paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
129b931c-4669-41ec-9959-e73a538a1dd1 | vuldeepecker-a-deep-learning-based-system-for-1 | 2001.02334 | null | https://arxiv.org/abs/2001.02334v1 | https://arxiv.org/pdf/2001.02334v1.pdf | $μ$VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection | Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detect... | ['Hai Jin', 'Deqing Zou', 'Sujuan Wang', 'Zhen Li', 'Shouhuai Xu'] | 2020-01-08 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.25786701e-01 -3.44362050e-01 -4.07746166e-01 -1.47433996e-01
-6.58092558e-01 -6.73754930e-01 1.04567595e-01 4.79684532e-01
1.15011365e-03 2.50043541e-01 -2.35437512e-01 -9.07571316e-01
-1.02453465e-02 -1.19558370e+00 -5.58269083e-01 -2.96562910e-01
-3.96643668e-01 -1.82737052e-01 5.10088444e-01 -2.87323117... | [7.06734561920166, 7.773303985595703] |
e22059c5-e021-4324-a35d-194a1c1c41a5 | textformer-a-query-based-end-to-end-text | 2306.03377 | null | https://arxiv.org/abs/2306.03377v1 | https://arxiv.org/pdf/2306.03377v1.pdf | TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision | End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Region-of-Interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these li... | ['Jianbing Shen', 'Xingping Dong', 'Sanyuan Zhao', 'Xiameng Qin', 'Xiaoqiang Zhang', 'Yukun Zhai'] | 2023-06-06 | null | null | null | null | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 4.61667329e-01 -1.14745699e-01 -1.52274370e-01 -6.67074025e-01
-1.02486074e+00 -3.85063320e-01 6.59399807e-01 -2.98155099e-02
-6.76733196e-01 1.86547369e-01 2.56759197e-01 -3.18340987e-01
3.61075372e-01 -4.52536434e-01 -6.37065470e-01 -5.99260032e-01
1.00651753e+00 6.17310941e-01 2.91393965e-01 1.07737854... | [11.988430976867676, 2.2465567588806152] |
fec2b315-86da-468c-95e7-b7e7428873ad | nirdizati-an-advanced-predictive-process | 2210.09688 | null | https://arxiv.org/abs/2210.09688v1 | https://arxiv.org/pdf/2210.09688v1.pdf | Nirdizati: an Advanced Predictive Process Monitoring Toolkit | Predictive Process Monitoring is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. The recent stream of publications in this field shows the need for tools able to support researchers and user... | ['Fabrizio Maria Maggi', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Williams Rizzi'] | 2022-10-18 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.83914232e-01 8.70049670e-02 -8.71599987e-02 -1.92829132e-01
8.95665213e-02 -2.91003019e-01 9.94134724e-01 8.49419355e-01
2.45344296e-01 1.52062073e-01 1.98617622e-01 -5.06829619e-01
-7.35626280e-01 -8.80874753e-01 2.19708562e-01 -2.52979696e-01
-4.50435966e-01 8.96444738e-01 1.39662385e-01 2.09893018... | [8.60063362121582, 6.012337684631348] |
a9ce3266-57d1-4ee2-b6ac-b5f1200d298b | image-segmentation-and-processing-for | 1803.04620 | null | http://arxiv.org/abs/1803.04620v1 | http://arxiv.org/pdf/1803.04620v1.pdf | Image Segmentation and Processing for Efficient Parking Space Analysis | In this paper, we develop a method to detect vacant parking spaces in an
environment with unclear segments and contours with the help of MATLAB image
processing capabilities. Due to the anomalies present in the parking spaces,
such as uneven illumination, distorted slot lines and overlapping of cars. The
present-day co... | ['N Ruban Rajesh Kumar Muthu', 'Chetan Sai Tutika', 'Karthik R', 'Bharath KP', 'Charan Vallapaneni'] | 2018-03-13 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.29969874e-01 -2.11246639e-01 3.90190423e-01 -2.53775418e-01
-1.01067968e-01 -3.03242177e-01 4.77170467e-01 -6.91319928e-02
-5.09443343e-01 7.35910356e-01 -6.09317005e-01 -6.14009261e-01
2.30461895e-01 -1.04293990e+00 -4.30410832e-01 -6.85411990e-01
1.91414505e-01 4.27303046e-01 9.63962436e-01 -2.68914551... | [8.093989372253418, -1.4038485288619995] |
9ef501ab-1072-417b-b364-7bf08a9ebb4c | deepswir-a-deep-learning-based-approach-for | 1905.02749 | null | https://arxiv.org/abs/1905.02749v1 | https://arxiv.org/pdf/1905.02749v1.pdf | DeepSWIR: A Deep Learning Based Approach for the Synthesis of Short-Wave InfraRed Band using Multi-Sensor Concurrent Datasets | Convolutional Neural Network (CNN) is achieving remarkable progress in various computer vision tasks. In the past few years, the remote sensing community has observed Deep Neural Network (DNN) finally taking off in several challenging fields. In this study, we propose a DNN to generate a predefined High Resolution (HR)... | ['S Manthira Moorthi', 'Indranil Mishra', 'Yatharath Bhateja', 'Debjyoti Dhar', 'Ankur Garg', 'Litu Rout'] | 2019-05-07 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 6.79063678e-01 -2.24395275e-01 2.94421315e-01 -3.45812172e-01
-6.46816790e-01 -6.35084391e-01 5.56030691e-01 -3.33010674e-01
-5.03257990e-01 9.14696038e-01 7.05991611e-02 -4.70948011e-01
-4.27330405e-01 -1.31973100e+00 -4.96396959e-01 -9.98677135e-01
-2.23999709e-01 -9.15529430e-02 -3.03658575e-01 -1.06591344... | [9.83893871307373, -1.7586522102355957] |
dd8589c3-2ae1-426d-a0ea-557f0f94edcf | reciprocal-sequential-recommendation | 2306.14712 | null | https://arxiv.org/abs/2306.14712v1 | https://arxiv.org/pdf/2306.14712v1.pdf | Reciprocal Sequential Recommendation | Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the... | ['HengShu Zhu', 'Yang song', 'Wayne Xin Zhao', 'Yupeng Hou', 'Bowen Zheng'] | 2023-06-26 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 4.40927520e-02 -3.74152780e-01 -3.63448650e-01 -5.89723110e-01
-4.68859673e-01 -8.24887931e-01 3.78594875e-01 1.56778499e-01
-3.78805518e-01 2.74820149e-01 2.69182235e-01 -5.82962871e-01
-4.92153555e-01 -9.70506728e-01 -7.44294345e-01 -5.22760987e-01
3.54659230e-01 4.20586735e-01 1.90480575e-01 -4.26143110... | [10.136153221130371, 5.630405426025391] |
ef5681a8-dfcc-43e6-ade1-41490f8314bc | learning-to-revise-references-for-faithful | 2204.10290 | null | https://arxiv.org/abs/2204.10290v2 | https://arxiv.org/pdf/2204.10290v2.pdf | Learning to Revise References for Faithful Summarization | In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text. On large news corpora, removing low quality samples has been shown to reduce model hallucinations. Yet, for smaller, and/or noisier corpora, filtering is det... | ['Noémie Elhadad', 'Kathleen McKeown', 'Christopher Winestock', 'Qing Sun', 'Han-Chin Shing', 'Griffin Adams'] | 2022-04-13 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 6.37853444e-01 6.42081559e-01 -3.57810169e-01 -3.55515331e-01
-1.54006350e+00 -3.34704965e-01 4.36288565e-01 6.48973167e-01
-3.01819116e-01 1.34935892e+00 1.26216674e+00 1.07183084e-02
-2.06662472e-02 -3.38988572e-01 -5.67817271e-01 -2.20339283e-01
4.56007719e-01 4.91870373e-01 -1.58331200e-01 -1.51576594... | [12.245513916015625, 9.338586807250977] |
8710f70e-2243-4420-ae13-f5d6fd850b5c | redwine-a-clinical-datamart-with-text | 2304.05929 | null | https://arxiv.org/abs/2304.05929v1 | https://arxiv.org/pdf/2304.05929v1.pdf | ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research | Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical tr... | ['Yanshan Wang', 'Elizabeth Skidmore', 'Anthony Delitto', 'Michael J. Becich', 'Jonathan C. Silverstein', 'Brian McLay', 'Shyam Visweswaran Nickie Cappella', 'Janet Freburger', 'Allyn Bove', 'Andi Saptono', 'Bambang Parmanto', 'David Oniani'] | 2023-04-12 | null | null | null | null | ['data-visualization', 'data-visualization'] | ['methodology', 'miscellaneous'] | [-6.01565838e-02 -3.43495756e-01 -9.32156205e-01 -1.14069134e-01
-1.00562799e+00 -1.08155504e-01 -1.26402467e-01 8.93968165e-01
-6.16216540e-01 6.50933802e-01 1.41208577e+00 -6.62042379e-01
-6.50244117e-01 -7.55554795e-01 -1.00121580e-01 -1.35382310e-01
3.53600197e-02 5.97183406e-01 -4.93484795e-01 2.53092617... | [7.987650394439697, 6.165011882781982] |
c6f4e1c5-f5c0-48e1-acb1-60b082d4416c | memd-absa-a-multi-element-multi-domain | 2306.16956 | null | https://arxiv.org/abs/2306.16956v1 | https://arxiv.org/pdf/2306.16956v1.pdf | MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elemen... | ['Rui Xia', 'Jianfei Yu', 'Shijie Liu', 'Siwei Wu', 'Ke Li', 'Qiankun Zhao', 'Qiming Xie', 'Zengzhi Wang', 'Nan Song', 'Hongjie Cai'] | 2023-06-29 | null | null | null | null | ['sentiment-analysis', 'opinion-mining'] | ['natural-language-processing', 'natural-language-processing'] | [-1.14124920e-02 4.36693318e-02 -1.54699281e-01 -8.41833830e-01
-9.95254159e-01 -6.05210662e-01 5.39480150e-01 -1.30115926e-01
-2.24044383e-01 7.45173335e-01 4.23545390e-01 -1.84528515e-01
1.39630690e-01 -8.50963414e-01 -6.40991867e-01 -5.12658000e-01
4.18329835e-01 6.43009663e-01 -1.19430520e-01 -5.35299897... | [11.482702255249023, 6.6503400802612305] |
82116bfb-5519-4eee-9c41-f3150cf66080 | dula-net-a-dual-projection-network-for | 1811.11977 | null | http://arxiv.org/abs/1811.11977v2 | http://arxiv.org/pdf/1811.11977v2.pdf | DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama | We present a deep learning framework, called DuLa-Net, to predict
Manhattan-world 3D room layouts from a single RGB panorama. To achieve better
prediction accuracy, our method leverages two projections of the panorama at
once, namely the equirectangular panorama-view and the perspective
ceiling-view, that each contains... | ['Hung-Kuo Chu', 'Min Sun', 'Peter Wonka', 'Chi-Han Peng', 'Fu-En Wang', 'Shang-Ta Yang'] | 2018-11-29 | dula-net-a-dual-projection-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_DuLa-Net_A_Dual-Projection_Network_for_Estimating_Room_Layouts_From_a_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_DuLa-Net_A_Dual-Projection_Network_for_Estimating_Room_Layouts_From_a_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [-8.97889212e-02 9.38274190e-02 4.23099607e-01 -7.65431345e-01
-6.83264256e-01 -5.05161583e-01 3.44091296e-01 -1.83368519e-01
2.51195937e-01 2.53669888e-01 5.13165355e-01 -4.36935455e-01
-1.44715875e-01 -1.14584303e+00 -9.90940750e-01 -6.08015239e-01
-8.23468193e-02 3.60576540e-01 -1.58699185e-01 -2.42057294... | [8.72728157043457, -2.8507766723632812] |
08bb7a56-4973-4dd7-8fec-bc76ef79c2ee | zero-shot-learning-of-a-conditional | 2210.14392 | null | https://arxiv.org/abs/2210.14392v1 | https://arxiv.org/pdf/2210.14392v1.pdf | Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization | We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. In... | ['Jungwon Lee', 'Mostafa El-Khamy', 'Yoojin Choi'] | 2022-10-26 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 4.57852453e-01 4.51785594e-01 8.48696604e-02 -5.65467000e-01
-8.29636455e-01 -1.53389946e-01 8.52714539e-01 -2.71511912e-01
-6.00998044e-01 8.11920524e-01 -1.75190538e-01 -2.15331018e-01
3.80212933e-01 -1.41124415e+00 -1.00747132e+00 -9.36658442e-01
2.60199636e-01 5.34506023e-01 3.47178996e-01 -1.49840891... | [11.504518508911133, -0.1356695145368576] |
