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19772a8f-f17a-4555-a74c-30fb364a6723 | commu-dataset-for-combinatorial-music | 2211.09385 | null | https://arxiv.org/abs/2211.09385v1 | https://arxiv.org/pdf/2211.09385v1.pdf | ComMU: Dataset for Combinatorial Music Generation | Commercial adoption of automatic music composition requires the capability of generating diverse and high-quality music suitable for the desired context (e.g., music for romantic movies, action games, restaurants, etc.). In this paper, we introduce combinatorial music generation, a new task to create varying background... | ['Seon Joo Kim', 'Sharang Han', 'Kwanho Park', 'Hyeonchan Hwang', 'Minjoo Ki', 'Hyolim Kang', 'Taehyun Kim', 'Lee Hyun'] | 2022-11-17 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.74219683e-01 -2.98854679e-01 1.60961188e-02 4.59161662e-02
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-1.07134618e-01 4.78622258e-01 1.03147969e-01 -4.53803778... | [15.93899917602539, 5.4590020179748535] |
743b5622-c815-4e32-a944-d3e3c5738187 | qualitative-and-quantitative-analysis-of-1 | null | null | https://openreview.net/forum?id=BxSvC2DvNH9 | https://openreview.net/pdf?id=BxSvC2DvNH9 | Qualitative and Quantitative Analysis of Diversity in Cross-document Coreference Resolution Datasets | Established cross-document coreference resolution (CDCR) datasets contain manually annotated event-centric mentions of events and entities that form coreference chains with identity relations. In this paper, we qualitatively and quantitatively compare the annotation schemes of ECB+, a CDCR dataset with identity corefer... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [-2.77583450e-01 4.59843516e-01 -6.16118073e-01 -1.30341113e-01
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3.93245369e-02 1.17744124e+00 1.41793385e-01 -5.68829238... | [9.318414688110352, 9.532918930053711] |
3167450d-3b3a-4f00-9139-9019c0bf2cfa | exploring-continual-learning-for-code | 2307.02435 | null | https://arxiv.org/abs/2307.02435v1 | https://arxiv.org/pdf/2307.02435v1.pdf | Exploring Continual Learning for Code Generation Models | Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance. However, libraries are upgraded or deprecated very frequently and re-training large-scale language models is computationally expensive. Therefore, Continual Learning (CL) is an important aspect that remains underexplored i... | ['Bing Xiang', 'Mohit Bansal', 'Murali Krishna Ramanathan', 'Ramesh Nallapati', 'Parminder Bhatia', 'Xiaofei Ma', 'Ming Tan', 'Dejiao Zhang', 'Xiaopeng Li', 'Hantian Ding', 'Qing Sun', 'Prateek Yadav'] | 2023-07-05 | null | null | null | null | ['code-generation', 'continual-learning'] | ['computer-code', 'methodology'] | [-7.66206160e-02 -1.83472320e-01 -2.83114046e-01 -1.99087203e-01
-9.65653956e-01 -6.15505219e-01 5.66564977e-01 4.24608141e-02
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2.26812765e-01 4.63869143e-03 2.54230440e-01 2.88202595... | [7.72246789932251, 7.894862651824951] |
238adb35-0099-4455-ae7a-24f0caada33a | epasad-ellipsoid-decision-boundary-based | 2204.04154 | null | https://arxiv.org/abs/2204.04154v1 | https://arxiv.org/pdf/2204.04154v1.pdf | EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector | Due to the importance of Critical Infrastructure (CI) in a nation's economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems (CPS) such as power grids, water, and sewage treatment facilities, oil and gas pipelines, etc. In recent times, these syste... | ['Sandeep Kumar Shukla', 'Saurabh Kumar', 'Rachit Agarwal', 'Vikas Maurya'] | 2022-04-08 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.16035128e-01 -2.62414306e-01 1.84633955e-01 4.94768232e-01
2.76940409e-02 -9.20669436e-01 8.74077201e-01 4.18500513e-01
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-6.55345738e-01 1.01524957e-01 7.81089365e-01 -1.19534738... | [5.3653082847595215, 7.189121723175049] |
75e190ec-60db-48dc-8d1a-42e9f0bd747f | actor-context-actor-relation-network-for | 2006.07976 | null | https://arxiv.org/abs/2006.07976v3 | https://arxiv.org/pdf/2006.07976v3.pdf | Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization | Localizing persons and recognizing their actions from videos is a challenging task towards high-level video understanding. Recent advances have been achieved by modeling direct pairwise relations between entities. In this paper, we take one step further, not only model direct relations between pairs but also take into ... | ['Yu Liu', 'Mike Zheng Shou', 'Siyu Chen', 'Junting Pan', 'Hongsheng Li', 'Jing Shao'] | 2020-06-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Pan_Actor-Context-Actor_Relation_Network_for_Spatio-Temporal_Action_Localization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Pan_Actor-Context-Actor_Relation_Network_for_Spatio-Temporal_Action_Localization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 6.30521923e-02 1.32756799e-01 -3.44375461e-01 -3.79349351e-01
-2.29969800e-01 -4.61411119e-01 1.04600453e+00 1.37298658e-01
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-5.82440376e-01 2.77061969e-01 3.13857079e-01 -1.70486480... | [8.36176872253418, 0.6340634822845459] |
6be627fd-48d9-473f-822e-d1b384ad9b0b | hierarchical-reinforcement-learning-for-open | 1909.07547 | null | https://arxiv.org/abs/1909.07547v3 | https://arxiv.org/pdf/1909.07547v3.pdf | Hierarchical Reinforcement Learning for Open-Domain Dialog | Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text. Reinforcement Lear... | ['Natasha Jaques', 'Abdelrhman Saleh', 'Rosalind Picard', 'Judy Hanwen Shen', 'Asma Ghandeharioun'] | 2019-09-17 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-6.10762369e-03 6.64241731e-01 -1.84472650e-01 -4.87356961e-01
-1.08571494e+00 -8.02915096e-01 7.97326922e-01 -1.41738564e-01
-2.70525545e-01 1.04644251e+00 7.47455716e-01 -3.51558059e-01
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1.06328391e-01 8.56774628e-01 4.63671377e-03 -7.08291888... | [12.82711124420166, 8.106203079223633] |
dcdcedff-ab0e-46d3-8f8b-fc4a8927bf5f | the-portiloop-a-deep-learning-based-open | 2107.13473 | null | https://arxiv.org/abs/2107.13473v3 | https://arxiv.org/pdf/2107.13473v3.pdf | The Portiloop: a deep learning-based open science tool for closed-loop brain stimulation | Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neu... | ['Emily B. J. Coffey', 'Milo Sobral', "Xavier L'Heureux", 'Giovanni Beltrame', 'Hugo R. Jourde', 'Yann Bouteiller', 'Nicolas Valenchon'] | 2021-07-28 | null | null | null | null | ['spindle-detection'] | ['medical'] | [ 7.82211646e-02 -8.43652897e-03 1.41968504e-01 -2.31651604e-01
-4.43374366e-01 -5.90473831e-01 1.40198022e-01 2.07279567e-02
-3.59559268e-01 8.31108749e-01 2.19340697e-01 -3.47138166e-01
-4.33471471e-01 -1.31252468e-01 -5.99325359e-01 -6.06095314e-01
-3.73277903e-01 6.19087756e-01 2.09300563e-01 -4.41894680... | [13.31008529663086, 3.44884991645813] |
d6a52b4a-9c39-403c-b228-dbc0031be850 | clotho-an-audio-captioning-dataset | 1910.09387 | null | https://arxiv.org/abs/1910.09387v1 | https://arxiv.org/pdf/1910.09387v1.pdf | Clotho: An Audio Captioning Dataset | Audio captioning is the novel task of general audio content description using free text. It is an intermodal translation task (not speech-to-text), where a system accepts as an input an audio signal and outputs the textual description (i.e. the caption) of that signal. In this paper we present Clotho, a dataset for aud... | ['Konstantinos Drossos', 'Tuomas Virtanen', 'Samuel Lipping'] | 2019-10-21 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 2.16287404e-01 2.50215232e-01 4.88281772e-02 -3.12909573e-01
-1.64597714e+00 -1.11879456e+00 4.02047127e-01 3.48157971e-03
-2.25659981e-01 7.53132641e-01 8.89369369e-01 -6.01823367e-02
3.65771770e-01 7.14516826e-03 -7.61528373e-01 -3.38376164e-01
1.56984068e-02 7.06459343e-01 -1.14879005e-01 -1.96622148... | [15.288493156433105, 4.948275566101074] |
933c4c6c-5e31-4729-8fe7-fdc634079fe0 | informer-beyond-efficient-transformer-for | 2012.07436 | null | https://arxiv.org/abs/2012.07436v3 | https://arxiv.org/pdf/2012.07436v3.pdf | Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting | Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input effici... | ['Wancai Zhang', 'Hui Xiong', 'JianXin Li', 'Shuai Zhang', 'Jieqi Peng', 'Shanghang Zhang', 'Haoyi Zhou'] | 2020-12-14 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 2.54283458e-01 -1.90895706e-01 -1.15490258e-01 -5.50363481e-01
-6.50114536e-01 -4.69510883e-01 2.32625455e-01 -2.63755083e-01
-4.16552685e-02 5.88768065e-01 1.86306536e-01 -6.04639888e-01
1.40378088e-01 -7.61700869e-01 -8.48846316e-01 -6.50367141e-01
-1.62865028e-01 4.39125061e-01 3.61023098e-02 -3.18370819... | [7.122158527374268, 2.910532236099243] |
5a25517d-7ee5-4af5-a00f-b7a18c9eedb8 | video-instance-segmentation-in-an-open-world | 2304.01200 | null | https://arxiv.org/abs/2304.01200v1 | https://arxiv.org/pdf/2304.01200v1.pdf | Video Instance Segmentation in an Open-World | Existing video instance segmentation (VIS) approaches generally follow a closed-world assumption, where only seen category instances are identified and spatio-temporally segmented at inference. Open-world formulation relaxes the close-world static-learning assumption as follows: (a) first, it distinguishes a set of kno... | ['Fahad Shahbaz Khan', 'Mubarak Shah', 'Jorma Laaksonen', 'Salman Khan', 'Rao Muhammad Anwer', 'Hisham Cholakkal', 'Sanath Narayan', 'Omkar Thawakar'] | 2023-04-03 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.56199235e-01 1.79903790e-01 -4.35047537e-01 -2.24806458e-01
-1.02112317e+00 -7.98537731e-01 6.97958887e-01 -9.20692831e-02
-4.61929232e-01 7.25840271e-01 -7.09975883e-02 -8.48780721e-02
5.71790291e-03 -6.56484604e-01 -1.09993923e+00 -8.06286693e-01
-1.37398988e-01 4.79392141e-01 7.85256803e-01 2.87165552... | [9.264726638793945, 0.06481468677520752] |
bb212395-8543-40fe-951c-637ddf9fecda | multi-step-greedy-policies-in-model-free-deep | null | null | https://openreview.net/forum?id=r1l7E1HFPH | https://openreview.net/pdf?id=r1l7E1HFPH | Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning | Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go). In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynam... | ['Mohammad Ghavamzadeh', 'Manan Tomar', 'Yonathan Efroni'] | 2019-09-25 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.16327129e-01 3.62153918e-01 -3.60204160e-01 2.87571490e-01
-6.47050083e-01 -5.05298197e-01 4.75806981e-01 1.92219645e-01
-8.32740247e-01 1.33930433e+00 -2.64917284e-01 -6.51820898e-01
-4.93375480e-01 -9.00757015e-01 -5.77047050e-01 -7.88446665e-01
-3.12503815e-01 7.35208035e-01 1.84857577e-01 -5.24643958... | [4.191315650939941, 2.1818580627441406] |
1f8b9a67-8589-4164-afea-2c14e73b8e62 | svcnet-scribble-based-video-colorization | 2303.11591 | null | https://arxiv.org/abs/2303.11591v1 | https://arxiv.org/pdf/2303.11591v1.pdf | SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation | In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, an... | ['Mengyang Liu', 'Yujia Zhang', 'Pengfei Xian', 'Wing-Yin Yu', 'Xuehui Wang', 'Kangcheng Liu', 'Lai-Man Po', 'Yuzhi Zhao'] | 2023-03-21 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 6.12022402e-03 -6.36072934e-01 -1.24884374e-01 -1.93356931e-01
-1.58335999e-01 -5.47008634e-01 2.20846925e-02 -5.84361970e-01
-4.14608240e-01 5.63106954e-01 -2.12403201e-02 -2.15862423e-01
2.07553953e-01 -7.31094956e-01 -8.69144082e-01 -7.29499936e-01
2.54605174e-01 -1.95752427e-01 5.12836218e-01 -6.29352406... | [11.113011360168457, -1.2280457019805908] |
84b4b3e9-07b1-4a30-be23-3c367e725259 | a-neural-template-matching-method-to-detect | 2209.11791 | null | https://arxiv.org/abs/2209.11791v1 | https://arxiv.org/pdf/2209.11791v1.pdf | A Neural Template Matching Method to Detect Knee Joint Areas | In this paper, new methods are considered to detect knee joint areas in bilateral PA fixed flexion knee X-ray images. The methods are of template matching type where the distance criterion is based on the negative normalized cross-correlation. The manual annotations are made on only one side of a single bilateral image... | ['Juha Tiirola'] | 2022-09-23 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.43616036e-01 3.75525773e-01 -4.12616253e-01 -2.73147196e-01
-8.07154953e-01 -2.11032573e-02 1.95406973e-01 8.51116404e-02
-7.04329729e-01 7.55251169e-01 -1.25781223e-01 3.03818852e-01
