.31330562e-01 6.28672689e-02 3.41597915e-01 -2.56024569e-01 8.69037807e-01 -9.42700505e-01 -8.39617848e-01 4.88300696e-02 -6.73537195e-01 -9.38381016e-01 -9.30958986e-02 2.17223719e-01 1.65478349e-01 1.43711850e-01 9.16735291e-01 5.66277266e-01 -3.93460097e-04 7.88141847e-01 -1.33326769e+00 -7.11449802e-01 9.02965307e-01 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0c093eaa-ecd2-4f0a-a848-9f0735ddb284	pointview-gcn-3d-shape-classification-with	null	null	https://ieeexplore.ieee.org/abstract/document/9506426	https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9506426	POINTVIEW-GCN: 3D SHAPE CLASSIFICATION WITH MULTI-VIEW POINT CLOUDS	We address 3D shape classification with partial point cloud inputs captured from multiple viewpoints around the object. Different from existing methods that perform classification on the complete point cloud by first registering multi-view capturing, we propose PointView-GCN with multi-level Graph Convolutional Networks (GCNs) to hierarchically aggregate the shape features of single-view point clouds, in order to encode both the geometrical cues of an object and their multi- view relations. With experiments on our novel single-view datasets, we prove that PointView-GCN produces a more de- scriptive global shape feature which stably improves the classification accuracy by ∼ 5% compared to the classifiers with single-view point clouds, and outperforms the state-of-the-art methods with the complete point clouds on ModelNet40.	['Alessio Del Bue', 'Yiming Wang', 'Seyed Saber Mohammadi']	2021-09-22	null	null	null	ieee-international-conference-on-image-5	['3d-shape-retrieval', '3d-point-cloud-classification']	['computer-vision', 'computer-vision']	[-3.89669240e-01 -2.50286669e-01 6.60961643e-02 -5.03648818e-01 -3.82500350e-01 -8.18585992e-01 8.36601734e-01 -1.86776444e-02 2.65390068e-01 -2.06645072e-01 -3.80748093e-01 -2.34133214e-01 -1.82027042e-01 -1.21227479e+00 -9.50204194e-01 -4.22740370e-01 1.55601814e-01 9.43383753e-01 3.78291845e-01 -4.77368146e-01 2.89387017e-01 1.36367500e+00 -1.68074167e+00 5.69519639e-01 4.78380919e-01 1.34865773e+00 4.39844616e-02 4.46380109e-01 -2.37926960e-01 1.25776440e-01 -1.62983999e-01 -4.73924696e-01 5.50897419e-01 5.28161585e-01 -4.66326594e-01 1.80568039e-01 1.44169235e+00 -1.49762064e-01 -2.59226501e-01 1.06726754e+00 1.37402162e-01 -1.60857990e-01 5.72182655e-01 -1.48544228e+00 -1.01035368e+00 -8.00549015e-02 -4.44718391e-01 -4.28937711e-02 2.65187114e-01 1.15283258e-01 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297c7cf9-cf7c-4d8c-b262-b83f94bfd362	autoregressive-gan-for-semantic-unconditional	2211.00987	null	https://arxiv.org/abs/2211.00987v2	https://arxiv.org/pdf/2211.00987v2.pdf	Autoregressive GAN for Semantic Unconditional Head Motion Generation	In this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose. Different from traditional audio-conditioned talking head generation that seldom puts emphasis on realistic head motions, we devise a GAN-based architecture that learns to synthesize rich head motion sequences over long duration while maintaining low error accumulation levels.In particular, the autoregressive generation of incremental outputs ensures smooth trajectories, while a multi-scale discriminator on input pairs drives generation toward better handling of high- and low-frequency signals and less mode collapse.We experimentally demonstrate the relevance of the proposed method and show its superiority compared to models that attained state-of-the-art performances on similar tasks.	['Dominique Vaufreydaz', 'Stéphane Lathuilière', 'Xavier Alameda-Pineda', 'Louis Airale']	2022-11-02	null	null	null	null	['talking-head-generation']	['computer-vision']	[ 4.69038822e-03 5.32340467e-01 2.68347025e-01 -4.33720291e-01 -1.11973882e+00 -8.74400213e-02 8.62187684e-01 -8.65317702e-01 -4.77776527e-02 8.78308833e-01 5.81206739e-01 3.18401963e-01 3.88981313e-01 -4.54521090e-01 -6.33343101e-01 -7.76663005e-01 7.10127503e-02 3.60929966e-01 -1.48552703e-02 -2.21840411e-01 -3.87982070e-01 2.93327689e-01 -1.74472713e+00 9.18309540e-02 5.23615003e-01 7.73867726e-01 7.64000192e-02 8.54707718e-01 4.71146196e-01 8.70896220e-01 -9.79062796e-01 -4.67497468e-01 -1.31391048e-01 -1.09199035e+00 -5.77199578e-01 1.99076176e-01 6.39092088e-01 -4.24051255e-01 -5.04762292e-01 7.02933133e-01 1.17669332e+00 4.10840482e-01 8.96964490e-01 -1.28338039e+00 -7.87245750e-01 3.67182851e-01 -2.89216697e-01 -6.91123828e-02 9.40703154e-01 5.28209329e-01 7.05587447e-01 -1.03961754e+00 8.54715168e-01 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ca22e54f-e926-49b4-b90b-a4453a49ab79	representation-learning-for-appliance	2209.03759	null	https://arxiv.org/abs/2209.03759v1	https://arxiv.org/pdf/2209.03759v1.pdf	Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning	Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning with deep neural networks is successfully applied to several related disciplines. The main advantage of representation learning lies in replacing an expert-driven, hand-crafted feature extraction with hierarchical learning from many representations in raw data format. In this paper, we show how the NILM processing-chain can be improved, reduced in complexity and alternatively designed with recent deep learning algorithms. On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing. We evaluate all approaches on two large-scale energy consumption datasets with more than 50,000 events of 44 appliances. We show that with the use of deep learning, we are able to reach and surpass the performance of the state-of-the-art classical machine learning approach for appliance recognition with an F-Score of 0.75 and 0.86 compared to 0.69 and 0.87 of the classical approach.	