5e08ab41-bd62-455f-b36d-e4f2e1002247 | metafill-text-infilling-for-meta-path | 2210.07488 | null | https://arxiv.org/abs/2210.07488v1 | https://arxiv.org/pdf/2210.07488v1.pdf | MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks | Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types, is the core technique to embed HINs. Since manually curating meta-paths is time-consuming, there is a pressing need to develop automated m... | ['Sheng Wang', 'Ming Zhang', 'Hanwen Xu', 'Junwei Yang', 'Kefei Duan', 'Zequn Liu'] | 2022-10-14 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 3.06895524e-01 3.41517001e-01 -6.68264627e-01 9.98095497e-02
-5.72777569e-01 -7.40332842e-01 6.25866413e-01 5.51629663e-01
-8.88579190e-02 5.92036009e-01 3.40523958e-01 -8.50638270e-01
-4.78609689e-02 -1.50753391e+00 -5.71082532e-01 -1.29892126e-01
-4.09684032e-01 4.05898064e-01 3.51592332e-01 -4.35351372... | [7.477383613586426, 6.436932563781738] |
2ece5a6b-3133-4976-949b-bf5805ab4f5a | an-ensemble-of-density-based-geometric-one | 2011.06388 | null | https://arxiv.org/abs/2011.06388v2 | https://arxiv.org/pdf/2011.06388v2.pdf | An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm | One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classification in dealing with some problematic data set or special cases. Generally, classification accuracy... | ['Jin Young Choi', 'Do Gyun Kim'] | 2020-10-02 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-9.41843316e-02 -1.57983243e-01 -1.72427565e-01 -4.13543880e-01
-4.06852931e-01 -3.91656756e-01 4.12681699e-01 5.30865490e-01
-2.01432824e-01 1.00609779e+00 -1.94374934e-01 -4.53104645e-01
-8.69946301e-01 -1.02325010e+00 -2.76220769e-01 -7.16794789e-01
-5.19745648e-02 7.37863481e-01 1.89104259e-01 -2.81011313... | [8.252730369567871, 4.21832275390625] |
4c263cae-7fa6-4bf9-8354-8d0c0b8b4c0b | hierarchical-scene-parsing-by-weakly | 1709.09490 | null | http://arxiv.org/abs/1709.09490v2 | http://arxiv.org/pdf/1709.09490v2.pdf | Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions | This paper investigates a fundamental problem of scene understanding: how to
parse a scene image into a structured configuration (i.e., a semantic object
hierarchy with object interaction relations). We propose a deep architecture
consisting of two networks: i) a convolutional neural network (CNN) extracting
the image ... | ['WangMeng Zuo', 'Guangrun Wang', 'Liang Lin', 'Meng Wang', 'Ruimao Zhang'] | 2017-09-27 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 5.80233037e-01 2.16603160e-01 -4.14546989e-02 -9.01173711e-01
-4.77120847e-01 -6.19940400e-01 3.45550954e-01 4.02115360e-02
-3.42807859e-01 3.94344598e-01 1.30098119e-01 -4.65935141e-01
2.37616390e-01 -1.06773114e+00 -1.19583797e+00 -5.60055137e-01
2.28013292e-01 4.43742067e-01 2.90234417e-01 9.21974774... | [9.644777297973633, 0.5714792609214783] |
0f72071d-19a2-4602-a7ed-9227b0fd2135 | amc-net-an-effective-network-for-automatic | 2304.00445 | null | https://arxiv.org/abs/2304.00445v1 | https://arxiv.org/pdf/2304.00445v1.pdf | AMC-Net: An Effective Network for Automatic Modulation Classification | Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have bee... | ['Shuyuan Yang', 'Zhixi Feng', 'Tiantian Wang', 'Jiawei Zhang'] | 2023-04-02 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 5.63023567e-01 -6.89757168e-01 -3.19358140e-01 -1.44180045e-01
-7.19116032e-01 -1.49781317e-01 3.02273244e-01 -4.56053987e-02
-3.48967075e-01 5.95403850e-01 9.69349965e-02 -5.06426156e-01
-4.94591475e-01 -4.22265381e-01 -6.55695871e-02 -8.23119938e-01
-2.74813801e-01 -4.30037051e-01 -1.82562664e-01 -3.08713824... | [6.482579231262207, 1.4806734323501587] |
6b5b5c86-284a-493a-a7e6-dab4a2e0900f | abstractive-text-summarization-for-sanskrit | null | null | https://aclanthology.org/2020.wildre-1.11 | https://aclanthology.org/2020.wildre-1.11.pdf | Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches | The authors present a work-in-progress in the field of Abstractive Text Summarization (ATS) for Sanskrit Prose {--} a first attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS and argue for the ones to be adopted for Sanskrit prose considering the unique properties of the lan... | ['Girish Jha', 'Shagun Sinha'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.79282898e-01 2.44193733e-01 -2.16653496e-01 -1.34915382e-01
-8.08955193e-01 -8.31897080e-01 6.83728158e-01 6.94689095e-01
-4.94530708e-01 1.12247109e+00 9.08388734e-01 -2.77162850e-01
-4.31075990e-01 -4.35379833e-01 -7.05227926e-02 -6.16846263e-01
2.77727723e-01 7.16200948e-01 1.12610385e-01 -6.08554304... | [12.34887409210205, 9.597572326660156] |
81ce36ea-8799-4659-8c12-3c5423a8f678 | winning-arguments-interaction-dynamics-and | 1602.01103 | null | http://arxiv.org/abs/1602.01103v2 | http://arxiv.org/pdf/1602.01103v2.pdf | Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions | Changing someone's opinion is arguably one of the most important challenges
of social interaction. The underlying process proves difficult to study: it is
hard to know how someone's opinions are formed and whether and how someone's
views shift. Fortunately, ChangeMyView, an active community on Reddit, provides
a platfo... | ['Cristian Danescu-Niculescu-Mizil', 'Vlad Niculae', 'Lillian Lee', 'Chenhao Tan'] | 2016-02-02 | null | null | null | null | ['persuasion-strategies'] | ['computer-vision'] | [ 1.26772285e-01 3.62406582e-01 -3.55910242e-01 -3.62681359e-01
-2.21222356e-01 -1.04996955e+00 9.64128077e-01 7.44120657e-01
-5.97210050e-01 8.89287412e-01 7.70391226e-01 -8.35336506e-01
4.51211706e-02 -8.13292444e-01 -3.49156708e-01 -5.99588573e-01
4.52551484e-01 2.40242779e-01 8.01047534e-02 -6.51587129... | [8.832167625427246, 10.0199613571167] |
868c13bb-0db5-46b3-8f1d-283456d8c50e | boltvos-box-level-tracking-for-video-object | 1904.04552 | null | https://arxiv.org/abs/1904.04552v2 | https://arxiv.org/pdf/1904.04552v2.pdf | BoLTVOS: Box-Level Tracking for Video Object Segmentation | We approach video object segmentation (VOS) by splitting the task into two sub-tasks: bounding box level tracking, followed by bounding box segmentation. Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which consists of an R-CNN detector conditioned on the first-frame bounding box to detect th... | ['Jonathon Luiten', 'Bastian Leibe', 'Paul Voigtlaender'] | 2019-04-09 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.51072398e-01 1.41952392e-02 -3.80353719e-01 -1.39066547e-01
-9.80335355e-01 -8.01136553e-01 3.66735846e-01 -1.54543996e-01
-5.62201977e-01 2.41509885e-01 -1.88765958e-01 -2.32379198e-01
5.15317678e-01 -2.68389851e-01 -1.32256031e+00 -3.55310798e-01
-1.33712932e-01 4.21605676e-01 1.17090440e+00 1.67804480... | [8.927364349365234, -0.1771388053894043] |
4838caa0-1e36-4e61-a277-a771fd11967c | abb-bert-a-bert-model-for-disambiguating | 2207.04008 | null | https://arxiv.org/abs/2207.04008v1 | https://arxiv.org/pdf/2207.04008v1.pdf | ABB-BERT: A BERT model for disambiguating abbreviations and contractions | Abbreviations and contractions are commonly found in text across different domains. For example, doctors' notes contain many contractions that can be personalized based on their choices. Existing spelling correction models are not suitable to handle expansions because of many reductions of characters in words. In this ... | ['Nimit Jain', 'Aswin Gridhar Subramanian', 'Andi Cupallari', 'Prateek Kacker'] | 2022-07-08 | null | https://aclanthology.org/2021.icon-main.35 | https://aclanthology.org/2021.icon-main.35.pdf | icon-2021-12 | ['spelling-correction'] | ['natural-language-processing'] | [-5.82020544e-02 -3.49632017e-02 -2.64828414e-01 -4.28347528e-01
-6.20290279e-01 -8.97738457e-01 3.79896909e-01 2.35673562e-01
-6.07290089e-01 9.91515160e-01 5.59142768e-01 -4.99728918e-01
-1.21043913e-01 -5.59521735e-01 -3.12850624e-01 -1.35823503e-01
1.86933726e-01 1.08658874e+00 2.03291818e-01 -4.78727311... | [11.008979797363281, 10.255229949951172] |
5650a1f1-b933-45f0-93e6-75969e7337b1 | neural-network-applications-in-earthquake | 1910.01178 | null | https://arxiv.org/abs/1910.01178v1 | https://arxiv.org/pdf/1910.01178v1.pdf | Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations | In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning expl... | ['Arnaud Mignan', 'Marco Broccardo'] | 2019-10-02 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-4.15572196e-01 1.70888454e-01 1.05280891e-01 -1.22274272e-01
-5.16003013e-01 -2.41949826e-01 8.08598101e-01 1.01368152e-01
-5.81172228e-01 9.18440282e-01 3.94386262e-01 -4.67936307e-01
-5.10295033e-01 -8.57837141e-01 -4.70781177e-01 -9.05989289e-01
-7.94756293e-01 5.11593997e-01 1.75038800e-01 -6.18797183... | [6.8110480308532715, 2.7920658588409424] |
2abdd492-c965-482d-bb91-48145a81e374 | group-activity-recognition-in-basketball | 2209.00451 | null | https://arxiv.org/abs/2209.00451v1 | https://arxiv.org/pdf/2209.00451v1.pdf | Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS) | Like many team sports, basketball involves two groups of players who engage in collaborative and adversarial activities to win a game. Players and teams are executing various complex strategies to gain an advantage over their opponents. Defining, identifying, and analyzing different types of activities is an important ... | ['Slobodan Vucetic', 'Sandro Hauri'] | 2022-08-31 | null | null | null | null | ['sports-analytics', 'group-activity-recognition'] | ['computer-vision', 'computer-vision'] | [-4.53757271e-02 -3.52366865e-01 -3.74403775e-01 -2.56568342e-01
-6.13247454e-01 -7.07465887e-01 3.54027748e-01 -4.58665239e-03
-6.87431395e-01 4.95880485e-01 3.80263329e-01 -7.11307600e-02
-1.58800557e-01 -1.04174984e+00 -8.63879681e-01 -5.18340826e-01
-2.85367072e-01 5.36004663e-01 5.11237502e-01 -3.26504856... | [6.758005142211914, 0.32413196563720703] |
a5c0ac02-ca00-4743-b327-01d193ef58f5 | one-embedder-any-task-instruction-finetuned | 2212.09741 | null | https://arxiv.org/abs/2212.09741v3 | https://arxiv.org/pdf/2212.09741v3.pdf | One Embedder, Any Task: Instruction-Finetuned Text Embeddings | We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate ... | ['Weijia Shi', 'Tao Yu', 'Luke Zettlemoyer', 'Noah A. Smith', 'Wen-tau Yih', 'Mari Ostendorf', 'Yushi Hu', 'Yizhong Wang', 'Jungo Kasai', 'Hongjin Su'] | 2022-12-19 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 2.29653820e-01 4.11004983e-02 -4.08290327e-01 -6.03932917e-01
-9.67079699e-01 -8.99775028e-01 7.08189905e-01 4.55365717e-01
-6.92678094e-01 5.85808396e-01 4.84091371e-01 -6.04710460e-01
1.40634343e-01 -3.87739211e-01 -6.29661679e-01 -1.90387845e-01
3.06083202e-01 5.93587518e-01 2.46050023e-02 -3.22517246... | [10.709805488586426, 8.454154014587402] |
c1f4e705-abe4-4f81-ac0a-f4f8a2d7146b | refinenet-multi-path-refinement-networks-for | 1611.06612 | null | http://arxiv.org/abs/1611.06612v3 | http://arxiv.org/pdf/1611.06612v3.pdf | RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation | Recently, very deep convolutional neural networks (CNNs) have shown
outstanding performance in object recognition and have also been the first
choice for dense classification problems such as semantic segmentation.