-3.46747249e-01 -3.91533017e-01 -5.90656042e-01 -6.07507527e-01
-2.84073591e-01 7.77670383e-01 7.81951547e-01 -2.90849715... | [13.952411651611328, -2.6635961532592773] |
3a646451-6322-4d1b-a478-c50eec61879a | egocol-egocentric-camera-pose-estimation-for | 2306.16606 | null | https://arxiv.org/abs/2306.16606v1 | https://arxiv.org/pdf/2306.16606v1.pdf | EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023 | We present EgoCOL, an egocentric camera pose estimation method for open-world 3D object localization. Our method leverages sparse camera pose reconstructions in a two-fold manner, video and scan independently, to estimate the camera pose of egocentric frames in 3D renders with high recall and precision. We extensively ... | ['Pablo Arbeláez', 'Kevis-Kokitsi Maninis', 'Jordi Pont-Tuset', 'Maria Escobar', 'Cristhian Forigua'] | 2023-06-29 | null | null | null | null | ['pose-estimation', 'object-localization'] | ['computer-vision', 'computer-vision'] | [-8.30167234e-01 -2.82019854e-01 -3.79439533e-01 -1.26918137e-01
-1.24096143e+00 -1.37329006e+00 6.31201506e-01 -4.28853244e-01
-2.89077163e-01 -3.21980268e-02 5.32673180e-01 1.30395219e-01
4.33010757e-01 -1.65563539e-01 -9.62094665e-01 -3.41933072e-01
1.14528142e-01 5.58916509e-01 1.81654662e-01 5.25306463... | [7.534502983093262, -2.390181303024292] |
159aeb3a-8312-4a09-ba8b-e110bf6c524e | towards-in-context-scene-understanding | 2306.01667 | null | https://arxiv.org/abs/2306.01667v1 | https://arxiv.org/pdf/2306.01667v1.pdf | Towards In-context Scene Understanding | In-context learning$\unicode{x2013}$the ability to configure a model's behavior with different prompts$\unicode{x2013}$has revolutionized the field of natural language processing, alleviating the need for task-specific models and paving the way for generalist models capable of assisting with any query. Computer vision,... | ['Olivier J. Hénaff', 'Relja Arandjelović', 'Nikhil Parthasarathy', 'David Steiner', 'Ivana Balažević'] | 2023-06-02 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 4.18644667e-01 3.08324099e-01 2.63982415e-01 -5.98958373e-01
-5.96308053e-01 -6.49894238e-01 5.61343491e-01 2.01184094e-01
-9.47888494e-01 4.03648406e-01 -3.33270550e-01 -4.80268598e-01
-4.04641390e-01 -8.29630852e-01 -7.41071284e-01 -6.58975244e-01
6.96026310e-02 5.98422527e-01 4.20579642e-01 -4.01569664... | [9.91576099395752, 1.4951705932617188] |
77da8f60-bd56-43ac-a5db-adc3040e220d | supervised-learning-of-the-next-best-view-for | 1905.05833 | null | https://arxiv.org/abs/1905.05833v1 | https://arxiv.org/pdf/1905.05833v1.pdf | Supervised Learning of the Next-Best-View for 3D Object Reconstruction | Motivated by the advances in 3D sensing technology and the spreading of low-cost robotic platforms, 3D object reconstruction has become a common task in many areas. Nevertheless, the selection of the optimal sensor pose that maximizes the reconstructed surface is a problem that remains open. It is known in the literatu... | ['Hind Taud', 'J. Irving Vasquez-Gomez', 'Carolina Reta', 'Miguel Mendoza', 'Luis Enrique Sucar'] | 2019-05-14 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.83552080e-01 2.83165306e-01 2.22510491e-02 -4.42507803e-01
-5.96895099e-01 -4.67587084e-01 4.21111852e-01 -3.27802777e-01
-2.26041913e-01 2.74871439e-01 1.27795249e-01 -2.33941749e-02
-3.35706472e-01 -8.83961976e-01 -1.03726709e+00 -4.18428510e-01
1.11427121e-01 7.50731111e-01 3.34694207e-01 -1.50490448... | [8.153594970703125, -2.8240268230438232] |
abc85d61-35a2-450a-aa2b-9e324ebc98d4 | temporal-dynamic-quantization-for-diffusion | 2306.02316 | null | https://arxiv.org/abs/2306.02316v1 | https://arxiv.org/pdf/2306.02316v1.pdf | Temporal Dynamic Quantization for Diffusion Models | The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility. However, high storage and computation demands, resulting from the model size and iterative generation, hinder its use on mobile devices. Existing quantization techniques struggle to maintain pe... | ['Eunhyeok Park', 'HyungJun Kim', 'Daehyun Ahn', 'Jungwon Lee', 'Junhyuk So'] | 2023-06-04 | null | null | null | null | ['quantization'] | ['methodology'] | [ 3.08526456e-01 -3.42515439e-01 -3.62532705e-01 -3.07378203e-01
-8.70038390e-01 -5.38832903e-01 7.02785313e-01 8.79333466e-02
-4.71419573e-01 6.51561141e-01 -2.72823013e-02 -3.19792628e-01
-4.45216745e-02 -7.98502564e-01 -3.45967382e-01 -8.18815589e-01
-1.20128281e-01 3.36858809e-01 4.38348681e-01 8.47952142... | [11.125394821166992, -0.42279669642448425] |
86233e2f-6b6c-43a2-a2ba-8bc1ebe80926 | the-usfd-spoken-language-translation-system | 1509.03870 | null | http://arxiv.org/abs/1509.03870v1 | http://arxiv.org/pdf/1509.03870v1.pdf | The USFD Spoken Language Translation System for IWSLT 2014 | The University of Sheffield (USFD) participated in the International Workshop
for Spoken Language Translation (IWSLT) in 2014. In this paper, we will
introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is
achieved by two multi-pass deep neural network systems with adaptation and
rescoring techni... | ['Ghada Alharbi', 'Mortaza Doulaty', 'Lucia Specia', 'Raymond W. M. Ng', 'Oscar Saz', 'Kashif Shah', 'Rama Doddipatla', 'Wilker Aziz', 'Thomas Hain', 'Madina Hasan'] | 2015-09-13 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.98607939e-01 2.21377864e-01 -1.10509964e-02 -5.07865787e-01
-1.62079298e+00 -5.71822166e-01 8.68823826e-01 -4.50823605e-02
-7.35880077e-01 9.21052814e-01 5.18424273e-01 -8.03744316e-01
1.06157601e-01 -2.61656046e-01 -6.24869943e-01 -2.97965884e-01
4.07039523e-01 1.02407217e+00 -1.23354718e-01 -4.19832557... | [14.451739311218262, 7.136730194091797] |
b1a6b2c8-a97c-495d-8b67-828d53e5f769 | learning-cross-modal-context-graph-for-visual | null | null | https://arxiv.org/pdf/1911.09042.pdf | https://arxiv.org/pdf/1911.09042.pdf | Learning Cross-modal Context Graph for Visual Grounding | Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding entities, strong context effect and the resulting semantic ambiguities. Prior works typically focus on learning representations of individual p... | ['Yongfei Liu; Bo Wan; Xiaodan Zhu; Xuming He'] | 2020-02-13 | null | null | null | aaai-2020-2020-2 | ['phrase-grounding', 'natural-language-visual-grounding'] | ['natural-language-processing', 'reasoning'] | [ 8.50324929e-02 1.97820619e-01 -2.14727089e-01 -2.37946287e-01
-7.80250847e-01 -6.88867748e-01 7.25393414e-01 4.24857795e-01
-2.44744509e-01 3.55388105e-01 3.55933905e-01 -3.30599695e-01
1.41085938e-01 -7.84769416e-01 -8.45014691e-01 -3.59186679e-01
-4.17145304e-02 2.38150969e-01 3.94798934e-01 -3.47909182... | [10.450016021728516, 1.508952260017395] |
5e400278-6bbc-4012-8e62-e5584af20863 | local-prediction-aggregation-a-frustratingly | 2205.04183 | null | https://arxiv.org/abs/2205.04183v3 | https://arxiv.org/pdf/2205.04183v3.pdf | Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation | We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. Th... | ['Shangling Jui', 'Joost Van de Weijer', 'Kai Wang', 'Yaxing Wang', 'Shiqi Yang'] | 2022-05-09 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-4.08602476e-01 -2.00014621e-01 -4.77320254e-01 -7.70666599e-01
-7.53614902e-01 -4.28474277e-01 5.12697399e-01 -4.86774221e-02
-2.10840389e-01 6.27254903e-01 2.88977802e-01 1.64048761e-01
-3.92754316e-01 -6.83133662e-01 -4.19543207e-01 -8.54051173e-01
-1.22510917e-01 5.36755085e-01 2.33470663e-01 -1.01511240... | [10.2052001953125, 3.029291868209839] |
99b6045e-5f2f-40ba-9902-a08d5231739e | hybrid-neural-networks-for-on-device | 2112.05893 | null | https://arxiv.org/abs/2112.05893v1 | https://arxiv.org/pdf/2112.05893v1.pdf | Hybrid Neural Networks for On-device Directional Hearing | On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements. While neural nets can achieve significantly better performance than traditional beamformers, all existing models fall short of supporting low-latency causal inference... | ['Shyamnath Gollakota', 'Hao Zhang', 'Maruchi Kim', 'Anran Wang'] | 2021-12-11 | hybrid-neural-networks-for-on-device-1 | https://directionalhearing.cs.washington.edu/ | https://arxiv.org/pdf/2112.05893 | aaai-2022-2 | ['directional-hearing', 'audio-source-separation', 'real-time-directional-hearing'] | ['audio', 'audio', 'audio'] | [ 3.72695446e-01 1.95502684e-01 -1.06663078e-01 -4.86112833e-01
-9.33818638e-01 -1.78336203e-01 1.57808408e-01 -2.87519712e-02
-5.44277370e-01 6.15106165e-01 9.28436697e-01 -7.34326065e-01
-2.40820870e-01 -7.46117234e-01 -6.18844807e-01 -3.37391794e-01
-4.88405645e-01 1.01756595e-01 3.27290595e-01 4.36475843... | [14.98604679107666, 5.670225143432617] |
1f6a17aa-c4c7-432d-b52f-7eefd101518c | pix3d-dataset-and-methods-for-single-image-3d | 1804.04610 | null | http://arxiv.org/abs/1804.04610v1 | http://arxiv.org/pdf/1804.04610v1.pdf | Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling | We study 3D shape modeling from a single image and make contributions to it
in three aspects. First, we present Pix3D, a large-scale benchmark of diverse
image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications
in shape-related tasks including reconstruction, retrieval, viewpoint
estimation, etc.... | ['Chengkai Zhang', 'Zhoutong Zhang', 'Xiuming Zhang', 'Jiajun Wu', 'Xingyuan Sun', 'William T. Freeman', 'Tianfan Xue', 'Joshua B. Tenenbaum'] | 2018-04-12 | pix3d-dataset-and-methods-for-single-image-3d-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Sun_Pix3D_Dataset_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Sun_Pix3D_Dataset_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-shape-modeling', 'viewpoint-estimation'] | ['computer-vision', 'computer-vision'] | [-2.92134136e-02 -3.47652704e-01 -3.96118641e-01 -3.41387182e-01
-1.15529013e+00 -6.74126506e-01 4.76443708e-01 -3.49740416e-01
1.47986999e-02 2.10079432e-01 4.08390582e-01 -9.74866897e-02
1.03101104e-01 -4.66074049e-01 -1.02803838e+00 -2.60858297e-01
5.01926541e-01 1.01309919e+00 2.28921905e-01 2.86069023... | [8.506453514099121, -3.1909584999084473] |
162712d2-3593-4544-8181-bef7ba092c81 | compressing-sentence-representation-via | null | null | https://openreview.net/forum?id=n3cvM4Phez9 | https://openreview.net/pdf?id=n3cvM4Phez9 | Compressing Sentence Representation via Homomorphic Projective Distillation | How to learn highly compact yet effective sentence representation? Pre-trained language models have been effective in many NLP tasks. However, these models are often huge and produce large sentence embeddings. Moreover, there is a big performance gap between large and small models. In this paper, we propose Homomorphic... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.62698194e-01 1.00625359e-01 -2.46473074e-01 -1.41782224e-01
-1.49082780e+00 -2.42292419e-01 7.61150181e-01 3.62844050e-01
-6.52118623e-01 5.42730033e-01 7.71447837e-01 -2.84831643e-01
2.21899033e-01 -8.37854266e-01 -6.80159450e-01 -2.48784825e-01
7.84529746e-02 4.45660591e-01 6.22920506e-02 -3.48919481... | [11.033600807189941, 8.396317481994629] |
dbc92207-658a-4f94-bd60-8a3a732287df | bridging-the-training-inference-gap-for-dense | 2210.13678 | null | https://arxiv.org/abs/2210.13678v1 | https://arxiv.org/pdf/2210.13678v1.pdf | Bridging the Training-Inference Gap for Dense Phrase Retrieval | Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these procedures are often misaligned in that training objectives do not exactly reflect the retrieval scenario at inference time. In this paper, we exp... | ['William Yang Wang', 'Yashar Mehdad', 'Yizhe Zhang', 'Wenhan Xiong', 'Barlas Oguz', 'Jinhyuk Lee', 'Gyuwan Kim'] | 2022-10-25 | null | null | null | null | ['passage-retrieval', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [-8.44425932e-02 -1.35227084e-01 -3.26556176e-01 -1.15563683e-01
-1.70395863e+00 -6.41831577e-01 2.86415279e-01 2.58965254e-01
-6.28600121e-01 9.19609010e-01 1.25633895e-01 -3.59030634e-01
-5.45145273e-01 -1.11982822e+00 -9.78009284e-01 -3.30920994e-01
2.25592598e-01 1.07597375e+00 2.33940661e-01 -4.67889667... | [11.458056449890137, 7.696648120880127] |
3f1b8080-c0a4-45b6-a10c-6a111f2e983e | multi-domain-dialogue-state-tracking-with-top | null | null | https://aclanthology.org/2022.sigdial-1.24 | https://aclanthology.org/2022.sigdial-1.24.pdf | Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention | As an important component of task-oriented dialogue systems, dialogue state tracking is designed to track the dialogue state through the conversations between users and systems. Multi-domain dialogue state tracking is a challenging task, in which the correlation among different domains and slots needs to consider. Rece... | ['Takahiro Shinozaki', 'Sheng Li', 'Jiyi Li', 'Longfei Yang'] | null | null | null | null | sigdial-acl-2022-9 | ['dialogue-state-tracking', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [-3.25005166e-02 1.69339150e-01 -3.76188546e-01 -3.23831022e-01
