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6babc05a-faab-46e7-838b-5fcfe251fa14 | what-makes-my-queries-slow-subgroup-discovery | 2108.03906 | null | https://arxiv.org/abs/2108.03906v1 | https://arxiv.org/pdf/2108.03906v1.pdf | "What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis | Among daily tasks of database administrators (DBAs), the analysis of query workloads to identify schema issues and improving performances is crucial. Although DBAs can easily pinpoint queries repeatedly causing performance issues, it remains challenging to automatically identify subsets of queries that share some prope... | ['Mehdi Kaytoue', 'Philippe Chaleat', 'Romain Mathonat', 'Anes Bendimerad', 'Youcef Remil'] | 2021-08-09 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [-1.74817830e-01 4.40880880e-02 -4.32243496e-01 -4.82362807e-01
-4.90201771e-01 -6.58830881e-01 5.44828996e-02 9.00824904e-01
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-3.93901139e-01 6.78037047e-01 6.22238994e-01 -2.16882810... | [8.965415954589844, 7.317539215087891] |
5da92c0a-90f3-4267-a39c-67b2991c90a2 | climategan-raising-climate-change-awareness | 2110.02871 | null | https://arxiv.org/abs/2110.02871v1 | https://arxiv.org/pdf/2110.02871v1.pdf | ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods | Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the pote... | ['Yoshua Bengio', 'Alex Hernandez-Garcia', 'Vahe Vardanyan', 'Adrien Juraver', 'Gautier Cosne', 'Sunand Raghupathi', 'Alexia Reynaud', 'Tianyu Zhang', 'Mélisande Teng', 'Alexandra Sasha Luccioni', 'Victor Schmidt'] | 2021-10-06 | climategan-raising-climate-change-awareness-1 | https://openreview.net/forum?id=EZNOb_uNpJk | https://openreview.net/pdf?id=EZNOb_uNpJk | iclr-2022-4 | ['conditional-image-generation'] | ['computer-vision'] | [ 6.11796439e-01 2.20330074e-01 4.34485048e-01 -5.07331073e-01
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3.85489352e-02 1.07586794e-01 -3.87908071e-02 -5.03340840... | [9.54433536529541, -1.6526341438293457] |
cba164e3-9495-414e-a023-80700e323060 | awq-activation-aware-weight-quantization-for | 2306.00978 | null | https://arxiv.org/abs/2306.00978v1 | https://arxiv.org/pdf/2306.00978v1.pdf | AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration | Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth). In this paper, we propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for... | ['Song Han', 'Xingyu Dang', 'Shang Yang', 'Haotian Tang', 'Jiaming Tang', 'Ji Lin'] | 2023-06-01 | null | null | null | null | ['quantization', 'common-sense-reasoning'] | ['methodology', 'reasoning'] | [-7.78537393e-02 -3.81324857e-01 -6.15141988e-01 -1.09047167e-01
-1.14449179e+00 -3.52220505e-01 2.26646289e-01 3.83157194e-01
-8.45798969e-01 2.95431077e-01 2.46789262e-01 -7.80370951e-01
2.23942176e-01 -6.22192979e-01 -7.56642997e-01 -7.01942980e-01
-2.59397298e-01 1.78113565e-01 5.48040211e-01 -3.60476762... | [8.672783851623535, 3.464522123336792] |
a571b5cb-051f-48c3-abb4-4561ac86549a | unsupervised-adaptation-with-domain | 1711.08010 | null | http://arxiv.org/abs/1711.08010v2 | http://arxiv.org/pdf/1711.08010v2.pdf | Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition | Unsupervised domain adaptation of speech signal aims at adapting a
well-trained source-domain acoustic model to the unlabeled data from target
domain. This can be achieved by adversarial training of deep neural network
(DNN) acoustic models to learn an intermediate deep representation that is both
senone-discriminative... | ['Zhuo Chen', 'Yifan Gong', 'Zhong Meng', 'Vadim Mazalov', 'Jinyu Li'] | 2017-11-21 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 6.21707022e-01 3.26437950e-01 1.03789657e-01 -5.42453349e-01
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3.21337998e-01 4.97262120e-01 -5.67403249e-02 -3.31016570... | [14.551300048828125, 6.386044979095459] |
95ca8973-5ae2-4284-8ff3-3cf839301b2f | behavior-cloned-transformers-are | 2210.07382 | null | https://arxiv.org/abs/2210.07382v2 | https://arxiv.org/pdf/2210.07382v2.pdf | Behavior Cloned Transformers are Neurosymbolic Reasoners | In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation. We test our agent's abilities in text games -- challenging benchmarks for evaluating the multi-step reasonin... | ['Prithviraj Ammanabrolu', 'Marc-Alexandre Côté', 'Peter Jansen', 'Ruoyao Wang'] | 2022-10-13 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.84540597e-02 4.73713517e-01 7.07402676e-02 8.57342184e-02
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-3.89540911e-01 9.45733666e-01 1.01308203e+00 -1.02039587... | [3.6539716720581055, 1.325090765953064] |
27249016-1426-40c0-86d8-9e89b24848a1 | using-knowledge-graphs-for-performance | 2301.09876 | null | https://arxiv.org/abs/2301.09876v1 | https://arxiv.org/pdf/2301.09876v1.pdf | Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms | Empirical data plays an important role in evolutionary computation research. To make better use of the available data, ontologies have been proposed in the literature to organize their storage in a structured way. However, the full potential of these formal methods to capture our domain knowledge has yet to be demonstr... | ['Carola Doerr', 'Tome Eftimov', 'Panče Panov', 'Sašo Džeroski', 'Diederick Vermetten', 'Ana Kostovska'] | 2023-01-24 | null | null | null | null | ['triple-classification', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [ 2.57888347e-01 1.89058900e-01 -2.73105264e-01 -3.45416695e-01
-8.54284912e-02 -4.18282479e-01 5.19275725e-01 6.85331285e-01
-2.10839882e-01 6.98363245e-01 -8.02602470e-02 -2.13903919e-01
-9.46200609e-01 -1.10135543e+00 -5.73916197e-01 -4.22056079e-01
-2.03367665e-01 5.92592180e-01 1.60016045e-01 -3.77657443... | [8.347735404968262, 4.599459171295166] |
52b56cd6-c224-4934-8bd1-e67655a43673 | gated-graph-sequence-neural-networks | 1511.05493 | null | http://arxiv.org/abs/1511.05493v4 | http://arxiv.org/pdf/1511.05493v4.pdf | Gated Graph Sequence Neural Networks | Graph-structured data appears frequently in domains including chemistry,
natural language semantics, social networks, and knowledge bases. In this work,
we study feature learning techniques for graph-structured inputs. Our starting
point is previous work on Graph Neural Networks (Scarselli et al., 2009), which
we modif... | ['Daniel Tarlow', 'Yujia Li', 'Marc Brockschmidt', 'Richard Zemel'] | 2015-11-17 | null | null | null | null | ['sql-to-text'] | ['computer-code'] | [ 6.79083347e-01 6.19195461e-01 -4.20034379e-01 -4.58376497e-01
-1.45211294e-01 -5.95608711e-01 4.21911329e-01 4.07459259e-01
-1.71780005e-01 6.44562721e-01 2.95060873e-02 -1.07201374e+00
1.03813529e-01 -1.30261326e+00 -1.21164048e+00 -1.63004681e-01
-5.33183455e-01 2.85039783e-01 -7.89353549e-02 -3.03660899... | [7.018618583679199, 6.508023738861084] |
327fbac3-7f5f-46c9-b691-87f0b8f8c2e0 | swifttron-an-efficient-hardware-accelerator | 2304.03986 | null | https://arxiv.org/abs/2304.03986v2 | https://arxiv.org/pdf/2304.03986v2.pdf | SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers | Transformers' compute-intensive operations pose enormous challenges for their deployment in resource-constrained EdgeAI / tinyML devices. As an established neural network compression technique, quantization reduces the hardware computational and memory resources. In particular, fixed-point quantization is desirable to ... | ['Muhammad Shafique', 'Guido Masera', 'Maurizio Martina', 'Maurizio Capra', 'Davide Dura', 'Alberto Marchisio'] | 2023-04-08 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 6.41072839e-02 1.11062676e-01 -5.81701458e-01 -4.46627468e-01
-1.26396224e-01 -7.56454468e-02 1.70880049e-01 2.79848903e-01
-7.26713896e-01 2.16857374e-01 7.78799281e-02 -8.24026763e-01
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1.09014869e-01 1.76426545e-01 6.82289526e-02 -2.00356334... | [8.432191848754883, 2.8759424686431885] |
d1003ea7-35d3-420b-9f03-cc58c665d4a1 | sep-stereo-visually-guided-stereophonic-audio | 2007.09902 | null | https://arxiv.org/abs/2007.09902v1 | https://arxiv.org/pdf/2007.09902v1.pdf | Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation | Stereophonic audio is an indispensable ingredient to enhance human auditory experience. Recent research has explored the usage of visual information as guidance to generate binaural or ambisonic audio from mono ones with stereo supervision. However, this fully supervised paradigm suffers from an inherent drawback: the ... | ['Xudong Xu', 'Hang Zhou', 'Ziwei Liu', 'Xiaogang Wang', 'Dahua Lin'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1483_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570052.pdf | eccv-2020-8 | ['audio-generation'] | ['audio'] | [ 3.32953453e-01 -2.92933792e-01 1.75018609e-01 -1.69173896e-01
-1.06453454e+00 -5.63340008e-01 3.56248826e-01 3.53716165e-02
8.34314059e-03 6.04785860e-01 5.62703252e-01 1.37583420e-01
-1.67726696e-01 -4.71045643e-01 -5.61090767e-01 -9.47638273e-01
4.18555588e-01 -1.32435840e-02 2.29610041e-01 -2.82151222... | [14.95418643951416, 5.077826499938965] |
fcaf1250-b07b-4b0a-bd2c-d821b66de82c | high-fidelity-and-freely-controllable-talking | 2304.10168 | null | https://arxiv.org/abs/2304.10168v1 | https://arxiv.org/pdf/2304.10168v1.pdf | High-Fidelity and Freely Controllable Talking Head Video Generation | Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face often has unexpected deformation and severe distortions. Second, the driving ima... | ['Yan Lu', 'Xiang Ming', 'Xiao Li', 'Jinglu Wang', 'Yuan Zhou', 'Yue Gao'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_High-Fidelity_and_Freely_Controllable_Talking_Head_Video_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_High-Fidelity_and_Freely_Controllable_Talking_Head_Video_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-head-generation', 'video-generation', 'face-model'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.09920710e-01 9.48593616e-02 -6.57158643e-02 -5.96523881e-01
-9.27097738e-01 -5.48096955e-01 5.96505225e-01 -7.93152750e-01
1.89931050e-01 5.04286528e-01 7.13927269e-01 5.88948548e-01
2.45501697e-01 -2.49959603e-01 -7.21072912e-01 -7.91142464e-01
1.36007398e-01 -5.99715076e-02 -1.86731443e-01 -1.20332345... | [13.111870765686035, -0.3991010785102844] |
ee931418-5ea4-4dca-b983-a83185dd6b34 | learning-logic-specifications-for-soft-policy | 2303.09172 | null | https://arxiv.org/abs/2303.09172v1 | https://arxiv.org/pdf/2303.09172v1.pdf | Learning Logic Specifications for Soft Policy Guidance in POMCP | Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by computing an approximation of the optimal policy locally and online, using a Monte Carlo Tree Search based strategy. However, POMCP suffers fro... | ['Alessandro Farinelli', 'Alberto Castellini', 'Daniele Meli', 'Giulio Mazzi'] | 2023-03-16 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 5.34023456e-02 3.08901548e-01 -4.91121441e-01 -1.24796920e-01
-9.87822473e-01 -8.27191234e-01 6.57009780e-01 2.24428028e-01
-4.51372176e-01 1.28332591e+00 3.41167331e-01 -5.63022912e-01
-3.40775847e-01 -8.58331323e-01 -8.80597591e-01 -5.90294182e-01
-4.73166049e-01 9.27069068e-01 3.55465323e-01 8.66314322... | [4.273430824279785, 2.1591553688049316] |
f4beca2a-b319-4a10-bade-46a8f52d5488 | sdfdiff-differentiable-rendering-of-signed | 1912.07109 | null | https://arxiv.org/abs/1912.07109v2 | https://arxiv.org/pdf/1912.07109v2.pdf | SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization | We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to other representations, SDFs have the advantage that they can represent shapes with arbitrary topology, and that they guarantee watertight surfa... | ['Zhizhong Han', 'Matthias Zwicker', 'Dantong Ji', 'Yue Jiang'] | 2019-12-15 | sdfdiff-differentiable-rendering-of-signed-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.pdf | cvpr-2020-6 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-9.90900770e-02 -3.29329744e-02 3.31173778e-01 -3.37468565e-01
-8.67921293e-01 -5.38941324e-01 5.52909195e-01 -2.93615043e-01
3.56353000e-02 3.01822752e-01 7.88104236e-02 -1.69513896e-01
-1.05699701e-02 -9.97750938e-01 -8.92409265e-01 -3.94960761e-01
-7.92376846e-02 8.24922740e-01 8.62875357e-02 -1.80982381... | [8.739187240600586, -3.5751750469207764] |
2d0f38cb-06c3-4ede-a9b6-db32a2ffe422 | discriminative-models-can-still-outperform-1 | 2206.02892 | null | https://arxiv.org/abs/2206.02892v1 | https://arxiv.org/pdf/2206.02892v1.pdf | Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis | Aspect-based Sentiment Analysis (ABSA) helps to explain customers' opinions towards products and services. In the past, ABSA models were discriminative, but more recently generative models have been used to generate aspects and polarities directly from text. In contrast, discriminative models commonly first select aspe... | ['Bilal Ghanem', 'Alona Fyshe', 'Dhruv Mullick'] | 2022-06-06 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.78237036e-01 6.75371066e-02 -3.78101587e-01 -9.06745255e-01
-1.06281352e+00 -1.02371955e+00 8.95292282e-01 5.14684990e-02
-1.51984468e-01 4.22722220e-01 5.70312142e-01 -3.85183156e-01