However, repeated subsampling operations like pooling or convolution striding
in deep CNNs lead to a sign... | ['Chunhua Shen', 'Anton Milan', 'Guosheng Lin', 'Ian Reid'] | 2016-11-20 | refinenet-multi-path-refinement-networks-for-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [ 0.47524485 -0.03379475 -0.11205299 -0.6062903 -0.65347725 -0.24205014
0.61166865 -0.06838425 -0.74427444 0.7567884 0.08551758 0.04963757
0.18280327 -0.9093931 -1.0133505 -0.6603318 0.04464113 -0.03788443
0.7256646 -0.0771222 0.02094576 0.6626334 -1.7192317 0.7120856
0.7545922 1.4531143 0.3... | [9.56521987915039, 0.19586703181266785] |
739e7050-5416-48f1-933e-ba5f2ca7f848 | mucic-at-comma-icon-multilingual-gender | null | null | https://aclanthology.org/2021.icon-multigen.9 | https://aclanthology.org/2021.icon-multigen.9.pdf | MUCIC at ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Using N-grams and Multilingual Sentence Encoders | Social media analytics are widely being explored by researchers for various applications. Prominent among them are identifying and blocking abusive contents especially targeting individuals and communities, for various reasons. The increasing abusive contents and the increasing number of users on social media demands a... | ['Alexander Gelbukh', 'Grigori Sidorov', 'Hosahalli Lakshmaiah Shashirekha', 'Oxana Vitman', 'Fazlourrahman Balouchzahi'] | null | null | null | null | icon-2021-12 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-2.17138425e-01 -1.89355180e-01 6.84121177e-02 -4.17132616e-01
-8.99143815e-01 -7.30408907e-01 6.31222248e-01 7.24184215e-01
-7.34853923e-01 9.64544237e-01 2.49726921e-01 -1.99090719e-01
8.77906233e-02 -3.13709110e-01 -1.47712484e-01 -3.28326076e-01
1.67535424e-01 5.70548117e-01 -6.05539826e-04 -5.19704163... | [8.887839317321777, 10.59411334991455] |
0c470ad7-085e-451a-a998-bbd420a30aee | guiding-safe-exploration-with-weakest | 2209.14148 | null | https://arxiv.org/abs/2209.14148v2 | https://arxiv.org/pdf/2209.14148v2.pdf | Guiding Safe Exploration with Weakest Preconditions | In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic w... | ['Isil Dillig', 'Swarat Chaudhuri', 'Greg Anderson'] | 2022-09-28 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-5.77890361e-03 2.14773268e-01 -6.11116827e-01 8.69785845e-02
-8.37888241e-01 -6.58873677e-01 4.97273684e-01 3.31735194e-01
-4.89324987e-01 1.18821037e+00 -1.60106733e-01 -8.40700865e-01
-4.56877142e-01 -6.50119543e-01 -9.62731898e-01 -6.28814995e-01
-7.23572671e-01 2.83402056e-01 3.95796657e-01 -1.78646147... | [4.568832874298096, 2.1731302738189697] |
b8461134-345d-4cdf-ae86-37bfded00e30 | sg-lstm-social-group-lstm-for-robot | 2303.04320 | null | https://arxiv.org/abs/2303.04320v1 | https://arxiv.org/pdf/2303.04320v1.pdf | SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds | With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to min... | ['Aniket Bera', 'Maurice Chiu', 'Rashmi Bhaskara'] | 2023-03-08 | null | null | null | null | ['social-navigation', 'robot-navigation'] | ['robots', 'robots'] | [-1.84889704e-01 3.26068819e-01 3.08033288e-01 -3.36944252e-01
6.76119328e-02 -9.21252891e-02 4.29206908e-01 2.41316751e-01
-9.82301533e-01 8.94603014e-01 3.72017771e-01 -3.21884632e-01
-1.73008069e-01 -8.66694868e-01 -6.71162665e-01 -3.13983381e-01
-6.81271374e-01 4.71479595e-01 3.90408099e-01 -5.73642790... | [4.843813896179199, 0.9227018356323242] |
709a4d2f-3d97-4b47-90b8-9456a7b04602 | object-counting-from-aerial-remote-sensing | 2306.10439 | null | https://arxiv.org/abs/2306.10439v1 | https://arxiv.org/pdf/2306.10439v1.pdf | Object counting from aerial remote sensing images: application to wildlife and marine mammals | Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods. This research study utilizes deep learning techniques to automate counting tasks. Inspired by previous studies on crowd and animal counting, a UNet model with various backbones is implemented, w... | ['Minh-Tan Pham', 'Hugo Gangloff', 'Tanya Singh'] | 2023-06-17 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-1.76736325e-01 -2.87579119e-01 2.36095980e-01 -9.44717452e-02
3.32321912e-01 -4.05742168e-01 4.30603862e-01 9.57258567e-02
-1.52194571e+00 8.33659053e-01 1.04210392e-01 -1.25718698e-01
2.07158402e-01 -1.15796506e+00 -1.59117833e-01 -4.45287287e-01
-7.50096202e-01 5.62754929e-01 3.94348860e-01 -2.47843340... | [8.399945259094238, -0.7655431628227234] |
39780be2-518e-4843-a9eb-526721e2bfa6 | epigraf-rethinking-training-of-3d-gans | 2206.10535 | null | https://arxiv.org/abs/2206.10535v2 | https://arxiv.org/pdf/2206.10535v2.pdf | EpiGRAF: Rethinking training of 3D GANs | A very recent trend in generative modeling is building 3D-aware generators from 2D image collections. To induce the 3D bias, such models typically rely on volumetric rendering, which is expensive to employ at high resolutions. During the past months, there appeared more than 10 works that address this scaling issue by ... | ['Peter Wonka', 'Yiqun Wang', 'Sergey Tulyakov', 'Ivan Skorokhodov'] | 2022-06-21 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.28454992e-01 4.46740761e-02 2.81487018e-01 -2.13600874e-01
-1.06033897e+00 -5.58646142e-01 6.17309690e-01 -2.79378176e-01
-1.66584402e-01 8.05393159e-01 -6.47871569e-02 -3.38219404e-02
1.45582721e-01 -1.12786698e+00 -9.81450617e-01 -1.01841605e+00
-1.27621032e-02 5.56595564e-01 4.13425356e-01 -1.24974027... | [9.269986152648926, -3.0960350036621094] |
2c22a87f-6e4b-4c91-b2e3-479007bb3bf0 | semi-supervised-3d-shape-segmentation-with | 2204.08824 | null | https://arxiv.org/abs/2204.08824v2 | https://arxiv.org/pdf/2204.08824v2.pdf | Semi-supervised 3D shape segmentation with multilevel consistency and part substitution | The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel ... | ['Heung-Yeung Shum', 'Yang Liu', 'Xin Tong', 'Peng-Shuai Wang', 'Hao-Xiang Guo', 'Yu-Qi Yang', 'Chun-Yu Sun'] | 2022-04-19 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.93484833e-02 5.49364328e-01 -3.13532084e-01 -9.12221372e-01
-7.46217966e-01 -7.56426930e-01 2.76872337e-01 -7.28890374e-02
1.87633157e-01 1.05623856e-01 -1.64910242e-01 -3.14232886e-01
2.61759967e-01 -6.22643948e-01 -9.36982930e-01 -2.38267437e-01
7.82343149e-02 1.08095753e+00 5.45116782e-01 9.34549198... | [7.991793632507324, -3.292900800704956] |
6b54e5a6-3051-4e02-b616-d3de704983d1 | disambiguated-attention-embedding-for-multi | 2305.16912 | null | https://arxiv.org/abs/2305.16912v1 | https://arxiv.org/pdf/2305.16912v1.pdf | Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning | In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favor... | ['Min-Ling Zhang', 'Weijia Zhang', 'Wei Tang'] | 2023-05-26 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.28532541e-01 1.59688994e-01 -7.16818929e-01 -5.57278454e-01
-1.03625631e+00 -2.66944230e-01 3.74037445e-01 6.02645099e-01
-3.09261829e-01 9.18844998e-01 -4.62788343e-02 -3.73685248e-02
-2.73031682e-01 -9.28706169e-01 -6.48529708e-01 -1.01560569e+00
6.56548217e-02 5.05464137e-01 1.61834478e-01 2.06948712... | [9.557820320129395, 3.985593795776367] |
c005433b-683e-44c8-a1e1-b2cb55c219fb | semi-supervised-object-detection-via-virtual-1 | 2207.03433 | null | https://arxiv.org/abs/2207.03433v2 | https://arxiv.org/pdf/2207.03433v2.pdf | Semi-supervised Object Detection via Virtual Category Learning | Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exac... | ['Jungong Han', 'Kurt Debattista', 'Changrui Chen'] | 2022-07-07 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.81898522e-01 4.06675935e-01 -3.95571798e-01 -6.07262969e-01
-6.88066304e-01 -5.49596190e-01 6.62606359e-01 3.99568707e-01
-6.96252763e-01 8.21466863e-01 -2.30288997e-01 -1.08766012e-01
-3.85865629e-01 -4.58748668e-01 -5.85983872e-01 -9.80654180e-01
9.20767710e-02 4.53068644e-01 3.43782067e-01 4.37168539... | [9.22596549987793, 3.824976682662964] |
a51ded75-d334-4d68-9351-28931ec7591e | deep-image-compression-using-scene-text | 2305.11373 | null | https://arxiv.org/abs/2305.11373v1 | https://arxiv.org/pdf/2305.11373v1.pdf | Deep Image Compression Using Scene Text Quality Assessment | Image compression is a fundamental technology for Internet communication engineering. However, a high compression rate with general methods may degrade images, resulting in unreadable texts. In this paper, we propose an image compression method for maintaining text quality. We developed a scene text image quality asses... | ['Shinichiro Omachi', 'Tomo Miyazaki', 'Shohei Uchigasaki'] | 2023-05-19 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 4.28564698e-01 -6.07844353e-01 -2.91967005e-01 -3.52265626e-01
-7.63820648e-01 3.51735264e-01 2.28114873e-01 1.84664913e-02
-3.35166991e-01 4.37763870e-01 3.74513686e-01 -1.29069060e-01
-1.19384073e-01 -9.41590965e-01 -5.70694089e-01 -5.32872677e-01
2.43126303e-02 -6.04151823e-02 3.86371650e-02 -6.33505806... | [11.39484691619873, -1.6788769960403442] |
7ae56d8c-5782-4e74-a4d3-a513757bbd6f | novel-feature-extraction-selection-and-fusion | 1511.04317 | null | http://arxiv.org/abs/1511.04317v2 | http://arxiv.org/pdf/1511.04317v2.pdf | Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification | Modern malware is designed with mutation characteristics, namely polymorphism
and metamorphism, which causes an enormous growth in the number of variants of
malware samples. Categorization of malware samples on the basis of their
behaviors is essential for the computer security community, because they
receive huge numb... | ['Giorgio Giacinto', 'Stanislav Semenov', 'Mansour Ahmadi', 'Dmitry Ulyanov', 'Mikhail Trofimov'] | 2015-11-13 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 1.40795052e-01 -7.08082557e-01 -1.21010661e-01 -2.45871142e-01
2.10108534e-02 -5.67017853e-01 9.24246788e-01 5.31789541e-01
-2.82189220e-01 3.92409682e-01 -7.48656392e-02 -3.97135407e-01
-1.17068008e-01 -7.89939702e-01 1.10857815e-01 -7.79643416e-01
-4.43295300e-01 3.44856083e-01 3.17633480e-01 -2.53935456... | [14.40305233001709, 9.654616355895996] |
986455c3-29dd-413e-ae82-160f24baf6f3 | light-field-super-resolution-via-graph-based | 1701.02141 | null | http://arxiv.org/abs/1701.02141v2 | http://arxiv.org/pdf/1701.02141v2.pdf | Light Field Super-Resolution Via Graph-Based Regularization | Light field cameras capture the 3D information in a scene with a single
exposure. This special feature makes light field cameras very appealing for a
variety of applications: from post-capture refocus, to depth estimation and
image-based rendering. However, light field cameras suffer by design from
strong limitations i... | ['Pascal Frossard', 'Mattia Rossi'] | 2017-01-09 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 5.39435983e-01 -3.60020489e-01 1.29272789e-01 -2.72017986e-01
-4.27525789e-01 -2.81158417e-01 4.99849707e-01 -1.44492969e-01
-3.64444286e-01 1.04383910e+00 7.71023408e-02 2.27108210e-01
-2.46828750e-01 -8.99940848e-01 -4.94443685e-01 -8.10005784e-01
5.31062543e-01 3.94639611e-01 7.39671826e-01 -2.15578631... | [9.508099555969238, -2.6217994689941406] |
345407e9-0491-4333-bf26-3d37e7475e92 | enhancing-diversity-of-defocus-blur-detectors | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Enhancing_Diversity_of_Defocus_Blur_Detectors_via_Cross-Ensemble_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Enhancing_Diversity_of_Defocus_Blur_Detectors_via_Cross-Ensemble_Network_CVPR_2019_paper.pdf | Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network | Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this p... | [' Huchuan Lu', ' Qiuhua Lin', ' Bowen Zheng', 'Wenda Zhao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['defocus-blur-detection', 'defocus-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.42250499e-02 -7.65254736e-01 2.34261051e-01 -4.66463536e-01
-1.43990040e-01 -2.25335017e-01 3.99180710e-01 -3.19552541e-01
-3.80884945e-01 9.51613963e-01 3.07187825e-01 2.01719031e-02
-4.18124676e-01 -4.93980646e-01 -5.59423804e-01 -9.89931285e-01
-3.45156081e-02 1.19458362e-02 4.54785556e-01 6.86117411... | [11.33456802368164, -2.7279977798461914] |
73d7cf1c-f0c3-466a-94d2-4bf29c682420 | ogmn-occlusion-guided-multi-task-network-for | 2304.11805 | null | https://arxiv.org/abs/2304.11805v1 | https://arxiv.org/pdf/2304.11805v1.pdf | OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images | Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confu... | ['Xian Sun', 'Xinming Li', 'Xiuhua Mao', 'Peng Gao', 'Yongqiang Mao', 'Wenhui Diao', 'Xuexue Li'] | 2023-04-24 | null | null | null | null | ['occlusion-estimation'] | ['computer-vision'] | [ 6.60497546e-02 -3.46382290e-01 3.02184410e-02 -1.55357853e-01
