paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
218189b5-ea66-4310-8732-a6aa1f6eab47 | semantic-segmentation-on-vspw-dataset-through | 2109.01316 | null | https://arxiv.org/abs/2109.01316v1 | https://arxiv.org/pdf/2109.01316v1.pdf | Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models | Semantic segmentation is an important task in computer vision, from which some important usage scenarios are derived, such as autonomous driving, scene parsing, etc. Due to the emphasis on the task of video semantic segmentation, we participated in this competition. In this report, we briefly introduce the solutions of... | ['Xiaotao Wang', 'Junhong Zou', 'Zixuan Chen'] | 2021-09-03 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 4.20437515e-01 3.00221115e-01 -1.10794650e-02 -6.04497731e-01
-6.55474365e-01 -4.84311193e-01 3.52793217e-01 -4.14456129e-01
-7.92301476e-01 4.05547500e-01 5.30749038e-02 -2.42118359e-01
4.98110116e-01 -5.52587271e-01 -8.97045434e-01 -5.16560555e-01
2.75134981e-01 2.95679212e-01 9.79834080e-01 -1.83445528... | [9.131417274475098, -0.10875509679317474] |
0f884c12-8571-4131-a38d-cdd8fe908707 | task-specific-optimization-of-virtual-channel | 2112.13569 | null | https://arxiv.org/abs/2112.13569v1 | https://arxiv.org/pdf/2112.13569v1.pdf | Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification | Developing a single-microphone speech denoising or dereverberation front-end for robust automatic speaker verification (ASV) in noisy far-field speaking scenarios is challenging. To address this problem, we present a novel front-end design that involves a recently proposed extension of the weighted prediction error (WP... | ['Joon-Hyuk Chang', 'Joon-Young Yang'] | 2021-12-27 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [ 3.09273720e-01 8.27827305e-03 6.72363400e-01 -3.98474604e-01
-1.36143053e+00 -4.48828340e-01 3.94888818e-01 -2.50794172e-01
-1.87491059e-01 3.21832031e-01 5.68493187e-01 -3.96607339e-01
-1.06872849e-01 -1.18063785e-01 -7.50938058e-01 -9.87273455e-01
8.43545645e-02 -2.59262651e-01 -2.61547655e-01 -3.46055299... | [14.939644813537598, 5.964614391326904] |
bb32f28c-d156-4e0d-95e9-e316a147cd6c | 190503716 | 1905.03716 | null | https://arxiv.org/abs/1905.03716v1 | https://arxiv.org/pdf/1905.03716v1.pdf | Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method | Fully parallel architecture at disparity-level for efficient semi-global matching (SGM) with refined rank method is presented. The improved SGM algorithm is implemented with the non-parametric unified rank model which is the combination of Rank filter/AD and Rank SAD. Rank SAD is a novel definition by introducing the c... | ['Yiwu Yao', 'Yuhua Cheng'] | 2019-05-07 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 4.14848536e-01 -1.40865251e-01 -2.96208113e-01 -5.38088083e-01
-4.97958899e-01 9.81996357e-02 4.21816260e-01 -1.45744562e-01
-2.03534096e-01 2.81328887e-01 2.56289303e-01 -9.85721424e-02
-3.86356056e-01 -8.34286988e-01 -3.48384768e-01 -4.96685803e-01
5.42778112e-02 3.15252990e-01 5.94106436e-01 -2.95757234... | [9.087697982788086, -2.2306604385375977] |
610c3425-8917-4f47-9181-83cf4ee043f5 | animeceleb-large-scale-animation-celebfaces | 2111.07640 | null | https://arxiv.org/abs/2111.07640v2 | https://arxiv.org/pdf/2111.07640v2.pdf | AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment | We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. T... | ['Jaegul Choo', 'Junsoo Lee', 'Sunghyo Chung', 'Jaeseong Lee', 'Sunghyun Park', 'Kangyeol Kim'] | 2021-11-15 | null | null | null | null | ['talking-head-generation', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [-4.74245578e-01 1.67810872e-01 1.31078567e-02 -2.87621766e-01
-7.81330109e-01 -2.43594497e-01 6.79920793e-01 -5.18278360e-01
-3.02490711e-01 4.32312638e-01 4.94687051e-01 1.24189265e-01
4.73355383e-01 -5.68129539e-01 -8.38555098e-01 -4.16747034e-01
-8.49541575e-02 1.00106823e+00 4.24808621e-01 -4.19583052... | [12.99600887298584, -0.4254414141178131] |
d6953f80-1063-4a4d-b3e4-93493f723e09 | a-survey-on-the-application-of-data-science | 2209.07528 | null | https://arxiv.org/abs/2209.07528v1 | https://arxiv.org/pdf/2209.07528v1.pdf | A Survey on the application of Data Science And Analytics in the field of Organised Sports | The application of Data Science and Analytics to optimize or predict outcomes is Ubiquitous in the Modern World. Data Science and Analytics have optimized almost every domain that exists in the market. In our survey, we focus on how the field of Analytics has been adopted in the field of sports, and how it has contribu... | ['C Nandini', 'Prithvi HV', 'Sachin Kumar S'] | 2022-09-15 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-3.79142433e-01 6.99645886e-03 -3.13257694e-01 -3.08037370e-01
5.17146252e-02 -4.49179530e-01 8.36552382e-02 8.22893858e-01
-5.23365855e-01 3.02123666e-01 3.40473592e-01 -4.43932489e-02
-6.04806006e-01 -1.15823030e+00 -2.67285377e-01 -1.52374372e-01
-2.22242549e-01 8.30954671e-01 3.69268030e-01 -7.99975276... | [6.641534328460693, 0.3495121896266937] |
a8cd36a9-d92a-46eb-8509-e41155cfffc1 | dynamic-dense-rgb-d-slam-using-learning-based | 2205.05916 | null | https://arxiv.org/abs/2205.05916v2 | https://arxiv.org/pdf/2205.05916v2.pdf | Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry | We propose a dense dynamic RGB-D SLAM pipeline based on a learning-based visual odometry, TartanVO. TartanVO, like other direct methods rather than feature-based, estimates camera pose through dense optical flow, which only applies to static scenes and disregards dynamic objects. Due to the color constancy assumption, ... | ['Guangzhao Li', 'Jiayi Qiu', 'Yilin Cai', 'Shihao Shen'] | 2022-05-12 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-1.06417537e-01 -1.73832521e-01 -8.04640204e-02 -7.24121630e-02
-1.03763148e-01 -9.12056029e-01 4.58721429e-01 -5.12463599e-02
-5.99584937e-01 6.90469325e-01 -1.60562798e-01 -8.42445046e-02
2.51850694e-01 -8.33784878e-01 -6.66731119e-01 -5.58494031e-01
3.41818571e-01 6.86715126e-01 6.53897464e-01 -5.05709536... | [8.286064147949219, -2.0307230949401855] |
3b6de07f-94f5-4923-b62c-799438dc37f8 | proceedings-of-the-second-workshop-on-10 | null | null | https://aclanthology.org/W16-0800 | https://aclanthology.org/W16-0800.pdf | Proceedings of the Second Workshop on Computational Approaches to Deception Detection | null | [''] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.220540523529053, 3.657142162322998] |
846601d3-5d89-4bfc-9211-76d1a7416f1b | sense-annotated-corpus-for-russian | null | null | https://aclanthology.org/2022.clib-1.15 | https://aclanthology.org/2022.clib-1.15.pdf | Sense-Annotated Corpus for Russian | We present a sense-annotated corpus for Russian. The resource was obtained my manually annotating texts from the OpenCorpora corpus, an open corpus for the Russian language, by senses of Russian wordnet RuWordNet. The annotation was used as a test collection for comparing unsupervised (Personalized Pagerank) and pseudo... | ['Dmitry Ilvovsky', 'Angelina Bolshina', 'Maksim Kulaev', 'Natalia Loukachevitch', 'Alexander Kirillovich'] | null | null | null | null | clib-2022-9 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.37335986e-01 5.75284779e-01 -5.62043905e-01 -2.37361193e-02
-5.03569603e-01 -8.59200835e-01 5.93772829e-01 7.47210860e-01
-8.44660759e-01 1.29880190e+00 7.87429154e-01 -6.40950620e-01
5.88186942e-02 -7.65685320e-01 3.22120965e-01 -2.55052924e-01
5.05065918e-01 8.15709770e-01 1.50512829e-01 -9.11523998... | [10.224127769470215, 9.229647636413574] |
cc191d34-dd65-4fa7-95fb-0e19758be17e | on-the-feasibility-of-attacking-thai-lpr | 2301.05506 | null | https://arxiv.org/abs/2301.05506v1 | https://arxiv.org/pdf/2301.05506v1.pdf | On the feasibility of attacking Thai LPR systems with adversarial examples | Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary ca... | ['Norrathep Rattanavipanon', 'Jakapan Suaboot', 'Chissanupong Jiamsuchon'] | 2023-01-13 | null | null | null | null | ['optical-character-recognition', 'license-plate-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.37074095e-01 -3.98127973e-01 1.06324330e-01 -1.62406713e-01
-5.37690163e-01 -1.01477742e+00 4.12829190e-01 -5.76551020e-01
-4.59929138e-01 3.72881562e-01 -3.61508578e-01 -8.49261999e-01
3.95087451e-01 -5.00378609e-01 -8.74383211e-01 -4.36524421e-01
4.70561124e-02 3.02898455e-02 3.57559472e-01 -3.83499600... | [5.619505405426025, 7.956948280334473] |
83f28e49-f5b4-4f71-a366-392edb844051 | visual-speech-language-models | 1809.06800 | null | http://arxiv.org/abs/1809.06800v1 | http://arxiv.org/pdf/1809.06800v1.pdf | Visual Speech Language Models | Language models (LM) are very powerful in lipreading systems. Language models
built upon the ground truth utterances of datasets learn grammar and structure
rules of words and sentences (the latter in the case of continuous speech).
However, visual co-articulation effects in visual speech signals damage the
performance... | [] | 2018-09-14 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-4.80004810e-02 4.19772536e-01 -4.27045524e-01 -1.05629787e-01
-7.33451068e-01 -4.17580009e-01 8.04870546e-01 -3.56139272e-01
-4.34881508e-01 6.86613858e-01 7.70015955e-01 -8.37857902e-01
6.03084922e-01 -2.31809705e-01 -7.50527501e-01 -4.52126801e-01
4.51946348e-01 5.30018359e-02 -1.12294890e-02 2.72203293... | [14.296006202697754, 4.975762367248535] |
72fb5b67-1e20-4fa0-bafd-fb5a18d89cdd | residual-attention-based-network-for-hand | 1901.05876 | null | http://arxiv.org/abs/1901.05876v1 | http://arxiv.org/pdf/1901.05876v1.pdf | Residual Attention based Network for Hand Bone Age Assessment | Computerized automatic methods have been employed to boost the productivity
as well as objectiveness of hand bone age assessment. These approaches make
predictions according to the whole X-ray images, which include other objects
that may introduce distractions. Instead, our framework is inspired by the
clinical workflo... | ['Shaoting Zhang', 'Junjie Bai', 'Yi Lu', 'Qi Song', 'Bin Kong', 'Siwei Lyu', 'Kunlin Cao', 'Youbing Yin', 'Feng Gao', 'Eric Wu', 'Xin Wang'] | 2018-12-21 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 5.65777905e-02 6.37688696e-01 2.11136658e-02 -3.35663527e-01
-6.88514829e-01 -1.74982876e-01 6.31935969e-02 1.87315911e-01
-6.02365494e-01 3.48791301e-01 2.95497805e-01 -1.71818286e-01
-2.81736664e-02 -9.24973011e-01 -6.25568748e-01 -7.78180182e-01
2.00439081e-01 6.78487241e-01 4.29350585e-01 7.93579593... | [14.76699447631836, -2.4541633129119873] |
52d42dfb-c0d1-4e3f-bb31-6f66d39f7a13 | seeking-commonness-and-inconsistencies-a | 2203.08060 | null | https://arxiv.org/abs/2203.08060v3 | https://arxiv.org/pdf/2203.08060v3.pdf | Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering | Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of the previous multi-view subspace clustering algorithms are still faced with two limitations. Fir... | ['Chang-Dong Wang', 'Guang-Yu Zhang', 'Dong Huang', 'Xiaosha Cai'] | 2022-03-15 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.61682391e-01 -6.52080536e-01 -2.22756237e-01 -1.90697566e-01
-6.93987250e-01 -7.46628702e-01 5.69748461e-01 -3.98239046e-01
1.37317032e-01 1.92153171e-01 6.05070412e-01 2.51280129e-01
-4.60478812e-01 -1.63325980e-01 -1.67869091e-01 -1.14230549e+00
2.48973146e-01 -1.92067735e-02 -5.18631004e-02 6.91895336... | [8.266192436218262, 4.607967853546143] |
3d16b116-dd06-43bd-9243-76c6213aa410 | joint-appearance-and-motion-learning-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.pdf | Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction | Rolling shutter correction (RSC) is becoming increasingly popular for RS cameras that are widely used in commercial and industrial applications. Despite the promising performance, existing RSC methods typically employ a two-stage network structure that ignores intrinsic information interactions and hinders fast inf... | ['Qi Liu', 'Zhexiong Wan', 'Yuchao Dai', 'Yuxin Mao', 'Bin Fan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unrolling'] | ['computer-vision'] | [ 3.59000593e-01 -3.28877926e-01 -5.45878112e-01 -2.52010554e-01
-7.47303784e-01 -2.16594189e-01 4.34698373e-01 -4.33476865e-01
-4.66777116e-01 4.02793288e-01 3.06370914e-01 -3.04814488e-01
1.46766946e-01 -3.51371109e-01 -8.93870473e-01 -7.68501461e-01
2.66123593e-01 -4.00300920e-01 5.61116576e-01 -5.10917790... | [10.693714141845703, -1.4047962427139282] |
b3dbd5e2-047d-4525-a08e-c86cc92b136c | deep-convolutional-generative-adversarial | 1812.10179 | null | http://arxiv.org/abs/1812.10179v1 | http://arxiv.org/pdf/1812.10179v1.pdf | Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data | Traditional machine learning algorithms using hand-crafted feature extraction
techniques (such as local binary pattern) have limited accuracy because of high
variation in images of the same class (or intra-class variation) for food
recognition task. In recent works, convolutional neural networks (CNN) have
been applied... | ['N. B. Puhan', 'Bappaditya Mandal', 'Avijit Verma'] | 2018-12-26 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 5.91715872e-01 -3.88138602e-03 -1.48101091e-01 -3.81711841e-01
-5.34764111e-01 -5.49752474e-01 4.98502463e-01 2.97474027e-01
-4.64091659e-01 8.77686381e-01 -9.18458924e-02 -2.71390118e-02
2.36776456e-01 -1.39985466e+00 -1.14729536e+00 -8.05710673e-01
-1.63838957e-02 2.04370618e-01 -6.77856430e-02 -5.82606457... | [11.572487831115723, 4.346249103546143] |