['Hans-Arno Jacobsen', 'Daniel Jorde', 'Matthias Kahl']	2022-08-26	null	null	null	null	['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring']	['knowledge-base', 'miscellaneous', 'time-series']	[ 2.96036750e-01 -1.30227581e-02 -1.52016848e-01 -4.26272929e-01 -9.99907970e-01 -2.16956556e-01 5.09347618e-01 4.07694668e-01 -6.14886805e-02 4.60909218e-01 1.72216654e-01 -1.59743279e-01 -4.28050309e-01 -7.98947752e-01 -4.91633624e-01 -7.50977457e-01 -1.80680245e-01 4.69967157e-01 -3.93551350e-01 -1.42148450e-01 -2.19148949e-01 6.27982736e-01 -1.78856802e+00 3.92388672e-01 2.44502619e-01 1.72763431e+00 -2.27654144e-01 5.01295269e-01 3.55006121e-02 1.13261139e+00 -9.55105066e-01 -8.44687037e-03 -7.57697448e-02 -1.55857727e-01 -7.41749763e-01 -7.65760764e-02 -2.45428216e-02 -3.61323148e-01 -3.95135462e-01 7.56029665e-01 6.85826719e-01 -8.06881785e-02 5.23402572e-01 -1.36837256e+00 -3.14620018e-01 1.03485596e+00 -3.65978479e-01 1.25037298e-01 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be09afd9-d604-4455-8996-59ca280c6346	semi-supervised-single-view-3d-reconstruction	2209.15383	null	https://arxiv.org/abs/2209.15383v1	https://arxiv.org/pdf/2209.15383v1.pdf	Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors	The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an alternative way to mitigate the need for manual labels, but remains unexplored in 3D reconstruction. Inspired by the recent success of semi-supervised image classification tasks, we propose SSP3D, a semi-supervised framework for 3D reconstruction. In particular, we introduce an attention-guided prototype shape prior module for guiding realistic object reconstruction. We further introduce a discriminator-guided module to incentivize better shape generation, as well as a regularizer to tolerate noisy training samples. On the ShapeNet benchmark, the proposed approach outperforms previous supervised methods by clear margins under various labeling ratios, (i.e., 1%, 5% , 10% and 20%). Moreover, our approach also performs well when transferring to real-world Pix3D datasets under labeling ratios of 10%. We also demonstrate our method could transfer to novel categories with few novel supervised data. Experiments on the popular ShapeNet dataset show that our method outperforms the zero-shot baseline by over 12% and we also perform rigorous ablations and analysis to validate our approach.	['Yu-Gang Jiang', 'Zuxuan Wu', 'Hengduo Li', 'Zhen Xing']	2022-09-30	null	null	null	null	['single-view-3d-reconstruction', 'semi-supervised-image-classification', 'object-reconstruction']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.88695058e-01 4.29217130e-01 -2.53391117e-01 -3.52522671e-01 -9.69109476e-01 -7.50396132e-01 7.57150829e-01 -1.35908023e-01 -1.20708473e-01 3.20151508e-01 2.97385395e-01 -1.34261027e-01 2.07359746e-01 -6.56767190e-01 -8.37465048e-01 -4.94224608e-01 4.25779551e-01 7.14248240e-01 3.98206294e-01 -1.11768097e-02 -1.17166722e-02 6.42385364e-01 -1.50093770e+00 5.73139787e-02 7.78314590e-01 1.01116323e+00 3.70929837e-01 1.17632218e-01 -3.76298130e-02 3.05870265e-01 -2.26280689e-01 -3.78193796e-01 5.94344199e-01 -1.27870804e-02 -6.83458328e-01 7.09829867e-01 5.87304056e-01 -4.76473123e-01 -1.67274877e-01 8.25775504e-01 7.29442775e-01 -4.10587043e-02 8.01620543e-01 -9.33857322e-01 -6.72074139e-01 2.59135276e-01 -5.57367861e-01 -3.21783960e-01 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e9bcdec2-3a64-48f7-9ee3-55a0ed279ea6	anticipating-traffic-accidents-with-adaptive	1804.02675	null	http://arxiv.org/abs/1804.02675v1	http://arxiv.org/pdf/1804.02675v1.pdf	Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB	In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database for anticipation. The proposed AdaLEA allows a model to gradually learn an earlier anticipation as training progresses. The loss function adaptively assigns penalty weights depending on how early the model can an- ticipate a traffic accident at each epoch. Additionally, we construct a Near-miss Incident DataBase for anticipation. This database contains an enormous number of traffic near- miss incident videos and annotations for detail evaluation of two tasks, risk anticipation and risk-factor anticipation. In our experimental results, we found our proposal achieved the highest scores for risk anticipation (+6.6% better on mean average precision (mAP) and 2.36 sec earlier than previous work on the average time-to-collision (ATTC)) and risk-factor anticipation (+4.3% better on mAP and 0.70 sec earlier than previous work on ATTC).	