-5.34331501e-01 -4.28592563e-01 2.45846197e-01 -1.62350520e-01
-2.68499643e-01 1.30292013e-01 -4.13175784e-02 -4.05449383e-02
2.14031190e-01 -5.07441282e-01 -6.66013956e-01 -7.68952608e-01
5.57528734e-02 1.65877238e-01 8.45183432e-01 1.00360483... | [8.776285171508789, -0.6632851362228394] |
9038e6af-f0d9-4c92-bf21-339a21d72350 | multimodal-contrastive-learning-via-uni-modal | 2210.14556 | null | https://arxiv.org/abs/2210.14556v1 | https://arxiv.org/pdf/2210.14556v1.pdf | Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis | Multimodal representation learning is a challenging task in which previous work mostly focus on either uni-modality pre-training or cross-modality fusion. In fact, we regard modeling multimodal representation as building a skyscraper, where laying stable foundation and designing the main structure are equally essential... | ['Haifeng Hu', 'Ronghao Lin'] | 2022-10-26 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 3.35140795e-01 -5.88883281e-01 -3.53016973e-01 -1.31137609e-01
-1.09197521e+00 -3.72528136e-01 7.75691926e-01 -4.42817546e-02
-9.04052109e-02 2.47943267e-01 5.59199572e-01 1.51284710e-01
-2.28456497e-01 -5.07326186e-01 -7.14655638e-01 -9.85671163e-01
1.60521105e-01 -5.88983633e-02 -2.16337845e-01 -8.00281703... | [13.147795677185059, 5.014715671539307] |
ac024f56-dc5e-4a59-816f-f90240938630 | joint-background-reconstruction-and | 1707.07584 | null | http://arxiv.org/abs/1707.07584v1 | http://arxiv.org/pdf/1707.07584v1.pdf | Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network | Foreground segmentation in video sequences is a classic topic in computer
vision. Due to the lack of semantic and prior knowledge, it is difficult for
existing methods to deal with sophisticated scenes well. Therefore, in this
paper, we propose an end-to-end two-stage deep convolutional neural network
(CNN) framework f... | ['Yingying Chen', 'Jinqiao Wang', 'Xu Zhao', 'Ming Tang'] | 2017-07-24 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.18054128e-01 -5.96016832e-02 3.02208979e-02 -3.41727644e-01
-2.85839081e-01 -1.66594148e-01 1.13353267e-01 -3.37456405e-01
-5.83868921e-01 4.97559547e-01 -6.55445978e-02 -2.48934910e-01
5.67664087e-01 -8.09255421e-01 -9.34469163e-01 -8.75449121e-01
4.51227456e-01 -2.81252153e-02 9.86199200e-01 4.03552473... | [9.255270957946777, -0.2885473966598511] |
8f951009-7a4d-4942-8bb5-06a73ace765c | secure-and-efficient-flexibility-service | 2306.17475 | null | https://arxiv.org/abs/2306.17475v1 | https://arxiv.org/pdf/2306.17475v1.pdf | Secure and Efficient Flexibility Service Procurement: A Game-Theoretic Approach | Procuring flexibility services from energy consumers has been a potential solution to accommodating renewable generations in future power system. However, efficiently and securely coordinating the behaviors of diverse market participants within a privacy-preserving environment remains a challenge. This paper addresses ... | ['Nima Monshizadeh', 'Jacquelien M. A. Scherpen', 'Koorosh Shomalzadeh', 'Xiupeng Chen'] | 2023-06-30 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-4.82545286e-01 1.04724281e-01 -1.13145567e-01 -1.55098304e-01
-4.36499506e-01 -1.17999256e+00 5.06555550e-02 -2.60712385e-01
-2.78208166e-01 1.19097722e+00 -1.53157249e-01 -3.89063358e-01
-4.01230991e-01 -1.27055478e+00 -1.54367670e-01 -1.20555067e+00
-3.28972161e-01 1.01470083e-01 -2.09664732e-01 -2.13220149... | [5.619180679321289, 2.5556743144989014] |
ca412f04-f49e-4e6a-8a9e-713873b3db5e | toward-better-target-representation-for | 2208.10531 | null | https://arxiv.org/abs/2208.10531v3 | https://arxiv.org/pdf/2208.10531v3.pdf | RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation | Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting on... | ['Chen Chen', 'Lichao Sun', 'Lingjuan Lyu', 'Zhengming Ding', 'Qucheng Peng'] | 2022-08-22 | null | null | null | null | ['self-knowledge-distillation', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 3.18562180e-01 2.68778712e-01 -4.54333395e-01 -4.51364905e-01
-8.48362088e-01 -8.94473970e-01 3.92825484e-01 -6.85742497e-02
-4.41544563e-01 9.30512667e-01 -1.63076390e-02 -2.40733743e-01
3.42049241e-01 -9.67905879e-01 -9.61460650e-01 -7.56253898e-01
2.58267432e-01 7.29733557e-02 2.80398369e-01 -3.51249501... | [10.366175651550293, 3.1830990314483643] |
9740b426-09c5-4a32-8006-c2622f0f9129 | camera-calibration-and-player-localization-in | 2104.09333 | null | https://arxiv.org/abs/2104.09333v1 | https://arxiv.org/pdf/2104.09333v1.pdf | Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting | Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera ... | ['Marc Van Droogenbroeck', 'Bernard Ghanem', 'Olivier Barnich', 'Silvio Giancola', 'Floriane Magera', 'Adrien Deliège', 'Anthony Cioppa'] | 2021-04-19 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [-7.76606351e-02 -3.07870060e-01 -2.25186154e-01 -2.15871125e-01
-8.54368389e-01 -5.77741444e-01 1.33716241e-01 -3.75130117e-01
-8.43051195e-01 4.92120475e-01 3.82756263e-01 1.92768663e-01
1.03143025e-02 -4.28798527e-01 -9.45028722e-01 -5.06544411e-01
2.24987239e-01 6.05162501e-01 4.33615953e-01 -6.10504746... | [7.921414852142334, 0.22359104454517365] |
fb50ce28-1814-4ce0-b70f-b77a3d3249ea | deep-learning-based-joint-control-of-acoustic | 2203.01793 | null | https://arxiv.org/abs/2203.01793v2 | https://arxiv.org/pdf/2203.01793v2.pdf | Deep Learning-Based Joint Control of Acoustic Echo Cancellation, Beamforming and Postfiltering | We introduce a novel method for controlling the functionality of a hands-free speech communication device which comprises a model-based acoustic echo canceller (AEC), minimum variance distortionless response (MVDR) beamformer (BF) and spectral postfilter (PF). While the AEC removes the early echo component, the MVDR BF... | ['Walter Kellermann', 'Thomas Haubner'] | 2022-03-03 | null | null | null | null | ['acoustic-echo-cancellation', 'speech-extraction', 'acoustic-echo-cancellation'] | ['medical', 'speech', 'speech'] | [ 1.09282747e-01 1.53607838e-02 6.48695827e-01 -6.00418486e-02
-7.41253138e-01 -4.74788487e-01 5.12017727e-01 -3.73813689e-01
-5.22711039e-01 2.67823786e-01 5.54928243e-01 -5.37128329e-01
-1.76275074e-02 -2.01072812e-01 -3.95573556e-01 -7.84798741e-01
-4.41522077e-02 -2.18746617e-01 2.05405757e-01 -5.66767603... | [15.11220645904541, 6.020047187805176] |
d3d9471a-e973-4e95-9a1e-0af40d0d3cc5 | a-riemannian-framework-for-matching-point | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Deng_A_Riemannian_Framework_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Deng_A_Riemannian_Framework_2014_CVPR_paper.pdf | A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform | In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrodinger distance transform (SDT) representation. This is achieved by solving a static Schrodinger equation instead of the corresponding static ... | ['Baba C. Vemuri', 'Yan Deng', 'Anand Rangarajan', 'Stephan Eisenschenk'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['set-matching'] | ['computer-vision'] | [ 6.25301152e-02 8.76841918e-02 1.46014810e-01 -2.97857583e-01
-6.35518491e-01 -4.28393632e-01 5.58785439e-01 -1.93136975e-01
-4.76034075e-01 2.62417555e-01 -3.07043612e-01 -4.57143858e-02
-3.47616643e-01 -7.35880911e-01 -7.30163038e-01 -7.63426244e-01
-1.18648283e-01 9.71683919e-01 -7.34920707e-03 -2.00147092... | [7.7991814613342285, -2.7597434520721436] |
46f03fb2-9557-4555-9483-e1d414bc87c7 | a-self-attentive-model-for-knowledge-tracing | 1907.06837 | null | https://arxiv.org/abs/1907.06837v1 | https://arxiv.org/pdf/1907.06837v1.pdf | A Self-Attentive model for Knowledge Tracing | Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized... | ['George Karypis', 'Shalini Pandey'] | 2019-07-16 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 3.42042907e-03 -9.33963284e-02 -6.58634722e-01 -2.04217628e-01
-2.31238633e-01 -4.90227491e-01 4.57990825e-01 3.11139435e-01
-3.94065440e-01 7.05000520e-01 3.76295686e-01 -2.77692080e-01
-6.46081388e-01 -1.03249109e+00 -7.91480541e-01 -3.67075235e-01
3.39384675e-01 3.09670568e-01 3.11841547e-01 -2.74762660... | [10.127674102783203, 7.12978982925415] |
c8a8f80c-27bd-4d8e-8971-b609414849ab | dense-event-ordering-with-a-multi-pass | null | null | https://aclanthology.org/Q14-1022 | https://aclanthology.org/Q14-1022.pdf | Dense Event Ordering with a Multi-Pass Architecture | The past 10 years of event ordering research has focused on learning partial orderings over document events and time expressions. The most popular corpus, the TimeBank, contains a small subset of the possible ordering graph. Many evaluations follow suit by only testing certain pairs of events (e.g., only main verbs of ... | ['Taylor Cassidy', 'Nathanael Chambers', 'Bill McDowell', 'Steven Bethard'] | 2014-01-01 | null | null | null | tacl-2014-1 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-5.77989072e-02 6.72426820e-01 -4.70443994e-01 -7.51641512e-01
-5.99458277e-01 -8.94467413e-01 9.77192819e-01 8.45127046e-01
-5.92285037e-01 9.96751487e-01 6.59719408e-01 -4.31731820e-01
-4.01135355e-01 -8.74192894e-01 -6.27762973e-01 -2.82735586e-01
-8.32638979e-01 1.12817311e+00 7.26007998e-01 -3.89559984... | [9.057971000671387, 9.171116828918457] |
d739129a-fa7b-4aef-893c-b56ee52f6c51 | on-the-stability-of-low-pass-graph-filter | 2110.07234 | null | https://arxiv.org/abs/2110.07234v1 | https://arxiv.org/pdf/2110.07234v1.pdf | On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires | Recently, the stability of graph filters has been studied as one of the key theoretical properties driving the highly successful graph convolutional neural networks (GCNs). The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamental building block for... | ['Hoi-To Wai', 'Yiran He', 'Hoang-Son Nguyen'] | 2021-10-14 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.59214770e-02 4.59486008e-01 1.43844873e-01 2.76688248e-01
4.03774798e-01 -7.83771217e-01 4.60097015e-01 3.76221120e-01
-2.13280216e-01 5.48153639e-01 6.25463994e-03 -5.12326479e-01
-3.96177977e-01 -9.70701933e-01 -1.10340595e+00 -9.71555829e-01
-4.45774049e-01 -2.99410462e-01 6.05673432e-01 -4.42821383... | [6.82613468170166, 6.063237190246582] |
01e06066-2a0f-47d7-9ccf-600a8ad32465 | time-series-kernel-similarities-for | 1801.06845 | null | http://arxiv.org/abs/1801.06845v2 | http://arxiv.org/pdf/1801.06845v2.pdf | Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs | We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to ... | ['Filippo Maria Bianchi', 'Miroslaw Malek', 'Jelena Milosevic', 'Alberto Ferrante', 'Lorenzo Livi'] | 2018-01-21 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.35481882e-01 -3.93297940e-01 1.50782436e-01 -5.48061207e-02
-3.85883451e-01 -8.00639093e-01 2.84022063e-01 6.63797557e-01
-4.14438188e-01 5.66794097e-01 -8.22640806e-02 -5.18946409e-01
-6.59086585e-01 -7.99649298e-01 -7.36098960e-02 -7.51384377e-01
-6.40733302e-01 4.42872375e-01 -2.40186706e-01 2.11036727... | [14.217367172241211, 3.234929323196411] |
37aa221a-f468-459c-b85e-2742c09da98a | deep-partial-multi-view-learning | 2011.06170 | null | https://arxiv.org/abs/2011.06170v1 | https://arxiv.org/pdf/2011.06170v1.pdf | Deep Partial Multi-View Learning | Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-... | ['QinGhua Hu', 'Huazhu Fu', 'Joey Tianyi Zhou', 'Zongbo Han', 'Yajie Cui', 'Changqing Zhang'] | 2020-11-12 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.86926627e-01 2.10399568e-01 -6.89310968e-01 -3.71597588e-01
-6.53326929e-01 -5.99524975e-01 4.69152153e-01 -4.86617297e-01
4.14198160e-01 6.72703683e-01 4.43960011e-01 1.78177655e-01
-4.97804314e-01 -7.35107660e-01 -8.81192684e-01 -7.31432498e-01
1.71255052e-01 1.82132393e-01 -6.63371444e-01 3.63872498... | [8.475774765014648, 4.549767017364502] |
494fcbc5-9fc6-424e-abe5-8633b363f4a7 | atlas-end-to-end-3d-scene-reconstruction-from | 2003.10432 | null | https://arxiv.org/abs/2003.10432v3 | https://arxiv.org/pdf/2003.10432v3.pdf | Atlas: End-to-End 3D Scene Reconstruction from Posed Images | We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene. We hypothesize... | ['Zak Murez', 'James Bartolozzi', 'Ayan Sinha', 'Vijay Badrinarayanan', 'Tarrence van As', 'Andrew Rabinovich'] | 2020-03-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/277_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520409.pdf | eccv-2020-8 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 5.88643849e-01 3.80492240e-01 2.19670072e-01 -9.39621568e-01
-1.04815984e+00 -6.85361445e-01 6.53925002e-01 5.65764830e-02
-5.20062029e-01 2.01973274e-01 8.37291777e-02 -1.09398291e-01