-5.27383745e-01 -5.60621977e-01 8.55464697e-01 7.40218386e-02
-3.40948045e-01 7.48937249e-01 6.78329349e-01 -2.27676719e-01
5.87380268e-02 -2.27034077e-01 3.38215679e-01 -4.08346385e-01
2.19197512e-01 8.27042878e-01 6.78510308e-01 -1.07615817... | [12.751171112060547, 7.848485946655273] |
0eb57beb-1870-4ee4-add0-21f537310a47 | towards-unsupervised-deep-graph-structure | 2201.06367 | null | https://arxiv.org/abs/2201.06367v1 | https://arxiv.org/pdf/2201.06367v1.pdf | Towards Unsupervised Deep Graph Structure Learning | In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the dependence on explicit structures prevents GNNs from being applied ... | ['Shirui Pan', 'Hao Peng', 'Hongxu Chen', 'Daokun Zhang', 'Yu Zheng', 'Yixin Liu'] | 2022-01-17 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.57074928e-01 2.91916698e-01 -2.46608973e-01 -3.90972883e-01
-1.65145442e-01 -4.16668922e-01 3.34909499e-01 2.67728060e-01
-1.97251573e-01 5.98215997e-01 -1.15922101e-01 -2.35038474e-01
-3.40793341e-01 -9.93623316e-01 -7.51466990e-01 -8.08929145e-01
-9.54833627e-02 2.89217651e-01 3.03572595e-01 -1.53964922... | [7.332619667053223, 6.247817516326904] |
af8917d0-588e-4b7a-ad36-9d0cd9527c41 | dnabert-2-efficient-foundation-model-and | 2306.15006 | null | https://arxiv.org/abs/2306.15006v1 | https://arxiv.org/pdf/2306.15006v1.pdf | DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome | Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made significant strides in this area. Existing works have largely hinged on k-mer, fixed-length permutations of A, T, C, and G, as the token of the geno... | ['Han Liu', 'Ramana Davuluri', 'Pratik Dutta', 'Weijian Li', 'Yanrong Ji', 'Zhihan Zhou'] | 2023-06-26 | null | null | null | null | ['splice-site-prediction', 'covid-variant-prediction', 'promoter-detection', 'transcription-factor-binding-site-prediction', 'dna-analysis', 'transcription-factor-binding-site-prediction-1', 'transcription-factor-binding-site-prediction-2', 'genome-understanding', 'core-promoter-detection', 'epigenetic-marks-prediction... | ['medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'time-series'] | [ 6.24985456e-01 -1.48777992e-01 -2.66542524e-01 -1.41331956e-01
-8.14928651e-01 -9.90903795e-01 2.11175252e-02 5.34351707e-01
-6.87723219e-01 7.53404975e-01 -1.10351332e-01 -7.80439854e-01
-6.08625039e-02 -7.95609236e-01 -1.05353284e+00 -7.03195274e-01
-8.93581361e-02 5.38042903e-01 -1.58037201e-01 2.91753896... | [10.789790153503418, 7.466174602508545] |
1f421e53-facb-4d78-8789-b53901328f5a | saliency-aware-spatio-temporal-artifact | 2301.01069 | null | https://arxiv.org/abs/2301.01069v1 | https://arxiv.org/pdf/2301.01069v1.pdf | Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment | Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investi... | ['Tiesong Zhao', 'Chengdong Lan', 'Weiling Chen', 'Yang Zheng', 'Liqun Lin'] | 2023-01-03 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 1.99036330e-01 -7.38057256e-01 -6.22765394e-03 -9.86221731e-02
-4.74812627e-01 -3.11411053e-01 1.97302788e-01 3.50493252e-01
2.94798752e-03 5.47178686e-01 3.93099368e-01 -5.68583980e-02
-2.46999115e-02 -2.87652194e-01 -6.23727083e-01 -5.13716042e-01
-3.84995192e-01 -9.58378732e-01 7.27445662e-01 -5.74665740... | [11.719966888427734, -1.9278206825256348] |
e88c56ee-a322-4d04-b3f1-f9187716abb6 | environmental-sound-classification-on | null | null | https://github.com/jonnor/ESC-CNN-microcontroller/blob/master/README.md#abstract | https://github.com/jonnor/ESC-CNN-microcontroller/releases/download/print1/report-print1.pdf | Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks | Noise is a growing problem in urban areas, and according to the WHO is the second environmental cause of health problems in Europe. Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems. It is desirable that these sensor systems, in addition to l... | ['Jon Nordby'] | 2019-05-15 | null | null | null | n-a-2019-5 | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.57378048e-01 -2.62244344e-01 5.43702960e-01 -3.30143839e-01
-4.80757356e-01 -1.29453853e-01 -1.95245430e-01 2.27022871e-01
-7.82415271e-01 3.37701321e-01 -2.72157103e-01 -4.97275710e-01
-1.51926070e-01 -1.24741411e+00 -3.17727834e-01 -7.97726333e-01
-3.19226831e-01 -6.11662157e-02 3.36230665e-01 9.51611772... | [14.620237350463867, 5.474834442138672] |
53bdb159-a1b8-498d-aab0-5b9896b30d6c | relational-reasoning-over-spatial-temporal | null | null | https://ieeexplore.ieee.org/abstract/document/9750933 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9750933 | Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization | In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal representations for video summarization. Most existing video summarization methods extract image-level features with ImageNet pre-trained deep models. Differently, our method exploits object-level and relation-level information to cap... | ['Jie zhou', 'Jiwen Lu', 'Yucheng Han', 'Wencheng Zhu'] | 2022-04-06 | null | null | null | ieee-transactions-on-image-processing-2022-4 | ['supervised-video-summarization', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 1.39163032e-01 1.76026151e-01 -4.56202596e-01 -2.70099103e-01
-4.63513047e-01 -1.44880980e-01 5.17143488e-01 4.52542663e-01
-2.19490439e-01 3.50253910e-01 7.98605144e-01 1.06676131e-01
-3.68316919e-02 -9.63024855e-01 -8.33499849e-01 -3.57095301e-01
-3.41830581e-01 -7.51844049e-02 6.23708010e-01 -3.01660281... | [9.844778060913086, 0.7471922636032104] |
8a8d89bd-e963-4f44-8341-bb9d669ba192 | d2c-diffusion-denoising-models-for-few-shot | 2106.06819 | null | https://arxiv.org/abs/2106.06819v1 | https://arxiv.org/pdf/2106.06819v1.pdf | D2C: Diffusion-Denoising Models for Few-shot Conditional Generation | Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for... | ['Stefano Ermon', 'Chenlin Meng', 'Jiaming Song', 'Abhishek Sinha'] | 2021-06-12 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.36821550e-01 5.01183629e-01 -3.26634884e-01 -1.56618834e-01
-9.09155130e-01 -4.21249717e-01 9.97985542e-01 -5.34179389e-01
-1.56526342e-01 8.34153354e-01 3.61140072e-01 1.00615732e-01
4.32563394e-01 -7.97381282e-01 -8.62689376e-01 -7.97627330e-01
2.66411901e-01 5.71405530e-01 -1.81386307e-01 -5.86381927... | [11.385379791259766, -0.21780355274677277] |
b507784c-4be9-412a-bf68-182eac50dace | segmentation-of-vhr-eo-images-using | 2108.04222 | null | https://arxiv.org/abs/2108.04222v2 | https://arxiv.org/pdf/2108.04222v2.pdf | Segmentation of VHR EO Images using Unsupervised Learning | Semantic segmentation is a crucial step in many Earth observation tasks. Large quantity of pixel-level annotation is required to train deep networks for semantic segmentation. Earth observation techniques are applied to varieties of applications and since classes vary widely depending on the applications, therefore, do... | ['Xiao Xiang Zhu', 'Muhammad Shahzad', 'Lichao Mou', 'Sudipan Saha'] | 2021-07-09 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.99934995e-01 2.77671311e-02 2.11662516e-01 -6.83633566e-01
-2.00128183e-01 -5.80264270e-01 1.90239504e-01 3.36891949e-01
-6.33758128e-01 4.21417803e-01 -2.56226629e-01 -2.81339020e-01
-7.92429820e-02 -1.03656363e+00 -4.31578368e-01 -9.11058426e-01
1.16923138e-01 4.30800557e-01 4.77858841e-01 3.43320966... | [9.498333930969238, -1.0298399925231934] |
1a6dc22a-696b-4aa4-8f43-55a8d5374ecf | multi-task-end-to-end-training-improves-1 | 2305.06218 | null | https://arxiv.org/abs/2305.06218v1 | https://arxiv.org/pdf/2305.06218v1.pdf | Multi-Task End-to-End Training Improves Conversational Recommendation | In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue. While previous works in this area adopt complex multi-component approaches where the dia... | ['Judith Yue Li', 'Ambarish Jash', 'Santiago Ontanon', 'Moustafa Farid Alzantot', 'Ellie Ka In Chio', 'Dima Kuzmin', 'Naveen Ram'] | 2023-05-08 | null | null | null | null | ['movie-recommendation', 'dialogue-management'] | ['miscellaneous', 'natural-language-processing'] | [ 3.65387380e-01 2.24919751e-01 -4.78689037e-02 -8.20601761e-01
-1.02283108e+00 -7.86995292e-01 7.47184694e-01 -9.09755081e-02
-3.80268931e-01 6.25495374e-01 8.77791703e-01 -3.40604037e-01
-1.74657464e-01 -4.28905219e-01 -3.44738156e-01 -3.37715745e-01
1.38857052e-01 1.00945544e+00 2.18281582e-01 -6.10526860... | [12.38705825805664, 7.570472717285156] |
2f03a2d7-eb37-46cb-8b58-a7d0cdddee02 | are-pre-trained-language-models-useful-for | 2305.15183 | null | https://arxiv.org/abs/2305.15183v1 | https://arxiv.org/pdf/2305.15183v1.pdf | Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction? | Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. We hypothesize that model ensemble based on the perplexity (PPL) computed by pre-trained language models (PLMs) should benefit the GEC system. To this end, we explore several ensemble strategies based on strong... | ['Yunfang Wu', 'Xiuyu Wu', 'Chenming Tang'] | 2023-05-24 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-2.32126638e-01 -4.42886120e-03 1.46760553e-01 -5.36810935e-01
-8.45427394e-01 -3.05029392e-01 6.13358200e-01 3.51977736e-01
-4.51256484e-01 8.86129677e-01 2.13529661e-01 -5.85438013e-01
1.67697862e-01 -5.99409699e-01 -5.75979352e-01 -6.00520611e-01
5.37507832e-02 4.32398885e-01 -8.93316716e-02 -6.82732165... | [11.090837478637695, 10.703198432922363] |
de8653fb-751e-48ba-ab16-6a9a3d998c4b | rgb-d-salient-object-detection-a-survey | 2008.00230 | null | https://arxiv.org/abs/2008.00230v4 | https://arxiv.org/pdf/2008.00230v4.pdf | RGB-D Salient Object Detection: A Survey | Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer vision tasks. Now, with the advent of depth sensors, depth maps with affluent spatial information that can be beneficial in boosting the perf... | ['Ming-Ming Cheng', 'Deng-Ping Fan', 'Jianbing Shen', 'Tao Zhou', 'Ling Shao'] | 2020-08-01 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 8.23387969e-03 -1.40143633e-01 -3.18030924e-01 -3.74017984e-01
-4.87069070e-01 -3.14092785e-01 3.02020758e-01 9.47051346e-02
-1.47546962e-01 5.22390425e-01 1.00973167e-01 -1.13386428e-02
-6.45980388e-02 -7.53376722e-01 -3.85602146e-01 -9.39703286e-01
3.51809636e-02 -1.35811031e-01 6.53083503e-01 -3.03312093... | [9.637410163879395, -0.7927422523498535] |
b4e18d5a-2f6f-49dd-8824-5287b2c4538d | incremental-few-shot-text-classification-with | 2104.11882 | null | https://arxiv.org/abs/2104.11882v1 | https://arxiv.org/pdf/2104.11882v1.pdf | Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System | Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled data. The system should be intelligent enough to recognize upcoming new classes with a few examples. In... | ['Philip Yu', 'Yihao Feng', 'Wenpeng Yin', 'Congying Xia'] | 2021-04-24 | null | https://aclanthology.org/2021.naacl-main.106 | https://aclanthology.org/2021.naacl-main.106.pdf | naacl-2021-4 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 6.05687261e-01 3.31689477e-01 -4.57101256e-01 -7.29799092e-01
-7.48002231e-01 -3.57815206e-01 5.88210583e-01 6.80570781e-01
-6.00460649e-01 8.91377628e-01 6.84741586e-02 -1.78814143e-01
1.55015633e-01 -9.71557677e-01 -2.58411974e-01 -4.65368271e-01
2.44093686e-01 8.82975519e-01 4.88251328e-01 -3.39039862... | [10.23849105834961, 3.7335102558135986] |
1d5354c7-d3af-4db1-9a39-5c762b2d27dd | attention-transfer-network-for-nature-image | null | null | https://www.semanticscholar.org/paper/Attention-Transfer-Network-for-Nature-Image-Matting-Zhou-Tian/426480d271a49efd97b4586b918188f8e44d20e4 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9197694&tag=1 | Attention Transfer Network for Nature Image Matting | Natural image matting is an important problem that widely applied in computer vision and graphics. Recent deep learning matting approaches have made an impressive process in both accuracy and efficiency. However, there are still two fundamental problems remain largely unsolved: 1) accurately separating an object from t... | ['and Zhiquan Qi', 'IEEE', 'Member', 'Yingjie Tian', 'Fenfen Zhou'] | 2020-09-15 | null | null | null | ieee-transactions-on-circuits-and-systems-for-8 | ['image-matting'] | ['computer-vision'] | [ 2.56392986e-01 -2.17808709e-01 1.12221219e-01 -3.09270054e-01