1.41801447e-01 -8.79508138e-01 -3.98845434e-01 -4.30337638e-01
5.56186497e-01 8.67332935e-01 -2.77342469e-01 -5.98619640... | [11.430286407470703, 6.716883182525635] |
40bdec32-a51b-4307-b947-7ec0ee2401a3 | simple-and-efficient-confidence-score-for | 2303.04604 | null | https://arxiv.org/abs/2303.04604v1 | https://arxiv.org/pdf/2303.04604v1.pdf | Simple and Efficient Confidence Score for Grading Whole Slide Images | Grading precancerous lesions on whole slide images is a challenging task: the continuous space of morphological phenotypes makes clear-cut decisions between different grades often difficult, leading to low inter- and intra-rater agreements. More and more Artificial Intelligence (AI) algorithms are developed to help pat... | ['Thomas Walter', 'Cécile Badoual', 'Rutger Fick', 'Yaëlle Bellahsen-Harrar', 'Mélanie Lubrano'] | 2023-03-08 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 4.55387414e-01 2.76259392e-01 -1.93682373e-01 -3.66393238e-01
-1.04432511e+00 -7.25349247e-01 3.59596610e-01 6.61863804e-01
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-4.01704699e-01 -7.19091356e-01 -1.14980981e-01 -1.12261593e+00
2.10210189e-01 8.32941234e-01 3.08399826e-01 1.93640605... | [15.120173454284668, -2.923309326171875] |
739f7fe1-16bc-4a07-a2ec-30f78c78a849 | minimizing-the-effect-of-noise-and-limited | 2208.10390 | null | https://arxiv.org/abs/2208.10390v1 | https://arxiv.org/pdf/2208.10390v1.pdf | Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning | Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns. We tackle these challenges through utilizing the framework of deep Multitask Learning (dMTL) and incorporating image depth estimation as an auxiliary task. On a custom... | ['Farzad Khalvati', 'Partoo Vafaeikia', 'Khashayar Namdar'] | 2022-08-22 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 1.57056689e-01 -7.29149356e-02 -1.18082501e-01 -5.27186215e-01
-1.16557276e+00 -5.51287174e-01 4.19556528e-01 -2.44792178e-02
-7.12462008e-01 6.55981779e-01 1.34247420e-02 -2.33676076e-01
1.51364073e-01 -6.28640652e-01 -9.95765209e-01 -6.17463052e-01
1.84189379e-01 2.89328098e-01 3.52268487e-01 2.42353976... | [9.530471801757812, 1.418951392173767] |
89801b7f-d173-4dc4-b838-60c9affd1706 | combining-vision-and-tactile-sensation-for | 2304.11193 | null | https://arxiv.org/abs/2304.11193v1 | https://arxiv.org/pdf/2304.11193v1.pdf | Combining Vision and Tactile Sensation for Video Prediction | In this paper, we explore the impact of adding tactile sensation to video prediction models for physical robot interactions. Predicting the impact of robotic actions on the environment is a fundamental challenge in robotics. Current methods leverage visual and robot action data to generate video predictions over a give... | ['Amir Ghalamzan-E', 'Willow Mandil'] | 2023-04-21 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 4.08301353e-01 1.27952144e-01 -2.33073369e-01 -3.16701710e-01
1.20209932e-01 -2.21811533e-01 4.31464761e-01 1.12730235e-01
-2.10044429e-01 3.41571957e-01 1.47609383e-01 1.58595100e-01
-2.08039373e-01 -5.90800047e-01 -1.11411095e+00 -2.61314750e-01
-1.10866353e-01 1.62262276e-01 5.97829223e-01 -2.35662505... | [4.862826347351074, 0.5889959931373596] |
d2a07263-9589-40dc-be8c-5cb30f3545f3 | exploring-length-generalization-in-large | 2207.04901 | null | https://arxiv.org/abs/2207.04901v2 | https://arxiv.org/pdf/2207.04901v2.pdf | Exploring Length Generalization in Large Language Models | The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare. These include theorem proving, solving quantitative mathematics problems, and reading/... | ['Behnam Neyshabur', 'Ethan Dyer', 'Guy Gur-Ari', 'Ambrose Slone', 'Vinay Ramasesh', 'Vedant Misra', 'Aitor Lewkowycz', 'Anders Andreassen', 'Yuhuai Wu', 'Cem Anil'] | 2022-07-11 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.84896749e-01 1.74118221e-01 -3.98594327e-02 -3.58728647e-01
-1.03790736e+00 -9.70353127e-01 3.96274537e-01 4.85437751e-01
-3.28366429e-01 6.14491642e-01 -8.25560745e-03 -1.15499473e+00
-6.03709698e-01 -1.03568494e+00 -9.23324585e-01 -1.27286136e-01
-1.26443177e-01 5.69816530e-01 2.41124406e-01 -3.11471939... | [9.487804412841797, 7.268215656280518] |
43babe3e-8010-4416-af0f-ac9549c0fc16 | adaptive-as-natural-as-possible-image | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Lin_Adaptive_As-Natural-As-Possible_Image_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Lin_Adaptive_As-Natural-As-Possible_Image_2015_CVPR_paper.pdf | Adaptive As-Natural-As-Possible Image Stitching | The goal of image stitching is to create natural-looking mosaics free of artifacts that may occur due to relative camera motion, illumination changes, and optical aberrations. In this paper, we propose a novel stitching method, that uses a smooth stitching field over the entire target image, while accounting for all th... | ['Karthikeyan Natesan Ramamurthy', 'Chung-Ching Lin', 'Aleksandr Y. Aravkin', 'Sharathchandra U. Pankanti'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['image-stitching'] | ['computer-vision'] | [ 8.29639256e-01 -4.03282255e-01 2.06934735e-01 -6.16970705e-03
-4.71252531e-01 -8.52030456e-01 6.68658674e-01 -3.66222352e-01
-1.12218827e-01 3.36590767e-01 1.07239008e-01 1.60419047e-01
-6.12664036e-02 -4.26195830e-01 -4.74824190e-01 -8.73761654e-01
2.39835799e-01 2.37802699e-01 5.42129934e-01 -1.10664509... | [9.356209754943848, -2.3856749534606934] |
4362f88f-d613-4b98-ac6e-81a48f4b7cbe | langevin-thompson-sampling-with-logarithmic | 2306.08803 | null | https://arxiv.org/abs/2306.08803v1 | https://arxiv.org/pdf/2306.08803v1.pdf | Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning | Thompson sampling (TS) is widely used in sequential decision making due to its ease of use and appealing empirical performance. However, many existing analytical and empirical results for TS rely on restrictive assumptions on reward distributions, such as belonging to conjugate families, which limits their applicabilit... | ['Siddharth Mitra', 'Yi-An Ma', 'Nikki Lijing Kuang', 'Amin Karbasi'] | 2023-06-15 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 1.54462561e-01 -1.25959694e-01 -4.50614274e-01 -3.32277566e-01
-1.01556814e+00 -5.68109870e-01 8.45655799e-02 3.01228702e-01
-5.89099646e-01 1.15514386e+00 -4.05883551e-01 -7.66203880e-01
-4.76956606e-01 -7.76310146e-01 -8.44517469e-01 -7.83540487e-01
-6.47913963e-02 7.67141640e-01 4.04327810e-02 7.28960186... | [4.48383903503418, 3.1819210052490234] |
58dae5fd-0e80-4328-9a2b-2370ae08a788 | dp-util-comprehensive-utility-analysis-of | 2112.12998 | null | https://arxiv.org/abs/2112.12998v1 | https://arxiv.org/pdf/2112.12998v1.pdf | DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning | Differential Privacy (DP) has emerged as a rigorous formalism to reason about quantifiable privacy leakage. In machine learning (ML), DP has been employed to limit inference/disclosure of training examples. Prior work leveraged DP across the ML pipeline, albeit in isolation, often focusing on mechanisms such as gradien... | ['Birhanu Eshete', 'Ismat Jarin'] | 2021-12-24 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.70717162e-01 2.29700267e-01 -5.68071231e-02 -2.66070664e-01
-7.66849279e-01 -1.03730083e+00 4.38647389e-01 3.38110059e-01
-4.46857810e-01 6.23409033e-01 2.06858173e-01 -7.42352188e-01
-2.18684971e-01 -6.53506994e-01 -7.50729084e-01 -8.01057398e-01
-1.05016299e-01 -1.55603677e-01 -4.75859672e-01 2.61908203... | [5.965097427368164, 6.969370365142822] |
543934bd-d0c9-44e0-8f50-8cb5450ff9ad | local-global-temporal-difference-learning-for | 2304.04421 | null | https://arxiv.org/abs/2304.04421v1 | https://arxiv.org/pdf/2304.04421v1.pdf | Local-Global Temporal Difference Learning for Satellite Video Super-Resolution | Optical-flow-based and kernel-based approaches have been widely explored for temporal compensation in satellite video super-resolution (VSR). However, these techniques involve high computational consumption and are prone to fail under complex motions. In this paper, we proposed to exploit the well-defined temporal diff... | ['Chia-Wen Lin', 'Liangpei Zhang', 'Jiang He', 'Xianyu Jin', 'Kui Jiang', 'Qiangqiang Yuan', 'Yi Xiao'] | 2023-04-10 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 5.55281714e-02 -5.69822073e-01 -3.52755934e-01 -3.10342669e-01
-5.85585296e-01 -3.03452700e-01 3.71009797e-01 -3.53449225e-01
-2.04511687e-01 6.38100445e-01 5.39690316e-01 3.10955554e-01
-1.65827021e-01 -4.67939496e-01 -3.80189985e-01 -9.38865721e-01
-1.65103450e-01 -6.46863997e-01 5.43568850e-01 -4.34076130... | [11.090127944946289, -1.8587006330490112] |
45a178a4-9156-47ed-8009-77e7b8cf0ffb | generalized-universal-domain-adaptation-with | 2305.04466 | null | https://arxiv.org/abs/2305.04466v1 | https://arxiv.org/pdf/2305.04466v1.pdf | Generalized Universal Domain Adaptation with Generative Flow Networks | We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants, essenti... | ['Chao Wu', 'Jun Xiao', 'Kun Kuang', 'Fei Wu', 'Jianye Hao', 'Yunfeng Shao', 'Yinchuan Li', 'Didi Zhu'] | 2023-05-08 | null | null | null | null | ['universal-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 2.26715922e-01 7.30127916e-02 -4.84472513e-01 -2.51841694e-01
-7.70109892e-01 -6.85078561e-01 4.39459562e-01 -3.40214998e-01
-1.92708075e-01 8.76122236e-01 9.08770561e-02 -1.97898611e-01
-9.67422947e-02 -8.28273058e-01 -3.70672733e-01 -1.04742122e+00
2.07622677e-01 6.09157383e-01 -6.48761168e-02 -4.84547131... | [10.32065486907959, 3.0520823001861572] |
41bfb318-1a1a-4e43-b1f4-a0b008164ce5 | ma2cl-masked-attentive-contrastive-learning | 2306.02006 | null | https://arxiv.org/abs/2306.02006v1 | https://arxiv.org/pdf/2306.02006v1.pdf | MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning | Recent approaches have utilized self-supervised auxiliary tasks as representation learning to improve the performance and sample efficiency of vision-based reinforcement learning algorithms in single-agent settings. However, in multi-agent reinforcement learning (MARL), these techniques face challenges because each age... | ['Houqiang Li', 'Wengang Zhou', 'Mingxiao Feng', 'Haolin Song'] | 2023-06-03 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.12873250e-01 -4.81954068e-02 -3.90397519e-01 2.56648138e-02
-7.57538259e-01 -3.01984251e-01 8.69863331e-01 1.12989899e-02
-5.30491889e-01 9.20858204e-01 1.02904990e-01 1.51183635e-01
-2.05861017e-01 -4.57422912e-01 -7.50122488e-01 -1.06132829e+00
-1.16057232e-01 7.25944340e-01 -9.26586986e-02 -1.82877511... | [4.2074174880981445, 1.4280364513397217] |
3fd441c8-5e7b-4bcb-b237-f67e04206053 | towards-large-scale-single-shot-millimeter | 2305.15750 | null | https://arxiv.org/abs/2305.15750v2 | https://arxiv.org/pdf/2305.15750v2.pdf | Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection | Millimeter-wave (MMW) imaging is emerging as a promising technique for safe security inspection. It achieves a delicate balance between imaging resolution, penetrability and human safety, resulting in higher resolution compared to low-frequency microwave, stronger penetrability compared to visible light, and stronger s... | ['Huteng Liu', 'Jun Zhang', 'Shiyong Li', 'Guoqiang Zhao', 'Xuyang Chang', 'Hanwen Xu', 'Chunyang Teng', 'Shuoguang Wang', 'Daoyu Li', 'Liheng Bian'] | 2023-05-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 4.28213656e-01 1.82079688e-01 2.84566998e-01 -3.37013483e-01
-1.07778943e+00 -4.82461751e-01 3.65333319e-01 -1.62832975e-01
-2.54207373e-01 3.35762501e-01 1.05188869e-01 -5.11997044e-01
-7.86487162e-01 -7.97475636e-01 -6.08036339e-01 -1.23954654e+00
-3.41767043e-01 6.31164312e-02 3.00677240e-01 1.23877108... | [6.77614164352417, 0.8785808682441711] |
8f2be92b-40e8-48dc-ab9c-96ebb8ff6f05 | model-aware-contrastive-learning-towards | 2207.07874 | null | https://arxiv.org/abs/2207.07874v4 | https://arxiv.org/pdf/2207.07874v4.pdf | Model-Aware Contrastive Learning: Towards Escaping the Dilemmas | Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains. However, the most common InfoNCE-based methods suffer from some dilemmas, such as \textit{uniformity-tolerance dilemma} (UTD) and \textit{gradient reduction}, both of which are related to a $\mathcal{P}_{ij}$ term. It has... | ['Chunlin Chen', 'Huaxiong Li', 'Ziqi Wen', 'Haoxing Chen', 'Bo wang', 'Chao Zhang', 'Zizheng Huang'] | 2022-07-16 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.51609635e-01 3.42553742e-02 -1.21911153e-01 -4.76701051e-01
-6.34234190e-01 -3.20910066e-01 4.21312213e-01 4.01236057e-01
-7.04115272e-01 6.54264867e-01 -1.49114519e-01 -4.00132090e-01
-3.72115076e-01 -5.21740437e-01 -6.31300867e-01 -9.18879330e-01
-1.37435002e-02 -1.24807134e-01 2.94503272e-01 -3.93919140... | [9.177568435668945, 3.2610669136047363] |
fc2f1c73-61ee-4055-ade1-f17356d15d51 | nuclick-from-clicks-in-the-nuclei-to-nuclear | 1909.03253 | null | https://arxiv.org/abs/1909.03253v1 | https://arxiv.org/pdf/1909.03253v1.pdf | NuClick: From Clicks in the Nuclei to Nuclear Boundaries | Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmentation is a very labor-intensive and time-consuming task. Thereby, providing a tool that can facilitate and speed up this procedure is very ... | ['Navid Alemi Koohbanani', 'Mostafa Jahanifar', 'Nasir Rajpoot'] | 2019-09-07 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 3.57302241e-02 3.30697298e-01 6.80850595e-02 -6.19956136e-01