e9842706-6136-47a9-a235-181e25d7d8a9 | visual-semantics-allow-for-textual-reasoning-1 | 2112.12916 | null | https://arxiv.org/abs/2112.12916v1 | https://arxiv.org/pdf/2112.12916v1.pdf | Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition | Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arb... | ['Bo Du', 'Chaoyue Wang', 'Fengxiang He', 'Juhua Liu', 'Jing Zhang', 'Chen Chen', 'Yue He'] | 2021-12-24 | visual-semantics-allow-for-textual-reasoning | https://arxiv.org/abs/2112.12916 | https://arxiv.org/pdf/2112.12916.pdf | aaai-2022-2021-12 | ['scene-text-recognition'] | ['computer-vision'] | [ 2.79875845e-01 1.62542522e-01 -2.13828698e-01 -4.66254622e-01
-5.37345529e-01 -8.22419763e-01 5.25079906e-01 2.36848012e-01
-2.24521548e-01 -1.31229786e-02 2.11528704e-01 -4.98800516e-01
3.42257500e-01 -8.31968069e-01 -9.20793176e-01 -4.39227015e-01
3.37812334e-01 4.95402694e-01 4.93882120e-01 -2.43185554... | [10.403214454650879, 1.4222530126571655] |
ca6a310c-bfc1-4792-aacf-6395dadd0fe4 | photorealistic-lip-sync-with-adversarial | 2002.08700 | null | https://arxiv.org/abs/2002.08700v2 | https://arxiv.org/pdf/2002.08700v2.pdf | A Neural Lip-Sync Framework for Synthesizing Photorealistic Virtual News Anchors | Lip sync has emerged as a promising technique for generating mouth movements from audio signals. However, synthesizing a high-resolution and photorealistic virtual news anchor is still challenging. Lack of natural appearance, visual consistency, and processing efficiency are the main problems with existing methods. Thi... | ['Zhou Zhu', 'Changjiang Ji', 'Ruobing Zheng', 'Bo Song'] | 2020-02-20 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 2.18845055e-01 -7.34313801e-02 1.82242766e-01 -3.54266286e-01
-9.16869760e-01 -2.69517452e-01 5.15844107e-01 -5.98056614e-01
1.24259010e-01 4.88632828e-01 1.67480513e-01 1.22853577e-01
4.74437416e-01 -5.30346513e-01 -8.27447653e-01 -4.12113398e-01
2.24862263e-01 -1.80404797e-01 1.45011351e-01 -3.34054023... | [13.180797576904297, -0.4407215714454651] |
59a8dd10-932d-4169-a6f0-bafce12211f3 | context-based-automated-scoring-of-complex | null | null | https://aclanthology.org/2020.bea-1.19 | https://aclanthology.org/2020.bea-1.19.pdf | Context-based Automated Scoring of Complex Mathematical Responses | The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately. In this paper we propose a method for automatically scoring responses that contain both text and algebraic expressions. Our method not only achieves high agreement with huma... | ['Dmytro Galochkin', 'Avijit Vajpayee', 'Aoife Cahill', 'Brian Riordan', 'James H Fife'] | 2020-07-01 | null | null | null | ws-2020-7 | ['explainable-models'] | ['computer-vision'] | [ 3.54892835e-02 3.72391015e-01 -1.50821730e-02 -7.53490031e-01
-1.17323887e+00 -8.41111720e-01 2.50225157e-01 4.97810930e-01
2.00232957e-03 8.71481955e-01 1.50390267e-01 -8.08681786e-01
-2.92027414e-01 -7.57851601e-01 -2.92988598e-01 7.77227059e-02
4.59074795e-01 4.87542331e-01 -2.27193963e-02 -4.87728894... | [11.278840065002441, 9.184999465942383] |
79ddc3cc-8a5e-4719-b9e4-2103ffec6a69 | data-driven-computing-in-elasticity-via-1 | null | null | https://www.sciencedirect.com/science/article/pii/S2095034918302071 | https://www.sciencedirect.com/science/article/pii/S2095034918302071 | Data-driven computing in elasticity via kernel regression | This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate ... | ['Yoshihiro Kanno'] | 2018-12-12 | null | null | null | theoretical-and-applied-mechanics-letters | ['stress-strain-relation'] | ['miscellaneous'] | [-3.16895962e-01 -2.43922040e-01 -1.81696981e-01 -3.36651027e-01
-5.62781036e-01 6.95516020e-02 -2.19265267e-01 -1.92194834e-01
-3.65458965e-01 8.73395622e-01 -2.86212806e-02 1.51530787e-01
-6.19488358e-01 -6.46208167e-01 -5.93884647e-01 -9.32437122e-01
-2.40661532e-01 1.00775123e+00 5.28112590e-01 -2.78874338... | [6.363703727722168, 3.4497745037078857] |
f7d66832-0e54-4f52-a386-968390def924 | align-your-latents-high-resolution-video | 2304.08818 | null | https://arxiv.org/abs/2304.08818v1 | https://arxiv.org/pdf/2304.08818v1.pdf | Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models | Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM... | ['Karsten Kreis', 'Sanja Fidler', 'Seung Wook Kim', 'Tim Dockhorn', 'Huan Ling', 'Robin Rombach', 'Andreas Blattmann'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Blattmann_Align_Your_Latents_High-Resolution_Video_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Blattmann_Align_Your_Latents_High-Resolution_Video_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-super-resolution', 'video-generation', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.82461816e-01 -2.71242112e-02 -1.47369161e-01 2.33964864e-02
-9.33332384e-01 -4.87258106e-01 8.21413815e-01 -8.27497125e-01
-8.85191485e-02 5.68838179e-01 4.87412572e-01 -1.62581831e-01
2.13346511e-01 -8.38355362e-01 -1.01643467e+00 -6.90164506e-01
-6.21596631e-03 2.45243520e-01 2.32251555e-01 -5.02827317... | [10.875605583190918, -0.6356833577156067] |
e9feabf5-60d6-4b3d-a621-1ca8e7d4d1dd | a-unified-sequence-to-sequence-front-end | 1911.04111 | null | https://arxiv.org/abs/1911.04111v1 | https://arxiv.org/pdf/1911.04111v1.pdf | A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis | In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech. Building a typical pipeline-based front-end which consists of multiple individual components requires extensive efforts. In this paper, we proposed a unified ... | ['Shichao Liu', 'Zhiling Zhang', 'Yuxuan Wang', 'Yang Zhang', 'Zejun Ma', 'Xiang Yin', 'Junjie Pan'] | 2019-11-11 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 4.43951301e-02 -8.84623826e-02 3.09029996e-01 -5.26864469e-01
-1.21069229e+00 -6.39532387e-01 1.94698796e-01 -4.30085391e-01
-3.02357227e-01 2.47040898e-01 4.97951984e-01 -6.93506300e-01
6.85486317e-01 -2.10219696e-01 -3.66893381e-01 -4.25715476e-01
3.79874378e-01 9.02123973e-02 2.87325114e-01 -3.70952755... | [14.737015724182129, 6.734172821044922] |
8549e099-9467-492a-bc85-af0a1738aa9a | bayesian-metric-learning-for-uncertainty | 2302.01332 | null | https://arxiv.org/abs/2302.01332v2 | https://arxiv.org/pdf/2302.01332v2.pdf | Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval | We propose the first Bayesian encoder for metric learning. Rather than relying on neural amortization as done in prior works, we learn a distribution over the network weights with the Laplace Approximation. We actualize this by first proving that the contrastive loss is a valid log-posterior. We then propose three meth... | ['Soren Hauberg', 'Silas Brack', 'Marco Miani', 'Frederik Warburg'] | 2023-02-02 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-8.43350887e-02 3.13959867e-01 -7.41092190e-02 -6.77758992e-01
-1.37600362e+00 -4.91887480e-01 4.68909800e-01 -2.06498399e-01
-6.35860085e-01 9.38303709e-01 -6.22012690e-02 -2.60841042e-01
-3.59409511e-01 -5.09814501e-01 -1.14314497e+00 -6.81143522e-01
-3.84117782e-01 4.90042597e-01 2.52366245e-01 4.04970735... | [7.214768886566162, 3.902141571044922] |
2e109c73-722d-4011-87e7-0ef71d2325e7 | comparison-of-u-net-based-convolutional | 1810.04017 | null | http://arxiv.org/abs/1810.04017v1 | http://arxiv.org/pdf/1810.04017v1.pdf | Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT | Various approaches for liver segmentation in CT have been proposed: Besides
statistical shape models, which played a major role in this research area,
novel approaches on the basis of convolutional neural networks have been
introduced recently. Using a set of 219 liver CT datasets with reference
segmentations from live... | ['Andrea Schenk', 'Grzegorz Chlebus', 'Hans Meine', 'Mohsen Ghafoorian', 'Itaru Endo'] | 2018-10-09 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-4.48933452e-01 1.34904608e-01 -3.37712020e-01 -6.33244812e-01
-4.43508297e-01 -4.63423461e-01 5.27391195e-01 7.09535241e-01
-4.61053759e-01 4.89864290e-01 2.91616261e-01 -6.32576287e-01
-1.66264787e-01 -7.79084563e-01 -1.93681940e-01 -8.22155714e-01
-7.96183407e-01 5.99426925e-01 1.08545870e-01 1.26668811... | [14.482341766357422, -2.6877851486206055] |
9f0634d4-28ad-4a61-aac3-56edc69cce28 | 190910367 | 1909.10367 | null | https://arxiv.org/abs/1909.10367v2 | https://arxiv.org/pdf/1909.10367v2.pdf | Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions | Reasoning about graphs evolving over time is a challenging concept in many domains, such as bioinformatics, physics, and social networks. We consider a common case in which edges can be short term interactions (e.g., messaging) or long term structural connections (e.g., friendship). In practice, long term edges are oft... | ['Graham W. Taylor', 'Carolyn Augusta', 'Boris Knyazev'] | 2019-09-23 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-8.89468864e-02 3.46887141e-01 -1.03908099e-01 -4.07609016e-01
-4.52598184e-02 -7.18757570e-01 8.84876072e-01 3.29115212e-01
-1.20513104e-01 5.70271790e-01 1.60272226e-01 -5.21630168e-01
-2.10325703e-01 -1.17469704e+00 -1.05305779e+00 -4.21683639e-01
-3.74705702e-01 5.86608231e-01 1.57433093e-01 -1.57718793... | [7.196505069732666, 6.028126239776611] |
bd3d7ae3-5e28-412b-b4b7-ed0e165bd733 | estimating-the-causal-effect-of-early | 2306.13891 | null | https://arxiv.org/abs/2306.13891v1 | https://arxiv.org/pdf/2306.13891v1.pdf | Estimating the Causal Effect of Early ArXiving on Paper Acceptance | What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this question. We use data from the ICLR conference (2018--2022) and apply methods from causal inference to estimate the effect of a... | ['Noah A. Smith', 'Bo Zhang', 'David Wadden', 'Jiayao Zhang', 'Yanai Elazar'] | 2023-06-24 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [-2.08950043e-01 1.39497042e-01 -5.56640685e-01 -7.81228021e-02
-5.79238832e-01 -5.01472771e-01 6.35266662e-01 8.55190635e-01
-7.63350546e-01 5.02203465e-01 5.59512079e-01 -1.06982887e+00
-3.56648237e-01 -8.97831738e-01 -1.19528639e+00 2.30132900e-02
1.39288545e-01 -9.42339823e-02 -7.36685842e-03 5.55689156... | [8.079489707946777, 5.588912487030029] |
76f3f425-31e9-4f45-b963-2162939c9082 | learning-sparse-temporal-video-mapping-for | 2301.06103 | null | https://arxiv.org/abs/2301.06103v1 | https://arxiv.org/pdf/2301.06103v1.pdf | Learning Sparse Temporal Video Mapping for Action Quality Assessment in Floor Gymnastics | Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance. It is crucial to comprehend local discriminative spatial dependencies and global semantics for accurate evaluation. However, existing benchmark datase... | ['Ajmal Mian', 'Ghulam Mubashar Hassan', 'Sania Zahan'] | 2023-01-15 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [-3.04962367e-01 -8.39361012e-01 -2.58080572e-01 -4.41556454e-01
-6.42709255e-01 -3.51155937e-01 4.23878998e-01 1.72038287e-01
-3.74646127e-01 4.96198088e-01 5.27703047e-01 4.48773533e-01
-4.22334462e-01 -6.33070767e-01 -7.15857148e-01 -4.36888874e-01
-4.71220344e-01 7.64806122e-02 4.17110592e-01 -4.36643034... | [8.093949317932129, 0.473542720079422] |
e54e642a-3c6c-4ade-be56-d8964536a3e4 | hardware-efficient-joint-radar-communications | 2104.08127 | null | https://arxiv.org/abs/2104.08127v1 | https://arxiv.org/pdf/2104.08127v1.pdf | Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization | In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain... | ['Fan Liu', 'Christos Masouros', 'Aryan Kaushik'] | 2021-04-16 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 7.17953682e-01 1.25744909e-01 -2.01142028e-01 -2.52532959e-01
-4.09458160e-01 -3.85845721e-01 2.10930318e-01 -3.64241898e-01
-2.77489066e-01 6.83768868e-01 2.08466724e-01 -4.00189966e-01
-8.19320619e-01 -9.75374103e-01 -3.53784598e-02 -9.98464346e-01
-4.77107555e-01 -2.80757517e-01 -5.58852971e-01 1.72158718... | [6.254347324371338, 1.3124860525131226] |
c10e88d2-8077-4f6d-b558-81013d3caa59 | vidstyleode-disentangled-video-editing-via | 2304.06020 | null | https://arxiv.org/abs/2304.06020v1 | https://arxiv.org/pdf/2304.06020v1.pdf | VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs | We propose $\textbf{VidStyleODE}$, a spatiotemporally continuous disentangled $\textbf{Vid}$eo representation based upon $\textbf{Style}$GAN and Neural-$\textbf{ODE}$s. Effective traversal of the latent space learned by Generative Adversarial Networks (GANs) has been the basis for recent breakthroughs in image editing.... | ['Aykut Erdem', 'Erkut Erdem', 'Levent Karacan', 'Duygu Ceylan', 'Tolga Birdal', 'Andrew Bond', 'Moayed Haji Ali'] | 2023-04-12 | null | null | null | null | ['video-generation', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 3.18147272e-01 -5.36770374e-02 4.07973193e-02 8.17867368e-02
-4.82441753e-01 -8.37713182e-01 5.39685845e-01 -9.28515196e-01
4.40551601e-02 8.07945192e-01 6.71982765e-02 -1.20959699e-01
8.15611929e-02 -6.99968636e-01 -1.03252935e+00 -9.70882952e-01
5.02190925e-02 -1.25350296e-01 -2.73070276e-01 -2.24990353... | [10.912286758422852, -0.655503511428833] |
45ad64dd-a0e3-48bb-9d9d-d7fdb8a3c7fe | posterior-sampling-for-deep-reinforcement | 2305.00477 | null | https://arxiv.org/abs/2305.00477v2 | https://arxiv.org/pdf/2305.00477v2.pdf | Posterior Sampling for Deep Reinforcement Learning | Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency by building an environment model that can be used for planning. Posterior Sampling for Reinforcement ... | ['Paulo Rauber', 'Michelangelo Conserva', 'Remo Sasso'] | 2023-04-30 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-1.77736267e-01 3.71903837e-01 -8.79098117e-01 -1.42770022e-01
-1.55932558e+00 -2.99727619e-01 9.92833376e-01 1.67065710e-01