3.32668453e-01 -7.04607606e-01 -1.09571791e+00 -3.44917357e-01
4.13213134e-01 1.01783764e+00 5.50662637e-01 -1.12042241... | [8.542558670043945, -2.8833975791931152] |
f5c6270b-4e70-481d-86c7-e5dea85076de | frustum-pointnets-for-3d-object-detection | 1711.08488 | null | http://arxiv.org/abs/1711.08488v2 | http://arxiv.org/pdf/1711.08488v2.pdf | Frustum PointNets for 3D Object Detection from RGB-D Data | In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach ... | ['Wei Liu', 'Leonidas J. Guibas', 'Chenxia Wu', 'Hao Su', 'Charles R. Qi'] | 2017-11-22 | frustum-pointnets-for-3d-object-detection-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Qi_Frustum_PointNets_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [-3.40012312e-02 -1.37952968e-01 9.96765494e-02 -1.48858875e-01
-6.87306941e-01 -9.49267387e-01 6.03965759e-01 1.92543909e-01
-3.98171484e-01 -7.71665573e-02 -3.70827019e-01 -3.76882225e-01
3.62758219e-01 -7.49139369e-01 -9.42387342e-01 -2.56848931e-01
-3.59332673e-02 8.62512589e-01 7.93021977e-01 -1.14640556... | [7.678022861480713, -2.6551260948181152] |
7d51dd4c-6c16-4d31-9e72-0de3b9f2ceac | evaluating-impact-of-user-cluster-targeted | 2305.04694 | null | https://arxiv.org/abs/2305.04694v1 | https://arxiv.org/pdf/2305.04694v1.pdf | Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders | In practice, users of a Recommender System (RS) fall into a few clusters based on their preferences. In this work, we conduct a systematic study on user-cluster targeted data poisoning attacks on Matrix Factorisation (MF) based RS, where an adversary injects fake users with falsely crafted user-item feedback to promote... | ['Douglas Leith', 'Sulthana Shams'] | 2023-05-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 4.78099510e-02 -2.75645763e-01 -2.94691175e-01 2.19660047e-02
-3.27652603e-01 -1.29249740e+00 4.28262383e-01 1.71438649e-01
-2.55149156e-01 3.61497372e-01 1.00312838e-02 -5.38718522e-01
-1.28236249e-01 -9.32214975e-01 -6.40519977e-01 -7.76368618e-01
-4.64523584e-01 7.53323063e-02 2.08675385e-01 -4.16651487... | [5.8349690437316895, 7.224748134613037] |
39ee0e96-0fc7-4b8a-baec-101b965fc1f2 | unbiased-scene-graph-generation-in-videos | 2304.00733 | null | https://arxiv.org/abs/2304.00733v3 | https://arxiv.org/pdf/2304.00733v3.pdf | Unbiased Scene Graph Generation in Videos | The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the visual relationships in addition to the already existing challenges in image-based SGG. Existing methods... | ['Amit K. Roy Chowdhury', 'Subarna Tripathi', 'Kyle Min', 'Sayak Nag'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 3.68889481e-01 3.26930322e-02 1.27087161e-02 -2.83650666e-01
-6.35956109e-01 -4.22537327e-01 7.86736608e-01 -2.96128005e-01
2.98655242e-01 7.28828073e-01 6.41275406e-01 -6.54249564e-02
-4.96965945e-02 -5.16139388e-01 -1.05407178e+00 -4.57433492e-01
-2.53227085e-01 5.70476234e-01 2.25586250e-01 -7.16057718... | [10.662290573120117, -0.18708930909633636] |
3298dd76-fce5-4167-80c1-bd5555b2de37 | dual-self-distillation-of-u-shaped-networks | 2306.03271 | null | https://arxiv.org/abs/2306.03271v1 | https://arxiv.org/pdf/2306.03271v1.pdf | Dual self-distillation of U-shaped networks for 3D medical image segmentation | U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework for U-shaped networks for 3D medical image segmentation. DSD distills knowledge from the ground-truth segmentation labels to the decoder layer... | ['Carri Glide-Hurst', 'Ming Dong', 'Soumyanil Banerjee'] | 2023-06-05 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.52328968e-01 8.12185049e-01 -1.37142986e-01 -5.70201933e-01
-6.25711977e-01 -6.04558885e-01 1.27765790e-01 8.05243384e-03
-3.10664684e-01 4.07926500e-01 -8.52167830e-02 -6.78259611e-01
3.12667161e-01 -7.90407360e-01 -7.24982440e-01 -5.13199091e-01
-3.17485929e-01 5.71470201e-01 6.10530257e-01 6.39086813... | [14.627425193786621, -2.4818644523620605] |
5a85ffe9-8c0f-48c4-b9f3-b8a25dc9b6bb | continuous-spectral-reconstruction-from-rgb | 2112.13003 | null | https://arxiv.org/abs/2112.13003v2 | https://arxiv.org/pdf/2112.13003v2.pdf | Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation | Existing methods for spectral reconstruction usually learn a discrete mapping from RGB images to a number of spectral bands. However, this modeling strategy ignores the continuous nature of spectral signature. In this paper, we propose Neural Spectral Reconstruction (NeSR) to lift this limitation, by introducing a nove... | ['Zhiwei Xiong', 'Lizhi Wang', 'Chang Chen', 'Mingde Yao', 'Ruikang Xu'] | 2021-12-24 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.15290940e-01 -3.35500151e-01 -1.73836157e-01 -5.51519156e-01
-5.75635552e-01 -3.75739425e-01 5.28915882e-01 -5.64208090e-01
-2.75694489e-01 5.42521596e-01 2.29269341e-01 -3.23145688e-02
-2.80235112e-01 -1.15969169e+00 -9.08405602e-01 -7.62331605e-01
4.26006436e-01 -1.96849450e-01 -6.18230738e-02 -3.41329485... | [10.249170303344727, -2.017453908920288] |
87fd8b28-f6a0-4abe-a835-09ac618e4aea | mug-multi-human-graph-network-for-3d-mesh | 2205.12583 | null | https://arxiv.org/abs/2205.12583v2 | https://arxiv.org/pdf/2205.12583v2.pdf | MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose | Reconstructing multi-human body mesh from a single monocular image is an important but challenging computer vision problem. In addition to the individual body mesh models, we need to estimate relative 3D positions among subjects to generate a coherent representation. In this work, through a single graph neural network,... | ['James Wang', 'Xianfeng Tang', 'Yandong Li', 'Chenyan Wu'] | 2022-05-25 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-9.72928330e-02 1.01146564e-01 1.96236111e-02 -1.38004750e-01
-4.01415914e-01 -4.44089696e-02 4.28738110e-02 -2.43047670e-01
3.02288570e-02 3.86697054e-01 6.90125227e-02 4.23190504e-01
9.45171192e-02 -9.46707606e-01 -9.01959538e-01 -2.13236541e-01
7.88481683e-02 1.03660297e+00 4.70984310e-01 -4.01505649... | [7.035866737365723, -1.0481677055358887] |
d0b473a5-4b43-4e02-b176-f712196891bb | unigeo-unifying-geometry-logical-reasoning | 2212.02746 | null | https://arxiv.org/abs/2212.02746v1 | https://arxiv.org/pdf/2212.02746v1.pdf | UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression | Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning capability on multipl... | ['Xiaodan Liang', 'Chongyu Chen', 'Liang Lin', 'Pan Lu', 'Jinghui Qin', 'Tong Li', 'Jiaqi Chen'] | 2022-12-06 | null | null | null | null | ['mathematical-reasoning', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [-7.48604462e-02 2.66966922e-03 1.26597032e-01 -1.69758961e-01
-8.09719861e-01 -6.58952236e-01 3.09838355e-01 -4.07355018e-02
-3.22528854e-02 6.24382079e-01 -9.52901468e-02 -7.41668105e-01
-3.35922807e-01 -1.39908957e+00 -1.35323715e+00 -2.01683044e-01
1.20760515e-01 3.81374419e-01 -4.76895683e-02 -4.86890882... | [9.487225532531738, 7.441948890686035] |
7a66892b-e4ac-4f1a-b3ec-608b8616631a | deep-neural-mel-subband-beamformer-for-in-car | 2211.12590 | null | https://arxiv.org/abs/2211.12590v2 | https://arxiv.org/pdf/2211.12590v2.pdf | Deep Neural Mel-Subband Beamformer for In-car Speech Separation | While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use. In this paper, we propo... | ['Dong Yu', 'Shi-Xiong Zhang', 'Meng Yu', 'Yong Xu', 'Vinay Kothapally'] | 2022-11-22 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 4.78837937e-02 -7.89941728e-01 1.33266985e-01 -9.35271382e-02
-1.03546739e+00 -5.15196025e-01 3.72962922e-01 1.66896671e-01
-3.56990874e-01 4.00138229e-01 5.57636797e-01 -3.72125626e-01
-4.10560727e-01 -5.38450480e-01 -2.25123629e-01 -1.06367576e+00
5.25266258e-03 -2.63265282e-01 3.56546074e-01 -1.31186903... | [15.038055419921875, 5.82928991317749] |
0174870c-3a2d-4135-ad9d-193d8e52640c | structured-matrix-completion-with | 1504.01823 | null | http://arxiv.org/abs/1504.01823v1 | http://arxiv.org/pdf/1504.01823v1.pdf | Structured Matrix Completion with Applications to Genomic Data Integration | Matrix completion has attracted significant recent attention in many fields
including statistics, applied mathematics and electrical engineering. Current
literature on matrix completion focuses primarily on independent sampling
models under which the individual observed entries are sampled independently.
Motivated by a... | ['Tianxi Cai', 'T. Tony Cai', 'Anru Zhang'] | 2015-04-08 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 6.89763904e-01 1.64382160e-01 -5.06158113e-01 -2.89344966e-01
-1.02813518e+00 -3.50630492e-01 1.63881734e-01 2.85349786e-01
-9.00765210e-02 9.69829142e-01 4.67466503e-01 -2.57625014e-01
-5.83130777e-01 -5.58898091e-01 -7.66757905e-01 -9.52278018e-01
-2.15165794e-01 2.41314918e-01 -5.97080529e-01 1.72973067... | [7.083174228668213, 4.619277477264404] |
eb4b1262-57ee-4640-97c3-b58ffde478f9 | medical-relation-extraction-with-manifold | null | null | https://aclanthology.org/P14-1078 | https://aclanthology.org/P14-1078.pdf | Medical Relation Extraction with Manifold Models | null | ['James Fan', 'Chang Wang'] | 2014-06-01 | null | null | null | acl-2014-6 | ['medical-relation-extraction'] | ['medical'] | [-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... | [-7.371366500854492, 3.60776948928833] |
d013a701-b933-4328-abae-028b421190e2 | badlad-a-large-multi-domain-bengali-document | 2303.05325 | null | https://arxiv.org/abs/2303.05325v3 | https://arxiv.org/pdf/2303.05325v3.pdf | BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset | While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA... | ['Md. Rezwanul Haque', 'Asif Shahriyar Sushmit', 'Ahmed Imtiaz Humayun', 'Tahsin Reasat', 'Farig Sadeque', 'Sayma Sultana Chowdhury', 'Marsia Haque Meghla', 'Akib Hasan Pavel', 'Souhardya Saha Dip', 'Shahriar Elahi Dhruvo', 'Fazle Rabbi Rakib', 'Intesur Ahmed', 'MD. Nazmuddoha Ansary', 'Syed Mobassir Hossen', 'Mahfuzur... | 2023-03-09 | null | null | null | null | ['optical-character-recognition', 'document-layout-analysis'] | ['computer-vision', 'computer-vision'] | [ 4.99165393e-02 -4.23392296e-01 9.92318988e-02 -2.61884600e-01
-9.62791502e-01 -1.10164940e+00 9.58264589e-01 1.55300528e-01
-3.82692724e-01 6.50911212e-01 3.53345811e-01 -5.89527488e-01
-2.03519747e-01 -8.02114487e-01 -9.63461876e-01 -5.21549463e-01
2.17132762e-01 9.17691290e-01 -4.78216708e-02 -1.35567471... | [11.794074058532715, 2.6332714557647705] |
f726a8cf-1367-4c72-a876-bebed467ef32 | catalyzing-clinical-diagnostic-pipelines | 2103.14969 | null | https://arxiv.org/abs/2103.14969v2 | https://arxiv.org/pdf/2103.14969v2.pdf | Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future | Deep learning has made a remarkable impact in the field of natural image processing over the past decade. Consequently, there is a great deal of interest in replicating this success across unsolved tasks in related domains, such as medical image analysis. Core to medical image analysis is the task of semantic segmentat... | ['Teofilo E. Zosa'] | 2021-03-27 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 5.48506916e-01 4.21741933e-01 -2.34657764e-01 -3.47814500e-01
-6.79920554e-01 -3.10031474e-01 2.25950703e-01 2.58450866e-01
-6.47987664e-01 4.64226902e-01 1.36047676e-01 -5.85520744e-01
-4.43338931e-01 -5.11703789e-01 -3.84899259e-01 -8.45598936e-01
-3.67137641e-01 5.74478984e-01 5.89727201e-02 -3.12839478... | [14.501468658447266, -2.5057566165924072] |
84461cab-7c59-4de9-8c7b-d1f9d2773c1e | the-second-dicova-challenge-dataset-and | 2110.01177 | null | https://arxiv.org/abs/2110.01177v3 | https://arxiv.org/pdf/2110.01177v3.pdf | The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics | The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for res... | ['Sriram Ganapathy', 'Pravin Mote', 'Debottam Dutta', 'Debarpan Bhattacharya', 'Srikanth Raj Chetupalli', 'Neeraj Kumar Sharma'] | 2021-10-04 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.91403258e-01 -5.07740796e-01 7.14755058e-01 -2.56590873e-01
-1.04525018e+00 -5.84376156e-01 7.60941058e-02 3.76233160e-01
-4.58392203e-01 3.04713994e-01 2.50296742e-01 1.64325014e-01
-8.92720371e-02 -1.21316664e-01 -1.18960030e-01 -7.98541844e-01
-4.94603842e-01 5.31351089e-01 1.04545452e-01 1.98576719... | [14.490256309509277, 4.021620750427246] |
b17a10e5-ac70-4a37-a2d9-71e7689e6655 | knowledge-base-question-answering-via | null | null | https://aclanthology.org/D18-1242 | https://aclanthology.org/D18-1242.pdf | Knowledge Base Question Answering via Encoding of Complex Query Graphs | Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question a... | ['Xusheng Luo', 'Fengli Lin', 'Kenny Zhu', 'Kangqi Luo'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.21079105e-01 2.86037832e-01 -1.25785530e-01 -2.90912628e-01