-8.74103427e-01 -7.01315641e-01 4.73435551e-01 4.51184720e-01
-8.04029167e-01 7.32571542e-01 -1.90722778e-01 -1.51775882e-01
2.60478109e-01 -8.53516877e-01 -8.20298851e-01 -9.48298395e-01
1.71993539e-01 9.77683961e-01 6.65451169e-01 1.36772692... | [14.733368873596191, -2.732520818710327] |
58742a1d-1837-44d7-9f57-403af8a2e280 | diffswap-high-fidelity-and-controllable-face | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_DiffSwap_High-Fidelity_and_Controllable_Face_Swapping_via_3D-Aware_Masked_Diffusion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_DiffSwap_High-Fidelity_and_Controllable_Face_Swapping_via_3D-Aware_Masked_Diffusion_CVPR_2023_paper.pdf | DiffSwap: High-Fidelity and Controllable Face Swapping via 3D-Aware Masked Diffusion | In this paper, we propose DiffSwap, a diffusion model based framework for high-fidelity and controllable face swapping. Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a cond... | ['Jiwen Lu', 'Jie zhou', 'Zuyan Liu', 'Weikang Shi', 'Yongming Rao', 'Wenliang Zhao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [ 2.49671742e-01 2.96271265e-01 -5.19122481e-02 -2.98822105e-01
-4.82497275e-01 -5.46573341e-01 5.49986303e-01 -6.88226640e-01
7.62917995e-02 7.32536852e-01 1.73086375e-01 2.38460779e-01
-1.26523346e-01 -8.45547616e-01 -6.94055021e-01 -9.39731002e-01
3.63077104e-01 2.08998948e-01 -1.55973285e-01 -2.17822924... | [12.628185272216797, -0.20709143579006195] |
6117dffd-b90c-4777-a207-4f157676b740 | is-chatgpt-a-good-keyphrase-generator-a | 2303.13001 | null | https://arxiv.org/abs/2303.13001v1 | https://arxiv.org/pdf/2303.13001v1.pdf | Is ChatGPT A Good Keyphrase Generator? A Preliminary Study | The emergence of ChatGPT has recently garnered significant attention from the computational linguistics community. To demonstrate its capabilities as a keyphrase generator, we conduct a preliminary evaluation of ChatGPT for the keyphrase generation task. We evaluate its performance in various aspects, including keyphra... | ['Liping Jing', 'Huafeng Liu', 'Yi Feng', 'Shilong Lu', 'Songfang Yao', 'Shuming Shi', 'Haiyun Jiang', 'Mingyang Song'] | 2023-03-23 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.84505686e-01 2.10280702e-01 -1.68425515e-01 3.12309951e-01
-1.24589634e+00 -1.23502433e+00 1.28877485e+00 5.74513435e-01
-3.96873027e-01 9.63863134e-01 8.75130594e-01 -4.23168927e-01
-2.63123453e-01 -5.81117392e-01 -3.15195054e-01 -2.90541559e-01
7.90046677e-02 4.28257376e-01 2.69243062e-01 -5.39767444... | [12.268296241760254, 8.898992538452148] |
a669e057-0608-43bf-8704-e4d431ec34d4 | the-exponentiated-gumbel-type-2-distribution | null | null | https://doi.org/10.1155/2016/5898356 | https://doi.org/10.1155/2016/5898356 | The Exponentiated Gumbel Type-2 Distribution: Properties and Application | We introduce a generalized version of the standard Gumble type-2 distribution. The new lifetime distribution is called the ExponentiatedGumbel (EG) type-2 distribution. The EG type-2 distribution has three nested submodels, namely, theGumbel type2 distribution, the Exponentiated Fr´echet (EF) distribution, and the Fr´e... | ['J.', 'A. C. Ohakwe', 'I. E. Akpanta', 'Okorie'] | 2016-07-10 | null | null | null | international-journal-of-mathematics-and | ['type'] | ['speech'] | [-7.00802207e-01 6.92892745e-02 -6.38717175e-01 -2.99801141e-01
-3.58797818e-01 -3.61455292e-01 5.35437226e-01 -2.82044802e-02
-3.30018550e-01 1.35800779e+00 -2.45307252e-01 -7.26358593e-01
-6.40125453e-01 -7.06455588e-01 -2.53686339e-01 -1.09167612e+00
-5.56557119e-01 5.93478143e-01 2.63475150e-01 2.20882297... | [6.980194568634033, 4.287303447723389] |
7ddf1810-8850-4ba8-bc38-e16019c1154f | picture-that-sketch-photorealistic-image | 2303.11162 | null | https://arxiv.org/abs/2303.11162v2 | https://arxiv.org/pdf/2303.11162v2.pdf | Picture that Sketch: Photorealistic Image Generation from Abstract Sketches | Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image - just like those shown in Fig. 1(a), all non-cherry-picked. We differ significantly from prior art in that we do not dictate an edgemap-like sketch to start with, but aim to work with a... | ['Yi-Zhe Song', 'Tao Xiang', 'Pinaki Nath Chowdhury', 'Aneeshan Sain', 'Ayan Kumar Bhunia', 'Subhadeep Koley'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Koley_Picture_That_Sketch_Photorealistic_Image_Generation_From_Abstract_Sketches_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Koley_Picture_That_Sketch_Photorealistic_Image_Generation_From_Abstract_Sketches_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 5.29757857e-01 3.34950179e-01 1.03035577e-01 -2.25487545e-01
-8.13881278e-01 -9.04209316e-01 1.15568256e+00 -6.66698217e-01
-5.08045740e-02 4.71554101e-01 3.68858576e-01 -4.56534699e-02
1.77615881e-01 -7.73602366e-01 -9.04418766e-01 -6.17274046e-01
5.22662938e-01 4.15098071e-01 -4.15942818e-01 -2.52479047... | [11.80747127532959, 0.20058897137641907] |
7d020165-9236-4f17-b032-bf28dd7f6b50 | grig-few-shot-generative-residual-image | 2304.12035 | null | https://arxiv.org/abs/2304.12035v1 | https://arxiv.org/pdf/2304.12035v1.pdf | GRIG: Few-Shot Generative Residual Image Inpainting | Image inpainting is the task of filling in missing or masked region of an image with semantically meaningful contents. Recent methods have shown significant improvement in dealing with large-scale missing regions. However, these methods usually require large training datasets to achieve satisfactory results and there h... | ['Hanli Zhao', 'Kaijie Shi', 'Tao Wang', 'Minglun Gong', 'Yong-Liang Yang', 'Xiaogang Jin', 'Xianta Jiang', 'Wanglong Lu'] | 2023-04-24 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.71816146e-01 6.75275549e-02 -6.12109005e-02 -2.40094766e-01
-1.15038490e+00 -2.09353462e-01 2.71469355e-01 -6.38836622e-01
2.61100739e-01 9.11091030e-01 2.08553135e-01 1.84211686e-01
3.53412092e-01 -9.86812472e-01 -1.10492146e+00 -6.45965695e-01
4.13522929e-01 -4.57213074e-02 1.24816947e-01 -3.08477461... | [11.519621849060059, -0.9140453338623047] |
5cc5a8db-216d-43f1-9eed-9f40ca59a1c5 | a-framework-for-provably-stable-and | 2305.12125 | null | https://arxiv.org/abs/2305.12125v1 | https://arxiv.org/pdf/2305.12125v1.pdf | A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks | We present a novel algorithm for training deep neural networks in supervised (classification and regression) and unsupervised (reinforcement learning) scenarios. This algorithm combines the standard stochastic gradient descent and the gradient clipping method. The output layer is updated using clipped gradients, the re... | ['Naman Saxena', 'Shalabh Bhatnagar', 'Arunselvan Ramaswamy'] | 2023-05-20 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 1.74902022e-01 3.01395118e-01 -8.51282030e-02 -1.68727368e-01
-3.70119989e-01 -4.72567499e-01 2.41814747e-01 2.37955451e-02
-8.87216449e-01 1.24881911e+00 -3.53756487e-01 -3.60593051e-01
-3.36455703e-01 -6.22164249e-01 -1.27249634e+00 -1.20415390e+00
-1.46646038e-01 4.06941250e-02 2.28860602e-01 -2.92895675... | [7.6360979080200195, 3.618036985397339] |
f6663462-75d6-4df2-8d9f-889114785a7e | spiral-contrastive-learning-an-efficient-3d | 2208.10694 | null | https://arxiv.org/abs/2208.10694v1 | https://arxiv.org/pdf/2208.10694v1.pdf | Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions | Computed tomography (CT) samples with pathological annotations are difficult to obtain. As a result, the computer-aided diagnosis (CAD) algorithms are trained on small datasets (e.g., LIDC-IDRI with 1,018 samples), limiting their accuracies and reliability. In the past five years, several works have tailored for unsupe... | ['Jinpeng Li', 'Xin Wei', 'Baolian Qi', 'Enwei Zhu', 'Penghua Zhai'] | 2022-08-23 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 3.26633990e-01 4.04321641e-01 -5.03119051e-01 -4.74021323e-02
-1.14182258e+00 1.90367736e-02 3.75024527e-01 -6.69047236e-02
-5.67763865e-01 4.69342291e-01 4.17376399e-01 -2.69339263e-01
-8.22786242e-02 -6.15939796e-01 -3.28585654e-01 -9.91987646e-01
-4.08657193e-02 6.45328820e-01 1.23622872e-01 2.84353137... | [14.814016342163086, -2.155107021331787] |
a514713d-fc2b-48c0-b5f6-2d0b369be077 | sign-coded-exposure-sensing-for-noise-robust | 2305.03226 | null | https://arxiv.org/abs/2305.03226v1 | https://arxiv.org/pdf/2305.03226v1.pdf | Sign-Coded Exposure Sensing for Noise-Robust High-Speed Imaging | We present a novel Fourier camera, an in-hardware optical compression of high-speed frames employing pixel-level sign-coded exposure where pixel intensities temporally modulated as positive and negative exposure are combined to yield Hadamard coefficients. The orthogonality of Walsh functions ensures that the noise is ... | ['Keigo Hirakawa', 'Vijayan Asari', 'R. Wes Baldwin'] | 2023-05-05 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 1.15328586e+00 -1.99584618e-01 3.29796672e-01 -7.73235336e-02
-2.56105632e-01 -2.67987370e-01 5.31134963e-01 -6.38429821e-01
-6.99645281e-01 7.78618932e-01 1.64591685e-01 -3.68492663e-01
-1.47835705e-02 -6.58774555e-01 -5.94146252e-01 -9.38417733e-01
-3.44398439e-01 -4.89610583e-01 2.45204791e-01 1.16696626... | [11.24655818939209, -2.2504427433013916] |
08475e7e-9838-4abd-9468-c3addd66ae67 | performance-of-gan-based-augmentation-for | 2304.09067 | null | https://arxiv.org/abs/2304.09067v1 | https://arxiv.org/pdf/2304.09067v1.pdf | Performance of GAN-based augmentation for deep learning COVID-19 image classification | The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In a classical approach image transformations i.e. rotations, cropping and brightness changes... | ['Rafał Możdżonek', 'Aleksander Ogonowski', 'Konrad Klimaszewski', 'Oleksandr Fedoruk'] | 2023-04-18 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 6.88715279e-01 3.93038154e-01 4.62450869e-02 -2.98878282e-01
-9.07705843e-01 -2.70858645e-01 6.58316433e-01 2.02536970e-01
-7.86877930e-01 8.28460813e-01 -1.87888563e-01 -4.00060534e-01
5.00521399e-02 -9.59284306e-01 -8.45087588e-01 -8.76873136e-01
1.88954815e-01 7.74784148e-01 -1.66858763e-01 -4.55485523... | [14.22390365600586, -1.983370065689087] |
b7eca03c-bdda-4ef8-8256-433fff86e321 | end-to-end-recovery-of-human-shape-and-pose | 1712.06584 | null | http://arxiv.org/abs/1712.06584v2 | http://arxiv.org/pdf/1712.06584v2.pdf | End-to-end Recovery of Human Shape and Pose | We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In
contrast to most current methods that compute 2D or 3D joint locations, we
produce a richer and more useful mesh representation that is parameterized by
shape and 3D joint angles.... | ['Michael J. Black', 'David W. Jacobs', 'Angjoo Kanazawa', 'Jitendra Malik'] | 2017-12-18 | end-to-end-recovery-of-human-shape-and-pose-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kanazawa_End-to-End_Recovery_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kanazawa_End-to-End_Recovery_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['monocular-3d-human-pose-estimation', '3d-multi-person-pose-estimation', 'weakly-supervised-3d-human-pose-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 8.48307088e-02 5.07770300e-01 1.78600401e-01 -2.69166619e-01
-1.14604783e+00 -6.51629448e-01 1.69108883e-01 -1.08365744e-01
-6.70530796e-01 2.13229850e-01 -2.76790351e-01 1.93167061e-01
6.44776225e-01 -4.79807973e-01 -1.25325692e+00 -2.14591488e-01
8.42429772e-02 1.14722764e+00 3.78150791e-01 -2.03163281... | [7.082042217254639, -1.1803479194641113] |
9fb4dc83-7c34-43aa-8d90-2210c58127c6 | temporal-recurrent-networks-for-online-action | 1811.07391 | null | http://arxiv.org/abs/1811.07391v2 | http://arxiv.org/pdf/1811.07391v2.pdf | Temporal Recurrent Networks for Online Action Detection | Most work on temporal action detection is formulated as an offline problem,
in which the start and end times of actions are determined after the entire
video is fully observed. However, important real-time applications including
surveillance and driver assistance systems require identifying actions as soon
as each vide... | ['Yi-Ting Chen', 'Mingfei Gao', 'Mingze Xu', 'Larry S. Davis', 'David J. Crandall'] | 2018-11-18 | temporal-recurrent-networks-for-online-action-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Xu_Temporal_Recurrent_Networks_for_Online_Action_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Temporal_Recurrent_Networks_for_Online_Action_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['online-action-detection'] | ['computer-vision'] | [ 4.42808092e-01 -2.40635604e-01 -5.28687298e-01 -3.14758241e-01
-4.07276094e-01 -2.92855740e-01 6.58538043e-01 -8.28115121e-02
-6.75686121e-01 4.22940999e-01 5.58279514e-01 -2.33625516e-01
7.47325346e-02 -4.20377851e-01 -2.93086231e-01 -4.45220023e-01
-1.53447419e-01 -8.70436728e-02 7.84425914e-01 2.84010824... | [8.250199317932129, 0.45918816328048706] |