-5.17198980e-01 -1.13805354e-01 2.58041441e-01 -2.75958031e-01
-1.10226154e-01 3.96610469e-01 4.50084358e-02 4.32409309e-02
1.38089225e-01 -8.13684046e-01 -9.61850226e-01 -8.95004988e-01
4.42928553e-01 1.18655227e-01 5.13309717e-01 3.78697701... | [10.591136932373047, -0.9438309669494629] |
30865d03-4f8c-475a-abb8-7a807add246f | how-helpful-is-inverse-reinforcement-learning | null | null | https://aclanthology.org/2021.acl-short.11 | https://aclanthology.org/2021.acl-short.11.pdf | How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? | Existing approaches for the Table-to-Text task suffer from issues such as missing information, hallucination and repetition. Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU. In this work, we instead pose the Table-to-Text task as Inverse Re... | ['Shashank Srivastava', 'Snigdha Chaturvedi', 'Zheng Qi', 'Sayan Ghosh'] | 2021-08-01 | null | null | null | acl-2021-5 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.95964867e-01 5.09678423e-01 -6.32250607e-01 -4.38314319e-01
-1.56049371e+00 -7.35870302e-01 9.18676257e-01 1.49777606e-01
-4.41013873e-01 1.18898177e+00 7.75376916e-01 -1.60398081e-01
1.50226131e-01 -3.43614548e-01 -8.57977808e-01 -3.23397249e-01
1.23818070e-01 8.39113235e-01 -3.56260180e-01 -4.93778765... | [11.702709197998047, 8.910517692565918] |
8b5e6563-0027-4c77-a843-37f580814cf2 | robust-proxy-improving-adversarial-robustness | 2306.15457 | null | https://arxiv.org/abs/2306.15457v1 | https://arxiv.org/pdf/2306.15457v1.pdf | Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning | Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense algorithms have been proposed. Recently, to improve adversarial robustness, many works try to enhance feature representation by imposing mo... | ['Yong Man Ro', 'Hong Joo Lee'] | 2023-06-27 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 1.22695580e-01 -7.24360645e-02 -1.24484068e-03 -3.23445082e-01
-9.54980791e-01 -1.06401920e+00 6.95421159e-01 -2.91485548e-01
-2.42557108e-01 7.68621981e-01 1.81411892e-01 -9.21723098e-02
-2.24370256e-01 -9.41564500e-01 -1.09288394e+00 -9.21375871e-01
6.14189729e-02 -2.96160996e-01 1.98276713e-01 -1.80533707... | [5.564391136169434, 7.924444198608398] |
55b5c39f-991e-4838-99e9-5c040b21cb14 | sample-efficient-social-navigation-using | 2106.10318 | null | https://arxiv.org/abs/2106.10318v1 | https://arxiv.org/pdf/2106.10318v1.pdf | Sample Efficient Social Navigation Using Inverse Reinforcement Learning | In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories. As mobile robots come to inhabit and traffic social spaces, they must account for social cues and behave in a socially compliant manner. We focus on learning such cues from example... | ['Gregory Dudek', 'Bobak H. Baghi'] | 2021-06-18 | null | null | null | null | ['social-navigation'] | ['robots'] | [-5.23906909e-02 3.12380403e-01 -2.59592682e-01 -2.92817533e-01
-5.96608043e-01 -5.17436206e-01 8.85986507e-01 -1.00133223e-02
-1.15336943e+00 1.25643277e+00 1.76719695e-01 -4.22566533e-01
-9.48418751e-02 -8.95873368e-01 -9.42356825e-01 -4.87493277e-01
-3.65782470e-01 6.89107776e-01 7.13169098e-01 -4.23981845... | [4.671984672546387, 1.1172970533370972] |
88dd2717-aceb-4a09-9c74-8b06068cdfad | smartbrush-text-and-shape-guided-object | 2212.05034 | null | https://arxiv.org/abs/2212.05034v1 | https://arxiv.org/pdf/2212.05034v1.pdf | SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model | Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, an... | ['Kun Zhang', 'Tobias Hinz', 'Zhe Lin', 'Zhifei Zhang', 'Shaoan Xie'] | 2022-12-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_SmartBrush_Text_and_Shape_Guided_Object_Inpainting_With_Diffusion_Model_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_SmartBrush_Text_and_Shape_Guided_Object_Inpainting_With_Diffusion_Model_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-inpainting'] | ['computer-vision'] | [ 3.54425758e-01 3.46132278e-01 -2.90528268e-01 -9.35118198e-02
-7.04769313e-01 -5.45170248e-01 4.99279559e-01 -2.29883492e-01
2.80097201e-02 7.75952697e-01 3.33806545e-01 6.68668468e-03
3.36105406e-01 -6.75768137e-01 -1.01399279e+00 -6.81343555e-01
4.98216450e-01 2.76332259e-01 3.15561235e-01 -8.43652040... | [11.423259735107422, -0.7251989841461182] |
2d27f67f-105f-46a8-8f29-c8cdc7b00fd6 | jpeg-compressed-images-can-bypass-protections | 2304.02234 | null | https://arxiv.org/abs/2304.02234v2 | https://arxiv.org/pdf/2304.02234v2.pdf | JPEG Compressed Images Can Bypass Protections Against AI Editing | Recently developed text-to-image diffusion models make it easy to edit or create high-quality images. Their ease of use has raised concerns about the potential for malicious editing or deepfake creation. Imperceptible perturbations have been proposed as a means of protecting images from malicious editing by preventing ... | ['Tom Goldstein', 'Jonas Geiping', 'Pedro Sandoval-Segura'] | 2023-04-05 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 5.63183665e-01 -1.53674558e-01 1.55233830e-01 1.29344931e-03
-4.33488041e-01 -8.65476847e-01 1.03799188e+00 3.90145555e-02
-4.16395098e-01 5.80929220e-01 2.55853355e-01 -5.24397850e-01
1.54687837e-01 -6.97032571e-01 -7.62012720e-01 -5.03244758e-01
-8.05599168e-02 -4.62127149e-01 1.45217359e-01 -2.65735388... | [12.419756889343262, 1.1325336694717407] |
ec9769b5-904a-4e7b-924b-fea87bc0dd9b | efficient-keyword-spotting-using-dilated | 1811.07684 | null | http://arxiv.org/abs/1811.07684v2 | http://arxiv.org/pdf/1811.07684v2.pdf | Efficient keyword spotting using dilated convolutions and gating | We explore the application of end-to-end stateless temporal modeling to
small-footprint keyword spotting as opposed to recurrent networks that model
long-term temporal dependencies using internal states. We propose a model
inspired by the recent success of dilated convolutions in sequence modeling
applications, allowin... | ['Mathieu Poumeyrol', 'Thibault Gisselbrecht', 'David Leroy', 'Mohammed Chlieh', 'Alice Coucke', 'Thibaut Lavril'] | 2018-11-19 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 3.82096380e-01 1.08683988e-01 -1.12372786e-01 -3.64412338e-01
-7.74502099e-01 -3.76573533e-01 5.99663854e-01 -1.87379792e-01
-9.25085783e-01 4.73633170e-01 4.71405119e-01 -3.12276989e-01
3.24922532e-01 -1.91265166e-01 -6.22068524e-01 -5.48937857e-01
-4.47353601e-01 -5.12068942e-02 1.79065436e-01 -8.20536315... | [14.319982528686523, 6.227598667144775] |
fa56a2e3-3277-4499-91ed-6540adbf5f14 | effective-data-augmentation-for-sentence | null | null | https://aclanthology.org/2022.coling-1.305 | https://aclanthology.org/2022.coling-1.305.pdf | Effective Data Augmentation for Sentence Classification Using One VAE per Class | In recent years, data augmentation has become an important field of machine learning. While images can use simple techniques such as cropping or rotating, textual data augmentation needs more complex manipulations to ensure that the generated examples are useful. Variational auto-encoders (VAE) and its conditional vari... | ['Philippe Langlais', 'Frédéric Piedboeuf'] | null | null | null | null | coling-2022-10 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.59202254e-01 4.29538906e-01 -1.74653515e-01 -1.94133967e-01
-3.69213372e-01 -5.35222888e-01 1.14709973e+00 1.11193106e-01
-4.72859204e-01 1.07350647e+00 -3.38637270e-02 -4.56935406e-01
4.71919745e-01 -9.53235388e-01 -7.99913406e-01 -7.43817925e-01
2.94811726e-01 7.21475422e-01 5.37199751e-02 -2.90277779... | [11.60489559173584, -0.18049122393131256] |
62e8483f-7976-473a-899f-198e9f55817f | a-review-of-benchmarks-for-visual-defect | 2305.13261 | null | https://arxiv.org/abs/2305.13261v1 | https://arxiv.org/pdf/2305.13261v1.pdf | A Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry | The field of industrial defect detection using machine learning and deep learning is a subject of active research. Datasets, also called benchmarks, are used to compare and assess research results. There is a number of datasets in industrial visual inspection, of varying quality. Thus, it is a difficult task to determi... | ['Yves GRANDVALET', 'Alexandre Durupt', 'Philippe Carvalho'] | 2023-05-05 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.17207119e-01 -2.98412174e-01 -1.04402892e-01 -6.79408073e-01
-2.10286111e-01 -3.28033775e-01 3.42065096e-02 5.14708698e-01
1.71058446e-01 5.03598928e-01 -4.97230530e-01 -5.03253520e-01
-3.51453602e-01 -1.01745844e+00 -4.89601463e-01 -4.03848767e-01
-3.58085968e-02 2.77370274e-01 3.26349400e-02 -1.19454719... | [7.347334384918213, 1.923999309539795] |
49ac7cc0-0c98-4d53-b9ea-5f3d6195bf32 | hopular-modern-hopfield-networks-for-tabular-1 | 2206.00664 | null | https://arxiv.org/abs/2206.00664v1 | https://arxiv.org/pdf/2206.00664v1.pdf | Hopular: Modern Hopfield Networks for Tabular Data | While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests, and Gradient Boosting are the best performing techniques with Gradient Boosting in the lead. Recen... | ['Sepp Hochreiter', 'Angela Bitto-Nemling', 'Lukas Gruber', 'Bernhard Schäfl'] | 2022-06-01 | hopular-modern-hopfield-networks-for-tabular | https://openreview.net/forum?id=3zJVXU311-Q | https://openreview.net/pdf?id=3zJVXU311-Q | null | ['classification'] | ['methodology'] | [-5.70051372e-01 -1.90126166e-01 -3.26538920e-01 -5.24319530e-01
-1.62590057e-01 -3.43268126e-01 7.21888542e-01 1.78008020e-01
-5.62203586e-01 1.10357511e+00 -1.70299020e-02 -4.62666899e-01
-2.54424423e-01 -1.12129545e+00 -7.31268466e-01 -7.71568894e-01
-3.67679089e-01 9.30266440e-01 1.42946988e-01 -2.94382066... | [8.608287811279297, 4.019047260284424] |
143842b4-36e8-4495-94c2-eda44297d6bf | estimating-optimal-policy-value-in-general | 2302.09451 | null | https://arxiv.org/abs/2302.09451v1 | https://arxiv.org/pdf/2302.09451v1.pdf | Estimating Optimal Policy Value in General Linear Contextual Bandits | In many bandit problems, the maximal reward achievable by a policy is often unknown in advance. We consider the problem of estimating the optimal policy value in the sublinear data regime before the optimal policy is even learnable. We refer to this as $V^*$ estimation. It was recently shown that fast $V^*$ estimation ... | ['Emma Brunskill', 'Vidya Muthukumar', 'Aldo Pacchiano', 'Weihao Kong', 'Jonathan N. Lee'] | 2023-02-19 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.07781816e-01 2.65184373e-01 -8.80197167e-01 -1.24584667e-01
-1.31581259e+00 -7.38921821e-01 -9.28917080e-02 1.84777588e-01
-6.57709837e-01 1.30640101e+00 -2.89928496e-01 -8.26322794e-01
-7.72571146e-01 -6.95264101e-01 -1.17469442e+00 -1.03037667e+00
-3.71183336e-01 8.25984359e-01 -8.08662921e-02 3.29194933... | [4.641083717346191, 3.4116344451904297] |
fb317051-dcf0-423f-a5b0-0d39b1263e48 | tabgsl-graph-structure-learning-for-tabular | 2305.15843 | null | https://arxiv.org/abs/2305.15843v1 | https://arxiv.org/pdf/2305.15843v1.pdf | TabGSL: Graph Structure Learning for Tabular Data Prediction | This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks. Despite the prevalence of tabular data in real-world applications, traditional deep learning methods often overlook the potentially valuable associations between data instances. Such association... | ['Cheng-Te Li', 'Jay Chiehen Liao'] | 2023-05-25 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-1.30668983e-01 3.85345995e-01 -4.76276487e-01 -3.03629100e-01
-2.88587600e-01 -5.40067136e-01 5.07286549e-01 7.28598416e-01
5.43682337e-01 6.93447888e-01 3.26706439e-01 -3.50587875e-01
-6.59360528e-01 -1.21262503e+00 -6.79933369e-01 -6.65852129e-01
-4.22224879e-01 7.18198657e-01 -2.92180359e-01 -1.48088649... | [7.102985382080078, 6.286863803863525] |
fd5a3b81-97c3-4c5b-a92d-24a28a576241 | increasingly-packing-multiple-facial | null | null | https://dl.acm.org/doi/10.1145/3323873.3325053 | https://dl.acm.org/doi/pdf/10.1145/3323873.3325053 | Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning | Simultaneously running multiple modules is a key requirement for a smart multimedia system for facial applications including face recognition, facial expression understanding, and gender identification. To effectively integrate them, a continual learning approach to learn new tasks without forgetting is introduced. Unl... | ['Chu-Song Chen', 'Yi-Ming Chan', 'Chein-Hung Chen', 'Jia-Hong Lee', 'Timmy S. T. Wan', 'Steven C. Y. Hung'] | 2019-06-10 | null | null | null | proceedings-of-the-2019-on-international | ['age-and-gender-classification', 'gender-prediction'] | ['computer-vision', 'computer-vision'] | [-2.41753105e-02 -1.24304138e-01 -2.80871391e-01 -5.28238714e-01