-5.67551434e-01 1.16098821e+00 3.36065263e-01 -4.99425709e-01
-3.88365656e-01 -8.72695982e-01 -9.43400979e-01 -6.59963369e-01
-4.58889455e-01 1.36481071e+00 2.33167514e-01 -1.26259923... | [4.112947940826416, 1.922719955444336] |
1f958c04-03ae-431c-8ac2-b32c8e0dbdad | chad-charlotte-anomaly-dataset | 2212.09258 | null | https://arxiv.org/abs/2212.09258v3 | https://arxiv.org/pdf/2212.09258v3.pdf | CHAD: Charlotte Anomaly Dataset | In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of a video contain abnormal behaviors. However, video anomaly detection is particularly context-specific, and the availability of representative ... | ['Hamed Tabkhi', 'Christopher Neff', 'Babak Rahimi Ardabili', 'Ghazal Alinezhad Noghre', 'Armin Danesh Pazho'] | 2022-12-19 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-1.20881140e-01 -6.36432588e-01 -4.92109507e-02 -1.48755312e-01
-5.35195768e-01 -2.29344204e-01 4.64949816e-01 1.96085945e-01
-3.74019474e-01 2.54944235e-01 2.89897680e-01 6.58239350e-02
4.01027620e-01 -5.04867733e-01 -6.75584733e-01 -3.68518978e-01
-3.65684181e-01 4.23745573e-01 4.61616606e-01 -3.12565900... | [7.8490400314331055, 1.437124252319336] |
e0e0cc9d-b0e5-432f-8fa2-be4309add77e | deep-neural-network-concepts-for-background | 1811.05255 | null | http://arxiv.org/abs/1811.05255v1 | http://arxiv.org/pdf/1811.05255v1.pdf | Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation | Conventional neural networks show a powerful framework for background
subtraction in video acquired by static cameras. Indeed, the well-known SOBS
method and its variants based on neural networks were the leader methods on the
largescale CDnet 2012 dataset during a long time. Recently, convolutional
neural networks whi... | ['Soon Ki Jung', 'Thierry Bouwmans', 'Sajid Javed', 'Maryam Sultana'] | 2018-11-13 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 3.88141304e-01 -5.66696465e-01 2.29444355e-01 -1.72885686e-01
9.74034145e-02 -2.16433406e-01 6.36665702e-01 -1.31286234e-01
-7.94361770e-01 9.19567347e-01 -3.04342419e-01 -3.46649289e-01
3.33544254e-01 -7.72784412e-01 -5.00911832e-01 -9.66342151e-01
6.86209649e-02 -5.61970733e-02 6.15542114e-01 -3.26784551... | [8.93152904510498, -0.6870083808898926] |
e3ab7dd2-a7e9-4b02-99be-001c62d88df6 | multi-crop-contrastive-learning-for | 2304.12235 | null | https://arxiv.org/abs/2304.12235v3 | https://arxiv.org/pdf/2304.12235v3.pdf | Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation | Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks. However, in the previous works, the negatives are sampled from the input image itself, which inspires us to design a data augmentation method to improve the quality of the select... | ['Cheng-Wei Hu', 'Zheng Yuan', 'Wei-Ling Cai', 'Chen Zhao'] | 2023-04-24 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 3.87203276e-01 -6.36049211e-02 -1.79179192e-01 -3.43187988e-01
-6.38137877e-01 -3.91443610e-01 6.59339488e-01 -5.56521773e-01
-2.13480636e-01 6.00473344e-01 3.07736993e-01 9.33450386e-02
1.71489179e-01 -8.43567550e-01 -9.52216864e-01 -9.22132790e-01
6.04285121e-01 1.05989240e-01 -6.43459707e-02 -3.19273323... | [11.679685592651367, -0.41255196928977966] |
470e4f06-dceb-42ad-a258-4f6285917989 | learning-and-visualizing-localized-geometric | 1711.04851 | null | http://arxiv.org/abs/1711.04851v3 | http://arxiv.org/pdf/1711.04851v3.pdf | Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes | 3D Convolutional Neural Networks (3D-CNN) have been used for object
recognition based on the voxelized shape of an object. However, interpreting
the decision making process of these 3D-CNNs is still an infeasible task. In
this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation
Mapping method (3D... | ['Adarsh Krishnamurthy', 'Sambit Ghadai', 'Aditya Balu', 'Soumik Sarkar'] | 2017-11-13 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-5.86510263e-02 2.54737228e-01 1.47083610e-01 -5.08804023e-01
-2.43024141e-01 -3.55815589e-01 3.82908732e-01 3.83036733e-01
1.00364953e-01 -3.35047767e-02 -3.35477978e-01 -5.98705828e-01
-3.75219345e-01 -9.15955663e-01 -6.33710742e-01 -2.63430178e-01
-9.77981985e-02 7.86456227e-01 1.21790178e-01 -2.54901797... | [8.055316925048828, -3.6610267162323] |
55d56ecf-b834-4ea9-b473-8474e46667ae | structure-and-automatic-segmentation-of | 2008.00756 | null | https://arxiv.org/abs/2008.00756v1 | https://arxiv.org/pdf/2008.00756v1.pdf | Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio | A Dhrupad vocal concert comprises a composition section that is interspersed with improvised episodes of increased rhythmic activity involving the interaction between the vocals and the percussion. Tracking the changing rhythmic density, in relation to the underlying metric tempo of the piece, thus facilitates the dete... | ['Rohit M. A.', 'Preeti Rao'] | 2020-08-03 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.04268008e-01 -3.87183487e-01 3.56967151e-01 2.54241556e-01
-6.70739770e-01 -9.81652677e-01 6.15928471e-01 3.01760316e-01
-1.13841571e-01 1.30369663e-01 5.77834964e-01 3.90079856e-01
-3.95749539e-01 -5.38719773e-01 -2.98037648e-01 -9.35404539e-01
-3.83674860e-01 4.14799899e-01 2.86161333e-01 -3.46532673... | [15.883774757385254, 5.354520797729492] |
6fb6107b-0893-422f-88da-5f643bdd833b | environment-sound-classification-using | 1908.11219 | null | https://arxiv.org/abs/1908.11219v9 | https://arxiv.org/pdf/1908.11219v9.pdf | Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network | In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coe... | ['Ole-Christoffer Granmo', 'Jivitesh Sharma', 'Morten Goodwin'] | 2019-08-28 | null | null | null | null | ['sound-classification'] | ['audio'] | [-6.79348735e-03 -3.89487147e-01 7.69152343e-01 -5.59652373e-02
-7.32810438e-01 -3.32430035e-01 5.29419839e-01 1.02108762e-01
-7.44218528e-01 4.20547545e-01 7.53147602e-02 -2.54620075e-01
-5.69620915e-03 -6.68064713e-01 -5.16790271e-01 -5.96609950e-01
-4.65583682e-01 -4.09407765e-01 3.51043314e-01 -2.43853509... | [15.172684669494629, 5.217771053314209] |
326b2612-4a5c-4e1f-8cbd-a28aa4e88778 | cell-detection-with-star-convex-polygons | 1806.03535 | null | http://arxiv.org/abs/1806.03535v2 | http://arxiv.org/pdf/1806.03535v2.pdf | Cell Detection with Star-convex Polygons | Automatic detection and segmentation of cells and nuclei in microscopy images
is important for many biological applications. Recent successful learning-based
approaches include per-pixel cell segmentation with subsequent pixel grouping,
or localization of bounding boxes with subsequent shape refinement. In
situations o... | ['Martin Weigert', 'Uwe Schmidt', 'Gene Myers', 'Coleman Broaddus'] | 2018-06-09 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.49740380e-01 7.77325854e-02 4.88000184e-01 -2.51107723e-01
-6.54454410e-01 -6.18864715e-01 3.06619734e-01 5.56842625e-01
-8.39500844e-01 1.09855342e+00 -5.41501462e-01 -5.86455576e-02
4.75306243e-01 -7.16594875e-01 -7.74294198e-01 -1.02622342e+00
2.07907811e-01 8.51305664e-01 6.28886759e-01 4.04166222... | [14.512176513671875, -3.1657588481903076] |
e1b26f15-b5e9-4c84-a203-576b02d38134 | sparse-representation-classification-via | 1906.01601 | null | https://arxiv.org/abs/1906.01601v1 | https://arxiv.org/pdf/1906.01601v1.pdf | Sparse Representation Classification via Screening for Graphs | The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency for ... | ['Cencheng Shen', 'Carey Priebe', 'Yuexiao Dong', 'Li Chen'] | 2019-06-04 | null | null | null | null | ['image-classification-shift-consistency'] | ['computer-vision'] | [ 3.67220104e-01 -2.18454935e-02 -6.45274818e-01 -3.03666592e-01
-8.05682003e-01 -1.83354169e-01 3.61354738e-01 -4.09493625e-01
2.48640582e-01 6.10109150e-01 1.74246550e-01 -3.13091427e-01
-3.47955763e-01 -3.68786871e-01 -5.37181675e-01 -9.98471260e-01
-1.76768556e-01 5.04335225e-01 2.91989110e-02 4.32306118... | [12.393863677978516, 0.4177156984806061] |
5527ef69-6337-42da-aedb-230ae88ced04 | eye-tracking-as-a-source-of-implicit-feedback | 2305.07516 | null | https://arxiv.org/abs/2305.07516v1 | https://arxiv.org/pdf/2305.07516v1.pdf | Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis | Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and add... | ['Maria Bielikova', 'Robert Moro', 'Santiago de Leon-Martinez'] | 2023-05-12 | null | null | null | null | ['movie-recommendation', 'collaborative-filtering'] | ['miscellaneous', 'miscellaneous'] | [-4.09682304e-01 -2.11252302e-01 -2.30589911e-01 -4.26643372e-01
1.80577517e-01 -8.75915885e-01 5.66252947e-01 7.63546944e-01
-6.45168900e-01 6.27980292e-01 5.36406815e-01 -4.90907967e-01
-7.36200273e-01 -5.93349397e-01 -1.61719382e-01 -6.56028092e-02
-3.24417353e-01 5.85018061e-02 8.02588046e-01 -5.87560952... | [10.05315113067627, 5.781005859375] |
9a24483a-7eb3-4621-b550-759d870ec657 | training-ibm-watson-using-automatically | 1611.03932 | null | http://arxiv.org/abs/1611.03932v1 | http://arxiv.org/pdf/1611.03932v1.pdf | Training IBM Watson using Automatically Generated Question-Answer Pairs | IBM Watson is a cognitive computing system capable of question answering in
natural languages. It is believed that IBM Watson can understand large corpora
and answer relevant questions more effectively than any other
question-answering system currently available. To unleash the full power of
Watson, however, we need to... | ['Minseok Kim', 'Jaeyoon Yoo', 'Gyuwan Kim', 'Sungroh Yoon', 'Jangho Lee', 'Changwoo Jung'] | 2016-11-12 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 1.67969003e-01 3.66896659e-01 6.32658482e-01 -3.70294601e-01
-1.22524655e+00 -9.88568723e-01 4.64286625e-01 5.32765985e-01
-4.67336208e-01 5.48736572e-01 2.45448798e-02 -1.00287795e+00
-7.18374178e-02 -1.10532761e+00 -4.15370673e-01 1.52597755e-01
3.55511963e-01 9.29643333e-01 6.35129094e-01 -7.80581236... | [11.31969928741455, 8.10158920288086] |
07067e94-be42-4996-9fbc-baf7c2312944 | from-random-to-regular-variation-in-the | 1910.10210 | null | http://arxiv.org/abs/1910.10210v1 | http://arxiv.org/pdf/1910.10210v1.pdf | From Random to Regular: Variation in the Patterning of Retinal Mosaics | The various types of retinal neurons are each positioned at their respective
depths within the retina where they are believed to be assembled as orderly
mosaics, in which like-type neurons minimize proximity to one another. Two
common statistical analyses for assessing the spatial properties of retinal
mosaics include ... | [] | 2019-10-22 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 3.35095018e-01 -2.04635426e-01 1.75228477e-01 -2.95169592e-01
-9.41051915e-02 -8.27300727e-01 6.96365893e-01 1.08462594e-01
-4.22367364e-01 4.50773686e-01 4.91577834e-01 -5.07277429e-01
-3.95305485e-01 -5.06403029e-01 -4.33244795e-01 -1.13855064e+00
4.28723507e-02 -6.94306288e-03 3.63408983e-01 -2.82390732... | [13.704546928405762, -3.0559699535369873] |
f49dcc3d-3ba1-4baf-a27c-4ec18f9b37ac | face-space-action-recognition-by-face-object | 1601.04293 | null | http://arxiv.org/abs/1601.04293v1 | http://arxiv.org/pdf/1601.04293v1.pdf | Face-space Action Recognition by Face-Object Interactions | Action recognition in still images has seen major improvement in recent years
due to advances in human pose estimation, object recognition and stronger
feature representations. However, there are still many cases in which
performance remains far from that of humans. In this paper, we approach the
problem by learning ex... | ['Shimon Ullman', 'Amir Rosenfeld'] | 2016-01-17 | null | null | null | null | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 6.51402414e-01 1.72515154e-01 -2.39938036e-01 -5.39953291e-01
-3.89046729e-01 -2.17782244e-01 1.05650866e+00 -2.36562759e-01
-5.04847825e-01 7.64599383e-01 6.49553895e-01 3.75723064e-01
-1.73213840e-01 -3.89696985e-01 -4.43744481e-01 -6.27365589e-01
-3.09860915e-01 7.36570179e-01 3.88419986e-01 -6.85854256... | [8.066529273986816, 0.374108225107193] |
677388a2-ff44-4f38-95a0-c69ba55115cb | transformer-based-model-for-monocular-visual | 2305.06121 | null | https://arxiv.org/abs/2305.06121v1 | https://arxiv.org/pdf/2305.06121v1.pdf | Transformer-based model for monocular visual odometry: a video understanding approach | Estimating the camera pose given images of a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and it often relies on geometric approaches that require engineering effort for a specific scenario. Deep learning methods have shown to be generali... | ['Marcos R. O. A. Maximo', 'André O. Françani'] | 2023-05-10 | null | null | null | null | ['video-understanding', 'monocular-visual-odometry', 'visual-odometry'] | ['computer-vision', 'robots', 'robots'] | [-4.52088892e-01 -4.03597653e-02 -2.59135991e-01 -4.44026709e-01
-4.27799344e-01 -3.85986984e-01 7.29785919e-01 -4.93949562e-01
-7.34114110e-01 1.00591987e-01 -1.46800354e-02 -1.18240826e-01
3.34459633e-01 -3.67758691e-01 -1.07968318e+00 -3.89515728e-01
1.68407321e-01 1.03894937e+00 2.93932140e-01 -2.71151513... | [8.106376647949219, -2.1065917015075684] |
f7d9e7f7-87e2-4af2-8aa3-419ca1346951 | identifying-well-formed-natural-language | 1808.09419 | null | http://arxiv.org/abs/1808.09419v1 | http://arxiv.org/pdf/1808.09419v1.pdf | Identifying Well-formed Natural Language Questions | Understanding search queries is a hard problem as it involves dealing with
"word salad" text ubiquitously issued by users. However, if a query resembles a
well-formed question, a natural language processing pipeline is able to perform
more accurate interpretation, thus reducing downstream compounding errors.