['Yoshimitsu Aoki', 'Tomoyuki Suzuki', 'Hirokatsu Kataoka', 'Yutaka Satoh']	2018-04-08	anticipating-traffic-accidents-with-adaptive-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Suzuki_Anticipating_Traffic_Accidents_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Suzuki_Anticipating_Traffic_Accidents_CVPR_2018_paper.pdf	cvpr-2018-6	['accident-anticipation']	['computer-vision']	[ 6.15313426e-02 6.58639148e-02 -1.11200735e-01 -4.84723777e-01 -1.42317092e+00 -2.28584856e-02 3.73226881e-01 3.12102765e-01 -9.23865736e-01 3.89736295e-01 1.96319476e-01 -3.65617365e-01 -6.84211403e-02 -4.09265548e-01 -7.72086203e-01 -4.87750322e-01 -7.24178731e-01 6.20882392e-01 8.96181226e-01 -2.00018659e-02 2.31907696e-01 5.67533672e-01 -1.52144742e+00 6.75055683e-01 5.90080678e-01 1.21707857e+00 8.89787674e-02 1.08415866e+00 3.48979920e-01 9.17732477e-01 -4.19918865e-01 -3.74783427e-01 5.51588714e-01 2.12819114e-01 -5.92637658e-01 -5.92509329e-01 3.46162021e-01 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5c9be18a-5b69-41f3-97cd-5184a3fe6a38	knowledge-representation-via-joint-learning	1609.07075	null	http://arxiv.org/abs/1609.07075v1	http://arxiv.org/pdf/1609.07075v1.pdf	Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs	Textual information is considered as significant supplement to knowledge representation learning (KRL). There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL. (2) How to dynamically select those informative sentences of the corresponding entities for KRL. In this paper, we propose the Sequential Text-embodied Knowledge Representation Learning to build knowledge representations from multiple sentences. Given each reference sentence of an entity, we first utilize recurrent neural network with pooling or long short-term memory network to encode the semantic information of the sentence with respect to the entity. Then we further design an attention model to measure the informativeness of each sentence, and build text-based representations of entities. We evaluate our method on two tasks, including triple classification and link prediction. Experimental results demonstrate that our method outperforms other baselines on both tasks, which indicates that our method is capable of selecting informative sentences and encoding the textual information well into knowledge representations.	['Ruobing Xie', 'Maosong Sun', 'Zhiyuan Liu', 'Jiawei Wu']	2016-09-22	null	null	null	null	['triple-classification']	['graphs']	[ 4.79471087e-02 3.32907468e-01 -5.75654626e-01 -2.84807056e-01 -8.29155147e-01 -2.97538251e-01 3.63244534e-01 1.99578702e-01 -4.09014165e-01 8.35995972e-01 8.50949347e-01 -1.23343341e-01 -1.86229348e-01 -1.00543964e+00 -8.75934899e-01 -3.25300157e-01 5.00341840e-02 3.66828054e-01 2.70978600e-01 -2.55527407e-01 2.40336522e-01 2.64139056e-01 -1.17707074e+00 5.13228416e-01 8.94014537e-01 1.07612062e+00 4.08807814e-01 4.24265116e-01 -5.84787965e-01 1.42035031e+00 -5.74176967e-01 -4.11780745e-01 -5.03902435e-01 -2.83597350e-01 -1.43955445e+00 -9.77629498e-02 1.18677726e-03 -3.22857022e-01 -6.93889320e-01 6.07152164e-01 4.00087178e-01 4.98792589e-01 6.64291620e-01 -7.29716182e-01 -1.29478371e+00 1.52932322e+00 -3.98940206e-01 4.56230491e-01 5.05602658e-01 -4.49774683e-01 1.39284241e+00 -1.24578750e+00 7.54796803e-01 1.29551387e+00 4.72898424e-01 1.64479360e-01 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-5.57280123e-01 3.56854707e-01 1.07643962e-01 -7.15123653e-01 -1.40830800e-01 1.61064103e-01]	[9.303086280822754, 8.404169082641602]
cea17c8d-d17c-4e23-8d68-75599ea53d9d	efficient-dense-point-cloud-object	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Kejie_Li_Efficient_Dense_Point_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Kejie_Li_Efficient_Dense_Point_ECCV_2018_paper.pdf	Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields	Most existing CNN-based methods for single-view 3D object reconstruction represent a 3D object as either a 3D voxel occupancy grid or multiple depth-mask image pairs. However, these representations are inefficient since empty voxels or background pixels are wasteful. We propose a novel approach that addresses this limitation by replacing masks with ''deformation-fields''. Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object. Each surface comprises a depth-map and corresponding deformation-field that ensures every pixel-depth pair in the depth-map lies on the object surface. These surfaces are then fused to form the full 3D shape. During training, we use a combination of per-view and multi-view losses. The novel multi-view loss encourages the 3D points back-projected from a particular view to be consistent across views. Extensive experiments demonstrate the efficiency and efficacy of our method on single-view 3D object reconstruction.	['Huangying Zhan', 'Trung Pham', 'Kejie Li', 'Ian Reid']	2018-09-01	null	null	null	eccv-2018-9	['3d-object-reconstruction']	['computer-vision']	[ 3.05312634e-01 1.79433763e-01 7.65808299e-02 -5.50731897e-01 -7.58697808e-01 -3.49478394e-01 3.87541503e-01 -3.32588226e-01 -9.75359902e-02 3.57810318e-01 4.45498116e-02 2.45908871e-01 2.50797749e-01 -9.67815876e-01 -1.06049538e+00 -5.50391555e-01 4.65045363e-01 7.13375807e-01 4.80535060e-01 2.44817451e-01 1.70420960e-01 9.13754821e-01 -1.48602128e+00 5.11285484e-01 3.42491537e-01 1.19265521e+00 5.05428493e-01 4.97347832e-01 -1.99875250e-01 2.28651196e-01 -1.98807627e-01 -2.76476085e-01 7.22648680e-01 -1.24671049e-01 -4.62267160e-01 6.50744915e-01 8.65318239e-01 -7.26337612e-01 -2.75280982e-01 9.64486122e-01 3.02943736e-01 -6.43282838e-04 5.54355383e-01 -8.70150924e-01 -4.31096405e-01 -2.29509979e-01 -9.44440663e-01 -2.33519226e-01 4.04083788e-01 2.43156264e-03 7.33519435e-01 -1.33165753e+00 9.32423890e-01 1.22292686e+00 6.16094410e-01 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-3.56287032e-01 -3.79687458e-01 -7.33810127e-01 3.06496561e-01 5.59403658e-01 -9.19579148e-01 -4.57259238e-01 6.76986426e-02]	[8.906929969787598, -3.1673736572265625]