77e6edd2-f4fe-42e2-aa8d-eea5e2573fe0 | recommendations-for-datasets-for-source-code | 1904.02660 | null | http://arxiv.org/abs/1904.02660v1 | http://arxiv.org/pdf/1904.02660v1.pdf | Recommendations for Datasets for Source Code Summarization | Source Code Summarization is the task of writing short, natural language
descriptions of source code. The main use for these descriptions is in software
documentation e.g. the one-sentence Java method descriptions in JavaDocs. Code
summarization is rapidly becoming a popular research problem, but progress is
restrained... | ['Alexander LeClair', 'Collin McMillan'] | 2019-04-04 | recommendations-for-datasets-for-source-code-1 | https://aclanthology.org/N19-1394 | https://aclanthology.org/N19-1394.pdf | naacl-2019-6 | ['code-summarization'] | ['computer-code'] | [ 2.70946950e-01 1.75207734e-01 -5.44661641e-01 -5.86485624e-01
-1.03209996e+00 -8.36206138e-01 4.62903351e-01 5.84383786e-01
-6.19782284e-02 4.98754203e-01 8.00928056e-01 -3.86147022e-01
5.47750341e-03 -7.57345790e-03 -4.72647190e-01 1.20283157e-01
7.70410001e-02 -2.00924844e-01 2.93535233e-01 -1.86529338... | [7.667816638946533, 7.910769462585449] |
9d9d28e7-df9b-418e-a0f4-fea2f582c38d | effectively-using-long-and-short-sessions-for | 2205.04366 | null | https://arxiv.org/abs/2205.04366v1 | https://arxiv.org/pdf/2205.04366v1.pdf | Effectively Using Long and Short Sessions for Multi-Session-based Recommendations | It is not accurate to make recommendations only based one single current session. Therefore, multi-session-based recommendation(MSBR) is a solution for the problem. Compared with the previous MSBR models, we have made three improvements in this paper. First, the previous work choose to use all the history sessions of t... | ['Yan Wang', 'Gang Wu', 'Zihan Wang'] | 2022-05-09 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.42110988e-01 -2.94983715e-01 -4.03337985e-01 -4.25656676e-01
-5.40281534e-02 -2.00376123e-01 1.64589107e-01 -2.40327179e-01
-4.22480762e-01 6.63574934e-01 5.30116200e-01 -3.81282456e-02
-3.96686226e-01 -1.06035638e+00 -4.73931789e-01 -7.36086607e-01
4.86170389e-02 1.99027389e-01 4.37359720e-01 -5.29040098... | [10.143501281738281, 5.6048688888549805] |
f79bc62f-8924-4e60-adfc-45edfa36191e | bridging-the-gap-between-language-models-and | 2204.05210 | null | https://arxiv.org/abs/2204.05210v1 | https://arxiv.org/pdf/2204.05210v1.pdf | Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling | Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages. Despite the great success, we draw an empir... | ['Daxin Jiang', 'Jian Pei', 'Ming Gong', 'Linjun Shou', 'Nuo Chen'] | 2022-04-11 | null | https://aclanthology.org/2022.naacl-main.139 | https://aclanthology.org/2022.naacl-main.139.pdf | naacl-2022-7 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.56648791e-01 4.76903245e-02 -5.67252815e-01 -6.12377465e-01
-1.26521385e+00 -6.33056760e-01 5.17391026e-01 8.18079486e-02
-7.07230926e-01 6.20607913e-01 4.49745685e-01 -7.03685224e-01
3.08134884e-01 -4.12422776e-01 -1.17992365e+00 -2.40064442e-01
4.29157168e-01 4.12432522e-01 1.50556847e-01 -3.81388515... | [11.012413024902344, 9.383408546447754] |
e5cf9241-1d63-476c-8b71-251c2027ffca | a-3d-shape-similarity-based-contrastive | 2211.02130 | null | https://arxiv.org/abs/2211.02130v1 | https://arxiv.org/pdf/2211.02130v1.pdf | A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning | Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that im... | ['Kangway V. Chuang', 'Gabriele Scalia', 'Tommaso Biancalani', 'Ziqing Lu', 'Nathaniel L. Diamant', 'Austin Atsango'] | 2022-11-03 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.31539285e-01 1.10433549e-01 -6.64256454e-01 -4.04319972e-01
-6.13567472e-01 -7.15868175e-01 6.09739840e-01 7.09197760e-01
-5.50697707e-02 9.87928450e-01 4.29931432e-02 -8.45224082e-01
-3.57492954e-01 -7.38664806e-01 -9.95455563e-01 -7.28172719e-01
-5.42696059e-01 5.63098311e-01 -3.27389017e-02 -6.06038570... | [5.139481067657471, 5.771726608276367] |
41fbcb82-a133-4cbd-8ab0-ae5cd70153e9 | bapose-bottom-up-pose-estimation-with | 2112.10716 | null | https://arxiv.org/abs/2112.10716v1 | https://arxiv.org/pdf/2112.10716v1.pdf | BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations | We propose BAPose, a novel bottom-up approach that achieves state-of-the-art results for multi-person pose estimation. Our end-to-end trainable framework leverages a disentangled multi-scale waterfall architecture and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes with occlusions... | ['Andreas Savakis', 'Bruno Artacho'] | 2021-12-20 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-3.90331388e-01 -2.46135935e-01 2.56856740e-01 -3.81891310e-01
-8.66412699e-01 -5.16937673e-01 4.39002514e-01 -3.08326602e-01
-7.05635965e-01 4.11373079e-01 6.59952998e-01 6.22740686e-01
-3.75764035e-02 -5.53323925e-01 -7.72234023e-01 -1.10808708e-01
-1.38652653e-01 7.43580997e-01 2.96782792e-01 -2.55712628... | [7.104533672332764, -0.8044403791427612] |
73282413-a963-4de7-809b-c8d8d561954f | feedback-linearization-of-car-dynamics-for | 2110.10441 | null | https://arxiv.org/abs/2110.10441v1 | https://arxiv.org/pdf/2110.10441v1.pdf | Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning | Through the method of Learning Feedback Linearization, we seek to learn a linearizing controller to simplify the process of controlling a car to race autonomously. A soft actor-critic approach is used to learn a decoupling matrix and drift vector that effectively correct for errors in a hand-designed linearizing contro... | ['Xiangyu Cai', 'Sida Li', 'Michael Estrada'] | 2021-10-20 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 9.00405943e-02 7.99988031e-01 -4.24342364e-01 -1.17309652e-02
-4.90498126e-01 -5.47697365e-01 2.98640877e-01 -3.32285196e-01
-2.69691288e-01 6.83774352e-01 -9.10746902e-02 -6.02195561e-01
-2.74880920e-02 -3.77574831e-01 -1.05947340e+00 -6.96490049e-01
6.35996014e-02 3.41355562e-01 6.99524954e-02 -8.71087849... | [4.855642318725586, 2.027982234954834] |
fecb8036-5b7d-4179-956a-7ab760bc4c1c | loglg-weakly-supervised-log-anomaly-detection | 2208.10833 | null | https://arxiv.org/abs/2208.10833v5 | https://arxiv.org/pdf/2208.10833v5.pdf | LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction | Fully supervised log anomaly detection methods suffer the heavy burden of annotating massive unlabeled log data. Recently, many semi-supervised methods have been proposed to reduce annotation costs with the help of parsed templates. However, these methods consider each keyword independently, which disregards the correl... | ['Bo Zhang', 'Liangfan Zheng', 'Weichao Hou', 'Tieqiao Zheng', 'Renjie Chen', 'Zhoujun Li', 'Jiaheng Liu', 'Jian Yang', 'Yuhui Guo', 'Hongcheng Guo'] | 2022-08-23 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.03227967e-01 -3.20312046e-02 -1.73443839e-01 -4.86265212e-01
-5.63081205e-01 -4.78067994e-01 2.61220723e-01 4.88534659e-01
-1.88646510e-01 3.32474858e-01 -2.58111730e-02 -3.35890710e-01
2.42130250e-01 -6.36734605e-01 -6.59983873e-01 -5.56069493e-01
-1.21276081e-01 4.10476267e-01 6.46494687e-01 2.76633799... | [7.47288703918457, 2.4986965656280518] |
ebf7f436-bc94-47ab-a6a4-9d18e5097347 | hyperbolic-audio-source-separation | 2212.05008 | null | https://arxiv.org/abs/2212.05008v1 | https://arxiv.org/pdf/2212.05008v1.pdf | Hyperbolic Audio Source Separation | We introduce a framework for audio source separation using embeddings on a hyperbolic manifold that compactly represent the hierarchical relationship between sound sources and time-frequency features. Inspired by recent successes modeling hierarchical relationships in text and images with hyperbolic embeddings, our alg... | ['Jonathan Le Roux', 'Aswin Subramanian', 'Gordon Wichern', 'Darius Petermann'] | 2022-12-09 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.76689921e-02 1.16848715e-01 3.73248875e-01 -9.87030938e-02
-1.16511536e+00 -9.23768163e-01 2.57963568e-01 2.38147914e-01
-2.24028364e-01 2.42303498e-02 8.08013141e-01 1.37218237e-01
-6.52442157e-01 -4.07839090e-01 -3.64753336e-01 -6.05509639e-01
-6.07780218e-01 -1.17687389e-01 1.09338149e-01 2.87277699... | [15.377578735351562, 5.6021223068237305] |
71a3be29-a34a-4067-999b-55d5360c45fe | neural-data-to-text-generation-with-lm-based | 2102.03556 | null | https://arxiv.org/abs/2102.03556v1 | https://arxiv.org/pdf/2102.03556v1.pdf | Neural Data-to-Text Generation with LM-based Text Augmentation | For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instances are available on the data side, often only very few text samples are available. To address this problem, we here propose a novel few-sho... | ['Hui Su', 'Vera Demberg', 'Dawei Zhu', 'Xiaoyu Shen', 'Ernie Chang'] | 2021-02-06 | null | https://aclanthology.org/2021.eacl-main.64 | https://aclanthology.org/2021.eacl-main.64.pdf | eacl-2021-2 | ['text-augmentation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.88757718e-01 3.75710875e-01 -1.34798065e-01 -4.13366705e-01
-1.12210548e+00 -6.03468060e-01 7.92241991e-01 4.10796255e-01
-6.02997780e-01 1.15874636e+00 3.16698849e-01 -1.39157280e-01
2.81152546e-01 -8.53632390e-01 -1.07539892e+00 -5.24293184e-01
5.56847572e-01 9.21865344e-01 4.04373296e-02 -2.94854283... | [11.659449577331543, 8.964717864990234] |
16beae60-2438-4dcc-8e94-835f51a942ab | seismic-data-interpolation-based-on-denoising | 2307.04226 | null | https://arxiv.org/abs/2307.04226v1 | https://arxiv.org/pdf/2307.04226v1.pdf | Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling | The incompleteness of the seismic data caused by missing traces along the spatial extension is a common issue in seismic acquisition due to the existence of obstacles and economic constraints, which severely impairs the imaging quality of subsurface geological structures. Recently, deep learning-based seismic interpola... | ['Sang-Woon Kim', 'Jiangshe Zhang', 'Baisong Jiang', 'Deng Xiong', 'Chengli Tan', 'Hongtao Wang', 'Chunxia Zhang', 'Xiaoli Wei'] | 2023-07-09 | null | null | null | null | ['denoising'] | ['computer-vision'] | [ 6.48498833e-02 -8.77432004e-02 3.08882505e-01 -9.62992013e-02
-9.49477315e-01 -2.87119355e-02 4.53286767e-01 -1.63622200e-01
-3.67858261e-01 8.50700438e-01 3.66117626e-01 3.40754874e-02
-4.06972677e-01 -1.07929075e+00 -8.32618654e-01 -1.23451221e+00
-6.77817017e-02 2.55290747e-01 3.80473077e-01 -1.80564240... | [6.833965301513672, 2.646696090698242] |
d856d620-505f-442e-b0ef-f94cbd684852 | misrobaerta-transformers-versus | 2304.07759 | null | https://arxiv.org/abs/2304.07759v1 | https://arxiv.org/pdf/2304.07759v1.pdf | MisRoBÆRTa: Transformers versus Misinformation | Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning ... | ['Elena-Simona Apostol', 'Ciprian-Octavian Truică'] | 2023-04-16 | null | null | null | null | ['misinformation', 'fake-news-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 1.23421058e-01 -3.98193076e-02 -1.78467900e-01 -1.32020041e-01
-6.73810720e-01 -6.60899043e-01 1.06543612e+00 2.10256800e-01
-4.07141060e-01 6.89882398e-01 3.38631600e-01 -7.75018096e-01
6.20100237e-02 -9.30667281e-01 -7.93918371e-01 -5.50629377e-01
1.83709174e-01 4.03730512e-01 -8.53006393e-02 -5.66613078... | [8.218731880187988, 10.262718200683594] |
760c7068-10ec-42d8-a250-98f8ac95933a | reqa-coarse-to-fine-assessment-of-image | 2209.01760 | null | https://arxiv.org/abs/2209.01760v4 | https://arxiv.org/pdf/2209.01760v4.pdf | REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect | Blind image quality assessment (BIQA) of user generated content (UGC) suffers from the range effect which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated; focusing on a particular range, the correlation is lower. The reason for the range effect is that ... | ['Fushuo Huo', 'Bingheng Li'] | 2022-09-05 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.07121408e-01 -7.03578413e-01 -7.26671293e-02 -4.93371725e-01
-5.81189036e-01 -2.87188768e-01 1.63217843e-01 4.50492688e-02
-2.30817124e-01 5.53046346e-01 3.86011690e-01 1.11908689e-01
-4.60762650e-01 -8.43880415e-01 -2.28057206e-01 -8.24379981e-01
2.96102405e-01 -4.80336785e-01 6.47560298e-01 -2.24042460... | [11.735082626342773, -1.9158419370651245] |
fe128a6c-3566-4cf3-82f3-0e34259dd267 | 190600551 | 1906.00551 | null | https://arxiv.org/abs/1906.00551v1 | https://arxiv.org/pdf/1906.00551v1.pdf | HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization | Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this pap... | ['Yidong Li', 'Congyan Lang', 'Yi Jin', 'Songhe Feng', 'Gengyu Lyu', 'Guojun Dai'] | 2019-06-03 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.44089055e-01 1.18242562e-01 -5.56241572e-01 -6.32307351e-01
-1.34047306e+00 -3.82981598e-01 2.28280082e-01 3.36280882e-01
-1.78289995e-01 5.96534908e-01 -7.90788140e-03 4.31563765e-01
-3.29834551e-01 -5.25220752e-01 -5.05331635e-01 -9.72636998e-01
2.62037247e-01 7.11462379e-01 -1.37729406e-01 5.94125807... | [9.528142929077148, 4.041176795959473] |