Hence, ide... | ['Manaal Faruqui', 'Dipanjan Das'] | 2018-08-28 | identifying-well-formed-natural-language-1 | https://aclanthology.org/D18-1091 | https://aclanthology.org/D18-1091.pdf | emnlp-2018-10 | ['query-wellformedness'] | ['natural-language-processing'] | [ 4.98464584e-01 2.96180785e-01 1.03301667e-02 -4.98428494e-01
-1.40827715e+00 -1.02174211e+00 4.47479188e-01 6.46946430e-01
-5.44508219e-01 5.30833960e-01 4.21829820e-01 -9.79640126e-01
7.33856708e-02 -8.06113064e-01 -8.96689057e-01 2.57836252e-01
5.45341730e-01 7.10797131e-01 5.17241538e-01 -4.15193558... | [11.409631729125977, 8.01941204071045] |
55949da2-0200-466e-bc9e-31b17357613f | learning-descriptive-image-captioning-via | 2306.13460 | null | https://arxiv.org/abs/2306.13460v2 | https://arxiv.org/pdf/2306.13460v2.pdf | Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation | Image captioning aims to describe visual content in natural language. As 'a picture is worth a thousand words', there could be various correct descriptions for an image. However, with maximum likelihood estimation as the training objective, the captioning model is penalized whenever its prediction mismatches with the l... | ['Qin Jin', 'Liang Zhang', 'Anwen Hu', 'Zihao Yue'] | 2023-06-23 | null | null | null | null | ['image-captioning', 'blocking'] | ['computer-vision', 'natural-language-processing'] | [ 5.38993537e-01 7.32284546e-01 -3.50972652e-01 -6.62443221e-01
-7.32422292e-01 -6.05996966e-01 6.31024778e-01 2.16604453e-02
-7.30935037e-02 8.60233426e-01 6.51474476e-01 -2.19499469e-01
5.81642747e-01 -3.92647177e-01 -9.10119414e-01 -2.87904650e-01
3.59951675e-01 4.04249758e-01 -2.93848842e-01 7.43843568... | [11.005861282348633, 1.0246013402938843] |
2f716b1f-7b3d-4329-a78a-69fd68fbbe4d | gshot-few-shot-generative-modeling-of-labeled | 2306.03480 | null | https://arxiv.org/abs/2306.03480v1 | https://arxiv.org/pdf/2306.03480v1.pdf | GSHOT: Few-shot Generative Modeling of Labeled Graphs | Deep graph generative modeling has gained enormous attraction in recent years due to its impressive ability to directly learn the underlying hidden graph distribution. Despite their initial success, these techniques, like much of the existing deep generative methods, require a large number of training samples to learn ... | ['Srikanta Bedathur', 'Sayan Ranu', 'Shubham Gupta', 'Sahil Manchanda'] | 2023-06-06 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 6.82205185e-02 3.50004464e-01 -3.19387048e-01 -2.62893379e-01
-6.44702435e-01 -3.23272556e-01 6.69643819e-01 1.76145598e-01
1.27828449e-01 7.96788216e-01 2.22615302e-01 -1.03015617e-01
1.21840931e-01 -1.08682346e+00 -6.13938570e-01 -6.56100154e-01
1.96205042e-02 8.53423238e-01 2.34606728e-01 -1.40362695... | [7.32040548324585, 6.201229572296143] |
8cb1ffca-b564-47dc-bf03-cefb497f2139 | phased-progressive-learning-with-coupling | 2205.12117 | null | https://arxiv.org/abs/2205.12117v3 | https://arxiv.org/pdf/2205.12117v3.pdf | Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification | Deep convolutional neural networks often perform poorly when faced with datasets that suffer from quantity imbalances and classification difficulties. Despite advances in the field, existing two-stage approaches still exhibit dataset bias or domain shift. To counter this, a phased progressive learning schedule has been... | ['Ronald X. Xu', 'Peng Yao', 'Shuwei Shen', 'Peng Liu', 'Pengfei Shao', 'Bingxuan Wu', 'Fan Zhang', 'Yi Cheng', 'Liang Xu'] | 2022-05-24 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.98593717e-02 -2.21336871e-01 -4.90078449e-01 -6.56482518e-01
-5.64471245e-01 -1.06828302e-01 2.20962152e-01 4.90565687e-01
-5.02820909e-01 1.00206316e+00 -1.28760576e-01 -8.17575157e-02
-1.84298337e-01 -7.75242090e-01 -5.71863770e-01 -5.95192075e-01
3.68682183e-02 4.99863237e-01 -6.11270070e-02 -2.71330774... | [9.147552490234375, 3.8997671604156494] |
115cdd2a-5148-4d73-b0c6-77d66f38c6b2 | is-bert-really-robust-natural-language-attack | 1907.11932 | null | https://arxiv.org/abs/1907.11932v6 | https://arxiv.org/pdf/1907.11932v6.pdf | Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment | Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this... | ['Joey Tianyi Zhou', 'Zhijing Jin', 'Peter Szolovits', 'Di Jin'] | 2019-07-27 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 5.63835502e-01 2.77743310e-01 3.17173123e-01 -2.26880550e-01
-9.90948379e-01 -1.13305712e+00 8.75798404e-01 1.08501427e-01
-3.22621882e-01 7.14614868e-01 1.48575649e-01 -6.01205826e-01
3.14540088e-01 -9.55251873e-01 -9.41563427e-01 -4.38960373e-01
-3.56464945e-02 4.76170868e-01 1.36688473e-02 -5.36392450... | [6.025905609130859, 8.11356258392334] |
dea543d0-feaa-42d0-bb88-300c664d20ea | on-the-relationship-between-counterfactual | 2207.04317 | null | https://arxiv.org/abs/2207.04317v2 | https://arxiv.org/pdf/2207.04317v2.pdf | On the Relationship Between Counterfactual Explainer and Recommender | Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular. However, the tangibility and trustworthiness of the recommendations are questionable due to the co... | ['Meng Jiang', 'Zheng Ning', 'Zhihan Zhang', 'Gang Liu'] | 2022-07-09 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-1.43550918e-01 2.63739496e-01 -3.03397119e-01 -5.75951099e-01
1.51153371e-01 -5.88805437e-01 8.22766483e-01 -1.31868511e-01
-1.66283637e-01 8.44453037e-01 7.10772991e-01 -6.11319542e-01
-7.57985890e-01 -8.25260878e-01 -8.92125487e-01 -3.56720120e-01
-1.36376964e-02 5.79398453e-01 -2.06434175e-01 -2.94507980... | [9.633727073669434, 5.6991376876831055] |
484c9017-81d6-4ac2-a295-aaed916f743b | stochastic-action-prediction-for-imitation | 2101.01055 | null | https://arxiv.org/abs/2101.01055v1 | https://arxiv.org/pdf/2101.01055v1.pdf | Stochastic Action Prediction for Imitation Learning | Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. When performing demonstrations, experts are not always consistent and might accomplish the same task in slightly different ways. In this paper, we demonstrate inhere... | ['Bharadwaj Amrutur', 'Shishir Kolathaya', 'Nihesh Rathod', 'Sagar Gubbi Venkatesh'] | 2020-12-26 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 7.07599223e-02 -6.65321201e-02 4.83378880e-02 -3.61675054e-01
-8.80640686e-01 -7.78944075e-01 9.24010932e-01 -3.18519056e-01
-5.24513006e-01 9.58937109e-01 2.42964759e-01 -3.58776897e-01
-2.62083024e-01 -3.25605273e-01 -1.21093774e+00 -4.42966849e-01
-3.79196405e-02 7.85943151e-01 1.40191779e-01 -2.05707312... | [4.384958744049072, 1.212282657623291] |
48409748-f43f-430f-999b-07d0cae8a167 | lights-light-specularity-dataset-for-specular | 2101.10772 | null | https://arxiv.org/abs/2101.10772v1 | https://arxiv.org/pdf/2101.10772v1.pdf | LIGHTS: LIGHT Specularity Dataset for specular detection in Multi-view | Specular highlights are commonplace in images, however, methods for detecting them and in turn removing the phenomenon are particularly challenging. A reason for this, is due to the difficulty of creating a dataset for training or evaluation, as in the real-world we lack the necessary control over the environment. Ther... | ['Stuart James', 'Alessio Del Bue', 'Theodore Tsesmelis', 'Mohamed Dahy Elkhouly'] | 2021-01-26 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 4.29540336e-01 -5.33082664e-01 5.77811837e-01 -9.17591378e-02
-6.17703795e-01 -7.73136020e-01 3.96640927e-01 -1.47670269e-01
8.69078785e-02 6.05684400e-01 2.00311780e-01 -9.59549658e-03
1.45945147e-01 -6.02335870e-01 -6.53530180e-01 -7.32098699e-01
-9.98953134e-02 -1.29099011e-01 7.41304100e-01 -1.48366809... | [10.453856468200684, -2.8025314807891846] |
d1b88146-bbca-4799-b607-8569d397a08d | subset-labeled-lda-for-large-scale-multi | 1709.05480 | null | http://arxiv.org/abs/1709.05480v1 | http://arxiv.org/pdf/1709.05480v1.pdf | Subset Labeled LDA for Large-Scale Multi-Label Classification | Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
multi-label learning tasks. Previous work has shown it to perform in par with
other state-of-the-art multi-label methods. Nonetheless, with increasing label
sets sizes LLDA enc... | ['Grigorios Tsoumakas', 'Yannis Papanikolaou'] | 2017-09-16 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [-1.48826733e-01 8.11415389e-02 -1.52236193e-01 -4.15785670e-01
-1.08652246e+00 -4.96610761e-01 7.77345955e-01 3.73303086e-01
-1.56965926e-01 6.93761230e-01 1.39472440e-01 2.65489370e-02
-2.50650197e-02 -4.82058913e-01 1.36959136e-01 -1.17871571e+00
-3.70721631e-02 1.44579029e+00 2.06561327e-01 3.29746515... | [9.541122436523438, 4.33111572265625] |
8979a301-bb7e-46d5-af5c-b9606b6ca483 | a-comparative-study-of-hierarchical-risk | 2210.00984 | null | https://arxiv.org/abs/2210.00984v1 | https://arxiv.org/pdf/2210.00984v1.pdf | A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks | Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a sys... | ['Abhishek Dutta', 'Jaydip Sen'] | 2022-10-03 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.51366574e-01 -3.14318687e-01 8.63752440e-02 -1.26061082e-01
-3.09008777e-01 -8.69255245e-01 5.91332376e-01 -3.75422716e-01
-1.13733836e-01 8.58287811e-01 3.83021265e-01 -7.29607701e-01
-7.61897147e-01 -9.23388422e-01 -1.58415884e-01 -6.52164102e-01
-2.24196881e-01 2.58295327e-01 2.36037076e-01 -3.56747806... | [4.722534656524658, 4.0338335037231445] |
10668414-5b83-482b-9da6-a92ffc5e0236 | conditional-wavegan | 1809.10636 | null | http://arxiv.org/abs/1809.10636v1 | http://arxiv.org/pdf/1809.10636v1.pdf | Conditional WaveGAN | Generative models are successfully used for image synthesis in the recent
years. But when it comes to other modalities like audio, text etc little
progress has been made. Recent works focus on generating audio from a
generative model in an unsupervised setting. We explore the possibility of
using generative models cond... | ['Woo-Jin Han', 'Gue Jun Jung', 'Anoop Toffy', 'Chae Young Lee'] | 2018-09-27 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.90682399e-01 2.63202071e-01 1.10889666e-01 -2.60390192e-01
-8.92942488e-01 -4.05732602e-01 1.03829801e+00 -3.08750212e-01
-2.29948342e-01 8.35859299e-01 4.56889093e-01 -9.13974196e-02
1.52096361e-01 -8.82815659e-01 -3.76574814e-01 -9.10988450e-01
2.00082976e-02 3.84421915e-01 5.23964502e-02 9.06400010... | [15.579279899597168, 5.635956764221191] |
5e3d9a5c-6435-4921-94a2-871f6b9e116c | state-based-episodic-memory-for-multi-agent | 2110.09817 | null | https://arxiv.org/abs/2110.09817v1 | https://arxiv.org/pdf/2110.09817v1.pdf | State-based Episodic Memory for Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) algorithms have made promising progress in recent years by leveraging the centralized training and decentralized execution (CTDE) paradigm. However, existing MARL algorithms still suffer from the sample inefficiency problem. In this paper, we propose a simple yet effective appr... | ['Wu-Jun Li', 'Xiao Ma'] | 2021-10-19 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.66033578e-01 -2.33108029e-01 -2.12082237e-01 1.66082337e-01
-6.38582230e-01 -2.71932751e-01 7.53604710e-01 4.74804848e-01
-7.98972249e-01 9.98817265e-01 -1.54628739e-01 -3.55325550e-01
-2.53274351e-01 -9.83637214e-01 -8.94438565e-01 -6.43702388e-01
-4.08344090e-01 7.26457834e-01 4.58554924e-01 -1.63686529... | [3.8529176712036133, 1.9515799283981323] |
25722f89-22d8-4f89-9b5e-62b3b45a35be | emotionally-enhanced-talking-face-generation | 2303.11548 | null | https://arxiv.org/abs/2303.11548v2 | https://arxiv.org/pdf/2303.11548v2.pdf | Emotionally Enhanced Talking Face Generation | Several works have developed end-to-end pipelines for generating lip-synced talking faces with various real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, ... | ['Rajiv Ratn Shah', 'Yifang Yin', 'Yi Yu', 'Sarthak Bhagat', 'Shagun Uppal', 'Sahil Goyal'] | 2023-03-21 | null | null | null | null | ['talking-head-generation', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.45073995e-01 -3.86868343e-02 1.86675102e-01 -6.54073775e-01