-5.65855801e-01 -2.69900560e-01 -1.28695220e-01 -1.58534095e-01
-5.95462382e-01 1.00852752e+00 -5.29916584e-01 -1.27139941e-01
-1.49178401e-01 -5.38067937e-01 -6.85806811e-01 -8.13845932e-01
-8.63914341e-02 4.63545710e-01 3.33143860e-01 7.80562758... | [9.711394309997559, 3.339000940322876] |
1bfe0e6c-528a-4239-bbfc-f7421d21e7b0 | probing-deep-speaker-embeddings-for-speaker | 2212.07068 | null | https://arxiv.org/abs/2212.07068v1 | https://arxiv.org/pdf/2212.07068v1.pdf | Probing Deep Speaker Embeddings for Speaker-related Tasks | Deep speaker embeddings have shown promising results in speaker recognition, as well as in other speaker-related tasks. However, some issues are still under explored, for instance, the information encoded in these representations and their influence on downstream tasks. Four deep speaker embeddings are studied in this ... | ['Rongzhi Gu', 'Junyi Peng', 'Ding Pan', 'Zifeng Zhao'] | 2022-12-14 | null | null | null | null | ['speaker-recognition', 'speaker-verification'] | ['speech', 'speech'] | [-2.12222219e-01 -1.42175183e-01 -6.57957643e-02 -6.80224776e-01
-7.10389197e-01 -5.40678501e-01 8.59735131e-01 7.01548485e-03
-3.04664314e-01 1.11290492e-01 8.77009571e-01 -2.64916956e-01
2.43925117e-02 -3.17159146e-01 -1.95358291e-01 -9.23089147e-01
-1.71739668e-01 5.51326992e-03 -3.18200067e-02 -2.87113279... | [14.279175758361816, 6.0721001625061035] |
c60400ee-87ba-49c5-9852-edec6c67274a | adversarial-multi-task-deep-learning-for | 2207.01691 | null | https://arxiv.org/abs/2207.01691v1 | https://arxiv.org/pdf/2207.01691v1.pdf | Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay | Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in both noisy and noise-free environments, while not introducing significant latency. In this work we propose using an adversarial multi-task ... | ['Zheng-Hua Tan', 'Peter Koch', 'Claus Meyer Larsen'] | 2022-07-04 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.59378037e-01 -2.86270946e-01 3.66839349e-01 3.24323088e-01
-1.01134098e+00 -5.92276454e-01 5.75842738e-01 4.69767600e-01
-6.47642672e-01 5.57937503e-01 -6.52045012e-02 -4.00579274e-01
-2.56400257e-01 -2.94065982e-01 -2.82483190e-01 -8.67384017e-01
-2.43141696e-01 2.43993789e-01 5.22336543e-01 -5.29778711... | [14.933513641357422, 5.792105674743652] |
315eae2b-27e5-49e3-84da-c49088274ac4 | applications-of-deep-learning-in-stock-market | 2003.01859 | null | https://arxiv.org/abs/2003.01859v1 | https://arxiv.org/pdf/2003.01859v1.pdf | Applications of deep learning in stock market prediction: recent progress | Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have b... | ['Weiwei Jiang'] | 2020-02-29 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-7.70798802e-01 -4.87860709e-01 -5.55173814e-01 -2.48876661e-01
-2.89703608e-01 -4.80267465e-01 6.11852765e-01 7.40473345e-02
-4.99570012e-01 7.11588323e-01 -9.60936397e-03 -2.55431861e-01
1.71879074e-03 -9.64931726e-01 -3.59182239e-01 -5.27376652e-01
-2.66219527e-01 2.42860883e-01 1.86597720e-01 -5.22507846... | [4.414448261260986, 4.265755653381348] |
a6d9ae3b-da5f-408e-b76f-000e0a5776b7 | re-think-and-re-design-graph-neural-networks | 2307.00222 | null | https://arxiv.org/abs/2307.00222v1 | https://arxiv.org/pdf/2307.00222v1.pdf | Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals | Graph neural networks (GNNs) are widely used in domains like social networks and biological systems. However, the locality assumption of GNNs, which limits information exchange to neighboring nodes, hampers their ability to capture long-range dependencies and global patterns in graphs. To address this, we propose a new... | ['Guorong Wu', 'Won Hwa Kim', 'Minjeong Kim', 'Shahar Z Kovalsky', 'Ziquan Wei', 'Jiaqi Ding', 'Tingting Dan'] | 2023-07-01 | null | null | null | null | ['image-denoising', 'graph-embedding', 'graph-learning'] | ['computer-vision', 'graphs', 'graphs'] | [ 4.30877618e-02 3.01520944e-01 1.54362721e-02 -6.24561422e-02
-6.51415437e-02 -6.46243393e-01 6.83832526e-01 3.73162851e-02
-2.97806412e-01 6.14314198e-01 1.67713240e-01 -3.49595904e-01
-2.02125460e-01 -1.31142724e+00 -9.10620868e-01 -7.77762234e-01
-2.52889633e-01 1.50030002e-01 1.34857133e-01 -3.36731583... | [6.928215503692627, 6.110955715179443] |
b99fcdea-50f3-4a90-aba2-571bf67121c5 | phishsim-aiding-phishing-website-detection | 2207.10801 | null | https://arxiv.org/abs/2207.10801v1 | https://arxiv.org/pdf/2207.10801v1.pdf | PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool | In this paper, we propose a feature-free method for detecting phishing websites using the Normalized Compression Distance (NCD), a parameter-free similarity measure which computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. It also removes any depende... | ['Sanjay Jha', 'Alan Blair', 'Arindam Pal', 'Rizka Purwanto'] | 2022-07-13 | null | null | null | null | ['phishing-website-detection'] | ['adversarial'] | [ 7.09486976e-02 -3.92927825e-01 -1.51292786e-01 -1.32248282e-01
-6.49619520e-01 -9.65475321e-01 5.33778429e-01 6.15187705e-01
-3.08697760e-01 4.65912044e-01 -5.04867375e-01 -4.92786050e-01
-1.47359341e-01 -9.44028795e-01 -2.95361280e-01 -8.64711881e-01
-3.39062393e-01 3.17851841e-01 8.88805151e-01 1.64652959... | [7.8212385177612305, 9.981996536254883] |
0945f26e-73af-4199-87d8-39664ed22fa6 | open-information-extraction-from-question | 1903.00172 | null | http://arxiv.org/abs/1903.00172v2 | http://arxiv.org/pdf/1903.00172v2.pdf | Open Information Extraction from Question-Answer Pairs | Open Information Extraction (OpenIE) extracts meaningful structured tuples
from free-form text. Most previous work on OpenIE considers extracting data
from one sentence at a time. We describe NeurON, a system for extracting tuples
from question-answer pairs. Since real questions and answers often contain
precisely the ... | ['Wang-Chiew Tan', 'Nikita Bhutani', 'Alon Halevy', 'Yoshihiko Suhara', 'H. V. Jagadish'] | 2019-03-01 | open-information-extraction-from-question-1 | https://aclanthology.org/N19-1239 | https://aclanthology.org/N19-1239.pdf | naacl-2019-6 | ['open-information-extraction'] | ['natural-language-processing'] | [ 6.84831068e-02 5.66060662e-01 -3.07996958e-01 -4.58939016e-01
-1.47428930e+00 -8.63334298e-01 1.11383528e-01 6.56410754e-01
-2.14816198e-01 1.44780719e+00 4.90007967e-01 -3.19994837e-01
-1.86203659e-01 -1.22018123e+00 -8.86888146e-01 1.89185351e-01
1.54082462e-01 8.45904469e-01 4.05945897e-01 -5.10325730... | [9.867986679077148, 8.481878280639648] |
51e2ff4e-bfd2-4101-9bfe-2d6e8c766550 | influence-of-segmentation-on-deep-iris | 1901.10431 | null | https://arxiv.org/abs/1901.10431v2 | https://arxiv.org/pdf/1901.10431v2.pdf | Influence of segmentation on deep iris recognition performance | Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies ... | ['Vitomir Štruc', 'Dejan Štepec', 'Juš Lozej', 'Peter Peer'] | 2019-01-29 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.68218338e-01 -5.43905124e-02 -1.07918225e-01 -6.41073167e-01
-2.09689423e-01 -3.45585108e-01 7.12785244e-01 4.98109236e-02
-5.45880556e-01 1.92876488e-01 1.13676369e-01 -4.43444908e-01
-3.48755062e-01 -4.32495058e-01 -3.90643865e-01 -7.16188371e-01
2.19635412e-01 4.98840392e-01 -1.95415959e-01 3.45364492... | [3.743502378463745, -3.6320319175720215] |
ceb769ff-c60d-4514-a619-946fd4e146d8 | self-pair-synthesizing-changes-from-single | 2212.10236 | null | https://arxiv.org/abs/2212.10236v1 | https://arxiv.org/pdf/2212.10236v1.pdf | Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery | For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision. To overcome this issue, single-temporal supervision which treats change labels as the difference of two semantic masks has been proposed. This novel method tr... | ['Junghoon Seo', 'Yongjin Jeon', 'Hakjin Lee', 'Minseok Seo'] | 2022-12-20 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.13092101e-01 -2.25968152e-01 3.14013243e-01 -6.49222612e-01
-4.36309040e-01 -6.81147099e-01 5.65543234e-01 1.72166303e-01
-4.30854678e-01 6.09676242e-01 -7.98552334e-02 -2.81591326e-01
-2.23717000e-02 -8.19602251e-01 -8.44468415e-01 -8.55377614e-01
9.26619917e-02 -2.55387694e-01 5.32123923e-01 -7.88279101... | [9.681890487670898, -1.2945058345794678] |
a8b539fb-f964-49fa-a24b-b4f7e19494a3 | how-large-language-models-are-transforming | 2210.03568 | null | https://arxiv.org/abs/2210.03568v3 | https://arxiv.org/pdf/2210.03568v3.pdf | How Large Language Models are Transforming Machine-Paraphrased Plagiarism | The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is ... | ['Bela Gipp', 'Frederic Kirstein', 'Terry Ruas', 'Jan Philip Wahle'] | 2022-10-07 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [-1.04429662e-01 3.29471827e-01 8.09875131e-02 2.02998579e-01
-1.27410865e+00 -1.01679516e+00 8.34580839e-01 3.86288851e-01
-1.77236378e-01 6.91338837e-01 5.55082321e-01 -7.49400139e-01
3.71445231e-02 -7.00981855e-01 -8.51204395e-01 8.68235249e-03
5.90704620e-01 3.82968307e-01 -2.72965312e-01 -1.38339937... | [8.610077857971191, 9.998034477233887] |
090a7d0f-f149-4bdc-beb3-a8e8dbf8ae3b | a-convex-optimal-control-framework-for | 2203.16870 | null | https://arxiv.org/abs/2203.16870v3 | https://arxiv.org/pdf/2203.16870v3.pdf | A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing | Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical ... | ['Simos A. Evangelou', 'Stelios Timotheou', 'Boli Chen', 'Xiao Pan'] | 2022-03-31 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.63144290e-01 3.79603505e-01 -4.00445461e-01 -5.93377789e-03
-4.34187025e-01 -6.98463857e-01 2.36469910e-01 2.71615952e-01
-6.19935989e-01 8.18384409e-01 -5.57720482e-01 -5.66940784e-01
-1.01635981e+00 -6.92780972e-01 -6.50298834e-01 -1.01016963e+00
-1.36319965e-01 5.27780414e-01 2.64542247e-03 -3.64214659... | [5.550437927246094, 1.9085668325424194] |
acc305cb-fa2f-42df-88f1-ea7cd421ebd9 | detecting-edit-failures-in-large-language | 2305.17553 | null | https://arxiv.org/abs/2305.17553v2 | https://arxiv.org/pdf/2305.17553v2.pdf | Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark | Recent model editing techniques promise to mitigate the problem of memorizing false or outdated associations during LLM training. However, we show that these techniques can introduce large unwanted side effects which are not detected by existing specificity benchmarks. We extend the existing CounterFact benchmark to in... | ['Fazl Barez', 'Ioannis Konstas', 'Esben Kran', 'Julia Persson', 'Jason Hoelscher-Obermaier'] | 2023-05-27 | null | null | null | null | ['specificity', 'model-editing'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.61272991e-01 2.56620854e-01 -6.59118772e-01 -3.53466302e-01
-8.55334520e-01 -6.03687167e-01 1.00320578e+00 4.72153313e-02
-7.16377020e-01 1.53208971e+00 1.40918583e-01 -4.10201967e-01
-8.39416981e-02 -4.44542170e-01 -1.05087399e+00 -2.86720276e-01
-3.10376465e-01 1.08512379e-01 7.70267025e-02 -3.81230898... | [10.365972518920898, 8.17609691619873] |
56213e73-5757-459a-987c-9aa0f07cb57f | modeling-the-distributional-uncertainty-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tian_Modeling_the_Distributional_Uncertainty_for_Salient_Object_Detection_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_Modeling_the_Distributional_Uncertainty_for_Salient_Object_Detection_Models_CVPR_2023_paper.pdf | Modeling the Distributional Uncertainty for Salient Object Detection Models | Most of the existing salient object detection (SOD) models focus on improving the overall model performance, without explicitly explaining the discrepancy between the training and testing distributions. In this paper, we investigate a particular type of epistemic uncertainty, namely distributional uncertainty, for ... | ['Yuchao Dai', 'Mochu Xiang', 'Jing Zhang', 'Xinyu Tian'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['salient-object-detection-1'] | ['computer-vision'] | [-2.70945784e-02 4.34081048e-01 -3.22668582e-01 -5.00526667e-01
-7.34632432e-01 -4.02665257e-01 5.51254034e-01 2.31753334e-01
-3.81837845e-01 7.59642482e-01 4.08667773e-02 -3.24365526e-01
-1.44229516e-01 -4.81842786e-01 -8.57436121e-01 -7.02286720e-01