67e87a5a-c7cb-42f8-9883-85e49dd34118 | heterogeneously-distributed-joint-radar | 2107.13838 | null | https://arxiv.org/abs/2107.13838v2 | https://arxiv.org/pdf/2107.13838v2.pdf | Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation | Due to spectrum scarcity, the coexistence of radar and wireless communication has gained substantial research interest recently. Among many scenarios, the heterogeneouslydistributed joint radar-communication system is promising due to its flexibility and compatibility of existing architectures. In this paper, we focus ... | ['Björn Ottersten', 'Bhavani Shankar M. R.', 'Kumar Vijay Mishra', 'Linlong Wu'] | 2021-07-29 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 3.22750241e-01 -1.93998083e-01 -1.61179423e-01 -1.44415468e-01
-8.70287538e-01 -2.61012852e-01 1.76040336e-01 -4.04620975e-01
-4.18318301e-01 1.17727602e+00 -1.33553669e-01 -4.15811330e-01
-7.62914419e-01 -6.87282562e-01 1.55673876e-01 -1.12394249e+00
-2.36538157e-01 3.68629903e-01 -3.90834004e-01 1.01992987... | [6.2727460861206055, 1.354749083518982] |
3fea2165-32a7-4727-80ff-0b9f016b1022 | multimodal-age-and-gender-classification | 1907.10081 | null | https://arxiv.org/abs/1907.10081v1 | https://arxiv.org/pdf/1907.10081v1.pdf | Multimodal Age and Gender Classification Using Ear and Profile Face Images | In this paper, we present multimodal deep neural network frameworks for age and gender classification, which take input a profile face image as well as an ear image. Our main objective is to enhance the accuracy of soft biometric trait extraction from profile face images by additionally utilizing a promising biometric ... | ['Hazim Kemal Ekenel', 'Fevziye Irem Eyiokur', 'Dogucan Yaman'] | 2019-07-23 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-1.76033694e-02 5.77815622e-02 3.72619624e-03 -9.04189229e-01
-8.26783121e-01 -1.91861331e-01 4.65284109e-01 -1.12791471e-01
-5.84233999e-01 7.53274679e-01 -1.07383601e-01 1.29713327e-01
-2.61799544e-01 -4.31508064e-01 -2.74610162e-01 -1.04417098e+00
-4.80378978e-02 3.72803688e-01 -7.70516455e-01 4.57881428... | [13.560420036315918, 0.950089693069458] |
8311bc2b-e01d-45e5-ad3c-1e625cf57bcb | r-mbo-a-multi-surrogate-approach-for | 2204.13166 | null | https://arxiv.org/abs/2204.13166v1 | https://arxiv.org/pdf/2204.13166v1.pdf | R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation | Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximated set of Pareto optimal solutions. In many real-world problems, a decision-maker has some preferenc... | ['Tinkle Chugh'] | 2022-04-27 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.63177744e-01 -2.23625824e-01 4.90429401e-02 -3.97582054e-01
-9.77602303e-01 -5.96189857e-01 2.95403272e-01 3.42133552e-01
-5.37503421e-01 1.15083539e+00 -6.52504265e-02 -2.28005182e-02
-1.06093299e+00 -8.02786946e-01 -4.15274352e-01 -1.11015689e+00
6.29805550e-02 8.11935723e-01 2.64508605e-01 -1.24248870... | [6.087121486663818, 3.6086814403533936] |
1f3b5779-7ae1-4a16-82b9-5f54ffe8ab01 | motrv3-release-fetch-supervision-for-end-to | 2305.14298 | null | https://arxiv.org/abs/2305.14298v1 | https://arxiv.org/pdf/2305.14298v1.pdf | MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking | Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisfactory convergence dynamics. While MOTRv2 partly addresses this problem, it demands an additional detection network for assistance. In this ... | ['Wenbing Tao', 'Xiangyu Zhang', 'Yuang Zhang', 'Zhuoling Li', 'Tiancai Wang', 'En Yu'] | 2023-05-23 | null | null | null | null | ['multi-object-tracking', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 1.46111652e-01 -8.64466205e-02 -4.20113206e-01 -2.96001047e-01
-7.49970675e-01 -4.82321471e-01 4.97023195e-01 1.37620606e-02
-5.24552226e-01 6.33028984e-01 -1.33575365e-01 -8.36255401e-02
7.05835521e-02 -3.37649673e-01 -7.31293380e-01 -9.31068480e-01
1.54036745e-01 5.45202136e-01 6.92014456e-01 8.73529762... | [6.2501726150512695, -2.0748770236968994] |
e42f8b02-1bf1-4401-b490-087fb27d8e0b | saf-bage-salient-approach-for-facial-soft | 1803.05719 | null | http://arxiv.org/abs/1803.05719v2 | http://arxiv.org/pdf/1803.05719v2.pdf | SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression | How can we improve the facial soft-biometric classification with help of the
human visual system? This paper explores the use of saliency which is
equivalent to the human visual system to classify Age, Gender and Facial
Expression soft-biometric for facial images. Using the Deep Multi-level Network
(ML-Net) [1] and off... | ['Viraj Mavani', 'Kenil Shah', 'Ayesha Gurnani', 'Yash Khandhediya', 'Vandit Gajjar'] | 2018-03-13 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [ 8.50889757e-02 4.23771828e-01 -7.61952326e-02 -6.73800707e-01
1.04660289e-02 6.56733215e-02 2.82171249e-01 -3.91342014e-01
-3.08652669e-01 3.97441536e-01 4.32273485e-02 2.73747027e-01
3.08208406e-01 -4.83787954e-01 -4.59856629e-01 -6.79569364e-01
-5.86877577e-02 -3.48891728e-02 1.43576056e-01 -4.05880511... | [13.48362922668457, 1.2658003568649292] |
0b88eb9f-cf0b-478b-a8c3-1bf5002090e2 | dense-label-encoding-for-boundary | 2011.09670 | null | https://arxiv.org/abs/2011.09670v4 | https://arxiv.org/pdf/2011.09670v4.pdf | Dense Label Encoding for Boundary Discontinuity Free Rotation Detection | Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this paper explores a relatively less-studied methodology based on classification. The hope is to in... | ['Junchi Yan', 'Wentao Wang', 'Yue Zhou', 'Liping Hou', 'Xue Yang'] | 2020-11-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.85628496e-02 -2.95418024e-01 -2.91191816e-01 -1.12950943e-01
-4.78821307e-01 -5.27845502e-01 6.48692369e-01 -9.57217999e-03
-2.35278383e-01 2.75937557e-01 2.59053886e-01 -3.68804723e-01
-1.79268450e-01 -6.12130642e-01 -4.16472703e-01 -8.44198585e-01
-1.18366413e-01 -5.18590920e-02 2.70002514e-01 -3.45886528... | [8.701553344726562, -0.8252307772636414] |
f336f3a3-8a41-497c-b780-a4b570a225d0 | shallow-bayesian-meta-learning-for-real-world | 2101.02833 | null | https://arxiv.org/abs/2101.02833v2 | https://arxiv.org/pdf/2101.02833v2.pdf | Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition | Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e.g. nearest centroid, classifiers. In this paper, we take an orthogonal approach that is agnostic to the features used and focus exclusively on meta-learning the actual classif... | ['Timothy Hospedales', 'Henry Gouk', 'Debin Meng', 'Xueting Zhang'] | 2021-01-08 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Shallow_Bayesian_Meta_Learning_for_Real-World_Few-Shot_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Shallow_Bayesian_Meta_Learning_for_Real-World_Few-Shot_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [-7.82809034e-02 -2.08618075e-01 -3.65402192e-01 -4.42395180e-01
-1.34700751e+00 -3.06834638e-01 7.41649687e-01 1.85116991e-01
-2.35193968e-01 7.82355547e-01 -3.55852470e-02 -1.09557115e-01
-6.54545486e-01 -7.84402728e-01 -4.73780811e-01 -7.64219284e-01
-1.46956399e-01 4.70396549e-01 2.07665965e-01 -4.02119845... | [9.88387393951416, 3.088408946990967] |
9ad1036e-308c-4504-8134-0c8a4ee6a74a | source-free-adaptive-gaze-estimation-by | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cai_Source-Free_Adaptive_Gaze_Estimation_by_Uncertainty_Reduction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cai_Source-Free_Adaptive_Gaze_Estimation_by_Uncertainty_Reduction_CVPR_2023_paper.pdf | Source-Free Adaptive Gaze Estimation by Uncertainty Reduction | Gaze estimation across domains has been explored recently because the training data are usually collected under controlled conditions while the trained gaze estimators are used in real and diverse environments. However, due to privacy and efficiency concerns, simultaneous access to annotated source data and to-be-p... | ['Xilin Chen', 'Shiguang Shan', 'Jiabei Zeng', 'Xin Cai'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['gaze-estimation', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 3.53443861e-01 1.95050001e-01 -2.65256673e-01 -7.60042369e-01
-5.68633974e-01 -3.92647833e-01 1.31464109e-01 -2.80676752e-01
-5.16222119e-01 9.23870325e-01 2.59358762e-03 3.06031942e-01
-4.94351014e-02 -3.76818590e-02 -7.50956416e-01 -6.28541827e-01
3.98266673e-01 3.80857550e-02 1.60769016e-01 2.59816259... | [14.122090339660645, 0.03078402206301689] |
ce12f6c7-bd2b-488b-8935-5e066acc8976 | triplet-based-embedding-distance-and | 1908.02283 | null | https://arxiv.org/abs/1908.02283v1 | https://arxiv.org/pdf/1908.02283v1.pdf | Triplet Based Embedding Distance and Similarity Learning for Text-independent Speaker Verification | Speaker embeddings become growing popular in the text-independent speaker verification task. In this paper, we propose two improvements during the training stage. The improvements are both based on triplet cause the training stage and the evaluation stage of the baseline x-vector system focus on different aims. Firstly... | ['Shugong Xu', 'Zongze Ren', 'Zhiyong Chen'] | 2019-08-06 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 4.27009016e-02 -1.72855392e-01 -8.53441563e-03 -8.04308176e-01
-1.11912608e+00 -3.76782894e-01 6.14580750e-01 -2.99060702e-01
-5.77738583e-01 1.09021120e-01 4.21632797e-01 -3.78288090e-01
-3.53801325e-02 1.42857134e-01 -2.48757109e-01 -9.51643288e-01
-8.19987655e-02 -6.06851056e-02 -3.97642910e-01 7.20816925... | [14.314634323120117, 6.110069751739502] |
d9644260-1b18-4fd5-baad-9838a3d08341 | realtime-global-attention-network-for | 2112.12939 | null | https://arxiv.org/abs/2112.12939v1 | https://arxiv.org/pdf/2112.12939v1.pdf | Realtime Global Attention Network for Semantic Segmentation | In this paper, we proposed an end-to-end realtime global attention neural network (RGANet) for the challenging task of semantic segmentation. Different from the encoding strategy deployed by self-attention paradigms, the proposed global attention module encodes global attention via depth-wise convolution and affine tra... | ['Xiangyu Chen', 'Xi Mo'] | 2021-12-24 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.64080322e-01 4.50991511e-01 7.40665123e-02 -5.78979135e-01
-6.02723181e-01 -1.55377790e-01 4.15465862e-01 -1.35946929e-01
-4.94471043e-01 4.95562315e-01 4.75169159e-02 6.53834920e-03
-2.52140611e-02 -6.59965038e-01 -1.01551044e+00 -5.90639889e-01
1.32491946e-01 4.16016340e-01 3.15733314e-01 -1.31680384... | [9.525145530700684, 0.18217137455940247] |
8c57d806-dd47-444a-afcc-7aab5b178870 | geometric-disentanglement-for-generative | 1908.06386 | null | https://arxiv.org/abs/1908.06386v1 | https://arxiv.org/pdf/1908.06386v1.pdf | Geometric Disentanglement for Generative Latent Shape Models | Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a generative model of ... | ['Tristan Aumentado-Armstrong', 'Stavros Tsogkas', 'Sven Dickinson', 'Allan Jepson'] | 2019-08-18 | geometric-disentanglement-for-generative-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Aumentado-Armstrong_Geometric_Disentanglement_for_Generative_Latent_Shape_Models_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Aumentado-Armstrong_Geometric_Disentanglement_for_Generative_Latent_Shape_Models_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-shape-generation', 'pose-transfer', '3d-shape-representation', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.53239059e-01 3.64139497e-01 2.14282826e-01 -3.20636332e-01
-3.54348749e-01 -9.32628393e-01 9.02718425e-01 -6.70373440e-02
-9.66182277e-02 2.70010084e-01 1.84209004e-01 -1.32644489e-01
-4.23212826e-01 -9.51273680e-01 -6.81526661e-01 -1.04275990e+00
2.25260824e-01 8.39942813e-01 -2.86614299e-01 -4.05643955... | [8.821550369262695, -3.3199331760406494] |
f4587095-a1cc-4e36-85b2-3124aa807b94 | posematcher-one-shot-6d-object-pose | 2304.01382 | null | https://arxiv.org/abs/2304.01382v1 | https://arxiv.org/pdf/2304.01382v1.pdf | PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching | Estimating the pose of an unseen object is the goal of the challenging one-shot pose estimation task. Previous methods have heavily relied on feature matching with great success. However, these methods are often inefficient and limited by their reliance on pre-trained models that have not be designed specifically for p... | ['Tae-Kyun Kim', 'Pedro Castro'] | 2023-04-03 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 8.23244825e-03 -1.86768517e-01 8.44454840e-02 -3.08130354e-01
-1.04297757e+00 -6.29348636e-01 6.51380658e-01 -2.37844616e-01
-4.23368782e-01 9.29920450e-02 -2.35907003e-01 2.05775425e-01
-6.71262443e-02 -5.39324999e-01 -1.07262015e+00 -3.47404301e-01
4.97954965e-01 9.40152466e-01 6.10418439e-01 -1.02300383... | [7.609992504119873, -2.601003408432007] |
d5435477-a811-4a50-9862-37207ab23966 | deep-svbrdf-estimation-on-real-materials | 2010.04143 | null | https://arxiv.org/abs/2010.04143v1 | https://arxiv.org/pdf/2010.04143v1.pdf | Deep SVBRDF Estimation on Real Materials | Recent work has demonstrated that deep learning approaches can successfully be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of a surface from as little as a single image. Closer inspection reveals, however, that most approaches in the literature are trained purely on synthetic data, which, ... | ['Jean-François Lalonde', 'Denis Laurendeau', 'Louis-Philippe Asselin'] | 2020-10-08 | null | null | null | null | ['svbrdf-estimation'] | ['computer-vision'] | [ 4.96587932e-01 -2.00229421e-01 9.89834219e-02 -3.23568612e-01