-2.56303698e-01 -5.98217249e-01 4.78300363e-01 -7.47931421e-01
-8.36269744e-03 6.11197472e-01 3.57780755e-01 -7.96058252e-02
3.82535964e-01 -4.75732923e-01 -4.92663056e-01 -4.36996996e-01
3.48970145e-01 -5.22852540e-02 -3.11817288e-01 -3.88896644... | [13.208642959594727, -0.38544368743896484] |
0766df42-a3e3-42a2-92a0-3c19bb90741e | teaching-independent-parts-separately-tips | 2205.05980 | null | https://arxiv.org/abs/2205.05980v4 | https://arxiv.org/pdf/2205.05980v4.pdf | "Teaching Independent Parts Separately" (TIPSy-GAN) : Improving Accuracy and Stability in Unsupervised Adversarial 2D to 3D Pose Estimation | We present TIPSy-GAN, a new approach to improve the accuracy and stability in unsupervised adversarial 2D to 3D human pose estimation. In our work we demonstrate that the human kinematic skeleton should not be assumed as a single spatially codependent structure; in fact, we posit when a full 2D pose is provided during ... | ['Hansung Kim', 'Srinandan Dasmahapatra', 'Peter Hardy'] | 2022-05-12 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 7.01329559e-02 5.61768413e-01 5.00670895e-02 -1.27575770e-01
-8.95167708e-01 -7.25419879e-01 5.15766561e-01 -3.84509623e-01
-4.54726368e-01 9.36147332e-01 2.68563390e-01 1.49731934e-01
9.00940001e-02 -4.75988299e-01 -1.00718975e+00 -7.15513289e-01
-1.24898009e-01 1.05418146e+00 1.80343673e-01 -4.09123838... | [6.91441011428833, -1.0301496982574463] |
6706d2a0-9f12-4df6-9a1f-4abc785682d9 | discern-and-answer-mitigating-the-impact-of | 2305.01579 | null | https://arxiv.org/abs/2305.01579v1 | https://arxiv.org/pdf/2305.01579v1.pdf | Discern and Answer: Mitigating the Impact of Misinformation in Retrieval-Augmented Models with Discriminators | Most existing retrieval-augmented language models (LMs) for question answering assume all retrieved information is factually correct. In this work, we study a more realistic scenario in which retrieved documents may contain misinformation, causing conflicts among them. We observe that the existing models are highly bri... | ['Joyce Jiyoung Whang', 'Sung-Hyon Myaeng', 'Junmo Kang', 'Jeonghwan Kim', 'Giwon Hong'] | 2023-05-02 | null | null | null | null | ['misinformation', 'open-domain-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 2.27819905e-01 2.60018289e-01 -3.70726854e-01 -3.12816083e-01
-1.69499338e+00 -8.82647097e-01 8.06191504e-01 2.90836036e-01
-4.48333651e-01 7.25695014e-01 4.23435450e-01 -6.03368580e-01
-5.95795572e-01 -8.28303576e-01 -5.91671586e-01 -2.13546976e-01
1.27195522e-01 7.67057717e-01 7.92693734e-01 -6.96245790... | [11.288288116455078, 7.8860039710998535] |
3d974af6-9d1b-4394-9025-1c2d48c35fa4 | cross-age-speaker-verification-learning-age | 2207.05929 | null | https://arxiv.org/abs/2207.05929v1 | https://arxiv.org/pdf/2207.05929v1.pdf | Cross-Age Speaker Verification: Learning Age-Invariant Speaker Embeddings | Automatic speaker verification has achieved remarkable progress in recent years. However, there is little research on cross-age speaker verification (CASV) due to insufficient relevant data. In this paper, we mine cross-age test sets based on the VoxCeleb dataset and propose our age-invariant speaker representation(AIS... | ['Ming Li', 'Dan Su', 'Chao Weng', 'Na Li', 'Xiaoyi Qin'] | 2022-07-13 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.28598973e-01 -1.38571113e-01 -2.79933494e-02 -5.67811549e-01
-1.23635960e+00 -4.61762995e-01 4.31520104e-01 -1.57668054e-01
-3.30232441e-01 4.28644806e-01 1.88950062e-01 -1.56509876e-01
2.66390800e-01 -3.29078823e-01 -3.98436844e-01 -8.92003000e-01
4.19352464e-02 7.33676404e-02 -2.21164063e-01 -1.78414620... | [14.0911283493042, 5.93014669418335] |
e424ed37-397c-422b-a713-6c186a66f011 | bidirectional-lstm-crf-for-clinical-concept | 1611.08373 | null | http://arxiv.org/abs/1611.08373v1 | http://arxiv.org/pdf/1611.08373v1.pdf | Bidirectional LSTM-CRF for Clinical Concept Extraction | Automated extraction of concepts from patient clinical records is an
essential facilitator of clinical research. For this reason, the 2010 i2b2/VA
Natural Language Processing Challenges for Clinical Records introduced a
concept extraction task aimed at identifying and classifying concepts into
predefined categories (i.... | ['Massimo Piccardi', 'Ehsan Zare Borzeshi', 'Raghavendra Chalapathy'] | 2016-11-25 | null | null | null | null | ['clinical-concept-extraction'] | ['medical'] | [ 3.46257150e-01 8.27304795e-02 -5.82756162e-01 -3.51376206e-01
-1.04773414e+00 -2.16777056e-01 5.41597605e-01 1.04766548e+00
-9.50424016e-01 1.01993251e+00 4.40053403e-01 -7.13929653e-01
-2.65247911e-01 -6.43877864e-01 -1.15355313e-01 -7.25367785e-01
-1.87421188e-01 8.77000272e-01 -4.98778373e-01 -9.77699533... | [8.471455574035645, 8.703399658203125] |
5df31e9e-e826-4b2f-a64b-b1b68200438d | 191013406 | 1910.13406 | null | https://arxiv.org/abs/1910.13406v2 | https://arxiv.org/pdf/1910.13406v2.pdf | Generalization of Reinforcement Learners with Working and Episodic Memory | Memory is an important aspect of intelligence and plays a role in many deep reinforcement learning models. However, little progress has been made in understanding when specific memory systems help more than others and how well they generalize. The field also has yet to see a prevalent consistent and rigorous approach f... | ['Joel Z. Leibo', 'Charles Blundell', 'Gavin Buttimore', 'Adrià Puigdomènech Badia', 'Steven Hansen', 'Ryan Faulkner', 'Melissa Tan', 'Charlie Deck', 'Meire Fortunato'] | 2019-10-29 | generalization-of-reinforcement-learners-with | http://papers.nips.cc/paper/9411-generalization-of-reinforcement-learners-with-working-and-episodic-memory | http://papers.nips.cc/paper/9411-generalization-of-reinforcement-learners-with-working-and-episodic-memory.pdf | neurips-2019-12 | ['holdout-set'] | ['computer-vision'] | [-4.22810316e-02 -1.96679845e-01 -1.57930572e-02 -2.20584258e-01
-4.83991086e-01 -8.85050535e-01 9.64741290e-01 3.16084445e-01
-8.79011869e-01 9.94430065e-01 -7.95711353e-02 -4.20703590e-01
-4.46336180e-01 -1.00371253e+00 -7.01769471e-01 -6.10385120e-01
-3.18948299e-01 9.74678218e-01 3.85251671e-01 -3.37795705... | [4.0392608642578125, 1.5902156829833984] |
005ed206-d4df-4e52-9326-4f2b1c058e04 | towards-detection-of-subjective-bias-using | 2002.06644 | null | https://arxiv.org/abs/2002.06644v1 | https://arxiv.org/pdf/2002.06644v1.pdf | Towards Detection of Subjective Bias using Contextualized Word Embeddings | Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization. This bias is introduced in natural language via inflammatory words and phrases, casting doubt over facts, and presupposing the truth. In this work, we perform comprehens... | ['Tanvi Dadu', 'Kartikey Pant', 'Radhika Mamidi'] | 2020-02-16 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 5.18898759e-03 2.77362585e-01 -7.78325737e-01 -5.54362774e-01
-6.71758354e-01 -7.27334142e-01 9.30788338e-01 6.42947197e-01
-9.10562813e-01 1.11287534e+00 6.85717642e-01 -4.37120527e-01
1.63857087e-01 -6.73564434e-01 -6.43025279e-01 -3.48217666e-01
4.80770040e-03 2.11393908e-01 7.02696070e-02 -9.62645054... | [8.976411819458008, 10.240798950195312] |
51ea9084-897d-4dfb-91e8-0e18a8423fde | damia-leveraging-domain-adaptation-as-a | 2005.08016 | null | https://arxiv.org/abs/2005.08016v1 | https://arxiv.org/pdf/2005.08016v1.pdf | DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks | Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an at... | ['Weiqi Luo', 'Yue Zhang', 'Anjia Yang', 'Jian Weng', 'Hongwei Huang', 'Guoqiang Zeng'] | 2020-05-16 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.19145250e-02 8.01947042e-02 -2.89156675e-01 -3.09000075e-01
-6.87088192e-01 -1.06846666e+00 4.11373347e-01 1.65096428e-02
-2.91095763e-01 5.24399340e-01 -3.67915004e-01 -8.28706980e-01
2.14283206e-02 -9.41496193e-01 -8.86383414e-01 -6.47814572e-01
9.04218387e-03 7.18379170e-02 9.69710872e-02 8.64200965... | [5.804412841796875, 7.481330394744873] |
615b7420-b35f-48c8-9e8d-e5160240c91f | dense-video-object-captioning-from-disjoint | 2306.11729 | null | https://arxiv.org/abs/2306.11729v1 | https://arxiv.org/pdf/2306.11729v1.pdf | Dense Video Object Captioning from Disjoint Supervision | We propose a new task and model for dense video object captioning -- detecting, tracking, and captioning trajectories of all objects in a video. This task unifies spatial and temporal understanding of the video, and requires fine-grained language description. Our model for dense video object captioning is trained end-t... | ['Cordelia Schmid', 'Chen Sun', 'Anurag Arnab', 'Xingyi Zhou'] | 2023-06-20 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 1.09921329e-01 1.64452553e-01 -2.96078861e-01 -8.97843912e-02
-1.08056521e+00 -7.06779480e-01 6.28821254e-01 -9.16171167e-03
-4.24788326e-01 4.64467049e-01 4.18958515e-01 -8.96820277e-02
3.29023749e-01 -4.90463734e-01 -1.34930313e+00 -3.34470540e-01
-2.38428593e-01 6.32344604e-01 7.99918890e-01 1.91409327... | [10.169473648071289, 0.7028518319129944] |
fdc4164f-1c1b-41ba-91b2-6514fd89095b | deep-reinforcement-learning-for-doom-using | 1807.01960 | null | http://arxiv.org/abs/1807.01960v1 | http://arxiv.org/pdf/1807.01960v1.pdf | Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks | Recent developments in deep reinforcement learning have enabled the creation
of agents for solving a large variety of games given a visual input. These
methods have been proven successful for 2D games, like the Atari games, or for
simple tasks, like navigating in mazes. It is still an open question, how to
address more... | ['Pericles A. Mitkas', 'Georgios Papoudakis', 'Kyriakos C. Chatzidimitriou'] | 2018-07-05 | null | null | null | null | ['game-of-doom'] | ['playing-games'] | [-2.99851596e-01 3.45452614e-02 1.01862438e-01 2.37429351e-01
-1.38546959e-01 -5.44376194e-01 6.11860037e-01 3.74779925e-02
-8.45467746e-01 1.15822005e+00 -2.05481410e-01 -3.95894676e-01
-4.06137854e-01 -9.72528338e-01 -4.32179868e-01 -7.03479469e-01
-4.72351611e-01 8.84238541e-01 6.71624660e-01 -9.48017478... | [3.745473623275757, 1.4796963930130005] |
4805f0e0-5f77-4d25-960f-0afc912c8db2 | only-text-only-image-or-both-predicting | null | null | https://aclanthology.org/2020.icon-main.60 | https://aclanthology.org/2020.icon-main.60.pdf | Only text? only image? or both? Predicting sentiment of internet memes | Nowadays, the spread of Internet memes on online social media platforms such as Instagram, Facebook, Reddit, and Twitter is very fast. Analyzing the sentiment of memes can provide various useful insights. Meme sentiment classification is a new area of research that is not explored yet. Recently SemEval provides a datas... | ['Asif Ekbal', 'Mamta .', 'Pranati Behera'] | null | null | null | null | icon-2020-12 | ['meme-classification'] | ['natural-language-processing'] | [-3.38305831e-01 -4.65887398e-01 -1.85939878e-01 -2.63796687e-01
-4.42096889e-01 -7.25861073e-01 5.23378909e-01 7.20128953e-01
-5.19676805e-01 7.48231530e-01 1.74178660e-01 -1.02737933e-01
3.63831639e-01 -1.00466597e+00 -3.84919196e-01 -3.86923999e-01
3.93074363e-01 -1.42653316e-01 3.02806854e-01 -3.39246005... | [8.523870468139648, 10.752058029174805] |
e2a76d41-6be3-4ea0-a530-e48231f5c477 | integrating-both-visual-and-audio-cues-for | 1711.08097 | null | http://arxiv.org/abs/1711.08097v2 | http://arxiv.org/pdf/1711.08097v2.pdf | Integrating both Visual and Audio Cues for Enhanced Video Caption | Video caption refers to generating a descriptive sentence for a specific
short video clip automatically, which has achieved remarkable success recently.