1.57448173e-01 5.61292648e-01 6.70759916e-01 3.46581548... | [9.632970809936523, 1.931174874305725] |
6fabcd94-576f-40b7-ab88-9c7307b247a4 | coherent-hierarchical-multi-label | 2010.10151 | null | https://arxiv.org/abs/2010.10151v1 | https://arxiv.org/pdf/2010.10151v1.pdf | Coherent Hierarchical Multi-Label Classification Networks | Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label cl... | ['Thomas Lukasiewicz', 'Eleonora Giunchiglia'] | 2020-10-20 | null | http://proceedings.neurips.cc/paper/2020/hash/6dd4e10e3296fa63738371ec0d5df818-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/6dd4e10e3296fa63738371ec0d5df818-Paper.pdf | neurips-2020-12 | ['protein-function-prediction'] | ['medical'] | [ 6.78208590e-01 4.45523858e-01 -4.31529075e-01 -5.83683074e-01
-7.15027928e-01 -3.80913377e-01 3.86374652e-01 4.86482948e-01
-1.94112480e-01 6.56841278e-01 -1.66285373e-02 -3.29808056e-01
-3.14655691e-01 -3.32105935e-01 -1.14347816e-01 -7.18125939e-01
1.86497360e-01 7.18274653e-01 3.75930011e-01 6.39790595... | [9.60743522644043, 4.357751846313477] |
1e2fd338-b3f0-4ea7-9beb-2f84e9cc2fea | simulating-analogue-film-damage-to-analyse | 2302.10004 | null | https://arxiv.org/abs/2302.10004v1 | https://arxiv.org/pdf/2302.10004v1.pdf | Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans | Digital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and dissemination of photographs of historical and cultural importance. While state-of-the-art deep learning models have shown impressive results in general... | ['Paul Henderson', 'John Williamson', 'Daniela Ivanova'] | 2023-02-20 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 8.52504790e-01 -7.94609189e-02 5.90994060e-01 -1.69092894e-01
-1.14421296e+00 -4.82090354e-01 6.37583971e-01 -2.85496432e-02
-3.17729264e-01 6.65369093e-01 3.32005829e-01 7.39638731e-02
-1.56193852e-01 -6.97944403e-01 -1.10992622e+00 -6.42319262e-01
3.78785208e-02 3.60974878e-01 2.12500334e-01 -2.51830667... | [11.279410362243652, -2.029863119125366] |
a07328f3-62d9-44ac-b760-b10a57dcf3e9 | viewformer-view-set-attention-for-multi-view | 2305.00161 | null | https://arxiv.org/abs/2305.00161v1 | https://arxiv.org/pdf/2305.00161v1.pdf | ViewFormer: View Set Attention for Multi-view 3D Shape Understanding | This paper presents ViewFormer, a simple yet effective model for multi-view 3d shape recognition and retrieval. We systematically investigate the existing methods for aggregating multi-view information and propose a novel ``view set" perspective, which minimizes the relation assumption about the views and releases the ... | ['Deying Li', 'Xudong Cai', 'Peng Wang', 'Yongcai Wang', 'Hongyu Sun'] | 2023-04-29 | null | null | null | null | ['3d-shape-recognition'] | ['computer-vision'] | [-1.76688299e-01 -1.62444055e-01 -1.12817183e-01 -5.59529841e-01
-1.06126010e+00 -9.28858817e-01 1.02200782e+00 -2.57490724e-01
-1.07145108e-01 -1.49161398e-01 4.40514058e-01 2.51080632e-01
-1.33128330e-01 -2.93528765e-01 -5.57243049e-01 -8.37799549e-01
8.89177620e-02 7.71435618e-01 3.93700413e-02 -1.48630545... | [8.230355262756348, -3.703024387359619] |
22ded831-7616-4091-a9ec-69b359158ed9 | yes-but-can-chatgpt-identify-entities-in | 2303.17322 | null | https://arxiv.org/abs/2303.17322v1 | https://arxiv.org/pdf/2303.17322v1.pdf | Yes but.. Can ChatGPT Identify Entities in Historical Documents? | Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents. For the last few months, the conversational agent ChatGPT has "prompted" a lot of interest in the scientific community and public due to its capacity of generatin... | ['Antoine Doucet', 'Jose G. Moreno', 'Ahmed Hamdi', 'Nancy Girdhar', 'Emanuela Boros', 'Carlos-Emiliano González-Gallardo'] | 2023-03-30 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-2.70913333e-01 3.48749459e-01 -1.14997938e-01 -1.01473115e-01
-1.22673678e+00 -9.29119945e-01 9.01781380e-01 4.84216243e-01
-7.03228831e-01 1.00519180e+00 7.90258348e-01 -5.36488712e-01
1.04690224e-01 -4.51116264e-01 -3.64560395e-01 -1.04069427e-01
1.50525570e-01 5.51604390e-01 2.28594482e-01 -3.10933053... | [9.798818588256836, 9.655640602111816] |
bfac7274-2344-4a62-9462-5c8fd6c4cbc2 | learning-to-selectively-learn-for-weakly | 2109.12457 | null | https://arxiv.org/abs/2109.12457v1 | https://arxiv.org/pdf/2109.12457v1.pdf | Learning to Selectively Learn for Weakly-supervised Paraphrase Generation | Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to address this issue, they may fail to generate meaningf... | ['Huan Liu', 'Yang Liu', 'Chenlei Guo', 'Xing Fan', 'Alexander Hanbo Li', 'Dingcheng Li', 'Kaize Ding'] | 2021-09-25 | null | https://aclanthology.org/2021.emnlp-main.480 | https://aclanthology.org/2021.emnlp-main.480.pdf | emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 6.14257872e-01 2.52707452e-02 -5.82288206e-01 -4.67656791e-01
-1.38139999e+00 -5.87219834e-01 7.28803098e-01 7.93863907e-02
-1.88420027e-01 9.59198058e-01 6.81929350e-01 -3.72444063e-01
1.02524832e-01 -5.35821617e-01 -8.89621079e-01 -3.37674320e-01
7.18587339e-01 6.45259798e-01 -1.37972385e-01 -4.18545067... | [11.654313087463379, 9.24806022644043] |
abebf61f-a9e8-4aa9-ba3f-0d9bd82dc6b7 | leveraging-a-bilingual-dictionary-to-learn | null | null | https://aclanthology.org/2022.lrec-1.124 | https://aclanthology.org/2022.lrec-1.124.pdf | Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations | Word embeddings (Mikolov et al., 2013; Pennington et al., 2014) have been used to bolster the performance of natural language processing systems in a wide variety of tasks, including information retrieval (Roy et al., 2018) and machine translation (Qi et al., 2018). However, approaches to learning word embeddings typic... | ['Paul Cook', 'Diego Bear'] | null | null | null | null | lrec-2022-6 | ['learning-word-embeddings', 'reverse-dictionary'] | ['methodology', 'natural-language-processing'] | [-9.85074341e-02 -1.42733872e-01 -5.72990298e-01 -1.28037512e-01
-4.51136351e-01 -7.84416318e-01 8.65889847e-01 4.77682143e-01
-8.79083514e-01 6.24291837e-01 4.88823146e-01 -7.24776328e-01
-5.35123870e-02 -9.55114484e-01 -4.54830498e-01 -2.87295133e-01
3.70970145e-02 3.90450627e-01 -3.11457843e-01 -6.63443506... | [10.835331916809082, 9.866747856140137] |
5e9b6ce2-4c99-4566-a544-0ec6c7c805ba | task-focused-few-shot-object-detection-for | 2201.12437 | null | https://arxiv.org/abs/2201.12437v2 | https://arxiv.org/pdf/2201.12437v2.pdf | Mobile Robot Manipulation using Pure Object Detection | This paper addresses the problem of mobile robot manipulation using object detection. Our approach uses detection and control as complimentary functions that learn from real-world interactions. We develop an end-to-end manipulation method based solely on detection and introduce Task-focused Few-shot Object Detection (T... | ['Brent Griffin'] | 2022-01-28 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.95238884e-02 9.90785360e-02 -1.90570325e-01 -2.37874806e-01
-5.64782977e-01 -7.05258250e-01 1.68755159e-01 -9.26571339e-03
-6.48364723e-01 3.89147311e-01 -4.02256370e-01 -6.54592365e-02
5.09062819e-02 -1.65071458e-01 -1.25456142e+00 -3.19026768e-01
-2.33406350e-01 7.38861442e-01 9.31193531e-01 -2.97996938... | [4.749980449676514, 0.45206817984580994] |
be0d497f-2555-4166-86ed-75031cf22141 | agent-environment-network-for-temporal-action | 2107.08323 | null | https://arxiv.org/abs/2107.08323v3 | https://arxiv.org/pdf/2107.08323v3.pdf | Agent-Environment Network for Temporal Action Proposal Generation | Temporal action proposal generation is an essential and challenging task that aims at localizing temporal intervals containing human actions in untrimmed videos. Most of existing approaches are unable to follow the human cognitive process of understanding the video context due to lack of attention mechanism to express ... | ['Minh-Triet Tran', 'Akihiro Sugimoto', 'Kashu Yamazaki', 'Ngan Le', 'Viet-Khoa Vo-Ho'] | 2021-07-17 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 4.28225696e-01 -2.59130057e-02 -6.35524914e-02 -7.53391609e-02
1.72422871e-01 -5.43119192e-01 1.12118435e+00 2.80519165e-02
-5.00849605e-01 8.10181797e-01 6.89587891e-01 1.38575304e-02
-8.39868113e-02 -6.95442021e-01 -6.09441757e-01 -7.87272990e-01
-3.25276583e-01 3.00381958e-01 6.19467318e-01 -1.64654538... | [8.452577590942383, 0.6816368699073792] |
15bd42ee-d84c-4825-a56e-681447ebb4d3 | kpi-edgar-a-novel-dataset-and-accompanying | 2210.09163 | null | https://arxiv.org/abs/2210.09163v1 | https://arxiv.org/pdf/2210.09163v1.pdf | KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents | We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link t... | ['Rafet Sifa', 'Christian Bauckhage', 'Basil Jacob', 'Desiana Nurchalifah', 'Lars Hillebrand', 'Syed Musharraf Ali', 'Tobias Deußer'] | 2022-10-17 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-4.47015494e-01 3.34258735e-01 -4.88952398e-01 -3.75732064e-01
-8.62697244e-01 -9.18644547e-01 7.06013620e-01 8.77359748e-01
-3.12547833e-01 7.78948784e-01 6.85610473e-01 -4.80547309e-01
-5.40013254e-01 -1.10381544e+00 -1.62282318e-01 -5.00402376e-02
-2.32214943e-01 6.35217845e-01 -3.04081086e-02 -4.14367765... | [9.357437133789062, 8.704753875732422] |
d5d53b06-917a-48c6-9a60-3e5c533776b9 | meta-learning-for-low-resource-unsupervised | 2010.09046 | null | https://arxiv.org/abs/2010.09046v2 | https://arxiv.org/pdf/2010.09046v2.pdf | Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning | Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neur... | ['Cheonbok Park', 'Jaegul Choo', 'Eunjeong Park', 'Mohammad Azam Khan', 'Soyoung Yang', 'Taehee Kim', 'Yunwon Tae'] | 2020-10-18 | null | https://aclanthology.org/2021.acl-long.225 | https://aclanthology.org/2021.acl-long.225.pdf | acl-2021-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.92911553e-01 -1.13362037e-01 -8.74387264e-01 -5.23470759e-01
-1.36203289e+00 -6.56033397e-01 7.81086504e-01 -1.96468577e-01
-5.61706185e-01 1.41471469e+00 8.17484856e-02 -6.31644666e-01
4.54384893e-01 -4.26196963e-01 -9.99521077e-01 -5.00835061e-01
6.73219979e-01 9.04732585e-01 9.24048796e-02 -3.52622628... | [11.620697021484375, 10.307747840881348] |
599b8002-09da-4c5c-93d0-512af0aaeef6 | adaptive-weighted-discriminator-for-training | 2012.03149 | null | https://arxiv.org/abs/2012.03149v2 | https://arxiv.org/pdf/2012.03149v2.pdf | Adaptive Weighted Discriminator for Training Generative Adversarial Networks | Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends o... | ['Qiang Ye', 'Qiang Cheng', 'Vasily Zadorozhnyy'] | 2020-12-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.pdf | cvpr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 2.27393836e-01 1.92401856e-01 1.01404920e-01 -3.76203150e-01
-6.59188867e-01 -5.63897252e-01 6.92971110e-01 -3.90267819e-01
-3.64098340e-01 9.35828328e-01 -1.66761562e-01 -1.06168598e-01
1.57870129e-01 -9.29191828e-01 -7.54384637e-01 -1.03118479e+00
-3.64425476e-03 3.70118022e-01 2.07587525e-01 -5.67849815... | [11.63056468963623, -0.2122114896774292] |
86c8d9bb-ed4d-4d6b-b0bb-6cad150d3063 | cnn-based-posture-free-hand-detection | 1809.10432 | null | http://arxiv.org/abs/1809.10432v1 | http://arxiv.org/pdf/1809.10432v1.pdf | CNN Based Posture-Free Hand Detection | Although many studies suggest high performance hand detection methods, those
methods are likely to be overfitting. Fortunately, the Convolution Neural
Network (CNN) based approach provides a better way that is less sensitive to
translation and hand poses. However the CNN approach is complex and can
increase computation... | ['Richard Adiguna', 'Yustinus Eko Soelistio'] | 2018-09-27 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-4.71670896e-01 -6.68686688e-01 -3.40547591e-01 9.59148258e-02
-1.40486851e-01 -4.08818543e-01 2.14016259e-01 -5.37469149e-01
-5.84685981e-01 4.85517383e-01 2.25149870e-01 -1.45017728e-01
2.40939051e-01 -7.00675130e-01 -2.72911638e-01 -6.43769681e-01
1.78953618e-01 4.15308803e-01 5.35157144e-01 -2.58460164... | [6.566018581390381, -0.6256058216094971] |
531aaaf0-fb89-46af-9605-a6fa5b85f60f | were-we-there-already-applying-minimal | null | null | https://aclanthology.org/2021.sigmorphon-1.29 | https://aclanthology.org/2021.sigmorphon-1.29.pdf | Were We There Already? Applying Minimal Generalization to the SIGMORPHON-UniMorph Shared Task on Cognitively Plausible Morphological Inflection | Morphological rules with various levels of specificity can be learned from example lexemes by recursive application of minimal generalization (Albright and Hayes, 2002, 2003).