-8.57711613e-01 -5.55316210e-01 4.22218263e-01 -3.46293956e-01
-9.04614702e-02 8.72969747e-01 -1.21033743e-01 -3.10734600e-01
-1.50922194e-01 -7.29466081e-01 -9.09703135e-01 -7.04521060e-01
1.44428357e-01 2.64471382e-01 2.07198575e-01 -3.49933244... | [9.725398063659668, -2.7667195796966553] |
32229abf-a30e-4bba-80e7-f3e7b314f9dc | a-benchmark-for-toxic-comment-classification | 2301.11125 | null | https://arxiv.org/abs/2301.11125v1 | https://arxiv.org/pdf/2301.11125v1.pdf | A benchmark for toxic comment classification on Civil Comments dataset | Toxic comment detection on social media has proven to be essential for content moderation. This paper compares a wide set of different models on a highly skewed multi-label hate speech dataset. We consider inference time and several metrics to measure performance and bias in our comparison. We show that all BERTs have ... | ['Reda Dehak', 'Pierre Guillaume', 'Henri Jamet', 'Corentin Duchene'] | 2023-01-26 | null | null | null | null | ['toxic-comment-classification'] | ['natural-language-processing'] | [-3.00196886e-01 -5.31098843e-02 -2.18006186e-02 -4.50561702e-01
-9.03702617e-01 -6.22676790e-01 9.01162505e-01 2.17951924e-01
-6.65117681e-01 8.52241933e-01 4.00214702e-01 -4.19573903e-01
5.30232526e-02 -5.73006988e-01 -4.20696974e-01 -6.27803206e-01
1.54624000e-01 4.20949966e-01 2.31770769e-01 -2.87167519... | [8.65045166015625, 10.367128372192383] |
843d9f3f-a1bc-469a-9fd5-0a29a3639077 | optimal-image-smoothing-and-its-applications | 2003.08210 | null | https://arxiv.org/abs/2003.08210v1 | https://arxiv.org/pdf/2003.08210v1.pdf | Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing | This paper is focused on deriving an optimal image smoother. The optimization is done through the minimization of the norm of the Laplace operator in the image coordinate system. Discretizing the Laplace operator and using the method of Euler-Lagrange result in a weighted average scheme for the optimal smoother. Satell... | ['M. Kiani'] | 2020-03-17 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 6.33297265e-02 -1.41916856e-01 4.00217503e-01 -1.58508852e-01
-5.10707796e-01 -3.00054193e-01 2.08766609e-01 1.39833242e-01
-5.65967321e-01 5.51827729e-01 -7.07829371e-02 -3.93455625e-01
-3.02549630e-01 -7.39635110e-01 -7.11739138e-02 -1.05454993e+00
-4.95638967e-01 -2.34662876e-01 2.63335884e-01 -3.96968305... | [10.314562797546387, -2.224564790725708] |
dca28ae2-5edd-4704-b568-ea83e842fb10 | comparing-of-term-clustering-frameworks-for | 1901.09037 | null | http://arxiv.org/abs/1901.09037v1 | http://arxiv.org/pdf/1901.09037v1.pdf | Comparing of Term Clustering Frameworks for Modular Ontology Learning | This paper aims to use term clustering to build a modular ontology according
to core ontology from domain-specific text. The acquisition of semantic
knowledge focuses on noun phrase appearing with the same syntactic roles in
relation to a verb or its preposition combination in a sentence. The
construction of this co-oc... | ['Ziwei Xu', 'Fabrice Guillet', 'Mounira Harzallah'] | 2019-01-25 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 1.95591733e-01 -1.28510654e-01 7.95229673e-02 -4.02712584e-01
-3.47555995e-01 -3.03159952e-01 5.00040591e-01 4.73742455e-01
-5.58159232e-01 4.21699643e-01 5.46590984e-01 1.14090264e-01
-8.94076407e-01 -8.02218676e-01 1.15875565e-01 -9.32625294e-01
-2.10160762e-01 4.92909849e-01 -2.05853313e-01 -3.32630277... | [10.25118350982666, 8.496562004089355] |
6f4bc9f6-3a8f-42a0-8db9-a38481e089e0 | un-nouvel-algorithme-pour-la-detection-des | 2112.13280 | null | https://arxiv.org/abs/2112.13280v1 | https://arxiv.org/pdf/2112.13280v1.pdf | Un nouvel algorithme pour la detection des transferts horizontaux de genes partiels entre les especes et pour la classification des transferts inferes | In this article we are describing a new algorithm for detecting and validating partial horizontal gene transfers (HGT). The presented algorithm is based on a sliding window procedure which analyzes fragments of the given multiple sequence alignment. A bootstrap procedure incorporated in our method can be used to estima... | ['Makarenkov Vladimir', 'Diallo Alpha Boubacar', 'Boc Alix'] | 2021-12-25 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 6.66018724e-01 1.15385853e-01 -1.30615145e-01 -3.93368751e-02
-1.11303635e-01 -6.08981073e-01 6.27109110e-01 6.35028899e-01
-4.27973390e-01 9.14170980e-01 -2.78301865e-01 -6.42104745e-01
-3.24123681e-01 -9.41043139e-01 -5.53326368e-01 -9.60132599e-01
-6.70640841e-02 8.61370802e-01 7.14895964e-01 -1.44230679... | [4.858279705047607, 5.186275005340576] |
1a6c3b96-c843-4878-967b-376042751c02 | renderers-are-good-zero-shot-representation | 2306.10721 | null | https://arxiv.org/abs/2306.10721v1 | https://arxiv.org/pdf/2306.10721v1.pdf | Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning | Can the latent spaces of modern generative neural rendering models serve as representations for 3D-aware discriminative visual understanding tasks? We use retrieval as a proxy for measuring the metric learning properties of the latent spaces of Shap-E, including capturing view-independence and enabling the aggregation ... | ['David Shustin', 'Michael Tang'] | 2023-06-19 | null | null | null | null | ['neural-rendering', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-1.66085824e-01 1.42793491e-01 -1.43129721e-01 -5.65742671e-01
-7.42211699e-01 -7.71186590e-01 1.08860409e+00 -5.23282647e-01
-6.39026389e-02 2.26663336e-01 7.21772194e-01 -1.09759659e-01
-1.02123708e-01 -6.49571121e-01 -6.82234764e-01 -5.12874365e-01
1.19367786e-01 5.67909360e-01 -1.05670810e-01 -1.07379153... | [8.675751686096191, -3.0969018936157227] |
c467205f-f9ef-49c5-9648-246dfc307d4a | topic-guided-sampling-for-data-efficient | 2306.00765 | null | https://arxiv.org/abs/2306.00765v1 | https://arxiv.org/pdf/2306.00765v1.pdf | Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection | Stance Detection is concerned with identifying the attitudes expressed by an author towards a target of interest. This task spans a variety of domains ranging from social media opinion identification to detecting the stance for a legal claim. However, the framing of the task varies within these domains, in terms of the... | ['Isabelle Augenstein', 'Arnav Arora', 'Erik Arakelyan'] | 2023-06-01 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.76969096e-01 1.08058169e-01 -4.12126750e-01 -6.50762260e-01
-1.47037745e+00 -8.91245008e-01 6.26775980e-01 3.34475696e-01
-4.61885035e-01 9.25338745e-01 6.33319467e-02 -2.13010699e-01
9.16889030e-03 -6.78573251e-01 -6.64113283e-01 -7.66163766e-01
2.39291623e-01 7.79707789e-01 3.60947311e-01 -3.67934257... | [8.926483154296875, 9.935812950134277] |
8d7252f0-a763-4f23-a743-fa416442b118 | sheffield-submissions-for-wmt18-multimodal | null | null | https://aclanthology.org/W18-6442 | https://aclanthology.org/W18-6442.pdf | Sheffield Submissions for WMT18 Multimodal Translation Shared Task | This paper describes the University of Sheffield{'}s submissions to the WMT18 Multimodal Machine Translation shared task. We participated in both tasks 1 and 1b. For task 1, we build on a standard sequence to sequence attention-based neural machine translation system (NMT) and investigate the utility of multimodal re-r... | ['Pranava Swaroop Madhyastha', 'Lucia Specia', 'Chiraag Lala', 'Carolina Scarton'] | 2018-10-01 | null | null | null | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 8.18187654e-01 1.02090776e-01 -2.16250300e-01 -3.35003257e-01
-1.74317551e+00 -9.93333280e-01 9.48093235e-01 3.58707011e-01
-9.28976476e-01 1.13885224e+00 6.39653087e-01 -7.23404109e-01
-3.08936127e-02 -5.30482642e-02 -6.03545666e-01 -2.62953937e-01
5.13696313e-01 1.25054145e+00 -2.56445467e-01 -6.51769638... | [11.50938606262207, 1.5520899295806885] |
2b54100b-80ac-4a3f-8de0-2f1b33ff626a | population-based-evolutionary-gaming-for | 2306.05236 | null | https://arxiv.org/abs/2306.05236v1 | https://arxiv.org/pdf/2306.05236v1.pdf | Population-Based Evolutionary Gaming for Unsupervised Person Re-identification | Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To addre... | ['Yonghong Tian', 'Xuesong Gao', 'Weiqiang Chen', 'Shiyong Li', 'Mengxi Jia', 'Peixi Peng', 'Yunpeng Zhai'] | 2023-06-08 | null | null | null | null | ['person-re-identification', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 2.55190611e-01 -1.07267536e-01 -1.43014237e-01 -1.83891252e-01
9.16278362e-02 -2.18612865e-01 3.95590365e-01 -1.82206810e-01
-6.76291943e-01 7.73933113e-01 -2.02040404e-01 3.11882168e-01
-6.13331318e-01 -9.29386377e-01 -2.78681964e-01 -9.26383436e-01
4.78479303e-02 6.86461627e-01 1.69553198e-02 -1.88143864... | [14.818310737609863, 1.1178867816925049] |
df8b007b-5806-462e-a639-95b770692d20 | continuous-conditional-random-field | 2110.06085 | null | https://arxiv.org/abs/2110.06085v1 | https://arxiv.org/pdf/2110.06085v1.pdf | Continuous Conditional Random Field Convolution for Point Cloud Segmentation | Point cloud segmentation is the foundation of 3D environmental perception for modern intelligent systems. To solve this problem and image segmentation, conditional random fields (CRFs) are usually formulated as discrete models in label space to encourage label consistency, which is actually a kind of postprocessing. In... | ['Zhong Jin', 'Huan Wang', 'Franck Davoine', 'Fei Yang'] | 2021-10-12 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.36280864e-01 1.28386825e-01 2.19641730e-01 -6.19704247e-01
-3.08878779e-01 -3.32533032e-01 3.91006261e-01 1.77873760e-01
-3.72045338e-01 1.83685988e-01 -3.06450099e-01 -2.68856853e-01
4.52450328e-02 -1.13550854e+00 -1.01315594e+00 -7.14145362e-01
1.25117227e-01 3.13620389e-01 4.13105547e-01 7.52261430... | [8.122750282287598, -3.1921639442443848] |
3ee90f04-e3e1-499b-a60a-210ed9f292dd | synthesizing-mixed-type-electronic-health | 2302.14679 | null | https://arxiv.org/abs/2302.14679v1 | https://arxiv.org/pdf/2302.14679v1.pdf | Synthesizing Mixed-type Electronic Health Records using Diffusion Models | Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data. Synthetic data generation is a promising solution to mitigate these risks, often relying on deep generative models such as Generative Adversarial Networks (GANs). However, recent studies have ... | ['David A. Clifton', 'Andrew P. Creagh', 'Tingting Zhu', 'Vinod Kumar Chauhan', 'Ghadeer O. Ghosheh', 'Taha Ceritli'] | 2023-02-28 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'type'] | ['medical', 'miscellaneous', 'speech'] | [ 1.87156916e-01 6.75645292e-01 1.57361284e-01 -2.73993224e-01
-1.09768665e+00 -4.76299256e-01 4.98879880e-01 1.74810439e-01
-1.50173202e-01 9.58284676e-01 6.63362145e-01 -2.66873270e-01
2.97922373e-01 -1.10338473e+00 -6.79362118e-01 -5.70818543e-01
1.14564866e-01 3.05790484e-01 -5.95014811e-01 6.55257106... | [6.245790481567383, 6.7914252281188965] |
3e3ba2a5-45a2-41d4-a50f-6e90b5bda38a | generative-adversarial-network-driven | 2202.07802 | null | https://arxiv.org/abs/2202.07802v1 | https://arxiv.org/pdf/2202.07802v1.pdf | Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing | Mobile Crowdsensing systems are vulnerable to various attacks as they build on non-dedicated and ubiquitous properties. Machine learning (ML)-based approaches are widely investigated to build attack detection systems and ensure MCS systems security. However, adversaries that aim to clog the sensing front-end and MCS ba... | ['Burak Kantarci', 'Zhiyan Chen'] | 2022-02-16 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 5.19737005e-01 1.87358916e-01 8.00553858e-02 1.65024787e-01
-8.03849816e-01 -1.00108361e+00 5.64041615e-01 -1.54236063e-01
-1.69828951e-01 9.19470131e-01 -2.40291849e-01 -5.79875350e-01
3.83673668e-01 -1.12566340e+00 -9.24574077e-01 -6.58083081e-01
-5.92946038e-02 -9.64521989e-02 2.92179942e-01 -4.85763013... | [13.62140941619873, 5.757315635681152] |
fd1da713-6f12-404e-bb26-5aa1a57bbc1a | sleep-quality-chronotype-and-social-jet-lag | 2103.10795 | null | https://arxiv.org/abs/2103.10795v1 | https://arxiv.org/pdf/2103.10795v1.pdf | Sleep quality, chronotype and social jet lag of adolescents from a population with a very late chronotype | Sleep disorders can be a negative factor both for learning as for the mental and physical development of adolescents. It has been shown that, in many populations, adolescents tend to have a poor sleep quality, and a very late chronotype. Furthermore, these features peak at adolescence, in the sense that adults tend to ... | ['Sebastián Risau-Gusman', 'Fernanda R. Román', 'Sabrina C. Riva', 'Mara López-Wortzman', 'Sergio Lindenbaum', 'Pablo M. Gleiser', 'D. Lorena Franco', 'Damián Dellavale', 'Romina A. Capellino', 'Andrés H. Calderón'] | 2021-03-19 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-5.58476031e-01 8.29493031e-02 -3.91074389e-01 -1.18296526e-01
1.96996368e-02 -1.47378162e-01 2.20394775e-01 6.73218131e-01
-8.54223251e-01 7.87088096e-01 2.77035952e-01 -4.38220054e-02
-2.57306784e-01 -7.35231996e-01 -8.21609721e-02 -5.73870778e-01