However, most of the existing methods focus more on visual information while
ignoring the synchronized audio cues. We propose three multimodal deep fusion
strategies t... | ['Zhao-Xiang Zhang', 'Wangli Hao', 'He Guan', 'Guibo Zhu'] | 2017-11-22 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 6.71920627e-02 -3.07627141e-01 -4.26425859e-02 -2.70287812e-01
-1.22011518e+00 -3.27502996e-01 7.50467539e-01 7.45961741e-02
-4.46846515e-01 4.31636244e-01 5.04035115e-01 1.78905904e-01
-6.33419156e-02 -3.21582466e-01 -7.80112743e-01 -6.53086305e-01
5.60793169e-02 -2.86405802e-01 1.84931666e-01 -1.74020231... | [13.604039192199707, 4.812338829040527] |
ff41cb52-d1cb-4584-8d2a-79fb30462a7b | edaps-enhanced-domain-adaptive-panoptic | 2304.14291 | null | https://arxiv.org/abs/2304.14291v1 | https://arxiv.org/pdf/2304.14291v1.pdf | EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation | With autonomous industries on the rise, domain adaptation of the visual perception stack is an important research direction due to the cost savings promise. Much prior art was dedicated to domain-adaptive semantic segmentation in the synthetic-to-real context. Despite being a crucial output of the perception stack, pan... | ['Luc van Gool', 'Dengxin Dai', 'Anton Obukhov', 'Lukas Hoyer', 'Suman Saha'] | 2023-04-27 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.26380193e-01 1.04632460e-01 -1.09573655e-01 -5.18236816e-01
-7.57238388e-01 -9.38212216e-01 6.77545071e-01 -2.07946301e-01
-1.22066744e-01 3.37376177e-01 9.84097421e-02 -3.48829448e-01
-7.55021349e-02 -7.97141731e-01 -3.86388063e-01 -6.18972778e-01
1.72065452e-01 7.01175630e-01 5.76033592e-01 -3.89351100... | [9.733711242675781, 1.2648735046386719] |
1347e14a-d88e-41ca-b2fe-8e462cdd8d3b | umt-unified-multi-modal-transformers-for | 2203.12745 | null | https://arxiv.org/abs/2203.12745v2 | https://arxiv.org/pdf/2203.12745v2.pdf | UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection | Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era. Nevertheless, jointly conducting moment retrieval and highlight detection is an emerging research topic, even though its component problems and... | ['XiaoHu Qie', 'Ying Shan', 'Chang Wen Chen', 'Yang Wu', 'Siyuan Li', 'Ye Liu'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['highlight-detection', 'moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 6.44523725e-02 -5.05640805e-01 -4.02469397e-01 8.53960663e-02
-1.57746720e+00 -6.11154079e-01 6.05833769e-01 -7.70435780e-02
-2.26912349e-01 3.29519928e-01 3.40167820e-01 1.17206655e-01
-2.23033860e-01 -3.16935599e-01 -5.65812051e-01 -6.98813260e-01
-1.94963291e-01 1.87269509e-01 4.21220541e-01 -1.51691213... | [10.17409896850586, 0.7212256789207458] |
bfebf544-7901-4ba9-9ee1-ded57edf0790 | missing-modality-robustness-in-semi | 2304.10756 | null | https://arxiv.org/abs/2304.10756v1 | https://arxiv.org/pdf/2304.10756v1.pdf | Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation | Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To a... | ['Zsolt Kira', 'Yen-Cheng Liu', 'Harsh Maheshwari'] | 2023-04-21 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 4.10949349e-01 5.31269014e-02 -2.97551811e-01 -3.78241658e-01
-1.81337559e+00 -8.25735271e-01 6.14479840e-01 -1.03262059e-01
-6.06300652e-01 6.31950200e-01 1.33797154e-01 -6.69961721e-02
1.99889019e-01 -1.70985430e-01 -9.77042556e-01 -8.17160666e-01
4.43407714e-01 7.25282192e-01 7.48461127e-01 -2.01627536... | [9.726480484008789, 1.1629880666732788] |
334859e5-d030-4fbc-8d3d-0dc646ec9a6b | contextual-similarity-aggregation-with-self | 2110.13430 | null | https://arxiv.org/abs/2110.13430v1 | https://arxiv.org/pdf/2110.13430v1.pdf | Contextual Similarity Aggregation with Self-attention for Visual Re-ranking | In content-based image retrieval, the first-round retrieval result by simple visual feature comparison may be unsatisfactory, which can be refined by visual re-ranking techniques. In image retrieval, it is observed that the contextual similarity among the top-ranked images is an important clue to distinguish the semant... | ['Houqiang Li', 'Wengang Zhou', 'Min Wang', 'Hui Wu', 'Jianbo Ouyang'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/18d10dc6e666eab6de9215ae5b3d54df-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/18d10dc6e666eab6de9215ae5b3d54df-Paper.pdf | neurips-2021-12 | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.42414868e-01 -4.94716644e-01 -2.79917926e-01 -2.23476917e-01
-9.68660533e-01 -4.58996385e-01 5.91856182e-01 5.95189214e-01
-2.55385190e-01 2.99322098e-01 4.03354675e-01 7.14469478e-02
-4.97461647e-01 -6.30102217e-01 -3.55381459e-01 -7.97120869e-01
2.87775844e-01 9.37037468e-02 3.42219770e-01 -1.62171498... | [10.866643905639648, 1.1086807250976562] |
603903db-5c3f-4ab6-87e9-c214967bdba8 | membership-inference-attack-using-self | 2205.13680 | null | https://arxiv.org/abs/2205.13680v1 | https://arxiv.org/pdf/2205.13680v1.pdf | Membership Inference Attack Using Self Influence Functions | Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model. Thus, MI is a major privacy threat to models trained on private sensitive data, such as medical records. In MI attacks one may consider the black-box settings, where the model's parameters and activation... | ['Raja Giryes', 'Gilad Cohen'] | 2022-05-26 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.83768946e-01 4.27157521e-01 -2.90152162e-01 -6.25658691e-01
-6.88835204e-01 -6.32564545e-01 4.26653147e-01 9.54246670e-02
-6.45454764e-01 7.06279695e-01 -7.94760063e-02 -6.12987638e-01
2.07924232e-01 -8.58273506e-01 -9.53369141e-01 -7.63957977e-01
-3.12409848e-01 4.45379227e-01 -2.50060409e-01 4.72822264... | [5.955318927764893, 7.077521800994873] |
47aecdbe-b966-40c0-bf03-06e756afab68 | generative-modeling-in-structural-hankel | 2211.13857 | null | https://arxiv.org/abs/2211.13857v1 | https://arxiv.org/pdf/2211.13857v1.pdf | Generative Modeling in Structural-Hankel Domain for Color Image Inpainting | In recent years, some researchers focused on using a single image to obtain a large number of samples through multi-scale features. This study intends to a brand-new idea that requires only ten or even fewer samples to construct the low-rank structural-Hankel matrices-assisted score-based generative model (SHGM) for co... | ['Qiegen Liu', 'Yuhao Wang', 'Wenbo Wan', 'Shenglin Wu', 'CHUNHUA WU', 'Zihao Li'] | 2022-11-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.61824721e-01 -1.14768326e-01 2.78932899e-01 -1.30664349e-01
-1.05929339e+00 3.03335767e-02 2.81186700e-01 -6.34302676e-01
-1.09568842e-01 9.70495760e-01 1.43776968e-01 3.49455088e-01
-2.37435892e-01 -6.12921357e-01 -7.26207674e-01 -1.06241035e+00
1.37203813e-01 4.13470089e-01 -2.94311106e-01 5.23731746... | [11.090115547180176, -1.8966466188430786] |
eb841b5b-820d-493c-bf72-dce397dd6527 | almanac-knowledge-grounded-language-models | 2303.01229 | null | https://arxiv.org/abs/2303.01229v2 | https://arxiv.org/pdf/2303.01229v2.pdf | Almanac: Retrieval-Augmented Language Models for Clinical Medicine | Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine, adoption of these models in real-world settings has been largely limited... | ['William Hiesinger', 'Joanna Nelson', 'Curt Langlotz', 'Karen Hirsch', 'Kathleen Boyd', 'Jack Boyd', 'Euan Ashley', 'Kevin Alexander', 'Michael Moor', 'Jennifer L. Kim', 'Alex R. Dalal', 'Rohan Shad', 'Akash Chaurasia', 'Cyril Zakka'] | 2023-03-01 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 3.06654125e-01 7.14907825e-01 -5.51379621e-01 -2.71464765e-01
-1.41560912e+00 -6.21769965e-01 5.36757767e-01 9.83133435e-01
-3.48743826e-01 9.74069357e-01 1.18570697e+00 -8.54601800e-01
-4.09677386e-01 -3.09263617e-01 6.56608120e-02 -1.82384089e-01
-5.14437109e-02 7.78580308e-01 -3.95469785e-01 -2.22837642... | [8.716221809387207, 8.448633193969727] |
5cc9cffb-322d-4e5a-8761-e44dc9ddf56b | blind-deblurring-of-hyperspectral-document | 2303.05130 | null | https://arxiv.org/abs/2303.05130v1 | https://arxiv.org/pdf/2303.05130v1.pdf | Blind deblurring of hyperspectral document images | Most computer vision and machine learning-based approaches for historical document analysis are tailored to grayscale or RGB images and thus, mostly exploit their spatial information. Multispectral (MS) and hyperspectral (HS) images contain, next to the spatial information, much richer spectral information than RGB ima... | ['A. Traviglia', 'G. Franceschin', 'P. Guzzonato', 'M. Ljubenovic'] | 2023-03-09 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 7.11444795e-01 -7.73076653e-01 1.15125321e-01 1.30528808e-01
-7.32238889e-01 -8.72334063e-01 6.66923702e-01 -1.09677300e-01
-3.19587827e-01 6.94119692e-01 3.15153599e-01 -2.57642478e-01
-3.77482802e-01 -5.27718306e-01 -2.34679967e-01 -1.33809566e+00
2.83158958e-01 -1.30031496e-01 -7.51586929e-02 3.16123962... | [10.194753646850586, -2.1580967903137207] |
e02ee229-77ac-4d59-aaac-6fb8c7864b8b | towards-end-to-end-speaker-diarization-in-the | 2211.01299 | null | https://arxiv.org/abs/2211.01299v1 | https://arxiv.org/pdf/2211.01299v1.pdf | Towards End-to-end Speaker Diarization in the Wild | Speaker diarization algorithms address the "who spoke when" problem in audio recordings. Algorithms trained end-to-end have proven superior to classical modular-cascaded systems in constrained scenarios with a small number of speakers. However, their performance for in-the-wild recordings containing more speakers with ... | ['Jonathan Le Roux', 'Aswin Subramanian', 'François G. Germain', 'Gordon Wichern', 'Zexu Pan'] | 2022-11-02 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 1.01840779e-01 2.70276308e-01 2.37333804e-01 -3.55437249e-01
-1.26300013e+00 -6.25723600e-01 3.62988472e-01 -2.01616913e-01
-2.84486890e-01 -3.68493772e-03 6.36449277e-01 -1.99676737e-01
2.35482737e-01 -9.18171778e-02 -6.17465854e-01 -6.06705606e-01
-1.83864936e-01 6.56374812e-01 -2.03859344e-01 -2.42941543... | [14.578354835510254, 6.020040035247803] |
16cda6ba-d6da-4972-b96d-b9b67652c555 | hdnet-hierarchical-dynamic-network-for-gait | 2211.00312 | null | https://arxiv.org/abs/2211.00312v1 | https://arxiv.org/pdf/2211.00312v1.pdf | HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar | Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveill... | ['Zhiguo Shi', 'Cheng Zhuo', 'Yu Fu', 'Chaojie Gu', 'Kun Shi', 'Yong Wang', 'Yanyan Huang'] | 2022-11-01 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-8.16310346e-02 -7.66585767e-01 1.51040763e-01 -1.92986459e-01
-5.15502505e-02 -2.62248963e-01 2.83973068e-01 -5.77230155e-01
-4.59193498e-01 5.72116673e-01 1.35640483e-02 -9.02903974e-02
-1.93770513e-01 -1.18638146e+00 -5.50025515e-02 -1.09896064e+00
-1.07286982e-01 -1.29840672e-01 2.34256536e-01 -6.81656450... | [6.962425231933594, 0.2936866283416748] |
78186105-39fd-4ca7-9c6a-bea7841d17d0 | lips-don-t-lie-a-generalisable-and-robust | 2012.07657 | null | https://arxiv.org/abs/2012.07657v3 | https://arxiv.org/pdf/2012.07657v3.pdf | Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection | Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operation... | ['Maja Pantic', 'Stavros Petridis', 'Konstantinos Vougioukas', 'Alexandros Haliassos'] | 2020-12-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.pdf | cvpr-2021-1 | ['lipreading'] | ['computer-vision'] | [ 3.36967736e-01 -7.71498680e-02 2.84464974e-02 -1.66646644e-01
-4.88135457e-01 -3.63844246e-01 7.80294776e-01 -2.71737665e-01
-2.13155508e-01 3.07276845e-01 2.15050578e-01 1.63880050e-01
1.91425905e-01 -4.45408881e-01 -8.43235195e-01 -8.04714978e-01
-2.14281991e-01 -8.28111097e-02 2.67345220e-01 -1.34628728... | [12.635919570922852, 1.094822645187378] |
6d2a4193-4c17-4925-8f9a-23e9d1ba2b86 | capsgan-using-dynamic-routing-for-generative | 1806.03968 | null | http://arxiv.org/abs/1806.03968v1 | http://arxiv.org/pdf/1806.03968v1.pdf | CapsGAN: Using Dynamic Routing for Generative Adversarial Networks | In this paper, we propose a novel technique for generating images in the 3D