A model that learns rules solely through minimal generalization was used to predict average human wug-test ratings from German, English, and Dut... | ['Jane S.Y. Li', 'Colin Wilson'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.09199697e-01 3.16026449e-01 -1.81713596e-01 -1.00916660e+00
-3.22791129e-01 -8.88084531e-01 5.56011558e-01 2.72858202e-01
-8.15119267e-01 9.05106902e-01 1.05435438e-01 -5.51835299e-01
-4.39760536e-01 -6.74359322e-01 -3.63204122e-01 -1.30792081e-01
-4.35706638e-02 4.68760192e-01 4.19795036e-01 -4.97068673... | [10.648760795593262, 9.642779350280762] |
0da68c68-fff9-437b-97cd-d838deedebd3 | neural-text-classification-by-jointly-1 | 2011.12184 | null | https://arxiv.org/abs/2011.12184v1 | https://arxiv.org/pdf/2011.12184v1.pdf | Neural Text Classification by Jointly Learning to Cluster and Align | Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids. We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a latent variable model and interacting with distrib... | ['Shuo Jin', 'Haidong Zhang', 'Yekun Chai'] | 2020-11-24 | neural-text-classification-by-jointly | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-1.81507081e-01 2.16114432e-01 -3.61522526e-01 -4.39247787e-01
-7.44920850e-01 -6.99590445e-01 1.10079813e+00 9.17645514e-01
-4.72619504e-01 -1.00834310e-01 7.30484426e-01 2.49859355e-02
-3.73446554e-01 -6.34991765e-01 -2.07583517e-01 -1.00739276e+00
-2.21193973e-02 7.38327861e-01 -5.59220791e-01 2.61089534... | [10.415709495544434, 6.796196937561035] |
68222bfb-b245-495d-b338-a83cad2653e2 | direction-of-arrival-estimation-for-a-vector | 2004.05671 | null | https://arxiv.org/abs/2004.05671v1 | https://arxiv.org/pdf/2004.05671v1.pdf | Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks | A vector sensor, a type of sensor array with six collocated antennas to measure all electromagnetic field components of incident waves, has been shown to be advantageous in estimating the angle of arrival and polarization of the incident sources. While angle estimation with machine learning for linear arrays has been w... | ['Jianyuan Yu', 'Daniel Tait', 'R. Michael Buehrer', 'William W. Howard'] | 2020-04-12 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.77698636e-01 -2.86307156e-01 1.99139029e-01 -4.15623069e-01
-5.65881789e-01 -5.12928843e-01 -2.32404470e-02 -2.11926788e-01
-1.57409906e-01 4.68111902e-01 9.37312469e-03 -4.04358238e-01
-6.89919412e-01 -7.64948845e-01 -6.65795982e-01 -9.79486525e-01
-3.51341188e-01 2.12785810e-01 -4.84561652e-01 3.15171108... | [6.517349720001221, 1.3100148439407349] |
c4d59f3d-6d89-435e-a946-4bb32ac9e5f5 | a-sequential-algorithm-for-training-text | cmp-lg/9407020 | null | https://arxiv.org/abs/cmp-lg/9407020v2 | https://arxiv.org/pdf/cmp-lg/9407020v2.pdf | A Sequential Algorithm for Training Text Classifiers | The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and teste... | ['William A. Gale', 'David D. Lewis'] | 1994-07-24 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 5.78295887e-01 3.53578866e-01 -5.61330616e-01 -9.79542613e-01
-1.11938727e+00 -5.17994285e-01 1.04749846e+00 8.12069416e-01
-9.60653722e-01 9.75382686e-01 6.73269853e-02 -6.53068244e-01
1.43913832e-02 -6.26571476e-01 -4.71613556e-01 -2.77688622e-01
2.44116232e-01 7.22617269e-01 1.31738096e-01 1.59819514... | [10.557442665100098, 8.013365745544434] |
3ed19591-ce79-44f7-a90d-03ac9f310ed1 | attention-mechanisms-for-object-recognition | 1807.09480 | null | http://arxiv.org/abs/1807.09480v2 | http://arxiv.org/pdf/1807.09480v2.pdf | Attention Mechanisms for Object Recognition with Event-Based Cameras | Event-based cameras are neuromorphic sensors capable of efficiently encoding
visual information in the form of sparse sequences of events. Being
biologically inspired, they are commonly used to exploit some of the
computational and power consumption benefits of biological vision. In this
paper we focus on a specific fe... | ['Marco Cannici', 'Matteo Matteucci', 'Andrea Romanoni', 'Marco Ciccone'] | 2018-07-25 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 6.69659317e-01 -2.82874823e-01 3.19144517e-01 -3.35854292e-01
-1.96765825e-01 -4.23024356e-01 1.10023272e+00 1.09468186e-02
-8.66136849e-01 6.51121736e-01 3.27037215e-01 3.14511359e-01
-2.75903523e-01 -4.89731163e-01 -1.03433955e+00 -9.28147793e-01
-1.04287947e-02 -2.91365534e-02 6.67451560e-01 2.99109638... | [8.252243041992188, 2.2564008235931396] |
86450c69-f797-46fe-9a1c-313b5198129c | saarland-vector-based-models-of-semantic | null | null | https://aclanthology.org/S12-1089 | https://aclanthology.org/S12-1089.pdf | Saarland: Vector-based models of semantic textual similarity | null | ['Georgiana Dinu', 'Stefan Thater'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['video-description'] | ['computer-vision'] | [-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... | [-7.3042192459106445, 3.6937756538391113] |
b9ed9431-226b-4239-b991-74f96f42f0e9 | ordered-tree-decomposition-for-hrg-rule | null | null | https://aclanthology.org/J19-2005 | https://aclanthology.org/J19-2005.pdf | Ordered Tree Decomposition for HRG Rule Extraction | We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure. The assumption of a fixed order f... | ['Xiaochang Peng', 'Giorgio Satta', 'Daniel Gildea'] | 2019-06-01 | null | null | null | cl-2019-6 | ['tree-decomposition'] | ['graphs'] | [ 8.87693524e-01 1.07339799e+00 -3.99789326e-02 -2.31383339e-01
-4.21782196e-01 -8.80654335e-01 6.78051859e-02 4.16664094e-01
-2.27150340e-02 5.26392579e-01 -9.26198736e-02 -8.76892090e-01
-1.99896380e-01 -1.58551323e+00 -7.78090715e-01 -2.51591057e-01
-2.67046303e-01 6.48554683e-01 5.74291110e-01 -1.29263207... | [10.287538528442383, 9.556445121765137] |
fa04ddc6-ad1c-4c73-bf3f-8d1e96b36790 | phrase-aware-unsupervised-constituency | null | null | https://openreview.net/forum?id=c9pFDJXSGa5 | https://openreview.net/pdf?id=c9pFDJXSGa5 | Phrase-aware Unsupervised Constituency Parsing | Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task. Despite their high accuracy in identifying low-level structures, prior arts tend to struggle in capturing high-level structures like clauses, since the MLM task usually only requires ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 3.59460503e-01 4.95124429e-01 -5.61533272e-01 -4.54832554e-01
-1.04532349e+00 -6.28401101e-01 2.59902447e-01 2.10883364e-01
-2.29169145e-01 4.97718811e-01 5.19712389e-01 -8.36682022e-01
3.47786754e-01 -7.69633293e-01 -7.44679391e-01 -4.95177418e-01
1.56411409e-01 4.58922863e-01 1.59239545e-01 -2.04268564... | [10.40538215637207, 9.596328735351562] |
3283281b-f968-4d97-868a-8788efcc9389 | instance-segmentation-gnns-for-one-shot | 2103.06509 | null | https://arxiv.org/abs/2103.06509v1 | https://arxiv.org/pdf/2103.06509v1.pdf | Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC | 3D instance segmentation remains a challenging problem in computer vision. Particle tracking at colliders like the LHC can be conceptualized as an instance segmentation task: beginning from a point cloud of hits in a particle detector, an algorithm must identify which hits belong to individual particle trajectories and... | ['Gage DeZoort', 'Savannah Thais'] | 2021-03-11 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-5.18022329e-02 3.00685763e-01 -1.20716967e-01 -2.26871207e-01
-6.65772557e-01 -7.68617868e-01 8.16038191e-01 4.51108187e-01
-5.26053786e-01 4.68014687e-01 -5.59717953e-01 -5.11753440e-01
-3.89624983e-01 -1.06733823e+00 -8.57390285e-01 -6.22989714e-01
-9.12158713e-02 1.62604010e+00 5.28872728e-01 -5.99937402... | [15.695473670959473, 2.9151132106781006] |
a5dda608-9bbe-4aee-9279-bdfc4561d947 | gnpm-geometric-aware-neural-parametric-models | 2209.10621 | null | https://arxiv.org/abs/2209.10621v1 | https://arxiv.org/pdf/2209.10621v1.pdf | GNPM: Geometric-Aware Neural Parametric Models | We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into account the local structure of data to learn disentangled shape and pose latent spaces of 4D dynamics, using a geometric-aware architecture on point clouds. Temporally consistent 3D deformations are estimated without the ne... | ['Lourdes Agapito', 'Mirgahney Mohamed'] | 2022-09-21 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [-2.14169651e-01 1.80501446e-01 -2.68039465e-01 -1.91547215e-01
-4.11909580e-01 -6.80638194e-01 7.99448192e-01 -2.30478808e-01
-2.71793343e-02 5.03450572e-01 3.82413745e-01 3.33172828e-01
3.95650268e-02 -6.59136117e-01 -1.19850469e+00 -5.26012957e-01
-3.06602716e-01 9.81729329e-01 -2.91523952e-02 -2.33414266... | [6.9492926597595215, -1.2362289428710938] |
206b317d-342f-44f8-87b9-af1dd266ec54 | semg-gesture-recognition-with-a-simple-model | 2006.03645 | null | https://arxiv.org/abs/2006.03645v2 | https://arxiv.org/pdf/2006.03645v2.pdf | sEMG Gesture Recognition with a Simple Model of Attention | Myoelectric control is one of the leading areas of research in the field of robotic prosthetics. We present our research in surface electromyography (sEMG) signal classification, where our simple and novel attention-based approach now leads the industry, universally beating more complex, state-of-the-art models. Our no... | ['Carson Drake', 'David Josephs', 'Andrew Heroy', 'John Santerre'] | 2020-06-05 | null | null | null | null | ['emg-gesture-recognition', 'electromyography-emg'] | ['medical', 'medical'] | [ 4.50039268e-01 7.18892738e-02 -4.31843042e-01 2.29654670e-01
-6.83639824e-01 -1.47869229e-01 7.35870451e-02 -7.40201354e-01
-4.18947846e-01 6.68542504e-01 4.73312765e-01 4.31676209e-02
-3.38496685e-01 -5.06974049e-02 -7.12765336e-01 -6.87487245e-01
-3.35704774e-01 2.23010376e-01 -2.05437958e-01 -3.54588360... | [6.8502302169799805, 0.18503722548484802] |
788f3f92-62a2-4052-afff-d0fcbd7b5d71 | cross-modality-deep-feature-learning-for | 2201.02356 | null | https://arxiv.org/abs/2201.02356v1 | https://arxiv.org/pdf/2201.02356v1.pdf | Cross-Modality Deep Feature Learning for Brain Tumor Segmentation | Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using deep convolutional neural networks. However, different from the RGB image data that are very widespread, the medical image data used in brain... | ['Yizhou Yu', 'Junwei Han', 'Jungong Han', 'Qiang Zhang', 'Guohai Huang', 'Dingwen Zhang'] | 2022-01-07 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.84362668e-01 -1.91575229e-01 -2.83323210e-02 -3.90476376e-01
-9.90941584e-01 -2.39048786e-02 5.62700629e-01 1.25127539e-01
-5.83798885e-01 5.42578697e-01 2.02203900e-01 -2.82919705e-01
-2.44351581e-01 -7.08033919e-01 -5.14326692e-01 -1.09952819e+00
2.77205825e-01 2.24251106e-01 3.28603178e-01 -1.34377733... | [14.484533309936523, -2.411526918411255] |
216533a8-e6a7-4b0e-971f-382168bc1df4 | globalmind-global-multi-head-interactive-self | 2304.08687 | null | https://arxiv.org/abs/2304.08687v1 | https://arxiv.org/pdf/2304.08687v1.pdf | GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection | High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a glo... | ['Liangpei Zhang', 'Chen Wu', 'Meiqi Hu'] | 2023-04-18 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.10993606e-01 -6.87351406e-01 1.26742125e-01 -3.66605520e-01
-4.30504203e-01 -5.19873917e-01 4.11483347e-01 5.53255603e-02
-2.23559886e-01 4.84560579e-01 -1.14830710e-01 -9.52507257e-02
-6.32352293e-01 -1.14525354e+00 -5.21585584e-01 -1.00399017e+00
-2.28249580e-01 4.37585190e-02 1.23513117e-01 -5.44313431... | [9.863794326782227, -1.4348989725112915] |
bd6fddf6-f6ea-48e5-b2f3-ef7fc700a301 | progressive-adversarial-networks-for-fine | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Progressive_Adversarial_Networks_for_Fine-Grained_Domain_Adaptation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Progressive_Adversarial_Networks_for_Fine-Grained_Domain_Adaptation_CVPR_2020_paper.pdf | Progressive Adversarial Networks for Fine-Grained Domain Adaptation | Fine-grained visual categorization has long been considered as an important problem, however, its real application is still restricted, since precisely annotating a large fine-grained image dataset is a laborious task and requires expert-level human knowledge. A solution to this problem is applying domain adaptation ap... | [' Jianmin Wang', ' Mingsheng Long', ' Yunbo Wang', ' Xinyang Chen', 'Sinan Wang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 3.08323890e-01 -2.89602041e-01 -2.34570473e-01 -3.84690523e-01
-6.36827528e-01 -1.07806373e+00 6.29328609e-01 1.28166750e-01
-4.41696882e-01 8.30268383e-01 3.88879366e-02 -2.50281077e-02
-2.05576003e-01 -7.48264253e-01 -5.65732658e-01 -9.21796918e-01
3.63285363e-01 6.13079250e-01 5.12098193e-01 -2.86193669... | [9.947298049926758, 2.4785122871398926] |
608ad736-a58d-4514-ab89-b33a8e374fac | aesop-abstract-encoding-of-stories-objects | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ravi_AESOP_Abstract_Encoding_of_Stories_Objects_and_Pictures_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ravi_AESOP_Abstract_Encoding_of_Stories_Objects_and_Pictures_ICCV_2021_paper.pdf | AESOP: Abstract Encoding of Stories, Objects, and Pictures | Visual storytelling and story comprehension are uniquely human skills that play a central role in how we learn about and experience the world. Despite remarkable progress in recent years in synthesis of visual and textual content in isolation and learning effective joint visual-linguistic representations, existing ... | ['Mubbasir Kapadia', 'Jonathan Brandt', 'Scott Cohen', 'Kushal Kafle', 'Hareesh Ravi'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['visual-storytelling', 'story-completion'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.05918133e-01 3.72586548e-02 5.85810766e-02 -1.23470113e-01