2132371e-dd4e-4121-8438-96e7755a4c80	transcending-scaling-laws-with-0-1-extra	2210.11399	null	https://arxiv.org/abs/2210.11399v2	https://arxiv.org/pdf/2210.11399v2.pdf	Transcending Scaling Laws with 0.1% Extra Compute	Scaling language models improves performance but comes with significant computational costs. This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. The key idea is to continue training a state-of-the-art large language model (e.g., PaLM) on a few more steps with UL2's mixture-of-denoiser objective. We show that, with almost negligible extra computational costs and no new sources of data, we are able to substantially improve the scaling properties of large language models on downstream metrics. In this paper, we continue training PaLM with UL2R, introducing a new set of models at 8B, 62B, and 540B scale which we call U-PaLM. Impressively, at 540B scale, we show an approximately 2x computational savings rate where U-PaLM achieves the same performance as the final PaLM 540B model at around half its computational budget (i.e., saving $\sim$4.4 million TPUv4 hours). We further show that this improved scaling curve leads to 'emergent abilities' on challenging BIG-Bench tasks -- for instance, U-PaLM does much better than PaLM on some tasks or demonstrates better quality at much smaller scale (62B as opposed to 540B). Overall, we show that U-PaLM outperforms PaLM on many few-shot setups, i.e., English NLP tasks (e.g., commonsense reasoning, question answering), reasoning tasks with chain-of-thought (e.g., GSM8K), multilingual tasks (MGSM, TydiQA), MMLU and challenging BIG-Bench tasks. Finally, we provide qualitative examples showing the new capabilities of U-PaLM for single and multi-span infilling.	['Mostafa Dehghani', 'Quoc V. Le', 'Neil Houlsby', 'Slav Petrov', 'Donald Metzler', 'Denny Zhou', 'Aakanksha Chowdhery', 'Jinfeng Rao', 'Huaixiu Steven Zheng', 'Xavier Garcia', 'Siamak Shakeri', 'David R. So', 'Vinh Q. Tran', 'Hyung Won Chung', 'Jason Wei', 'Yi Tay']	2022-10-20	null	null	null	null	['multi-task-language-understanding', 'gsm8k', 'cross-lingual-question-answering', 'arithmetic-reasoning']	['methodology', 'natural-language-processing', 'natural-language-processing', 'reasoning']	[-2.35596418e-01 6.89073280e-02 -1.38920248e-01 -7.38584325e-02 -1.70190620e+00 -8.18260789e-01 5.59195817e-01 -1.28790468e-01 -6.21111333e-01 7.48602390e-01 3.22201461e-01 -8.83169711e-01 -3.34145539e-02 -6.93346143e-01 -9.42341208e-01 -2.82767802e-01 2.36387298e-01 6.64896011e-01 -7.16850907e-02 -7.25613832e-01 -3.61525565e-02 2.14948148e-01 -8.77359152e-01 3.88140261e-01 8.11020613e-01 4.19867992e-01 2.71230459e-01 9.34073806e-01 -8.34189653e-02 1.16597021e+00 -4.48654920e-01 -7.36309290e-01 2.26139829e-01 -1.63057968e-01 -9.90521491e-01 -2.66672760e-01 4.72122937e-01 -2.92437315e-01 -4.57367152e-01 9.09466565e-01 5.95785558e-01 2.50673085e-01 1.80752397e-01 -1.06925333e+00 -9.47539389e-01 1.34257269e+00 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-2.19576806e-01 1.80435255e-02 5.47039688e-01 1.01756966e+00 3.27142686e-01 5.73553145e-01 3.35092917e-02 -4.01013583e-01 -1.18568289e+00 -1.29709208e+00 -4.21400338e-01 2.73256809e-01 -1.80182643e-02 -5.77563886e-03 -4.87248510e-01 -6.22089095e-02]	[10.74299430847168, 8.218887329101562]
623becb0-ad94-4cd1-afcc-dae02e89e2f9	wav2seq-pre-training-speech-to-text-encoder	2205.01086	null	https://arxiv.org/abs/2205.01086v1	https://arxiv.org/pdf/2205.01086v1.pdf	Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages	We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task -- transcribing audio inputs into pseudo subword sequences. This process stands on its own, or can be applied as low-cost second-stage pre-training. We experiment with automatic speech recognition (ASR), spoken named entity recognition, and speech-to-text translation. We set new state-of-the-art results for end-to-end spoken named entity recognition, and show consistent improvements on 20 language pairs for speech-to-text translation, even when competing methods use additional text data for training. Finally, on ASR, our approach enables encoder-decoder methods to benefit from pre-training for all parts of the network, and shows comparable performance to highly optimized recent methods.	