domain from images with high degree of geometrical transformations. By
coalescing two popular concurrent methods that have seen rapid ascension to the
machine learning zeitgeist in recent years: GANs (Goodfellow et. al.) and
Capsule networks (S... | ['Sal Vivona', 'Raeid Saqur'] | 2018-06-07 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 3.41924518e-01 6.86297357e-01 3.84801954e-01 -4.40976694e-02
-5.12027562e-01 -6.21410668e-01 1.12503421e+00 -7.11172462e-01
-1.94113608e-02 9.59889710e-01 3.27465326e-01 1.32630125e-03
7.82879591e-02 -7.44811773e-01 -8.59912694e-01 -8.92846048e-01
-1.92688927e-02 3.53652179e-01 -2.54479468e-01 -3.29173595... | [11.655219078063965, -0.3829164206981659] |
a5a0122b-a479-4bc6-b4d6-e6940b257cdd | misspelling-correction-with-pre-trained | 2101.03204 | null | https://arxiv.org/abs/2101.03204v1 | https://arxiv.org/pdf/2101.03204v1.pdf | Misspelling Correction with Pre-trained Contextual Language Model | Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete fun... | ['Julia Taylor Rayz', 'Youlim Ko', 'Xiaonan Jing', 'Yifei Hu'] | 2021-01-08 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 4.37801272e-01 -1.69918641e-01 2.06051216e-01 -4.53358144e-01
-3.72756898e-01 -8.43325794e-01 6.14963651e-01 9.60946739e-01
-7.76370049e-01 5.34199715e-01 1.46747038e-01 -6.74889207e-01
1.50392085e-01 -7.96508312e-01 -6.99718177e-01 -1.08547740e-01
2.91881144e-01 3.11250150e-01 2.12699622e-01 -5.38663328... | [10.965306282043457, 10.589861869812012] |
8dddad41-b85e-405b-bef9-afcca441b324 | neurips-22-cross-domain-metadl-competition | 2208.14686 | null | https://arxiv.org/abs/2208.14686v1 | https://arxiv.org/pdf/2208.14686v1.pdf | NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results | We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data,... | ['Wenwu Zhu', 'Xin Wang', 'Ihsan Ullah', 'Isabelle Guyon', 'Chaoyu Guan', 'Sergio Escalera', 'Adrian El Baz', 'Hong Chen', 'Dustin Carrión-Ojeda'] | 2022-08-31 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.20204277e-01 -2.51628458e-02 -1.80581138e-01 -1.49276152e-01
-9.89028990e-01 -1.22997098e-01 5.89424253e-01 1.67071521e-01
-7.96123087e-01 6.83639467e-01 1.77481800e-01 -1.28316283e-01
-4.15491194e-01 -4.88699973e-01 -7.12551773e-01 -5.36715150e-01
-1.53495014e-01 3.89997542e-01 1.22757931e-03 -5.80737293... | [9.965813636779785, 2.8915536403656006] |
1a29cef5-e1ec-4b22-b9bd-de0aeb5e9074 | a-hybrid-ann-snn-architecture-for-low-power | 2303.14176 | null | https://arxiv.org/abs/2303.14176v1 | https://arxiv.org/pdf/2303.14176v1.pdf | A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception | Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificia... | ['Davide Scaramuzza', 'Daniel Gehrig', 'Mathias Gehrig', 'Asude Aydin'] | 2023-03-24 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 4.55362916e-01 1.35300294e-01 6.39029732e-03 4.46437206e-03
-8.03509206e-02 -3.73106956e-01 7.46965170e-01 1.62727326e-01
-7.60927737e-01 9.65532422e-01 -3.53516787e-01 7.26009458e-02
1.49545372e-01 -9.35987592e-01 -7.71403372e-01 -7.66224086e-01
-8.53753090e-02 3.63637090e-01 7.26772547e-01 3.27146947... | [8.221450805664062, 2.4197959899902344] |
df6c1fba-0e9e-484f-ae74-b0466ff2ed6c | decentralized-social-navigation-with-non | 2306.08815 | null | https://arxiv.org/abs/2306.08815v1 | https://arxiv.org/pdf/2306.08815v1.pdf | Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization | This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiating right of way at a corridor intersection. Our contribution is a new realtime bi-level optimization algorithm, in which the top-level opti... | ['Joydeep Biswas', 'Arya Anantula', 'Zayne Sprague', 'Rahul Menon', 'Rohan Chandra'] | 2023-06-15 | null | null | null | null | ['multi-agent-reinforcement-learning', 'navigate', 'social-navigation', 'robot-navigation'] | ['methodology', 'reasoning', 'robots', 'robots'] | [-3.09595555e-01 2.98970371e-01 3.82430494e-01 2.33446173e-02
-5.21402478e-01 -4.42634493e-01 3.96056026e-01 4.19114739e-01
-1.37514913e+00 1.37734044e+00 -4.31907326e-01 -6.53003633e-01
-7.44400203e-01 -1.28326499e+00 -7.12811887e-01 -6.35957837e-01
-1.03574431e+00 1.05590653e+00 6.40867829e-01 -1.17984450... | [4.86391544342041, 1.3636339902877808] |
25506482-36e9-46f6-97af-5a94d56258ec | modeling-the-mitral-valve | 1902.00018 | null | http://arxiv.org/abs/1902.00018v1 | http://arxiv.org/pdf/1902.00018v1.pdf | Modeling the Mitral Valve | This work is concerned with modeling and simulation of the mitral valve, one
of the four valves in the human heart. The valve is composed of leaflets, the
free edges of which are supported by a system of chordae, which themselves are
anchored to the papillary muscles inside the left ventricle. First, we examine
valve a... | ['David M. McQueen', 'Charles S. Peskin', 'Alexander D. Kaiser'] | 2019-01-31 | null | null | null | null | ['stress-strain-relation'] | ['miscellaneous'] | [-1.45808935e-01 4.29816455e-01 1.49285868e-01 5.80632806e-01
4.32255089e-01 -1.00084746e+00 6.11451603e-02 -1.85923576e-01
1.75217204e-02 7.54505038e-01 -1.15233682e-01 -5.60040057e-01
-1.22400790e-01 -4.37235177e-01 -2.11731717e-01 -6.89421296e-01
-3.91162515e-01 4.88970429e-01 5.20933449e-01 -7.87570700... | [13.59971809387207, -2.9662671089172363] |
1d7eb214-3396-4ab2-9f62-e922fced647a | a-novel-counterfactual-method-for-aspect | 2306.11260 | null | https://arxiv.org/abs/2306.11260v2 | https://arxiv.org/pdf/2306.11260v2.pdf | A novel Counterfactual method for aspect-based sentiment analysis | Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which analyze the emotional polarity of the evaluation aspects. However, previous works only focus on the identification of opinion expressions, forget that the diversity of opinion expressions also has great impacts on the ABSA task. T... | ['Zhaoshu Shi', 'Chao Chen', 'Lulu Wen', 'Dongming Wu'] | 2023-06-20 | null | null | null | null | ['sentiment-analysis', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.21021104e-01 -1.57065347e-01 -1.78896144e-01 -6.05375111e-01
-5.32477617e-01 -5.48627019e-01 6.00778580e-01 -2.79089268e-02
-2.42589310e-01 8.20454240e-01 6.73327565e-01 -2.94368595e-01
3.68839771e-01 -6.78174257e-01 -3.58301967e-01 -7.57061362e-01
4.59203005e-01 -1.95803836e-01 -4.55687344e-01 -5.24833739... | [11.484403610229492, 6.679473400115967] |
6f6fa5cf-dfbb-4f5b-94e2-7ed3064d1dbc | self-supervised-video-representation-learning-1 | 1611.06646 | null | http://arxiv.org/abs/1611.06646v4 | http://arxiv.org/pdf/1611.06646v4.pdf | Self-Supervised Video Representation Learning With Odd-One-Out Networks | We propose a new self-supervised CNN pre-training technique based on a novel
auxiliary task called "odd-one-out learning". In this task, the machine is
asked to identify the unrelated or odd element from a set of otherwise related
elements. We apply this technique to self-supervised video representation
learning where ... | ['Hakan Bilen', 'Efstratios Gavves', 'Stephen Gould', 'Basura Fernando'] | 2016-11-21 | self-supervised-video-representation-learning-2 | http://openaccess.thecvf.com/content_cvpr_2017/html/Fernando_Self-Supervised_Video_Representation_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Fernando_Self-Supervised_Video_Representation_CVPR_2017_paper.pdf | cvpr-2017-7 | ['self-supervised-action-recognition', 'odd-one-out'] | ['computer-vision', 'reasoning'] | [ 6.89154685e-01 8.80475193e-02 -6.97264493e-01 -4.71145660e-01
-7.02807009e-01 -3.39788526e-01 2.86326408e-01 -3.02640438e-01
-5.11381447e-01 8.43163133e-01 1.95738867e-01 -7.08134845e-02
2.45541021e-01 -5.33623219e-01 -1.21278965e+00 -6.07926786e-01
-2.37699524e-01 2.94809997e-01 4.79055882e-01 5.60591929... | [8.576009750366211, 0.7519879937171936] |
93c93845-44df-4719-9114-b56a4c8a60d8 | 3dti-net-learn-inner-transform-invariant-3d | 1812.06254 | null | http://arxiv.org/abs/1812.06254v1 | http://arxiv.org/pdf/1812.06254v1.pdf | 3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN | Deep learning on point clouds has made a lot of progress recently. Many point
cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have
shown advantages in accuracy and speed comparing to those using traditional 3D
convolution algorithms. However, nearly all of these methods face a challenge,
sinc... | ['Rendong Ying', 'Jun Wang', 'Guanghua Pan', 'Peilin Liu'] | 2018-12-15 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-5.38801491e-01 -3.49449873e-01 -9.30866823e-02 -3.87828976e-01
-2.29190990e-01 -4.33531612e-01 4.64281261e-01 -1.48318142e-01
-3.44224185e-01 -1.23273328e-01 -4.92454112e-01 -5.48988044e-01
-4.47412841e-02 -1.01834297e+00 -9.33800697e-01 -3.80231261e-01
-1.24678582e-01 6.52418792e-01 2.44274095e-01 -1.96950093... | [7.941301345825195, -3.6281542778015137] |
c58e182d-f95b-44da-aa28-a40bfe58a57d | deep-learning-based-automated-covid-19 | 2111.11191 | null | https://arxiv.org/abs/2111.11191v5 | https://arxiv.org/pdf/2111.11191v5.pdf | Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images | A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. The method aims at increasing the predictive performance for COVID-19 diagnosis while more complex model architecture. Firstly, the CNN model includes four similar convolutional l... | ['Devrim Unay', 'Kenan Morani'] | 2021-11-22 | null | null | null | null | ['3d-classification', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 3.22385550e-01 5.39369643e-01 4.40941192e-02 -5.84072471e-01
-7.89220273e-01 -2.41959929e-01 1.81652099e-01 2.73745716e-01
-9.49028134e-01 4.51688975e-01 -1.27805948e-01 -5.22807360e-01
-4.66086894e-01 -7.53843904e-01 -4.20813084e-01 -8.99518847e-01
-5.21457493e-01 6.58173263e-01 6.51196659e-01 2.03256652... | [14.924344062805176, -2.2853152751922607] |
7793d443-6d58-49a1-b0f7-903b8959a61f | music-mixing-style-transfer-a-contrastive | 2211.02247 | null | https://arxiv.org/abs/2211.02247v3 | https://arxiv.org/pdf/2211.02247v3.pdf | Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects | We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are ... | ['Marco A. Martínez-Ramírez', 'Yuki Mitsufuji', 'Kyogu Lee', 'Stefan Uhlich', 'Wei-Hsiang Liao', 'Junghyun Koo'] | 2022-11-04 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 5.46035409e-01 -3.36692303e-01 2.77043670e-01 -2.46530503e-01
-1.17509842e+00 -8.60421658e-01 3.68461698e-01 -4.08767700e-01
-1.66423097e-01 5.61787963e-01 4.56021845e-01 2.90647209e-01
-3.24692488e-01 -4.84184355e-01 -7.76953459e-01 -7.80982852e-01
2.65906811e-01 1.85981870e-01 -3.88903975e-01 -2.20277861... | [15.54665470123291, 5.692589282989502] |
78cbe849-8f2a-464f-ac84-5cedd6dfa75e | id2image-leakage-of-non-id-information-into | 2304.07522 | null | https://arxiv.org/abs/2304.07522v1 | https://arxiv.org/pdf/2304.07522v1.pdf | ID2image: Leakage of non-ID information into face descriptors and inversion from descriptors to images | Embedding a face image to a descriptor vector using a deep CNN is a widely used technique in face recognition. Via several possible training strategies, such embeddings are supposed to capture only identity information. Information about the environment (such as background and lighting) or changeable aspects of the fac... | ['Patrik Huber', 'William A. P. Smith', 'Mingrui Li'] | 2023-04-15 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 1.47769347e-01 2.01824903e-01 2.34993115e-01 -7.01109290e-01
-1.10841170e-02 -7.79193759e-01 9.03715730e-01 -4.42170799e-01
-2.43951261e-01 6.93435073e-01 8.31037089e-02 2.01391876e-01
-4.05849852e-02 -7.78327346e-01 -8.88615370e-01 -9.16955113e-01
-1.20944087e-03 5.84904313e-01 -1.11672908e-01 -3.70145738... | [12.932302474975586, 0.11277931183576584] |
f58276a1-3e49-4a47-af8c-acb30c28947a | missing-data-reconstruction-in-remote-sensing | 1802.08369 | null | http://arxiv.org/abs/1802.08369v1 | http://arxiv.org/pdf/1802.08369v1.pdf | Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network | Because of the internal malfunction of satellite sensors and poor atmospheric