-4.04411048e-01 -9.86214578e-01 1.45539773e+00 3.66196007e-01
2.11290002e-01 4.24640387e-01 1.14209890e+00 -9.84504074e-02
-1.20351970e-01 -7.64308870e-01 -6.25533462e-01 -1.09152727e-01
8.33854005e-02 3.48234326e-01 -1.32738411e-01 -3.92709762... | [11.190916061401367, 0.9023920893669128] |
855fd7cc-7c40-410b-9cd0-aaa80e01a21a | change-detection-under-global-viewpoint | 1703.00552 | null | http://arxiv.org/abs/1703.00552v1 | http://arxiv.org/pdf/1703.00552v1.pdf | Change Detection under Global Viewpoint Uncertainty | This paper addresses the problem of change detection from a novel perspective
of long-term map learning. We are particularly interested in designing an
approach that can scale to large maps and that can function under global
uncertainty in the viewpoint (i.e., GPS-denied situations). Our approach, which
utilizes a comp... | ['Tanaka Kanji', 'Murase Tomoya'] | 2017-03-01 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.77389407e-01 -6.17407620e-01 -1.38540808e-02 -3.50962192e-01
-7.77243316e-01 -7.26620615e-01 9.96528566e-01 -3.72582711e-02
-2.16041535e-01 5.25530636e-01 2.71307200e-01 1.20060019e-01
-2.17803866e-01 -8.10975015e-01 -1.02490687e+00 -9.20423090e-01
-2.14336544e-01 1.83778182e-01 8.17731500e-01 -3.99537683... | [7.730288505554199, -1.9069499969482422] |
4de678d0-f33d-425c-b57c-2da490a5a9f9 | a-shallow-neural-network-for-native-language | null | null | https://aclanthology.org/W17-5027 | https://aclanthology.org/W17-5027.pdf | A Shallow Neural Network for Native Language Identification with Character N-grams | This paper describes the systems submitted by GadjahMada team to the Native Language Identification (NLI) Shared Task 2017. Our models used a continuous representation of character n-grams which are learned jointly with feed-forward neural network classifier. Character n-grams have been proved to be effective for style... | ['Meisyarah Dwiastuti', 'Yunita Sari', 'Muhammad Rifqi Fatchurrahman'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-2.20292658e-01 -2.68378377e-01 -5.36446691e-01 -5.71374953e-01
-7.32044339e-01 -7.98144102e-01 8.40274572e-01 7.47690275e-02
-5.96453726e-01 6.26812339e-01 2.68669665e-01 -4.35916781e-01
2.00960636e-01 -3.80906641e-01 -1.17503040e-01 -1.31991088e-01
2.39750847e-01 6.82359040e-01 -4.74195838e-01 -1.49725303... | [10.378216743469238, 10.510923385620117] |
62d8b7ce-b00f-4d80-9008-cbf610cd1c6c | disentangled-makeup-transfer-with-generative | 1907.01144 | null | https://arxiv.org/abs/1907.01144v1 | https://arxiv.org/pdf/1907.01144v1.pdf | Disentangled Makeup Transfer with Generative Adversarial Network | Facial makeup transfer is a widely-used technology that aims to transfer the makeup style from a reference face image to a non-makeup face. Existing literature leverage the adversarial loss so that the generated faces are of high quality and realistic as real ones, but are only able to produce fixed outputs. Inspired b... | ['Yaohui Jin', 'Wenqing Chen', 'Honglun Zhang', 'Hao He'] | 2019-07-02 | null | null | null | null | ['facial-makeup-transfer'] | ['computer-vision'] | [ 4.04486388e-01 4.07520801e-01 -5.98558895e-02 -4.28401381e-01
-7.04835236e-01 -9.93363976e-01 5.76327026e-01 -1.19019973e+00
3.16819400e-01 8.36321533e-01 4.93953452e-02 2.10774750e-01
5.15653491e-01 -9.87296641e-01 -9.65818822e-01 -8.82032216e-01
4.54985321e-01 2.46238485e-01 -6.31333768e-01 -2.43001774... | [12.693376541137695, 0.0876995176076889] |
c75a71d2-cd7f-4a6f-9485-5d7710b4d3ee | improving-sequential-recommendation | 2106.14031 | null | https://arxiv.org/abs/2106.14031v2 | https://arxiv.org/pdf/2106.14031v2.pdf | Improving Sequential Recommendation Consistency with Self-Supervised Imitation | Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the sequential recommender is prone to make inconsistent predictions. In this paper, we... | ['Bo Long', 'Zhen He', 'Zhuoye Ding', 'Xiaofang Zhao', 'Yonghao Song', 'Hongshen Chen', 'Xu Yuan'] | 2021-06-26 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-2.46031344e-01 -3.29637587e-01 -5.93589246e-01 -5.38059711e-01
-1.85181662e-01 -5.12747586e-01 5.27640760e-01 -2.99899966e-01
8.25579762e-02 5.42139530e-01 4.10568953e-01 1.84296623e-01
-7.32602358e-01 -5.22584140e-01 -6.82566047e-01 -5.03773689e-01
-3.33790593e-02 2.55674154e-01 -6.12934912e-03 -3.66246551... | [10.105229377746582, 5.584603309631348] |
afa1b163-7fb2-4640-9d35-aa1aabe5d45a | 3d-human-pose-estimation-in-video-with | 1811.11742 | null | http://arxiv.org/abs/1811.11742v2 | http://arxiv.org/pdf/1811.11742v2.pdf | 3D human pose estimation in video with temporal convolutions and semi-supervised training | In this work, we demonstrate that 3D poses in video can be effectively
estimated with a fully convolutional model based on dilated temporal
convolutions over 2D keypoints. We also introduce back-projection, a simple and
effective semi-supervised training method that leverages unlabeled video data.
We start with predict... | ['Michael Auli', 'David Grangier', 'Dario Pavllo', 'Christoph Feichtenhofer'] | 2018-11-28 | 3d-human-pose-estimation-in-video-with-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Pavllo_3D_Human_Pose_Estimation_in_Video_With_Temporal_Convolutions_and_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Pavllo_3D_Human_Pose_Estimation_in_Video_With_Temporal_Convolutions_and_CVPR_2019_paper.pdf | cvpr-2019-6 | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.26649040e-01 2.00264245e-01 -2.93885022e-01 -4.11318868e-01
-8.95853162e-01 -7.19462633e-01 4.52322662e-01 -5.51048458e-01
-6.50014222e-01 3.60715330e-01 3.83534431e-01 -9.95673425e-03
4.01584715e-01 -3.36465128e-02 -1.29592776e+00 -3.15876991e-01
-2.90046483e-01 5.02599895e-01 1.86323851e-01 1.80540353... | [7.1279096603393555, -1.0015801191329956] |
c03993d8-928e-42af-b3da-935a7eb0b308 | cloud-removal-using-atmosphere-model | 2210.01981 | null | https://arxiv.org/abs/2210.01981v1 | https://arxiv.org/pdf/2210.01981v1.pdf | Cloud removal Using Atmosphere Model | Cloud removal is an essential task in remote sensing data analysis. As the image sensors are distant from the earth ground, it is likely that part of the area of interests is covered by cloud. Moreover, the atmosphere in between creates a constant haze layer upon the acquired images. To recover the ground image, we pro... | ['Zhuo Wang', 'Feng Li', 'Yi Guo'] | 2022-10-05 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 6.00681007e-01 -3.53282839e-01 4.78467613e-01 3.95218283e-02
-5.85652113e-01 -4.46102232e-01 4.64192957e-01 -1.67353392e-01
-2.55141526e-01 5.14918447e-01 -1.19397685e-01 -1.53994057e-02
-4.65612143e-01 -8.13163936e-01 -4.98888731e-01 -1.14182281e+00
-1.55814946e-01 4.94443893e-01 -5.69591597e-02 -3.18203777... | [10.030427932739258, -2.007946252822876] |
572fba95-c71c-4e50-87b8-5099fc0b8d58 | point-process-modelling-of-rumour-dynamics-in | null | null | https://aclanthology.org/P15-2085 | https://aclanthology.org/P15-2085.pdf | Point Process Modelling of Rumour Dynamics in Social Media | null | ['Kalina Bontcheva', 'Michal Lukasik', 'Trevor Cohn'] | 2015-07-01 | point-process-modelling-of-rumour-dynamics-in-1 | https://aclanthology.org/P15-2085 | https://aclanthology.org/P15-2085.pdf | ijcnlp-2015-7 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.397706508636475, 3.655409574508667] |
7b5ca255-6c94-4403-a381-5ee767e4e11e | theres-a-time-and-place-for-reasoning-beyond | null | null | https://openreview.net/forum?id=2Po_v-AG9u | https://openreview.net/pdf?id=2Po_v-AG9u | There’s a Time and Place for Reasoning Beyond the Image | Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understa... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['image-clustering'] | ['computer-vision'] | [-1.89350978e-01 2.61923611e-01 -2.45678544e-01 -5.05472183e-01
-8.94105971e-01 -4.67512131e-01 9.39542055e-01 3.03511858e-01
-5.85097492e-01 7.37214744e-01 5.83753645e-01 -1.08892046e-01
-1.82118803e-01 -7.45111465e-01 -8.94820511e-01 -4.29842204e-01
1.87566146e-01 7.51869977e-01 4.44950968e-01 -1.21573120... | [10.697948455810547, 1.3101729154586792] |
355f82cf-342b-487f-bb8a-a9be785b209f | learning-decoupling-features-through | 2203.16772 | null | https://arxiv.org/abs/2203.16772v1 | https://arxiv.org/pdf/2203.16772v1.pdf | Learning Decoupling Features Through Orthogonality Regularization | Keyword spotting (KWS) and speaker verification (SV) are two important tasks in speech applications. Research shows that the state-of-art KWS and SV models are trained independently using different datasets since they expect to learn distinctive acoustic features. However, humans can distinguish language content and th... | ['Yuexian Zou', 'Yujun Wang', 'Peng Gao', 'Weiji Zhuang', 'Rongzhi Gu', 'Li Wang'] | 2022-03-31 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.01430970e-02 -7.48800412e-02 -2.34073848e-01 -6.57883346e-01
-9.19627190e-01 -3.12578142e-01 4.48290706e-01 -5.54890454e-01
-1.54550627e-01 2.71150649e-01 4.32434380e-01 -5.09164691e-01
1.14013635e-01 -9.28414706e-03 -3.22728246e-01 -7.76125610e-01
1.71097010e-01 -2.75217086e-01 -9.25618857e-02 -1.05375312... | [14.389182090759277, 6.1090264320373535] |
c0f396e4-52a3-476c-90a9-12ea7f9b97fe | multiresolution-fully-convolutional-networks | 2201.02350 | null | https://arxiv.org/abs/2201.02350v1 | https://arxiv.org/pdf/2201.02350v1.pdf | Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images | Clouds and snow have similar spectral features in the visible and near-infrared (VNIR) range and are thus difficult to distinguish from each other in high resolution VNIR images. We address this issue by introducing a shortwave-infrared (SWIR) band where clouds are highly reflective, and snow is absorptive. As SWIR is ... | ['Bhaskar Ramachandra Nikam', 'Prasun Kumar Gupta', 'Claudio Persello', 'Debvrat Varshney'] | 2022-01-07 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 6.05502188e-01 -6.94943070e-01 1.03234962e-01 -3.39311302e-01
-8.65981996e-01 -7.33987570e-01 3.49460810e-01 -2.22935677e-01
-1.70908287e-01 8.50129664e-01 -2.22806677e-01 -3.97282213e-01
-3.29540282e-01 -1.18435884e+00 -4.58618492e-01 -9.90431011e-01
-6.98210821e-02 2.64606655e-01 -1.57510698e-01 -5.38314164... | [9.767284393310547, -1.7205301523208618] |
3e7e520c-49e0-4a16-b10c-3888dfac04da | a-hierarchical-approach-for-generating | 1611.06607 | null | http://arxiv.org/abs/1611.06607v2 | http://arxiv.org/pdf/1611.06607v2.pdf | A Hierarchical Approach for Generating Descriptive Image Paragraphs | Recent progress on image captioning has made it possible to generate novel
sentences describing images in natural language, but compressing an image into
a single sentence can describe visual content in only coarse detail. While one
new captioning approach, dense captioning, can potentially describe images in
finer lev... | ['Li Fei-Fei', 'Justin Johnson', 'Ranjay Krishna', 'Jonathan Krause'] | 2016-11-20 | a-hierarchical-approach-for-generating-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Krause_A_Hierarchical_Approach_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Krause_A_Hierarchical_Approach_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-captioning'] | ['computer-vision'] | [ 6.35568261e-01 6.56099558e-01 -1.99225634e-01 -4.47176456e-01
-9.71973121e-01 -5.43496192e-01 7.88751662e-01 1.02228165e-01
1.98417991e-01 1.00982141e+00 8.48239779e-01 -1.88544124e-01
6.02672219e-01 -7.44424284e-01 -1.01922214e+00 -3.12104583e-01
3.44668716e-01 1.98611066e-01 1.05112463e-01 -8.45658034... | [11.076837539672852, 0.9444981813430786] |
54284512-53d6-4fd0-9850-5865c994605d | joint-monocular-3d-vehicle-detection-and | 1811.10742 | null | https://arxiv.org/abs/1811.10742v3 | https://arxiv.org/pdf/1811.10742v3.pdf | Joint Monocular 3D Vehicle Detection and Tracking | Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper, we propose a novel online framework for 3D vehicle detection and tracking from monocular videos. The framework can not only associate detec... | ['Philipp Krähenbühl', 'Ji Lin', 'Dequan Wang', 'Qi-Zhi Cai', 'Hou-Ning Hu', 'Min Sun', 'Fisher Yu', 'Trevor Darrell'] | 2018-11-26 | joint-monocular-3d-vehicle-detection-and-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Hu_Joint_Monocular_3D_Vehicle_Detection_and_Tracking_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Hu_Joint_Monocular_3D_Vehicle_Detection_and_Tracking_ICCV_2019_paper.pdf | iccv-2019-10 | ['online-multi-object-tracking'] | ['computer-vision'] | [-5.84704459e-01 -4.33868885e-01 -4.72907513e-01 -4.72894669e-01
-7.70935237e-01 -9.29611444e-01 8.12718868e-01 -1.19322330e-01
-4.66839790e-01 4.19401735e-01 -7.59946778e-02 -5.55190742e-01
3.18083048e-01 -6.40253127e-01 -8.98833454e-01 -4.55003351e-01
-6.20273829e-01 5.39129138e-01 7.09445834e-01 3.66057694... | [6.764579772949219, -2.1914148330688477] |
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