['Yoav Artzi', 'Kilian Q. Weinberger', 'Ryan Mcdonald', 'Kyu Han', 'Shinji Watanabe', 'Kwangyoun Kim', 'Felix Wu']	2022-05-02	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 6.52351201e-01 6.33231580e-01 -1.13346905e-01 -8.32846403e-01 -1.84186924e+00 -6.34358108e-01 6.49710298e-01 -2.96381086e-01 -6.01850390e-01 8.50319922e-01 7.83000052e-01 -6.44410789e-01 7.97905266e-01 -8.48613456e-02 -9.11494911e-01 -2.32574418e-01 -1.03827221e-02 9.08928633e-01 -4.77423817e-02 -2.02972740e-01 -3.26263815e-01 7.09647015e-02 -9.92787242e-01 6.95674360e-01 6.18663967e-01 6.30355477e-01 2.47139245e-01 1.14388156e+00 -3.41725528e-01 8.26622725e-01 -6.67764723e-01 -4.62156951e-01 -5.16697532e-03 -9.01024103e-01 -9.91259873e-01 1.70768663e-01 2.02149242e-01 -2.62279183e-01 -5.65001965e-01 7.29524255e-01 8.29403460e-01 -7.59500191e-02 6.14156187e-01 -6.72543883e-01 -6.48300588e-01 1.08907354e+00 1.01720475e-01 2.60716174e-02 6.10566080e-01 8.65255892e-02 1.01944268e+00 -1.36072850e+00 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8.73544931e-01 3.30535024e-01 1.02279973e+00 -8.38355303e-01 -1.00549924e+00 -1.19716227e+00 -2.47605458e-01 -2.10041534e-02 6.88905776e-01 2.37523064e-01 -3.94414216e-01 3.43293369e-01]	[14.491472244262695, 7.099369049072266]
dce229a6-d8a5-413e-8a01-d9dcf278651f	the-area-under-the-roc-curve-as-a-measure-of	2009.02400	null	https://arxiv.org/abs/2009.02400v2	https://arxiv.org/pdf/2009.02400v2.pdf	The Area Under the ROC Curve as a Measure of Clustering Quality	The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we elaborate on the fact that, in the context of internal/relative clustering validation as we consider, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a much more efficient algorithmic procedure. Our theoretical findings are supported by experimental results. These results show that, in addition to an effective and robust quantitative evaluation provided by AUCC, visual inspection of the ROC curves themselves can be useful to further assess a candidate clustering solution from a broader, qualitative perspective as well.	['Ricardo José Gabrielli Barreto Campello', 'Pablo Andretta Jaskowiak', 'Ivan Gesteira Costa']	2020-09-04	null	null	null	null	['clustering-algorithms-evaluation']	['methodology']	[ 5.86651228e-02 4.98554856e-02 -1.18458852e-01 -3.57040137e-01 -5.64346373e-01 -7.47462273e-01 5.18513620e-01 8.40489089e-01 -6.40851855e-01 4.15806204e-01 -1.83100566e-01 -5.47832787e-01 -5.99101484e-01 -5.98014534e-01 -1.76463917e-01 -9.74250257e-01 -2.60714442e-01 4.25589085e-01 9.28873345e-02 2.94823408e-01 2.12544292e-01 5.79435229e-01 -1.74038517e+00 -2.22947881e-01 9.11790013e-01 9.20225799e-01 -1.00381538e-01 4.25564080e-01 3.02448630e-01 1.20256409e-01 -8.17846596e-01 -5.20659328e-01 3.56942825e-02 -4.83908445e-01 -7.18167722e-01 3.04336905e-01 -1.03475861e-01 2.54177541e-01 4.24207896e-01 9.68049765e-01 2.05429807e-01 -6.38750419e-02 1.11380160e+00 -1.31517076e+00 -8.71536136e-02 4.86257255e-01 -3.94070208e-01 -6.44262508e-03 3.81104052e-01 1.29769966e-02 9.39084530e-01 -4.53117847e-01 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af52e238-9b61-4762-9a97-7a8c3325c006	learning-task-aware-energy-disaggregation-a	2204.06767	null	https://arxiv.org/abs/2204.06767v2	https://arxiv.org/pdf/2204.06767v2.pdf	Learning Task-Aware Energy Disaggregation: a Federated Approach	We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on aggregated meter measurements, a machine learning model is usually trained based on large amount of training data coming from a number of residential homes. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data, while load data coming from different regions or electricity users may exhibit heterogeneous usage patterns. Both practical concerns make training a single, centralized NILM model challenging. In this paper, we propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively. Simulation results on benchmark dataset validate proposed algorithm's performance on efficiently inferring appliance-level consumption for a variety of homes and appliances.	['Yize Chen', 'Ruohong Liu']	2022-04-14	null	null	null	null	['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring']	['knowledge-base', 'miscellaneous', 'time-series']	[ 1.14550836e-01 -3.43076997e-02 -2.86794186e-01 -8.02215755e-01 -1.29372644e+00 -3.99124473e-01 3.35019469e-01 8.83958563e-02 1.14032272e-02 8.79884124e-01 3.69043887e-01 -2.44218810e-03 -1.67114407e-01 -8.27198505e-01 -4.65192646e-01 -1.04962683e+00 -1.45179376e-01 6.47623658e-01 -6.62626088e-01 5.26918292e-01 -2.84406304e-01 1.27522573e-01 -1.17032456e+00 2.71539658e-01 1.06573975e+00 1.20734608e+00 3.69185656e-01 4.30178821e-01 1.11187316e-01 9.75634933e-01 -7.33206630e-01 2.90682521e-02 1.10349692e-01 -6.58366382e-02 -8.12780917e-01 2.20418274e-01 -2.00781807e-01 -5.95922887e-01 1.50495157e-01 1.10036266e+00 6.78217828e-01 7.97222704e-02 7.77678967e-01 -1.84772444e+00 -3.31665844e-01 1.28155661e+00 -5.55828154e-01 4.20649685e-02 1.79664493e-01 5.60635291e-02 1.02706718e+00 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-1.00204504e+00 -1.01857615e+00 -1.13042450e+00 -1.24273777e+00 5.34052789e-01 5.69987833e-01 -1.68810919e-01 -3.60254705e-01 8.95658284e-02]	[16.030864715576172, 7.560460090637207]