conditions such as thick cloud, the acquired remote sensing data often suffer
from missing information, i.e., the data usability is greatly reduced. In this
paper, a novel method of missing information reconstruction in remote sensing
images ... | ['Xinghua Li', 'Yancong Wei', 'Chao Zeng', 'Qiang Zhang', 'Qiangqiang Yuan'] | 2018-02-23 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.97133797e-01 -6.84529543e-01 -8.49672407e-02 -5.93779922e-01
-8.86671841e-01 -3.26271266e-01 5.21560162e-02 -3.29850107e-01
-3.30593467e-01 8.84885371e-01 8.22452977e-02 -4.90142822e-01
-4.05040801e-01 -9.70104396e-01 -6.05084419e-01 -8.68441582e-01
6.28472790e-02 -8.35289061e-02 -1.42823428e-01 -4.70862865... | [9.81849193572998, -1.756570816040039] |
83903a0a-f1d9-443c-8c51-13afd775adaa | weighted-structure-tensor-total-variation-for | 2306.10482 | null | https://arxiv.org/abs/2306.10482v1 | https://arxiv.org/pdf/2306.10482v1.pdf | Weighted structure tensor total variation for image denoising | Based on the variational framework of the image denoising problem, we introduce a novel image denoising regularizer that combines anisotropic total variation model (ATV) and structure tensor total variation model (STV) in this paper. The model can effectively capture the first-order information of the image and maintai... | ['Jingya Changa', 'Xiuhan Sheng'] | 2023-06-18 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 2.33645499e-01 -5.14950156e-01 4.57420290e-01 -1.50727760e-02
-4.28751916e-01 -2.59006768e-01 3.22061092e-01 -3.71165723e-01
-3.97721797e-01 4.67224181e-01 2.22948566e-01 6.02465682e-02
-5.31214237e-01 -6.16348982e-01 -3.97447646e-01 -1.37434506e+00
2.38928944e-01 -3.80788237e-01 1.38019994e-01 -4.76512641... | [11.412068367004395, -2.488023281097412] |
ef20dc30-2905-46dd-b1e1-88c01fdf3f3f | translating-implicit-discourse-connectives | null | null | https://aclanthology.org/W17-4812 | https://aclanthology.org/W17-4812.pdf | Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment | Implicit discourse connectives and relations are distributed more widely in Chinese texts, when translating into English, such connectives are usually translated explicitly. Towards Chinese-English MT, in this paper we describe cross-lingual annotation and alignment of dis-course connectives in a parallel corpus, descr... | ['Philippe Langlais', 'Hongzheng Li', 'Yaohong Jin'] | 2017-09-01 | null | null | null | ws-2017-9 | ['implicit-relations'] | ['natural-language-processing'] | [ 2.65887994e-02 7.74105430e-01 -7.18778670e-01 -5.07790089e-01
-7.25697577e-01 -9.02830422e-01 7.94002354e-01 1.55213848e-01
-5.16595483e-01 1.31978905e+00 7.20887363e-01 -8.95535290e-01
2.66849339e-01 -3.95012647e-01 -2.12631121e-01 -2.14274287e-01
-4.60585468e-02 7.65582025e-01 1.73339516e-01 -6.27869129... | [10.755189895629883, 9.317850112915039] |
133d20ae-29b4-4258-bf26-fe27fdf92cf7 | towards-nir-vis-masked-face-recognition | 2104.06761 | null | https://arxiv.org/abs/2104.06761v1 | https://arxiv.org/pdf/2104.06761v1.pdf | Towards NIR-VIS Masked Face Recognition | Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different modalities. Existing deep learning based methods have made remarkable progress in NIR-VIS face recognition, while it encounters certain new... | ['Tao Mei', 'Dan Zeng', 'Yinglu Liu', 'Hailin Shi', 'Hang Du'] | 2021-04-14 | null | null | null | null | ['heterogeneous-face-recognition', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.57762665e-01 -1.24584131e-01 9.39582586e-02 -2.63261676e-01
-5.61661065e-01 -4.12134320e-01 3.57546389e-01 -7.94304967e-01
8.14516023e-02 6.16842151e-01 6.76678941e-02 5.83662726e-02
-1.72076494e-01 -5.57271779e-01 -5.22754669e-01 -1.26204908e+00
4.15954858e-01 5.24985492e-02 -6.27580404e-01 -3.21596533... | [13.133201599121094, 0.47284314036369324] |
e17d4f84-ad89-47b2-9285-e53fc00e775f | a-general-class-of-transfer-learning | 2006.13228 | null | https://arxiv.org/abs/2006.13228v2 | https://arxiv.org/pdf/2006.13228v2.pdf | A General Class of Transfer Learning Regression without Implementation Cost | We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By ... | ['Shunya Minami', 'Stephen Wu', 'Kenji Fukumizu', 'Song Liu', 'Ryo Yoshida'] | 2020-06-23 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.33776575e-01 -6.08060956e-02 -4.47429448e-01 -5.37980855e-01
-8.39749157e-01 -2.48217821e-01 5.28727710e-01 -3.14200193e-01
-4.87533152e-01 8.43027294e-01 -3.08204025e-01 -1.89939156e-01
-1.72191069e-01 -8.34068537e-01 -8.13625097e-01 -8.40607345e-01
4.86635566e-01 4.04646724e-01 4.29582953e-01 -1.66691497... | [9.799644470214844, 3.218618154525757] |
1ec894d1-7c43-4714-8d88-cf221a32790c | empirical-assessment-of-end-to-end-iris | 2303.12742 | null | https://arxiv.org/abs/2303.12742v1 | https://arxiv.org/pdf/2303.12742v1.pdf | Empirical Assessment of End-to-End Iris Recognition System Capacity | Iris is an established modality in biometric recognition applications including consumer electronics, e-commerce, border security, forensics, and de-duplication of identity at a national scale. In light of the expanding usage of biometric recognition, identity clash (when templates from two different people match) is a... | ['Stephanie Schuckers', 'Joseph Skufca', 'Matthew Valenti', 'Natalia Schmid', 'Veeru Talreja', 'Richard Plesh', 'Priyanka Das'] | 2023-03-20 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 3.77961755e-01 -3.54654819e-01 -8.02007020e-02 -2.27910534e-01
-5.19971013e-01 -9.48682249e-01 2.61055380e-01 5.83980940e-02
-4.79246140e-01 2.58306116e-01 -7.89790228e-02 -7.38747537e-01
-3.35181057e-01 -3.65533054e-01 -2.02641264e-01 -4.78441983e-01
1.97094530e-01 1.73958272e-01 -6.33075893e-01 2.85506785... | [3.7514760494232178, -3.6228601932525635] |
fd67a9ea-25e3-4e21-8602-e973247a91cb | expectation-maximization-for-learning | 1411.1088 | null | http://arxiv.org/abs/1411.1088v1 | http://arxiv.org/pdf/1411.1088v1.pdf | Expectation-Maximization for Learning Determinantal Point Processes | A determinantal point process (DPP) is a probabilistic model of set diversity
compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP
to a given task, we would like to learn the entries of its kernel matrix by
maximizing the log-likelihood of the available data. However, log-likelihood is
non-co... | ['Ben Taskar', 'Emily Fox', 'Jennifer Gillenwater', 'Alex Kulesza'] | 2014-11-04 | expectation-maximization-for-learning-1 | http://papers.nips.cc/paper/5564-expectation-maximization-for-learning-determinantal-point-processes | http://papers.nips.cc/paper/5564-expectation-maximization-for-learning-determinantal-point-processes.pdf | neurips-2014-12 | ['product-recommendation'] | ['miscellaneous'] | [ 1.14132695e-01 -4.73904423e-02 -3.55513811e-01 -2.94837862e-01
-8.82190287e-01 -8.49598587e-01 1.79692388e-01 9.40093845e-02
-3.38187277e-01 4.14367616e-01 1.12130143e-01 -4.45326120e-01
-4.22467172e-01 -5.40110290e-01 -8.30209613e-01 -7.55358219e-01
-3.24129939e-01 6.08576238e-01 3.03109940e-02 7.61136338... | [7.434115886688232, 4.377988338470459] |
06fb3263-e8cd-4aaa-bd99-6a2a6be97b40 | importance-of-methodological-choices-in-data | 2302.10672 | null | https://arxiv.org/abs/2302.10672v1 | https://arxiv.org/pdf/2302.10672v1.pdf | Importance of methodological choices in data manipulation for validating epileptic seizure detection models | Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients. Despite advances in machine learning and IoT, small, nonstigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not... | ['David Atienza', 'Tomas Teijeiro', 'Una Pale'] | 2023-02-21 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 3.76297742e-01 -3.69925290e-01 -3.81149262e-01 -4.86759156e-01
-7.30991006e-01 -4.33759540e-01 9.95602608e-02 5.00518620e-01
-4.71295029e-01 8.71500194e-01 3.47092927e-01 -3.35286498e-01
-5.49606144e-01 -2.08557785e-01 -1.86436757e-01 -7.00284958e-01
-1.50009871e-01 6.21276498e-01 -2.05311850e-01 3.21215183... | [13.240202903747559, 3.4949951171875] |
ee956122-441a-42c9-850f-5d290a63e870 | graph-neural-networks-in-tensorflow-and-keras | 2006.12138 | null | https://arxiv.org/abs/2006.12138v1 | https://arxiv.org/pdf/2006.12138v1.pdf | Graph Neural Networks in TensorFlow and Keras with Spektral | In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processin... | ['Cesare Alippi', 'Daniele Grattarola'] | 2020-06-22 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-7.20103383e-01 4.54313159e-02 -1.06280863e-01 -4.71402168e-01
2.01082781e-01 -4.44169849e-01 3.69277388e-01 5.90365589e-01
-1.69884458e-01 2.77201056e-01 -1.03384636e-01 -7.94766068e-01
-6.07704036e-02 -1.34646583e+00 -4.32138145e-01 -5.55070937e-01
-7.15424478e-01 1.27543017e-01 -1.07127823e-01 -1.87403083... | [6.954776287078857, 5.829831123352051] |
30784565-e58a-4e54-8fa1-9a9844e62f64 | learning-to-detect-good-keypoints-to-match | 2212.09589 | null | https://arxiv.org/abs/2212.09589v1 | https://arxiv.org/pdf/2212.09589v1.pdf | Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images | We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor, as a ground-truth to train a convol... | ['Erickson R. Nascimento', 'Renato Martins', 'Felipe Cadar', 'Guilherme Potje', 'Welerson Melo'] | 2022-12-13 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [-5.70260808e-02 4.80738208e-02 -2.58803606e-01 -2.43057683e-01
-1.23993671e+00 -7.58700132e-01 8.31597924e-01 1.54044822e-01
-6.29731715e-01 8.57561678e-02 -1.20734714e-01 2.74445355e-01
-3.36004458e-02 -5.63392043e-01 -1.39752436e+00 -4.89407301e-01
7.38682821e-02 7.67266750e-01 5.18176615e-01 -1.42780408... | [8.069181442260742, -2.136471748352051] |
69338d51-fc2d-4335-a804-9c2aa2028214 | din-sql-decomposed-in-context-learning-of | 2304.11015 | null | https://arxiv.org/abs/2304.11015v2 | https://arxiv.org/pdf/2304.11015v2.pdf | DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction | We study the problem of decomposing a complex text-to-sql task into smaller sub-tasks and how such a decomposition can significantly improve the performance of Large Language Models (LLMs) in the reasoning process. There is currently a significant gap between the performance of fine-tuned models and prompting approache... | ['Davood Rafiei', 'Mohammadreza Pourreza'] | 2023-04-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-1.80201873e-01 3.99137765e-01 -3.63661110e-01 -5.08794248e-01
-1.27302194e+00 -7.88302839e-01 6.36235952e-01 4.23271328e-01
-1.48825392e-01 2.80623019e-01 5.35541713e-01 -8.23376060e-01
-1.50968045e-01 -9.30633664e-01 -9.52781439e-01 2.79208750e-01
5.37127815e-02 1.05978203e+00 6.43892646e-01 -3.41103524... | [9.729874610900879, 7.871181011199951] |
f136b69c-291c-4d11-a6bb-205f60cde737 | evolution-leads-to-a-diversity-of-motion | 1804.02508 | null | http://arxiv.org/abs/1804.02508v2 | http://arxiv.org/pdf/1804.02508v2.pdf | Evolution leads to a diversity of motion-detection neuronal circuits | A central goal of evolutionary biology is to explain the origins and
distribution of diversity across life. Beyond species or genetic diversity, we
also observe diversity in the circuits (genetic or otherwise) underlying
complex functional traits. However, while the theory behind the origins and
maintenance of genetic ... | ['Christoph Adami', 'Ali Tehrani-Saleh', 'Thomas LaBar'] | 2018-04-07 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.75452220e-01 -1.65736362e-01 2.63596237e-01 -1.63102567e-01
4.53671396e-01 -1.00415599e+00 4.73115146e-01 -6.18214309e-02
-4.14145410e-01 5.76144755e-01 1.81985363e-01 -6.09083831e-01
-3.49320918e-01 -6.83364272e-01 -6.31769896e-01 -6.95063174e-01
-1.63188353e-01 7.39782955e-03 4.51097459e-01 -5.45073986... | [5.665654182434082, 4.112533092498779] |
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