1.13120921e-01 6.15509033e-01 3.39214593e-01 -2.30136618... | [13.50318431854248, 3.477952718734741] |
1a4e5072-d94c-4fc8-b3ec-968908cd2cdc | even-the-simplest-baseline-needs-careful-re | null | null | https://aclanthology.org/2022.naacl-main.145 | https://aclanthology.org/2022.naacl-main.145.pdf | Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN | The power and the potential of deep learning models attract many researchers to design advanced and sophisticated architectures. Nevertheless, the progress is sometimes unreal due to various possible reasons. In this work, through an astonishing example we argue that more efforts should be paid to ensure the progress i... | ['Chih-Jen Lin', 'Hsuan-Tien Lin', 'Tsung-Han Yang', 'Jie-Jyun Liu', 'Si-An Chen'] | null | null | null | null | naacl-2022-7 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 1.65414304e-01 1.20179012e-01 -1.37917757e-01 -7.54043102e-01
-6.54479861e-01 -5.69791257e-01 7.40216911e-01 3.69506210e-01
-7.60017276e-01 6.45386875e-01 2.46555775e-01 -7.22531497e-01
-2.97882795e-01 -4.91384715e-01 -3.88684958e-01 -6.53362393e-01
4.45838273e-01 5.65732300e-01 -3.78489941e-02 -2.47771740... | [9.533815383911133, 4.634410858154297] |
3ea86dbc-0526-4689-acb5-74a902b96550 | romo-her-robust-model-based-hindsight | 2306.16061 | null | https://arxiv.org/abs/2306.16061v1 | https://arxiv.org/pdf/2306.16061v1.pdf | RoMo-HER: Robust Model-based Hindsight Experience Replay | Sparse rewards are one of the factors leading to low sample efficiency in multi-goal reinforcement learning (RL). Based on Hindsight Experience Replay (HER), model-based relabeling methods have been proposed to relabel goals using virtual trajectories obtained by interacting with the trained model, which can effectivel... | ['Bin Ren', 'Yuming Huang'] | 2023-06-28 | null | null | null | null | ['multi-goal-reinforcement-learning', 'robot-manipulation'] | ['methodology', 'robots'] | [-3.77290964e-01 9.97395590e-02 -3.37426096e-01 3.24639492e-02
-5.81918299e-01 -2.40755469e-01 5.63543320e-01 -1.00207016e-01
-7.11718261e-01 8.87548983e-01 1.70240983e-01 -7.25895539e-02
-4.41474468e-01 -5.38127780e-01 -6.66942835e-01 -7.32162178e-01
-3.98114949e-01 5.65313876e-01 1.47361577e-01 -6.83570743... | [4.216062545776367, 1.6197943687438965] |
180645c8-536a-4bc8-bcb6-2ad48e341022 | an-automatic-image-content-retrieval-method | 2108.12068 | null | https://arxiv.org/abs/2108.12068v1 | https://arxiv.org/pdf/2108.12068v1.pdf | An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences | A growing number of commercially available mobile phones come with integrated high-resolution digital cameras. That enables a new class of dedicated applications to image analysis such as mobile visual search, image cropping, object detection, content-based image retrieval, image classification. In this paper, a new mo... | ['Alessandro Bruno'] | 2021-08-26 | null | null | null | null | ['content-based-image-retrieval', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 5.39834619e-01 -1.97346732e-01 -3.56899738e-01 -4.99624908e-02
-4.14293945e-01 -3.84567618e-01 4.04603928e-01 4.28882897e-01
-3.67531180e-01 1.60250083e-01 -2.04354078e-01 -6.12987876e-02
-2.48989016e-02 -7.51805186e-01 -5.20575643e-01 -6.47316456e-01
1.87600970e-01 -1.93833977e-01 7.82486796e-01 -2.78993994... | [9.947887420654297, -0.4740324914455414] |
a2710ae6-ed26-4b72-8730-f46874a84f9c | energy-based-processes-for-exchangeable-data | 2003.07521 | null | https://arxiv.org/abs/2003.07521v2 | https://arxiv.org/pdf/2003.07521v2.pdf | Energy-Based Processes for Exchangeable Data | Recently there has been growing interest in modeling sets with exchangeability such as point clouds. A shortcoming of current approaches is that they restrict the cardinality of the sets considered or can only express limited forms of distribution over unobserved data. To overcome these limitations, we introduce Energy... | ['Dale Schuurmans', 'Hanjun Dai', 'Bo Dai', 'Mengjiao Yang'] | 2020-03-17 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2926-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2926-Paper.pdf | icml-2020-1 | ['point-cloud-generation'] | ['computer-vision'] | [ 1.73504367e-01 -2.67511487e-01 1.33424059e-01 -3.10608864e-01
-8.38686049e-01 -5.11516929e-01 7.35105753e-01 8.71200040e-02
-1.95946902e-01 7.41818011e-01 1.92238782e-02 -1.87920574e-02
-3.34569097e-01 -1.20907998e+00 -9.54771221e-01 -7.22354293e-01
2.87485886e-02 7.17134237e-01 1.34896589e-02 2.25943141... | [8.866793632507324, -3.6494011878967285] |
544f4a6c-9ef6-4f76-a967-a3dedd88e028 | radial-distortion-homography | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Kukelova_Radial_Distortion_Homography_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Kukelova_Radial_Distortion_Homography_2015_CVPR_paper.pdf | Radial Distortion Homography | The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more. Ignoring the radial distortion component in homography e... | ['Jan Heller', 'Zuzana Kukelova', 'Tomas Pajdla', 'Martin Bujnak'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['image-stitching', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.09763759e-01 -3.69818002e-01 6.74799457e-02 -8.03495422e-02
-2.54786491e-01 -7.23702610e-01 5.12636781e-01 -2.92373091e-01
-2.98868895e-01 5.75124204e-01 -3.08686346e-01 -3.25489610e-01
-2.17586786e-01 -3.47684324e-01 -5.50315559e-01 -7.19755054e-01
5.26560485e-01 7.35635161e-01 2.97956556e-01 -1.07405275... | [8.018314361572266, -2.3238368034362793] |
f810dd61-228f-448b-8c74-df1a9c2e668b | forecasting-human-dynamics-from-static-images | 1704.03432 | null | http://arxiv.org/abs/1704.03432v1 | http://arxiv.org/pdf/1704.03432v1.pdf | Forecasting Human Dynamics from Static Images | This paper presents the first study on forecasting human dynamics from static
images. The problem is to input a single RGB image and generate a sequence of
upcoming human body poses in 3D. To address the problem, we propose the 3D Pose
Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on
single-im... | ['Yu-Wei Chao', 'Brian Price', 'Jia Deng', 'Scott Cohen', 'Jimei Yang'] | 2017-04-11 | forecasting-human-dynamics-from-static-images-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Chao_Forecasting_Human_Dynamics_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Chao_Forecasting_Human_Dynamics_CVPR_2017_paper.pdf | cvpr-2017-7 | ['human-dynamics'] | ['computer-vision'] | [ 1.50415853e-01 8.76078382e-02 -1.01360507e-01 -2.49019668e-01
-5.20880640e-01 -3.21865708e-01 4.10906643e-01 -1.01216269e+00
-5.53059757e-01 5.60891688e-01 5.92748165e-01 1.97100103e-01
4.87940639e-01 -2.68347144e-01 -9.00095046e-01 -1.37230933e-01
-3.28465551e-01 8.50311875e-01 1.57179549e-01 -4.68995571... | [7.12231969833374, -0.6259563565254211] |
b75f5f43-1a8b-4767-ad73-e854f2b647a1 | towards-a-privacy-preserving-deep-learning | 2106.06765 | null | https://arxiv.org/abs/2106.06765v1 | https://arxiv.org/pdf/2106.06765v1.pdf | Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services | Data Distribution Service (DDS) is an innovative approach towards communication in ICS/IoT infrastructure and robotics. Being based on the cross-platform and cross-language API to be applicable in any computerised device, it offers the benefits of modern programming languages and the opportunities to develop more compl... | ['Stanislav Abaimov'] | 2021-06-12 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-2.45997801e-01 1.65451039e-02 -1.23670176e-01 -1.25636384e-01
5.23097329e-02 -6.87214136e-01 9.00586188e-01 3.97257149e-01
-3.31463039e-01 5.39024293e-01 -4.40548152e-01 -7.62092590e-01
-4.59199101e-01 -1.02412188e+00 -4.19308335e-01 -7.05595553e-01
-5.30919552e-01 5.18066645e-01 6.29887879e-01 -2.58966804... | [5.252415180206299, 7.157679080963135] |
d25be932-963b-46dd-a593-34107a6b4266 | fully-unsupervised-feature-alignment-for | 1907.09204 | null | https://arxiv.org/abs/1907.09204v2 | https://arxiv.org/pdf/1907.09204v2.pdf | Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information | The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets are designed and maintained... | ['Gabriel Michau', 'Olga Fink'] | 2019-07-22 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 4.36571389e-01 4.51197699e-02 1.46180704e-01 -3.01794130e-02
-3.23676974e-01 -6.48496509e-01 5.90657473e-01 4.43577707e-01
-3.91710438e-02 9.12308753e-01 -7.56722033e-01 -2.48128399e-01
-6.35238826e-01 -8.63708615e-01 -7.88675785e-01 -1.07206285e+00
-3.58105987e-01 3.84501994e-01 1.97135761e-01 4.15490562... | [6.735410690307617, 2.396639347076416] |
790f178b-9481-45f1-9f81-b56e08696ed9 | multilingual-representation-distillation-with | 2210.05033 | null | https://arxiv.org/abs/2210.05033v2 | https://arxiv.org/pdf/2210.05033v2.pdf | Multilingual Representation Distillation with Contrastive Learning | Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estim... | ['Philipp Koehn', 'Holger Schwenk', 'Kevin Heffernan', 'Weiting Tan'] | 2022-10-10 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.49987409e-01 -3.35979164e-01 -4.14837658e-01 -4.62229401e-01
-1.53806448e+00 -6.98901296e-01 6.41760945e-01 7.08212495e-01
-8.76949012e-01 8.31525624e-01 7.00609744e-01 -2.70335734e-01
1.16664872e-01 -6.61262453e-01 -9.53079224e-01 1.45125717e-01
4.64384109e-01 7.41595864e-01 4.78763655e-02 -6.43518269... | [11.138365745544434, 9.83722972869873] |
7a05a2d5-99dc-4466-8627-7cb02d6bee4e | patch-wise-spatial-temporal-quality | null | null | https://www.researchgate.net/publication/353113483_Patch-Wise_Spatial-Temporal_Quality_Enhancement_for_HEVC_Compressed_Video | https://www.researchgate.net/publication/353113483_Patch-Wise_Spatial-Temporal_Quality_Enhancement_for_HEVC_Compressed_Video | Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed Video | Recently, many deep learning based researches are conducted to explore the potential quality improvement of compressed videos. These methods mostly utilize either the spatial or temporal information to perform frame-level video enhancement. However, they fail in combining different spatial-temporal information to adapt... | ['Mai Xu', 'Hao Yang', 'Liangwei Yu', 'Liquan Shen', 'Qing Ding'] | 2021-07-08 | null | null | null | journal-2021-7 | ['video-enhancement'] | ['computer-vision'] | [ 1.89789101e-01 -8.26075613e-01 -1.98757201e-01 -3.64559174e-01
-7.11911440e-01 -2.45101061e-02 1.82333142e-01 9.68602747e-02
-5.53562164e-01 5.33947468e-01 5.20549059e-01 -9.09396857e-02
-3.65181297e-01 -7.27059603e-01 -5.72451651e-01 -8.54462683e-01
-4.40575093e-01 -7.46497691e-01 3.64118963e-01 -9.75972563... | [11.236333847045898, -1.7677173614501953] |
424619fd-6c3a-4c86-a3a8-2a2f82e7ea17 | vi-net-view-invariant-quality-of-human | 2008.04999 | null | https://arxiv.org/abs/2008.04999v1 | https://arxiv.org/pdf/2008.04999v1.pdf | VI-Net: View-Invariant Quality of Human Movement Assessment | We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the colle... | ['Adeline Paiement', 'Sion Hannuna', 'Majid Mirmehdi', 'Faegheh Sardari'] | 2020-08-11 | null | null | null | null | ['action-analysis', 'action-assessment', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.91922799e-01 -7.51868337e-02 -2.19714746e-01 -2.37400964e-01
-9.37517881e-01 -4.20650512e-01 3.62238765e-01 -4.17958647e-01
-7.38533080e-01 3.48288298e-01 6.83942437e-01 6.55475974e-01
-1.77536413e-01 -3.86635870e-01 -6.41674876e-01 -3.60532880e-01
-2.36404061e-01 3.96955401e-01 2.97590733e-01 -3.90856534... | [7.118964195251465, -0.677645742893219] |
2d5ef8c0-8c0e-4621-8404-087f89874738 | a-koopman-approach-to-understanding-sequence | 2102.07824 | null | https://arxiv.org/abs/2102.07824v4 | https://arxiv.org/pdf/2102.07824v4.pdf | An Operator Theoretic Approach for Analyzing Sequence Neural Networks | Analyzing the inner mechanisms of deep neural networks is a fundamental task in machine learning. Existing work provides limited analysis or it depends on local theories, such as fixed-point analysis. In contrast, we propose to analyze trained neural networks using an operator theoretic approach which is rooted in Koop... | ['Omri Azencot', 'Ilan Naiman'] | 2021-02-15 | a-koopman-approach-to-understanding-sequence-1 | https://openreview.net/forum?id=4j4qVy8OQA1 | https://openreview.net/pdf?id=4j4qVy8OQA1 | null | ['ecg-classification'] | ['medical'] | [-4.66012321e-02 3.87957543e-01 -2.64298648e-01 -4.59760427e-02
3.27570230e-01 -5.57266831e-01 2.84596324e-01 -1.62097048e-02
-1.99886277e-01 1.07032649e-01 1.46925390e-01 -3.40335965e-01
-7.71245539e-01 -4.00996715e-01 -6.46154761e-01 -9.20778275e-01
-5.97109556e-01 -2.89470434e-01 -2.00869143e-01 -4.74237889... | [7.928850173950195, 3.588822603225708] |
49af5643-010a-4701-8938-11b1448a7fda | prediction-of-reynolds-stresses-in-high-mach | 1808.07752 | null | http://arxiv.org/abs/1808.07752v1 | http://arxiv.org/pdf/1808.07752v1.pdf | Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning | Modeled Reynolds stress is a major source of model-form uncertainties in
Reynolds-averaged Navier-Stokes (RANS) simulations. Recently, a
physics-informed machine-learning (PIML) approach has been proposed for
reconstructing the discrepancies in RANS-modeled Reynolds stresses. The merits