e27d3688-9343-46dd-a501-4c22285a8182	a-survey-of-active-learning-algorithms-for	2104.07784	null	https://arxiv.org/abs/2104.07784v1	https://arxiv.org/pdf/2104.07784v1.pdf	A survey of active learning algorithms for supervised remote sensing image classification	Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.	['Jordi Munoz-Mari', 'Mikhail Kanevski', 'Loris Copa', 'Michele Volpi', 'Devis Tuia']	2021-04-15	null	null	null	null	['remote-sensing-image-classification']	['miscellaneous']	[ 7.13896334e-01 2.39737406e-02 -5.51321387e-01 -6.55238450e-01 -9.66651618e-01 -6.23230278e-01 2.86018342e-01 2.14670539e-01 -4.99267578e-01 1.13247311e+00 -3.33070546e-01 -5.23328781e-01 -7.88287044e-01 -6.96018100e-01 -9.76878256e-02 -1.28586745e+00 -1.84374288e-01 7.45704532e-01 -1.17869295e-01 3.61998141e-01 4.46995944e-01 7.11082399e-01 -1.58771718e+00 -6.91721812e-02 1.40901196e+00 1.01205862e+00 4.10823792e-01 4.63616490e-01 -8.60554650e-02 6.64499879e-01 -3.56673777e-01 1.79113150e-01 4.31595504e-01 -3.54284048e-01 -6.47768080e-01 6.98577285e-01 4.75286655e-02 -1.44173384e-01 6.96677327e-01 1.01718104e+00 4.28971946e-01 3.28518689e-01 9.91982996e-01 -8.84840369e-01 -1.05940737e-01 7.03292131e-01 -5.21744192e-01 4.79599647e-02 -2.81966537e-01 8.12229961e-02 9.90080893e-01 -8.94172311e-01 1.65969163e-01 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2ea300b8-5f88-42a1-98f3-6c27b8911449	emogen-eliminating-subjective-bias-in	2307.01229	null	https://arxiv.org/abs/2307.01229v1	https://arxiv.org/pdf/2307.01229v1.pdf	EmoGen: Eliminating Subjective Bias in Emotional Music Generation	Music is used to convey emotions, and thus generating emotional music is important in automatic music generation. Previous work on emotional music generation directly uses annotated emotion labels as control signals, which suffers from subjective bias: different people may annotate different emotions on the same music, and one person may feel different emotions under different situations. Therefore, directly mapping emotion labels to music sequences in an end-to-end way would confuse the learning process and hinder the model from generating music with general emotions. In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning. Both stages are beneficial: in the first stage, the attribute values around the clustering center represent the general emotions of these samples, which help eliminate the impacts of the subjective bias of emotion labels; in the second stage, the generation is completely disentangled from emotion labels and thus free from the subjective bias. Both subjective and objective evaluations show that EmoGen outperforms previous methods on emotion control accuracy and music quality respectively, which demonstrate our superiority in generating emotional music. Music samples generated by EmoGen are available via this link:https://ai-muzic.github.io/emogen/, and the code is available at this link:https://github.com/microsoft/muzic/.	['Jiang Bian', 'Shikun Zhang', 'Wei Ye', 'Xu Tan', 'Botao Yu', 'Peiling Lu', 'Chenfei Kang']	2023-07-03	null	null	null	null	['music-generation', 'self-supervised-learning', 'clustering', 'music-generation']	['audio', 'computer-vision', 'methodology', 'music']	[-1.20281659e-01 -7.56047070e-02 -2.01617554e-01 -1.17223382e-01 -6.31141484e-01 -7.04802036e-01 3.54213007e-02 -2.99456298e-01 1.01814605e-01 5.45043051e-01 4.67593223e-01 4.90707457e-01 4.45995145e-02 -8.08635116e-01 -3.17974031e-01 -6.90308809e-01 2.54202396e-01 2.54409850e-01 -7.16442525e-01 -2.80806035e-01 -5.95845580e-02 -2.63766319e-01 -1.56408632e+00 4.48089451e-01 9.17605698e-01 1.14658761e+00 -1.87780008e-01 6.39734805e-01 -9.95776802e-02 6.00590587e-01 -7.43816495e-01 -5.29777944e-01 2.11015508e-01 -9.25374746e-01 -3.74874562e-01 -6.43117949e-02 -1.90529943e-01 2.80995011e-01 1.09528407e-01 1.19157600e+00 9.54208195e-01 1.74749210e-01 6.48036957e-01 -1.62941158e+00 -7.58667648e-01 8.95635962e-01 -3.77441883e-01 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ff1ec6c5-00b4-4a8d-968a-923ab5ccdfc6	relational-memory-based-knowledge-graph	1907.06080	null	https://arxiv.org/abs/1907.06080v2	https://arxiv.org/pdf/1907.06080v2.pdf	A Relational Memory-based Embedding Model for Triple Classification and Search Personalization	Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in relationship triples. R-MeN considers each triple as a sequence of 3 input vectors that recurrently interact with a memory using a transformer self-attention mechanism. Thus R-MeN encodes new information from interactions between the memory and each input vector to return a corresponding vector. Consequently, R-MeN feeds these 3 returned vectors to a convolutional neural network-based decoder to produce a scalar score for the triple. Experimental results show that our proposed R-MeN obtains state-of-the-art results on SEARCH17 for the search personalization task, and on WN11 and FB13 for the triple classification task.	