-1.18170476e+00 -9.22722936e-01 4.32775736e-01 7.05385983e-01
-5.61684966e-01 9.62642074e-01 3.40294808e-01 -5.05602419e-01
-3.81158203e-01 -1.10197258e+00 -5.65350771e-01 2.11669222e-01
3.61930192e-01 1.16588795e+00 9.20973241e-01 -8.98851037... | [10.58431625366211, 7.9632110595703125] |
ba1faee1-75f2-4eef-aafa-6567970039b7 | weakly-supervised-action-localization-and | 2012.09542 | null | https://arxiv.org/abs/2012.09542v3 | https://arxiv.org/pdf/2012.09542v3.pdf | Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN | 3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the information loss seems unavoidable. To improve the visual explanations and classification in 3D CNN, we propose two approaches; i) aggregate l... | ['Takio Kurita', 'Muthu Subash Kavitha', 'Novanto Yudistira'] | 2020-12-17 | null | null | null | null | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 0.2006249 0.1910245 -0.5464479 -0.23186015 -0.3139077 -0.08641758
0.6332336 -0.1185597 -0.11673658 0.5736321 0.6193378 0.03021615
0.05600588 -0.44459003 -0.77959806 -0.6547611 -0.32424986 -0.18220565
0.40990293 0.28865254 0.46269786 0.92996126 -1.497337 0.79317176
0.49055034 1.5084934 0.... | [8.170072555541992, 0.5319777727127075] |
89005bb2-912c-4234-a45a-c012be15f1ce | domain-adaptation-for-inertial-measurement | 2304.06489 | null | https://arxiv.org/abs/2304.06489v1 | https://arxiv.org/pdf/2304.06489v1.pdf | Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey | Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc. However, the widespread adoption of these WHAR models is imped... | ['Nirmalya Roy', 'Indrajeet Ghosh', 'Abu Zaher Md Faridee', 'Avijoy Chakma'] | 2023-04-07 | null | null | null | null | ['sports-analytics', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 2.16244519e-01 -3.31893593e-01 -7.71533549e-01 -3.39406103e-01
-1.33347705e-01 6.83730915e-02 3.18044156e-01 2.72335082e-01
-4.20417905e-01 1.04258549e+00 5.76221228e-01 8.19521770e-02
-3.28129381e-01 -6.74393833e-01 -4.77162957e-01 -5.07369161e-01
-2.47397363e-01 2.27965727e-01 1.77597627e-02 1.04374975... | [7.435060977935791, 0.7763146758079529] |
a82b9aff-88a4-4604-b050-b06bf831b516 | s-textsuperscript-2-fpn-scale-ware-strip | 2206.07298 | null | https://arxiv.org/abs/2206.07298v2 | https://arxiv.org/pdf/2206.07298v2.pdf | S$^2$-FPN: Scale-ware Strip Attention Guided Feature Pyramid Network for Real-time Semantic Segmentation | Modern high-performance semantic segmentation methods employ a heavy backbone and dilated convolution to extract the relevant feature. Although extracting features with both contextual and semantic information is critical for the segmentation tasks, it brings a memory footprint and high computation cost for real-time a... | ['Xin Hong', 'Tewodros Legesse Munea', 'Chenxi Huang', 'Chenhui Yang', 'Mohammed A. M. Elhassan'] | 2022-06-15 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 3.26637119e-01 -1.19326532e-01 -1.87412411e-01 -4.60871041e-01
-8.19422960e-01 -6.19581714e-02 1.48365542e-01 6.36131829e-03
-4.93987501e-01 5.12198329e-01 -6.43650740e-02 -2.10028335e-01
8.43906179e-02 -1.06696343e+00 -8.78558457e-01 -5.66484571e-01
-4.10681441e-02 -1.53241411e-01 7.36090004e-01 -2.11919203... | [9.347980499267578, -0.43111512064933777] |
36b88120-21ea-41ae-9b13-658985e781bc | tomosam-a-3d-slicer-extension-using-sam-for | 2306.08609 | null | https://arxiv.org/abs/2306.08609v1 | https://arxiv.org/pdf/2306.08609v1.pdf | TomoSAM: a 3D Slicer extension using SAM for tomography segmentation | TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer, a highly capable software platform used for 3D image processing and visualization. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner, based only on a ... | ['Joseph C. Ferguson', 'Sergio Fraile Izquierdo', 'Alexandre Quintart', 'Federico Semeraro'] | 2023-06-14 | null | null | null | null | ['zero-shot-segmentation', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.32165447e-01 -2.89190173e-01 2.07773358e-01 -4.39045697e-01
-8.40339124e-01 -6.94949925e-01 4.55961764e-01 1.69999555e-01
-5.48230827e-01 1.91703826e-01 -4.31074888e-01 -6.85514390e-01
8.38038772e-02 -5.46870232e-01 -2.70102769e-01 -6.90660477e-01
-2.26674285e-02 8.85526955e-01 4.88645434e-01 2.63984621... | [14.05407428741455, -2.889786958694458] |
be79cd85-c757-4114-a9cb-06ee6944b3c2 | perceptual-multi-exposure-fusion | 2210.09604 | null | https://arxiv.org/abs/2210.09604v2 | https://arxiv.org/pdf/2210.09604v2.pdf | Perceptual Multi-Exposure Fusion | As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are to... | ['Xiaoning Liu'] | 2022-10-18 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 6.57816052e-01 -9.18897629e-01 4.94384944e-01 -2.31667757e-01
-7.95836449e-01 -3.83441299e-01 4.70580637e-01 -3.65106873e-02
-4.20915335e-01 6.93479955e-01 1.61786526e-01 -1.87742501e-01
-3.49951148e-01 -7.80826330e-01 -2.84914076e-01 -9.50544655e-01
1.33094974e-02 -6.32314622e-01 4.94887888e-01 -7.19866872... | [10.903839111328125, -2.450108766555786] |
07af6273-5765-45c1-b382-319acebb5072 | smash-a-semantic-enabled-multi-agent-approach | 2105.14915 | null | https://arxiv.org/abs/2105.14915v1 | https://arxiv.org/pdf/2105.14915v1.pdf | SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT | Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the u... | ['Olivier Boissier', 'Fano Ramparany', 'Iago Felipe Trentin', 'Hamed Rahimi'] | 2021-05-31 | null | null | null | null | ['multi-agent-integration'] | ['natural-language-processing'] | [-3.18401694e-01 2.91834831e-01 1.62061676e-01 -4.71544504e-01
1.73209980e-01 -3.45480233e-01 7.24989295e-01 3.00611973e-01
-3.21960717e-01 8.44777107e-01 3.21368039e-01 2.38735288e-01
-5.90649784e-01 -1.03894067e+00 -1.08055267e-02 -7.32462823e-01
6.43097311e-02 9.47765946e-01 5.28181434e-01 -5.19167125... | [8.722533226013184, 6.900644302368164] |
48159a2f-a0e5-452d-aceb-0b3225a40025 | open-world-semi-supervised-generalized | 2305.13533 | null | https://arxiv.org/abs/2305.13533v1 | https://arxiv.org/pdf/2305.13533v1.pdf | Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting | Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled texts belong to novel classes, thereby limiting the practicality of these methods. We argue that the OpenRE setting should be more aligned wit... | ['Jingbo Shang', 'Jiacheng Li', 'William Hogan'] | 2023-05-22 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 8.09026733e-02 5.94330549e-01 -7.79875278e-01 -5.78532040e-01
-7.07388580e-01 -7.76542187e-01 6.26952112e-01 3.46216530e-01
-1.88019872e-01 1.24637330e+00 3.42398673e-01 -3.64283532e-01
-2.26968944e-01 -8.75256240e-01 -6.15374863e-01 -2.85520554e-01
-4.16843668e-02 7.87750900e-01 2.40458578e-01 -2.84480393... | [9.292445182800293, 8.594095230102539] |
edb946b5-617d-412a-972d-86b02160789f | topological-pooling-on-graphs | 2303.14543 | null | https://arxiv.org/abs/2303.14543v1 | https://arxiv.org/pdf/2303.14543v1.pdf | Topological Pooling on Graphs | Graph neural networks (GNNs) have demonstrated a significant success in various graph learning tasks, from graph classification to anomaly detection. There recently has emerged a number of approaches adopting a graph pooling operation within GNNs, with a goal to preserve graph attributive and structural features during... | ['Yulia R. Gel', 'Yuzhou Chen'] | 2023-03-25 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-7.93355256e-02 1.84833080e-01 -2.76463628e-01 -2.52940208e-01
4.85899821e-02 -5.40518224e-01 7.44232893e-01 6.54072225e-01
-1.52319968e-01 3.38009983e-01 2.25123033e-01 -2.09285915e-01
-3.71697277e-01 -1.42441475e+00 -5.32301903e-01 -7.16720939e-01
-7.01517522e-01 1.55989736e-01 4.87267852e-01 -3.83452594... | [6.978485107421875, 6.1916069984436035] |
4fc47f66-43ed-4042-8d5a-96abbc38e59a | rationale-augmented-ensembles-in-language | 2207.00747 | null | https://arxiv.org/abs/2207.00747v1 | https://arxiv.org/pdf/2207.00747v1.pdf | Rationale-Augmented Ensembles in Language Models | Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks. We reconsider rationale-augmented prompting for few-shot in-context learning, where (input -> output) prompts are expanded to (input, rationale -> output) prompts. For rational... | ['Denny Zhou', 'Ed Chi', 'Quoc Le', 'Dale Schuurmans', 'Jason Wei', 'Xuezhi Wang'] | 2022-07-02 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.47509575e-01 2.93726027e-01 6.61183745e-02 -8.48204553e-01
-1.16420019e+00 -7.44820714e-01 6.74418271e-01 5.74339807e-01
-3.22694808e-01 5.11339903e-01 6.57500148e-01 -8.41358840e-01
-4.05666500e-01 -5.39205015e-01 -3.20343256e-01 -1.24801055e-01
4.04304475e-01 4.06211168e-01 1.75614357e-02 -5.26719093... | [10.242905616760254, 7.5866923332214355] |
b96b30b4-b643-4a99-b989-38d3d715c353 | continuous-time-spatiotemporal-calibration-of | 2108.07200 | null | https://arxiv.org/abs/2108.07200v1 | https://arxiv.org/pdf/2108.07200v1.pdf | Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera---IMU System | The rolling shutter (RS) mechanism is widely used by consumer-grade cameras, which are essential parts in smartphones and autonomous vehicles. The RS effect leads to image distortion upon relative motion between a camera and the scene. This effect needs to be considered in video stabilization, structure from motion, an... | ['Yukai Lin', 'Qicheng Yuan', 'Yuan Zhuang', 'Jianzhu Huai'] | 2021-08-16 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 6.87527135e-02 -2.64372736e-01 -1.51123583e-01 -2.02362895e-01
-5.56405902e-01 -5.36823809e-01 3.79503697e-01 -5.74163914e-01
-3.98456931e-01 4.22276437e-01 -3.66756439e-01 -4.44688112e-01
2.82299459e-01 -3.69499356e-01 -1.30688417e+00 -5.87828696e-01
3.38290453e-01 -5.59700429e-02 4.12699610e-01 -1.29347648... | [7.931204319000244, -2.1909754276275635] |
36df7d9a-9f6f-43b9-ba6e-18d9c6eaf890 | analysing-dense-passage-retrieval-for-multi | 2106.08433 | null | https://arxiv.org/abs/2106.08433v2 | https://arxiv.org/pdf/2106.08433v2.pdf | Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering | In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reason... | ['Evangelos Kanoulas', 'Svitlana Vakulenko', 'Nikos Voskarides', 'Georgios Sidiropoulos'] | 2021-06-15 | null | https://aclanthology.org/2021.sustainlp-1.7 | https://aclanthology.org/2021.sustainlp-1.7.pdf | emnlp-sustainlp-2021-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.07358086e-03 -1.62105948e-01 -1.65216208e-01 -3.26837935e-02
-2.03431940e+00 -7.44100153e-01 6.03177667e-01 6.28442764e-01
-4.03698981e-01 8.29543293e-01 3.92887354e-01 -3.57639939e-01
-6.21088386e-01 -9.52825010e-01 -5.47364295e-01 -1.84105664e-01
2.01354533e-01 1.10279441e+00 6.49366021e-01 -6.04784489... | [11.416923522949219, 7.770296096801758] |
8693c6d3-08e4-48de-819c-750f7c4acd6a | food-recommendations-for-reducing-water | null | null | https://www.mdpi.com/2071-1050/14/7/3833 | https://www.mdpi.com/2071-1050/14/7/3833/pdf | Food Recommendations for Reducing Water Footprint | Most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, kitchen assistance, or nutritional and caloric estimation of dishes, ignoring personalized and conscious food recommendations resources of the planet. Therefore, in this work, we present a personalized food... | ['Andrea Turconi', 'Riccardo La Grassa', 'Nicola Landro', 'Ignazio Gallo'] | 2022-04-24 | null | null | null | sustainability-2022-4 | ['food-recommendation'] | ['miscellaneous'] | [-2.70553619e-01 7.44177168e-03 -7.43906438e-01 -2.26491243e-01
2.48342842e-01 -7.60790586e-01 -5.53533658e-02 1.11479104e+00
-1.93723232e-01 2.10599348e-01 7.03845561e-01 -1.68633685e-01
-3.15062881e-01 -1.25256944e+00 -1.70211941e-01 -5.53966939e-01
2.13780701e-01 2.94890583e-01 9.57499593e-02 -4.84319270... | [11.534833908081055, 4.4875617027282715] |
030e5842-469b-4523-a359-7b6064c0f377 | incremental-learning-techniques-for-semantic | 1907.13372 | null | https://arxiv.org/abs/1907.13372v4 | https://arxiv.org/pdf/1907.13372v4.pdf | Incremental Learning Techniques for Semantic Segmentation | Deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when they are required to incrementally learn new tasks. Contemporary incremental learning frameworks focus on image classification and object detection while in this work we formally introduce the incremental learning pro... | ['Umberto Michieli', 'Pietro Zanuttigh'] | 2019-07-31 | null | null | null | null | ['overlapped-100-5', 'overlapped-10-1', 'disjoint-15-5', 'disjoint-10-1', 'disjoint-15-1', 'overlapped-15-5', 'overlapped-15-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 7.62708962e-01 3.07150811e-01 -9.18361247e-02 -5.40619731e-01