of the PIML framework has been d... | ['Jian-Xun Wang', 'Lian Duan', 'Heng Xiao', 'Junji Huang'] | 2018-08-19 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-3.91563594e-01 -9.29646671e-01 1.54639423e-01 -8.10249709e-03
-5.23812532e-01 -4.97787356e-01 6.20830774e-01 1.92777708e-01
-1.56800434e-01 1.00810087e+00 -2.18250901e-01 -7.90644169e-01
-3.19545716e-01 -4.86016572e-01 -4.56948839e-02 -7.57066429e-01
-3.14098746e-01 3.92888755e-01 -5.43519016e-03 -3.03211540... | [6.375761032104492, 3.3202733993530273] |
ef3358a7-63b9-4b8f-8da0-c90f89809b93 | ma-net-a-multi-scale-attention-network-for | null | null | https://ieeexplore.ieee.org/document/9201310 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9201310 | MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation | Automatic assessing the location and extent of liver and liver tumor is critical for radiologists, diagnosis and the clinical process. In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation. Unlike the ... | ['Hongrui Wang', 'Yan Li', 'Guanglei Wang', 'Tongle Fan'] | 2020-09-21 | null | null | null | ieee-access-2020-9 | ['tumor-segmentation'] | ['computer-vision'] | [-1.91804707e-01 -2.38089979e-01 8.00380111e-03 -4.94165510e-01
-6.86836541e-01 -1.69483572e-01 3.63628209e-01 4.13383275e-01
-5.17303348e-01 4.22620505e-01 6.08144403e-01 -1.06156923e-01
-1.71039626e-01 -7.18851745e-01 -5.97231984e-01 -8.49494219e-01
-1.31235868e-01 -9.20406953e-02 5.55836678e-01 -2.61176121... | [14.59321117401123, -2.6279382705688477] |
41f0719a-ffe7-4d66-9397-6c479d84b73e | handwriting-recognition-for-scottish-gaelic | null | null | https://aclanthology.org/2022.cltw-1.9 | https://aclanthology.org/2022.cltw-1.9.pdf | Handwriting recognition for Scottish Gaelic | Like most other minority languages, Scottish Gaelic has limited tools and resources available for Natural Language Processing research and applications. These limitations restrict the potential of the language to participate in modern speech technology, while also restricting research in fields such as corpus linguisti... | ['Mark Sinclair', 'Beatrice Alex', 'William Lamb'] | null | null | null | null | cltw-lrec-2022-6 | ['handwriting-recognition'] | ['computer-vision'] | [ 2.61855423e-01 2.37547472e-01 -2.15682343e-01 -3.05227607e-01
-9.20208275e-01 -7.99998164e-01 1.11770451e+00 2.04164878e-01
-4.57011729e-01 6.19016171e-01 8.82325947e-01 -6.86405957e-01
-5.95327094e-02 -5.21096826e-01 -1.84079885e-01 -4.41778481e-01
4.06361341e-01 5.00206649e-01 5.73452301e-02 -2.66580135... | [10.415834426879883, 10.138031959533691] |
c4a7d0f6-5d4c-466a-9a31-51e94d39dd77 | robust-outlier-rejection-for-3d-registration | 2304.01514 | null | https://arxiv.org/abs/2304.01514v1 | https://arxiv.org/pdf/2304.01514v1.pdf | Robust Outlier Rejection for 3D Registration with Variational Bayes | Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. In this paper, we develop a novel variat... | ['Mathieu Salzmann', 'Jian Yang', 'Jin Xie', 'Zhen Wei', 'Zheng Dang', 'Haobo Jiang'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Robust_Outlier_Rejection_for_3D_Registration_With_Variational_Bayes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Robust_Outlier_Rejection_for_3D_Registration_With_Variational_Bayes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['bayesian-inference'] | ['methodology'] | [-1.94139645e-01 4.06306703e-03 -2.63782233e-01 -4.24412727e-01
-1.35034871e+00 -2.51884729e-01 4.82313693e-01 3.19458134e-02
2.39174180e-02 3.56780261e-01 2.73325950e-01 2.17217699e-01
-5.97115159e-01 -4.58952636e-01 -9.31523263e-01 -9.23678041e-01
1.58929393e-01 8.33782434e-01 -4.28316519e-02 1.86040416... | [7.820902347564697, -2.9573278427124023] |
6e55e27f-320d-40dd-8c5c-340d43bca6ec | improving-shadow-suppression-for-illumination | 1710.05073 | null | http://arxiv.org/abs/1710.05073v1 | http://arxiv.org/pdf/1710.05073v1.pdf | Improving Shadow Suppression for Illumination Robust Face Recognition | 2D face analysis techniques, such as face landmarking, face recognition and
face verification, are reasonably dependent on illumination conditions which
are usually uncontrolled and unpredictable in the real world. An illumination
robust preprocessing method thus remains a significant challenge in reliable
face analysi... | ['Liming Chen', 'Jean-Marie Morvan', 'Xi Zhao', 'Wuming Zhang'] | 2017-10-13 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 5.23132801e-01 -5.50830007e-01 5.31484783e-01 -6.02964580e-01
-1.68380737e-01 -7.71452904e-01 6.34495378e-01 -2.63038993e-01
-1.50435328e-01 5.68862975e-01 -2.24248618e-01 1.00310527e-01
-1.11894019e-01 -5.24719954e-01 -4.36393201e-01 -1.04131782e+00
3.22017372e-01 1.58500317e-02 -2.77384728e-01 8.26572701... | [13.127579689025879, 0.6567527055740356] |
8e298381-af84-4e6f-9a7e-93e94ec35948 | jointly-complementary-competitive-influence | 2302.09620 | null | https://arxiv.org/abs/2302.09620v1 | https://arxiv.org/pdf/2302.09620v1.pdf | Jointly Complementary&Competitive Influence Maximization with Concurrent Ally-Boosting and Rival-Preventing | In this paper, we propose a new influence spread model, namely, Complementary\&Competitive Independent Cascade (C$^2$IC) model. C$^2$IC model generalizes three well known influence model, i.e., influence boosting (IB) model, campaign oblivious (CO)IC model and the IC-N (IC model with negative opinions) model. This is t... | ['Minghui Wu', 'Can Wang', 'Mengqi Xue', 'Wujian Yang', 'Wenjie Tian', 'Qihao Shi'] | 2023-02-19 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 3.92041802e-01 4.44614559e-01 -6.11055791e-01 -7.83965960e-02
6.29236773e-02 -7.48518348e-01 7.61843741e-01 -1.40180290e-01
-2.31272936e-01 1.15048611e+00 1.69208080e-01 -3.98938149e-01
-6.26303494e-01 -1.03402197e+00 -6.17310166e-01 -8.96386564e-01
-5.46554506e-01 7.65902102e-01 4.14160192e-01 -6.87635124... | [6.859138011932373, 5.365538597106934] |
bc8be382-01fe-4f91-b996-8ce9d38d96ce | class-anchor-margin-loss-for-content-based | 2306.00630 | null | https://arxiv.org/abs/2306.00630v2 | https://arxiv.org/pdf/2306.00630v2.pdf | Class Anchor Margin Loss for Content-Based Image Retrieval | The performance of neural networks in content-based image retrieval (CBIR) is highly influenced by the chosen loss (objective) function. The majority of objective functions for neural models can be divided into metric learning and statistical learning. Metric learning approaches require a pair mining strategy that ofte... | ['Radu Tudor Ionescu', 'Alexandru Ghita'] | 2023-06-01 | null | null | null | null | ['metric-learning', 'content-based-image-retrieval', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.67858443e-02 -2.92046756e-01 -2.33272761e-01 -5.61665356e-01
-1.03785944e+00 -3.34221721e-01 6.70004487e-01 3.43993515e-01
-7.80275643e-01 5.53086579e-01 2.13825256e-02 8.73492807e-02
-7.65477896e-01 -8.64338100e-01 -5.41671693e-01 -8.06806028e-01
-1.52410060e-01 3.71707261e-01 1.76656559e-01 -3.25139850... | [9.522726058959961, 3.031306505203247] |
5224c0e0-6f62-44d3-b1ff-9115aca7163b | synchronous-speech-recognition-and-speech-to | 1912.07240 | null | https://arxiv.org/abs/1912.07240v1 | https://arxiv.org/pdf/1912.07240v1.pdf | Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding | Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional pipeline system, the end-to-end ST model has potential benefits of lower latency, smaller model size, and less error propagation. However, it... | ['Cheng-qing Zong', 'Zhongjun He', 'Jiajun Zhang', 'Hua Wu', 'Yuchen Liu', 'Long Zhou', 'Hao Xiong', 'Haifeng Wang'] | 2019-12-16 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.15075105e-01 3.62930931e-02 -1.29184201e-01 -4.36032057e-01
-1.33381569e+00 -4.19751704e-01 5.38755000e-01 -4.33490604e-01
-2.45855063e-01 5.42441487e-01 4.17921633e-01 -6.79912329e-01
7.92063236e-01 -3.74261647e-01 -7.61565208e-01 -5.80099583e-01
6.28123164e-01 5.10154366e-01 2.67196685e-01 -1.92712590... | [14.473341941833496, 7.097713947296143] |
9b27faee-3213-4ca8-adeb-90c08465ebeb | ease-entity-aware-contrastive-learning-of-1 | 2205.04260 | null | https://arxiv.org/abs/2205.04260v1 | https://arxiv.org/pdf/2205.04260v1.pdf | EASE: Entity-Aware Contrastive Learning of Sentence Embedding | We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities. The advantage of using entity supervision is twofold: (1) entities have been shown to be a strong indicator of text semantics and thus should provide rich training signals for sentence... | ['Isao Echizen', 'Yoshimasa Tsuruoka', 'Ikuya Yamada', 'Ryokan Ri', 'Sosuke Nishikawa'] | 2022-05-09 | null | https://aclanthology.org/2022.naacl-main.284 | https://aclanthology.org/2022.naacl-main.284.pdf | naacl-2022-7 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-2.75445551e-01 -4.63050976e-02 -3.69408756e-01 -6.60797238e-01
-9.57317948e-01 -6.64338708e-01 9.26050127e-01 8.73555601e-01
-9.82421935e-01 5.63650191e-01 8.39599609e-01 -2.09390596e-01
4.74408157e-02 -3.36120576e-01 -6.27401769e-01 -2.02411562e-01
7.75897084e-03 6.35506988e-01 -2.06280667e-02 -3.16726536... | [10.903192520141602, 9.68709659576416] |
d47bb9f2-99c3-478d-bbf5-3b8df2473636 | improving-multi-label-emotion-classification | null | null | https://aclanthology.org/D18-1137 | https://aclanthology.org/D18-1137.pdf | Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network | In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the gen... | ["Lu{\\'\\i}s Marujo", 'Jing Jiang', 'William Brendel', 'Jianfei Yu', 'Pradeep Karuturi'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['stock-market-prediction'] | ['time-series'] | [-4.09888662e-02 -2.22706214e-01 -1.43447876e-01 -6.68046474e-01
-6.25730693e-01 -7.83323199e-02 1.90763578e-01 7.55484104e-02
-4.86087531e-01 6.76837325e-01 3.05187851e-01 1.37575790e-01
1.68176532e-01 -4.13527489e-01 -2.44181260e-01 -7.57233560e-01
4.20662284e-01 -1.03443764e-01 -3.67547274e-01 -3.62876832... | [11.561187744140625, 6.660256385803223] |
263fedf7-3e81-44ac-b873-0e525bdb1b72 | late-reverberation-suppression-using-u-nets | 2110.02144 | null | https://arxiv.org/abs/2110.02144v1 | https://arxiv.org/pdf/2110.02144v1.pdf | Late reverberation suppression using U-nets | In real-world settings, speech signals are almost always affected by reverberation produced by the working environment; these corrupted signals need to be \emph{dereverberated} prior to performing, e.g., speech recognition, speech-to-text conversion, compression, or general audio enhancement. In this paper, we propose ... | ['Felipe Tobar', 'Diego León'] | 2021-10-05 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.93541425e-01 -1.45424716e-02 5.08192182e-01 -1.93902329e-01
-6.27646923e-01 -3.31801146e-01 2.87801981e-01 -3.26920033e-01
-3.63577567e-02 7.06074953e-01 6.91215038e-01 -4.28747386e-01
4.96926568e-02 -5.11130333e-01 -8.97901773e-01 -7.05596805e-01
7.84347877e-02 -3.75638545e-01 -1.39058009e-01 -3.82309288... | [15.046734809875488, 5.935602188110352] |
75d6394a-169e-4d6b-b2ba-65979d235ca9 | predicting-eye-gaze-location-on-websites | 2211.08074 | null | https://arxiv.org/abs/2211.08074v3 | https://arxiv.org/pdf/2211.08074v3.pdf | Predicting Eye Gaze Location on Websites | World-wide-web, with the website and webpage as the main interface, facilitates the dissemination of important information. Hence it is crucial to optimize them for better user interaction, which is primarily done by analyzing users' behavior, especially users' eye-gaze locations. However, gathering these data is still... | ['Steffen Staab', 'Decky Aspandi', 'Ciheng Zhang'] | 2022-11-15 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 1.21982500e-01 -7.82064423e-02 -1.49775282e-01 -2.29952753e-01
-3.74884427e-01 -4.22451973e-01 4.24646229e-01 7.14035705e-02
-2.54083276e-01 1.73420697e-01 1.70267895e-01 -5.45464039e-01
-1.76293701e-01 -4.82361078e-01 -6.50351346e-01 -5.61458528e-01
2.35154912e-01 -2.43239671e-01 1.41045049e-01 -2.16455311... | [14.094232559204102, 0.08388006687164307] |
bba49847-220c-44de-af0c-e1906ad2298a | lets-take-this-online-adapting-scene | 1906.08744 | null | https://arxiv.org/abs/1906.08744v1 | https://arxiv.org/pdf/1906.08744v1.pdf | Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation | Many applications require a camera to be relocalised online, without expensive offline training on the target scene. Whilst both keyframe and sparse keypoint matching methods can be used online, the former often fail away from the training trajectory, and the latter can struggle in textureless regions. By contrast, sce... | ['Stuart Golodetz', 'Philip Torr', 'Tommaso Cavallari', 'Jishnu Mukhoti', 'Luca Bertinetto'] | 2019-06-20 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 4.17373687e-01 -1.88932851e-01 -1.28138736e-01 -3.76194715e-01
-7.94680357e-01 -6.92267179e-01 6.43832147e-01 -4.53055371e-03
-5.74238896e-01 3.55879486e-01 -7.71306902e-02 -3.16075295e-01
-2.08777152e-02 -7.07270741e-01 -8.34408998e-01 -5.24545670e-01
-9.08648297e-02 5.07247686e-01 7.46460557e-01 -7.83135965... | [7.7704901695251465, -2.216183662414551] |
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