['Dai Quoc Nguyen', 'Dinh Phung', 'Tu Dinh Nguyen']	2019-07-13	a-relational-memory-based-embedding-model-for	https://aclanthology.org/2020.acl-main.313	https://aclanthology.org/2020.acl-main.313.pdf	acl-2020-6	['triple-classification']	['graphs']	[-2.40072906e-02 4.42446291e-01 -9.39057410e-01 -3.47342402e-01 -6.37433052e-01 -1.24920301e-01 6.75329626e-01 4.30684566e-01 -5.04955649e-01 6.24092102e-01 5.88348925e-01 -3.47029805e-01 -1.05291203e-01 -1.18758988e+00 -1.06968260e+00 -2.61598617e-01 -6.33625239e-02 7.24342108e-01 7.47512579e-02 -4.92195338e-01 4.91661020e-02 5.41804619e-02 -1.52128804e+00 5.73591530e-01 7.39537060e-01 1.13044798e+00 8.89215618e-03 4.24330324e-01 -1.46618962e-01 8.67704511e-01 -1.38681158e-01 -8.16921532e-01 -1.28798991e-01 -1.15201861e-01 -1.10092473e+00 -5.28819919e-01 1.15644097e-01 -4.61281613e-02 -1.17392540e+00 6.89473689e-01 3.78150761e-01 2.94093490e-01 4.88819957e-01 -9.28805709e-01 -1.64860237e+00 1.36953735e+00 -2.09302932e-01 4.64305282e-01 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afac8bdd-a8de-4d6c-b945-1c635a1fba70	generalizing-mlps-with-dropouts-batch	2108.08186	null	https://arxiv.org/abs/2108.08186v2	https://arxiv.org/pdf/2108.08186v2.pdf	Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections	A multilayer perceptron (MLP) is typically made of multiple fully connected layers with nonlinear activation functions. There have been several approaches to make them better (e.g. faster convergence, better convergence limit, etc.). But the researches lack structured ways to test them. We test different MLP architectures by carrying out the experiments on the age and gender datasets. We empirically show that by whitening inputs before every linear layer and adding skip connections, our proposed MLP architecture can result in better performance. Since the whitening process includes dropouts, it can also be used to approximate Bayesian inference. We have open sourced our code, and released models and docker images at https://github.com/tae898/age-gender/	['Taewoon Kim']	2021-08-18	generalizing-mlps-with-dropouts-batch-1	https://openreview.net/forum?id=XbatFr32NRm	https://openreview.net/pdf?id=XbatFr32NRm	null	['age-and-gender-classification', 'age-estimation', 'gender-prediction', 'age-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous']	[-3.25158447e-01 4.50307839e-02 -3.41629446e-01 -8.61956179e-01 -1.19984955e-01 -1.80052943e-04 3.17458391e-01 -1.25674508e-03 -6.94832802e-01 1.12355864e+00 1.97363421e-01 -4.34974313e-01 7.08175078e-02 -8.83793771e-01 -1.00929856e+00 -7.12802112e-01 2.66366340e-02 3.99580002e-01 1.88235492e-01 4.25122172e-01 1.73359364e-01 2.72821516e-01 -1.21171033e+00 3.03248316e-01 9.59523678e-01 6.61041081e-01 8.36192910e-03 7.16353059e-01 -1.92287713e-01 8.58086884e-01 -6.40416861e-01 -6.52591705e-01 -1.04727343e-01 -2.04865515e-01 -7.19714403e-01 -6.08330309e-01 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d6e90b67-0144-4a94-8c0c-c77fd2158dca	a-computational-acquisition-model-for	2205.05974	null	https://arxiv.org/abs/2205.05974v1	https://arxiv.org/pdf/2205.05974v1.pdf	A Computational Acquisition Model for Multimodal Word Categorization	Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals. However, prior studies have been limited by their reliance on vision models trained on large image datasets annotated with a pre-defined set of depicted object categories. This is (a) not faithful to the information children receive and (b) prohibits the evaluation of such models with respect to category learning tasks, due to the pre-imposed category structure. We address this gap, and present a cognitively-inspired, multimodal acquisition model, trained from image-caption pairs on naturalistic data using cross-modal self-supervision. We show that the model learns word categories and object recognition abilities, and presents trends reminiscent of those reported in the developmental literature. We make our code and trained models public for future reference and use.	['Lea Frermann', 'Omri Abend', 'Gabriel Stanovsky', 'Uri Berger']	2022-05-12	null	https://aclanthology.org/2022.naacl-main.280	https://aclanthology.org/2022.naacl-main.280.pdf	naacl-2022-7	['language-acquisition']	['natural-language-processing']	[ 6.52463257e-01 2.68203348e-01 -1.28382310e-01 -6.91647470e-01 -2.77094722e-01 -8.49405468e-01 1.07800579e+00 5.74833632e-01 -5.77156126e-01 1.84086010e-01 3.37846875e-01 -2.82852590e-01 -2.17524678e-01 -5.98505795e-01 -9.18165684e-01 -3.05488884e-01 -9.54029325e-04 6.90040231e-01 1.37332767e-01 9.93778557e-03 2.84868300e-01 8.34431034e-03 -1.97165680e+00 6.03645205e-01 7.88827777e-01 5.88032246e-01 3.35736901e-01 4.57822174e-01 -1.03352763e-01 8.31474781e-01 2.54626828e-03 -7.99064696e-01 -2.10994840e-01 -5.10727108e-01 -8.91549647e-01 2.65451372e-01 9.29961801e-01 -4.92526323e-01 -1.31163836e-01 1.06753933e+00 1.25117406e-01 -1.73137426e-01 1.15847504e+00 -1.13579726e+00 -1.07900298e+00 7.42906928e-01 -3.73298556e-01 2.53381163e-01 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6.67818248e-01 1.16172945e+00 -2.41410568e-01 2.95136124e-02 1.53683349e-02 -8.59191656e-01 -2.50321776e-01 -1.03489172e+00 -4.26869690e-01 6.89668417e-01 3.11723709e-01 -7.59818375e-01 -1.99115902e-01 5.17767966e-01]	[10.201016426086426, 8.61330795288086]