-3.88667732e-01 -3.87953818e-01 6.48319483e-01 4.39634383e-01
-9.56427217e-01 1.02406096e+00 -4.25971180e-01 -7.91699812e-03
-1.31138787e-01 -7.16707110e-01 -1.09572208e+00 -8.20929348e-01
-1.25269771e-01 5.00332177e-01 9.34921622e-01 2.54392475... | [9.401266098022461, 1.9618067741394043] |
a0780901-0ecd-46f8-bdd8-1d8b6a465c93 | graph-neural-network-policies-and-imitation | 2210.05252 | null | https://arxiv.org/abs/2210.05252v1 | https://arxiv.org/pdf/2210.05252v1.pdf | Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues | Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with m... | ['Lina M. Rojas-Barahona', 'Fabrice Lefèvre', 'Tanguy Urvoy', 'Thibault Cordier'] | 2022-10-11 | null | https://aclanthology.org/2022.sigdial-1.10 | https://aclanthology.org/2022.sigdial-1.10.pdf | sigdial-acl-2022-9 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 5.10049164e-02 4.89331573e-01 -9.64492410e-02 -2.13091582e-01
-3.03642929e-01 -6.75574064e-01 9.14071798e-01 2.95066237e-02
-6.70580685e-01 1.42930400e+00 2.26536199e-01 -3.48888263e-02
-4.36480455e-02 -5.17097771e-01 -1.14655904e-01 -4.06880021e-01
-1.85491860e-01 1.05679774e+00 4.41420794e-01 -7.98791349... | [13.067849159240723, 8.061893463134766] |
46694094-25a2-4c86-9971-0300cbf70739 | safe-q-learning-for-continuous-time-linear | 2304.13573 | null | https://arxiv.org/abs/2304.13573v1 | https://arxiv.org/pdf/2304.13573v1.pdf | Safe Q-learning for continuous-time linear systems | Q-learning is a promising method for solving optimal control problems for uncertain systems without the explicit need for system identification. However, approaches for continuous-time Q-learning have limited provable safety guarantees, which restrict their applicability to real-time safety-critical systems. This paper... | ['Shubhendu Bhasin', 'Soutrik Bandyopadhyay'] | 2023-04-26 | null | null | null | null | ['q-learning'] | ['methodology'] | [-9.15424302e-02 6.72977269e-01 -7.50997066e-01 2.11485654e-01
-1.08076870e+00 -6.83275402e-01 5.19601218e-02 2.48596266e-01
-3.82625043e-01 1.36470854e+00 -5.12003183e-01 -9.16637182e-01
-6.55379653e-01 -3.82202864e-01 -7.08180130e-01 -8.73763740e-01
-4.05509472e-01 1.95587069e-01 9.25278664e-02 -3.54058504... | [4.80553674697876, 2.281172752380371] |
dcaa0b39-25e3-4c90-9488-c5062404532e | switch-to-generalize-domain-switch-learning | null | null | https://openreview.net/forum?id=H-iABMvzIc | https://openreview.net/pdf?id=H-iABMvzIc | Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification | This paper considers few-shot learning under the cross-domain scenario. The cross-domain setting imposes a critical challenge, i.e., using very few (support) samples to generalize the already-learned model to a novel domain. We hold a hypothesis, i.e., if a deep model is capable to fast generalize itself to different d... | ['Yi Yang', 'Yifan Sun', 'Zhengdong Hu'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.40531862e-01 -1.77113578e-01 -3.75197381e-01 -5.38732708e-01
-4.67358470e-01 -4.81636673e-01 5.87588131e-01 5.96142709e-02
-4.12308723e-01 8.86211693e-01 -2.19586894e-01 -8.64527654e-03
-1.43967211e-01 -1.07555139e+00 -7.90803432e-01 -5.89156806e-01
1.28515996e-02 4.35940653e-01 8.37247849e-01 -3.28429252... | [10.070869445800781, 3.0899853706359863] |
71b54459-670e-40ee-9b86-133463081310 | challenges-facing-the-explainability-of-age | 2303.06640 | null | https://arxiv.org/abs/2303.06640v1 | https://arxiv.org/pdf/2303.06640v1.pdf | Challenges facing the explainability of age prediction models: case study for two modalities | The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology. Despite the growing number of models and their increasing performance, we still know little about how these models work. Numerous examples of failures of AI systems show that ... | ['Przemyslaw Biecek', 'Jacek Rogala', 'Jaroslaw Zygierewicz', 'Weronika Hryniewska-Guzik', 'Mikolaj Spytek'] | 2023-03-12 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.96809147e-02 4.67651486e-01 -3.60803008e-01 -6.99934781e-01
1.17748328e-01 3.05569082e-01 3.70071918e-01 2.90151536e-01
-1.43193230e-01 1.10109484e+00 9.67161655e-02 -4.54083830e-01
-7.34965742e-01 -6.28810227e-01 -4.33573484e-01 -5.36968589e-01
-1.65658116e-01 7.23827183e-01 -1.18173234e-01 1.61725506... | [8.439630508422852, 5.411478042602539] |
8a2303b8-2ca7-49e2-81c4-be93b77a740c | social-biases-in-automatic-evaluation-metrics | 2210.08859 | null | https://arxiv.org/abs/2210.08859v1 | https://arxiv.org/pdf/2210.08859v1.pdf | Social Biases in Automatic Evaluation Metrics for NLG | Many studies have revealed that word embeddings, language models, and models for specific downstream tasks in NLP are prone to social biases, especially gender bias. Recently these techniques have been gradually applied to automatic evaluation metrics for text generation. In the paper, we propose an evaluation method b... | ['Xiaojun Wan', 'Mingqi Gao'] | 2022-10-17 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-4.10604514e-02 4.90161151e-01 -3.85838836e-01 -7.14731216e-01
-4.35510516e-01 -2.72649676e-01 1.12777591e+00 5.75918317e-01
-8.25880826e-01 7.90361643e-01 7.06344247e-01 -2.44996428e-01
-4.43140371e-03 -8.08034062e-01 -4.13007647e-01 -5.38912416e-01
4.22143370e-01 4.90211576e-01 -2.70782471e-01 -3.56523544... | [9.401008605957031, 10.199834823608398] |
f677e065-8258-43b4-b339-e691492f1a31 | federated-distillation-of-natural-language | 2110.02432 | null | https://arxiv.org/abs/2110.02432v2 | https://arxiv.org/pdf/2110.02432v2.pdf | KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks | We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network. KNOT aims to train a (global) student model by learning to minimize the optimal transport cost of its assigned probability distributio... | ['Soujanya Poria', 'Tushar Vaidya', 'Rishabh Bhardwaj'] | 2021-10-06 | federated-distillation-of-natural-language-1 | https://aclanthology.org/2022.coling-1.425 | https://aclanthology.org/2022.coling-1.425.pdf | coling-2022-10 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.18743412e-01 8.21800172e-01 -2.70006388e-01 -6.58433378e-01
-8.45236659e-01 -8.01855087e-01 7.82906175e-01 2.66926348e-01
-6.29667521e-01 1.11626267e+00 -6.52506500e-02 -2.17888877e-01
-2.17743665e-01 -9.22316432e-01 -9.02093768e-01 -6.27887905e-01
2.56095529e-01 8.37579727e-01 4.80598748e-01 2.89902419... | [9.541259765625, 3.4697346687316895] |
a8408a8b-42e5-4802-aaab-d3bdef2dccc8 | pednet-a-persona-enhanced-dual-alternating | null | null | https://aclanthology.org/2020.coling-main.361 | https://aclanthology.org/2020.coling-main.361.pdf | PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation | Endowing a chatbot with a personality is essential to deliver more realistic conversations. Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined persona information. However, generating personalized responses is still a challenging task since th... | ['Liang Pang', 'Shihan Wang', 'Chao Yang', 'Jingxu Yang', 'Wanyue Zhou', 'Bin Jiang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 1.01908930e-01 4.98564601e-01 3.33448023e-01 -6.55956388e-01
-7.22176671e-01 -4.36199814e-01 8.77594233e-01 -4.56432849e-01
-2.25868270e-01 1.02903771e+00 8.68736207e-01 2.48216093e-01
2.06089914e-01 -6.46931350e-01 -1.84456930e-01 -5.33137918e-01
5.28555393e-01 7.43845820e-01 -8.57647657e-02 -7.28430450... | [12.682513236999512, 8.178566932678223] |
de0417b6-4080-4928-9100-2d043323c7b0 | audio-visual-segmentation-with-semantics | 2301.13190 | null | https://arxiv.org/abs/2301.13190v1 | https://arxiv.org/pdf/2301.13190v1.pdf | Audio-Visual Segmentation with Semantics | We propose a new problem called audio-visual segmentation (AVS), in which the goal is to output a pixel-level map of the object(s) that produce sound at the time of the image frame. To facilitate this research, we construct the first audio-visual segmentation benchmark, i.e., AVSBench, providing pixel-wise annotations ... | ['Yiran Zhong', 'Meng Wang', 'Lingpeng Kong', 'Dan Guo', 'Stan Birchfield', 'Jing Zhang', 'Weixuan Sun', 'Jiayi Zhang', 'Jianyuan Wang', 'Xuyang Shen', 'Jinxing Zhou'] | 2023-01-30 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 3.46257448e-01 -2.94600725e-02 2.57221665e-02 -3.55268776e-01
-9.57465768e-01 -6.77860737e-01 3.94265503e-01 1.09455502e-02
-2.18146101e-01 1.88155770e-01 7.86486790e-02 -1.99877053e-01
3.07020605e-01 -5.29487312e-01 -9.34116721e-01 -7.57585406e-01
8.96081179e-02 1.31043315e-01 7.02532351e-01 1.10863119... | [14.819478034973145, 4.747314929962158] |
5f8f77ba-2306-453c-b8d6-aadfd1328404 | prosody-learning-mechanism-for-speech | 2008.05656 | null | https://arxiv.org/abs/2008.05656v1 | https://arxiv.org/pdf/2008.05656v1.pdf | Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit | Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of prosody and the correlation between prosody and semantics together. In this paper, a prosody learning mechanism is proposed to model the proso... | ['Jianzong Wang', 'Zhen Zeng', 'Jing Xiao', 'Ning Cheng'] | 2020-08-13 | null | null | null | null | ['prosody-prediction'] | ['natural-language-processing'] | [-3.65978777e-02 -4.52473313e-02 -5.06475687e-01 -2.58712232e-01
-4.68224496e-01 -2.04050869e-01 1.12371966e-01 -2.49077410e-01
-3.06455493e-01 5.17886281e-01 8.14193666e-01 -1.01943225e-01
4.84122634e-01 -5.78258276e-01 -5.97070098e-01 -6.72060370e-01
5.82316399e-01 -1.78274080e-01 2.26149231e-01 -5.36921859... | [14.952876091003418, 6.575819492340088] |
0e9d14e7-cb40-428f-a6c1-bdf94d1152db | learning-dense-features-for-point-cloud | 2206.06731 | null | https://arxiv.org/abs/2206.06731v2 | https://arxiv.org/pdf/2206.06731v2.pdf | Learning Dense Features for Point Cloud Registration Using a Graph Attention Network | Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning based methods require high computing capacity for processing a large number... | ['Quoc Vinh Lai Dang', 'Hojun Jin', 'Sarvar Hussain Nengroo'] | 2022-06-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.19931409e-01 -4.16955650e-01 -4.83466722e-02 -7.51883760e-02
-8.50287557e-01 -1.25357151e-01 5.41067958e-01 4.73270327e-01
-6.42466307e-01 3.32861096e-01 -2.69492745e-01 1.25195920e-01
-4.21226233e-01 -1.06130159e+00 -7.36296296e-01 -7.33003497e-01
-3.03342879e-01 8.08952928e-01 5.25816023e-01 -1.71294827... | [7.69967794418335, -3.050834894180298] |
b604c9c2-7ba1-4766-9b29-b563d6107fbb | multi-genre-music-transformer-composing-full | 2301.02385 | null | https://arxiv.org/abs/2301.02385v1 | https://arxiv.org/pdf/2301.02385v1.pdf | Multi-Genre Music Transformer -- Composing Full Length Musical Piece | In the task of generating music, the art factor plays a big role and is a great challenge for AI. Previous work involving adversarial training to produce new music pieces and modeling the compatibility of variety in music (beats, tempo, musical stems) demonstrated great examples of learning this task. Though this was l... | ['Abhinav Kaushal Keshari'] | 2023-01-06 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.03284460e-01 -9.02593285e-02 3.26446593e-01 1.44489259e-01
-9.80590701e-01 -1.24399364e+00 7.88969040e-01 -4.35294420e-01
-4.83912081e-02 9.03555036e-01 5.46661794e-01 1.56182989e-01
-1.20231412e-01 -8.61593187e-01 -8.68168354e-01 -4.31453049e-01
-1.22986831e-01 8.16231191e-01 9.08246264e-03 -9.02397275... | [16.03482437133789, 5.520203590393066] |
bd4a9cb2-21f2-4617-b26e-d48ec7c5dbb9 | gpu-acclerated-automated-feature-extraction | 1304.3992 | null | http://arxiv.org/abs/1304.3992v1 | http://arxiv.org/pdf/1304.3992v1.pdf | GPU Acclerated Automated Feature Extraction from Satellite Images | The availability of large volumes of remote sensing data insists on higher
degree of automation in feature extraction, making it a need of the hour.The
huge quantum of data that needs to be processed entails accelerated processing
to be enabled.GPUs, which were originally designed to provide efficient
visualization, ar... | ['D. Shanmukha Rao', 'K. Phani Tejaswi', 'A. V. V. Prasad', 'Thara Nair'] | 2013-04-15 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 5.83499730e-01 -5.28601527e-01 6.75035655e-01 -1.52026594e-01
-3.25227052e-01 -7.40036607e-01 4.57440764e-01 4.02010500e-01
-6.26757264e-01 5.00948906e-01 -2.06502154e-01 -5.22408366e-01
-4.58078772e-01 -1.04372275e+00 7.20256791e-02 -1.09538698e+00
-2.19025061e-01 -7.94547424e-02 5.84086291e-02 -2.08078071... | [9.709338188171387, -1.786978840827942] |