9160dbc6-8fb7-41ee-a6aa-37b73d177f4b	evaluating-deep-music-generation-methods	2201.00052	null	https://arxiv.org/abs/2201.00052v1	https://arxiv.org/pdf/2201.00052v1.pdf	Evaluating Deep Music Generation Methods Using Data Augmentation	Despite advances in deep algorithmic music generation, evaluation of generated samples often relies on human evaluation, which is subjective and costly. We focus on designing a homogeneous, objective framework for evaluating samples of algorithmically generated music. Any engineered measures to evaluate generated music typically attempt to define the samples' musicality, but do not capture qualities of music such as theme or mood. We do not seek to assess the musical merit of generated music, but instead explore whether generated samples contain meaningful information pertaining to emotion or mood/theme. We achieve this by measuring the change in predictive performance of a music mood/theme classifier after augmenting its training data with generated samples. We analyse music samples generated by three models -- SampleRNN, Jukebox, and DDSP -- and employ a homogeneous framework across all methods to allow for objective comparison. This is the first attempt at augmenting a music genre classification dataset with conditionally generated music. We investigate the classification performance improvement using deep music generation and the ability of the generators to make emotional music by using an additional, emotion annotation of the dataset. Finally, we use a classifier trained on real data to evaluate the label validity of class-conditionally generated samples.	['Bjoern W. Schuller', 'Vincent Brisse', 'Najla D. Al Futaisi', 'Alice Baird', 'Georgios Rizos', 'Toby Godwin']	2021-12-31	null	null	null	null	['music-generation', 'genre-classification', 'music-generation']	['audio', 'computer-vision', 'music']	[ 4.24713314e-01 2.08430558e-01 2.77608540e-02 -2.65724927e-01 -1.05408812e+00 -8.57060850e-01 5.48753619e-01 8.50836858e-02 -1.11043759e-01 6.26894951e-01 3.51695240e-01 4.24797624e-01 -1.98215425e-01 -7.50810504e-01 -3.27641398e-01 -5.95226228e-01 -2.47446653e-02 4.95940119e-01 -4.22447324e-01 -2.90774815e-02 2.16783449e-01 4.34211735e-03 -1.88115859e+00 6.51380658e-01 5.44173419e-01 1.17536092e+00 -3.07719111e-01 8.68220448e-01 7.44044408e-02 7.42066562e-01 -1.25573194e+00 -5.36133647e-01 2.67774254e-01 -9.92462695e-01 -6.24912620e-01 -1.82586815e-02 2.88206100e-01 1.87314913e-01 5.73610544e-01 8.86353493e-01 6.61555707e-01 -7.83557713e-04 8.84746194e-01 -1.38644278e+00 -3.49914104e-01 1.20183814e+00 8.73503909e-02 -3.54767174e-01 6.83933377e-01 2.68657863e-01 1.40804660e+00 -5.09586215e-01 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afacbeb3-8741-4258-a9ce-415b3cc36f75	privacy-preserving-image-queries-for-camera	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Speciale_Privacy_Preserving_Image_Queries_for_Camera_Localization_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Speciale_Privacy_Preserving_Image_Queries_for_Camera_Localization_ICCV_2019_paper.pdf	Privacy Preserving Image Queries for Camera Localization	Augmented/mixed reality and robotic applications are increasingly relying on cloud-based localization services, which require users to upload query images to perform camera pose estimation on a server. This raises significant privacy concerns when consumers use such services in their homes or in confidential industrial settings. Even if only image features are uploaded, the privacy concerns remain as the images can be reconstructed fairly well from feature locations and descriptors. We propose to conceal the content of the query images from an adversary on the server or a man-in-the-middle intruder. The key insight is to replace the 2D image feature points in the query image with randomly oriented 2D lines passing through their original 2D positions. It will be shown that this feature representation hides the image contents, and thereby protects user privacy, yet still provides sufficient geometric constraints to enable robust and accurate 6-DOF camera pose estimation from feature correspondences. Our proposed method can handle single- and multi-image queries as well as exploit additional information about known structure, gravity, and scale. Numerous experiments demonstrate the high practical relevance of our approach.	[' Marc Pollefeys', ' Sudipta N. Sinha', ' Johannes L. Schonberger', 'Pablo Speciale']	2019-10-01	null	null	null	iccv-2019-10	['camera-localization']	['computer-vision']	[ 1.36820069e-02 1.61166146e-01 -7.44946348e-03 -2.44830742e-01 -9.20794129e-01 -1.15424764e+00 3.02083969e-01 -9.68389660e-02 -5.18127739e-01 5.40799141e-01 -3.66894990e-01 -2.77843568e-02 2.83945743e-02 -4.77059871e-01 -8.46685290e-01 -9.06929314e-01 -4.99764341e-04 3.94990772e-01 1.01803102e-01 2.40905825e-02 3.18128496e-01 9.65520084e-01 -1.15299022e+00 -3.08181077e-01 4.07450497e-01 1.29777563e+00 1.33349761e-01 6.43706381e-01 4.29440677e-01 2.47758195e-01 -6.67371988e-01 -7.27071702e-01 6.87065840e-01 3.10565114e-01 -3.24037641e-01 3.21961492e-01 4.47657496e-01 -8.98517787e-01 -6.86349928e-01 1.44847775e+00 2.45123878e-01 -1.97990552e-01 -1.25475200e-02 -1.72511494e+00 -5.15089512e-01 -4.48963672e-01 -6.56291783e-01 -5.39553821e-01 1.01372159e+00 -6.26164004e-02 4.69035536e-01 -5.24340689e-01 8.67031038e-01 7.27042317e-01 6.02406323e-01 2.18131468e-01 -8.16585183e-01 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6.61864042e-01 -4.36180890e-01 -5.54323375e-01 -9.59748179e-02]	[7.554337501525879, -2.1682517528533936]

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