65ac61a1-200b-4fce-851e-a7b0a96a646f | a-graph-to-sequence-model-for-joint-intent | null | null | https://openreview.net/forum?id=T4q0_LdnUbX | https://openreview.net/pdf?id=T4q0_LdnUbX | A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems | Effectively decoding semantic frames in task-oriented dialogue systems remains a challenge, which typically includes intent detection and slot filling. Although RNN-based neural models show promising results by jointly learning of these two tasks, dominant RNNs are primarily focusing on modeling sequential dependencies... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['graph-to-sequence', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.99388951e-01 4.99723852e-01 -1.41249433e-01 -7.01550066e-01
-4.19667304e-01 -2.45931402e-01 5.88720500e-01 1.94920689e-01
-4.05447453e-01 4.19799209e-01 6.98330402e-01 -3.96853447e-01
3.07492465e-01 -6.24342561e-01 -4.37745690e-01 -3.32301170e-01
-5.45621812e-02 7.01823533e-01 2.99303621e-01 -5.24337292... | [12.509243965148926, 7.729620933532715] |
e2701b0f-8ee9-471c-9157-7371b8770124 | towards-a-unified-model-for-generating | 2301.10799 | null | https://arxiv.org/abs/2301.10799v2 | https://arxiv.org/pdf/2301.10799v2.pdf | Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering | The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. To address this,... | ['Pranava Madhyastha', 'Tillman Weyde', 'Chenxi Whitehouse'] | 2023-01-25 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 7.91641884e-03 4.72078621e-01 -5.82057089e-02 -7.05053568e-01
-1.42599261e+00 -5.85989416e-01 7.93682814e-01 1.68413311e-01
1.77906360e-02 6.81799352e-01 6.78996563e-01 -6.04653418e-01
3.16979617e-01 -4.13067400e-01 -6.78641081e-01 1.11383456e-03
5.55334151e-01 8.43911767e-01 1.98636398e-01 -5.36747098... | [10.890806198120117, 1.8762896060943604] |
85fe8546-cc99-42f8-8308-2ac2cdf7e280 | riddle-lidar-data-compression-with-range-1 | 2206.01738 | null | https://arxiv.org/abs/2206.01738v1 | https://arxiv.org/pdf/2206.01738v1.pdf | RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding | Lidars are depth measuring sensors widely used in autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high costs in data storage and transmission. While lidar data can be represented as two interchangeable representations: 3D point clouds and range images, most pre... | ['Dragomir Anguelov', 'Yin Zhou', 'Charles R. Qi', 'Xuanyu Zhou'] | 2022-06-02 | riddle-lidar-data-compression-with-range | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.pdf | cvpr-2022-1 | ['data-compression'] | ['time-series'] | [ 5.79511523e-01 -2.87164748e-01 -1.70406997e-01 -5.61395049e-01
-5.50118744e-01 -3.90942723e-01 6.69559598e-01 1.08036980e-01
-5.21177888e-01 4.60131079e-01 1.26373366e-01 -3.57673049e-01
-1.80897653e-01 -1.36324155e+00 -1.02496386e+00 -2.38375083e-01
-1.98207527e-01 9.52995002e-01 3.39830697e-01 -1.57055080... | [8.179213523864746, -2.9725100994110107] |
d0e582a1-5d4b-4674-99f4-09fffe1abe50 | detecting-adversarial-directions-in-deep | 2306.05873 | null | https://arxiv.org/abs/2306.05873v1 | https://arxiv.org/pdf/2306.05873v1.pdf | Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions | Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design. However, this incline in complexity, and furthermore the increase in the dimensions of the observation came at the cost of volatility that can be taken advantage of v... | ['Jonah Brown-Cohen', 'Ezgi Korkmaz'] | 2023-06-09 | null | null | null | null | ['adversarial-attack', 'atari-games'] | ['adversarial', 'playing-games'] | [ 2.69522481e-02 2.10799471e-01 -2.45961592e-01 1.30129144e-01
-9.30618882e-01 -1.00908577e+00 6.80630624e-01 2.08513632e-01
-5.67480206e-01 8.32361996e-01 -1.87233314e-01 -5.70324838e-01
-4.07315344e-01 -7.33495533e-01 -9.76414859e-01 -1.03103209e+00
-5.29474378e-01 2.46434182e-01 1.10279649e-01 -3.71722817... | [4.260656833648682, 2.310225009918213] |
552bb4b2-6357-40c3-b0a0-03aa26c02986 | image-free-domain-generalization-via-clip-for | 2210.16788 | null | https://arxiv.org/abs/2210.16788v1 | https://arxiv.org/pdf/2210.16788v1.pdf | Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation | RGB-based 3D hand pose estimation has been successful for decades thanks to large-scale databases and deep learning. However, the hand pose estimation network does not operate well for hand pose images whose characteristics are far different from the training data. This is caused by various factors such as illumination... | ['Seungryul Baek', 'Muhammadjon Boboev', 'Jihyeon Kim', 'Dong Uk Kim', 'Hansoo Park', 'Seongyeong Lee'] | 2022-10-30 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-5.77078722e-02 -5.62259376e-01 -2.20223770e-01 -3.88856888e-01
-6.40476704e-01 -4.93894488e-01 1.91340134e-01 -7.16798007e-01
-5.93465924e-01 8.72437239e-01 1.32004440e-01 2.25862086e-01
7.78114200e-02 -4.74428207e-01 -7.00049758e-01 -8.71017873e-01
2.77197212e-01 8.76773417e-01 2.82015026e-01 -3.59318674... | [6.627363204956055, -0.7226851582527161] |
5da16b77-b8b1-42af-a5e0-c66749486976 | range-only-bearing-estimator-for-localization | 2304.08182 | null | https://arxiv.org/abs/2304.08182v1 | https://arxiv.org/pdf/2304.08182v1.pdf | Range-Only Bearing Estimator for Localization and Mapping | Navigation and exploration within unknown environments are typical examples in which simultaneous localization and mapping (SLAM) algorithms are applied. When mobile agents deploy only range sensors without bearing information, the agents must estimate the bearing using the online distance measurement for the localizat... | ['Kerstin Bunte', 'Bayu Jayawardhana', 'Matteo Marcantoni'] | 2023-04-17 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-8.51384103e-02 2.98042390e-02 -1.52278170e-01 -2.61289865e-01
-6.88717186e-01 -6.43344522e-01 4.49732512e-01 1.62317976e-01
-1.01958060e+00 1.06340384e+00 -4.19774294e-01 -2.86398202e-01
-4.25743848e-01 -7.77191520e-01 -7.85165310e-01 -7.82537997e-01
-5.94008327e-01 4.50147718e-01 9.95364562e-02 -3.00041199... | [7.208559036254883, -1.8890053033828735] |
0b866b8c-2003-4bde-90e0-d013d60299c5 | alquist-2-0-alexa-prize-socialbot-based-on | 2011.03259 | null | https://arxiv.org/abs/2011.03259v1 | https://arxiv.org/pdf/2011.03259v1.pdf | Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models | This paper presents the second version of the dialogue system named Alquist competing in Amazon Alexa Prize 2018. We introduce a system leveraging ontology-based topic structure called topic nodes. Each of the nodes consists of several sub-dialogues, and each sub-dialogue has its own LSTM-based model for dialogue manag... | ['Jan Šedivý', 'Martin Matulík', 'Jakub Konrád', 'Petr Marek', 'Jan Pichl'] | 2020-11-06 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-4.35394436e-01 8.89925957e-01 -2.51365572e-01 -4.59228605e-01
-1.42037511e-01 -6.32698715e-01 9.16157484e-01 -5.45251705e-02
-2.43799925e-01 8.00533354e-01 2.21825644e-01 -2.33423650e-01
1.34659633e-01 -1.00202024e+00 1.34363487e-01 -2.20430240e-01
-1.80063725e-01 8.43031108e-01 6.55679643e-01 -7.66525209... | [12.862043380737305, 7.934828758239746] |
f8cf80e3-26eb-425d-8bba-5aa89ca8b583 | covtanet-a-hybrid-tri-level-attention-based | 2101.00691 | null | https://arxiv.org/abs/2101.00691v1 | https://arxiv.org/pdf/2101.00691v1.pdf | CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans | Rapid and precise diagnosis of COVID-19 is one of the major challenges faced by the global community to control the spread of this overgrowing pandemic. In this paper, a hybrid neural network is proposed, named CovTANet, to provide an end-to-end clinical diagnostic tool for early diagnosis, lesion segmentation, and sev... | ['Mohammad Saquib', 'Shaikh Anowarul Fattah', 'Md Maisoon Rahman', 'Shams Nafisa Ali', 'Sakib Chowdhury', 'Md. Jahin Alam', 'Tanvir Mahmud'] | 2021-01-03 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 4.37848806e-01 -2.54646271e-01 -3.46283317e-02 -2.17538372e-01
-5.93869984e-01 -1.61739171e-01 7.67542943e-02 3.29917192e-01
-5.84941566e-01 4.47144985e-01 1.24027327e-01 -1.35286167e-01
-2.88937509e-01 -4.95949805e-01 -2.67953128e-01 -7.75640309e-01
-1.19333051e-01 5.86029470e-01 1.40003845e-01 -4.16402817... | [15.477103233337402, -1.8052953481674194] |
25c948d8-9e78-421a-9883-46efcd67e003 | 3dmaterialgan-learning-3d-shape | 2007.13887 | null | https://arxiv.org/abs/2007.13887v1 | https://arxiv.org/pdf/2007.13887v1.pdf | 3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications | In the field of computer vision, unsupervised learning for 2D object generation has advanced rapidly in the past few years. However, 3D object generation has not garnered the same attention or success as its predecessor. To facilitate novel progress at the intersection of computer vision and materials science, we propo... | ['B. S. Manjunath', 'Devendra K. Jangid', 'Sam Daly', 'Neal R. Brodnik', 'Amil Khan', 'Tresa M. Pollock', 'McLean P. Echlin'] | 2020-07-27 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 5.71666956e-01 4.10631239e-01 3.10500503e-01 -1.62988678e-01
-7.97446966e-01 -5.87859750e-01 8.74607086e-01 -1.11126445e-01
8.78823549e-02 5.59001267e-01 -2.36443251e-01 -1.66742310e-01
-1.12253845e-01 -1.18520033e+00 -9.17254269e-01 -1.17336655e+00
1.86664149e-01 1.20466566e+00 -1.25953868e-01 -2.35205904... | [9.010005950927734, -3.5835235118865967] |
d4b78357-995d-4396-a61a-0d990d767a89 | bi-directional-interaction-network-for-person | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.pdf | Bi-Directional Interaction Network for Person Search | Existing works have designed end-to-end frameworks based on Faster-RCNN for person search. Due to the large receptive fields in deep networks, the feature maps of each proposal, cropped from the stem feature maps, involve redundant context information outside the bounding boxes. However, person search is a fine-grained... | [' Tieniu Tan', ' Chunfeng Song', ' Zhaoxiang Zhang', 'Wenkai Dong'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['person-search'] | ['computer-vision'] | [-1.32684931e-01 -3.71639311e-01 9.14124586e-03 -6.00308239e-01
-2.39415973e-01 -2.84153491e-01 4.30279970e-01 -2.45988250e-01
-8.12456608e-01 4.88572210e-01 1.58963814e-01 2.12915152e-01
-1.59255221e-01 -8.60950232e-01 -5.46078146e-01 -6.58769310e-01
6.85672760e-02 7.41877317e-01 2.61802584e-01 -1.57009602... | [14.84157657623291, 0.7914525866508484] |
a3980b3a-e64d-4fae-bfc9-5209e17e7470 | towards-explainable-artificial-intelligence-1 | 2108.00273 | null | https://arxiv.org/abs/2108.00273v2 | https://arxiv.org/pdf/2108.00273v2.pdf | Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents | Traffic accident anticipation is a vital function of Automated Driving Systems (ADSs) for providing a safety-guaranteed driving experience. An accident anticipation model aims to predict accidents promptly and accurately before they occur. Existing Artificial Intelligence (AI) models of accident anticipation lack a hum... | ['Ruwen Qin', 'Yu Li', 'Muhammad Monjurul Karim'] | 2021-07-31 | null | null | null | null | ['accident-anticipation'] | ['computer-vision'] | [ 1.33777320e-01 5.52900493e-01 -1.44119024e-01 -4.96777475e-01
-4.09930587e-01 1.53781176e-01 3.14015716e-01 -5.89403212e-02
-2.38213062e-01 4.55209047e-01 3.76410782e-01 -6.45899296e-01
-1.94325253e-01 -4.11721200e-01 -7.54901767e-01 -2.93781281e-01
-5.89520521e-02 1.42697096e-01 3.26445729e-01 -5.56745708... | [7.530207633972168, 0.07156240195035934] |
6905df0d-448f-4fbe-bc39-be144563126f | cs-trd-a-cross-sections-tree-ring-detection | 2305.10809 | null | https://arxiv.org/abs/2305.10809v1 | https://arxiv.org/pdf/2305.10809v1.pdf | CS-TRD: a Cross Sections Tree Ring Detection method | This work describes a Tree Ring Detection method for complete Cross-Sections of trees (CS-TRD). The method is based on the detection, processing, and connection of edges corresponding to the tree's growth rings. The method depends on the parameters for the Canny Devernay edge detector ($\sigma$ and two thresholds), a r... | ['Gregory Randall', 'Diego Passarella', 'Henry Marichal'] | 2023-05-18 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.59238964e-01 -8.94386917e-02 6.75167367e-02 -1.15803808e-01
-3.86708260e-01 -3.26700330e-01 3.65164816e-01 6.16000414e-01
-4.06482726e-01 1.09176770e-01 -5.72572291e-01 -6.75890446e-01
-9.16021615e-02 -1.15699041e+00 -1.81238651e-01 -5.19084036e-01
-4.87804443e-01 4.18465614e-01 1.11869204e+00 5.85589781... | [8.337270736694336, -1.4620441198349] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.