paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
80a5353d-822d-4d2d-b717-cf0f3fac2ab3 | pinpointing-why-object-recognition | 2304.05391 | null | https://arxiv.org/abs/2304.05391v1 | https://arxiv.org/pdf/2304.05391v1.pdf | Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies | Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of inequity. Addressing such performance gaps remains a challenge, as little is understood about why performance degrades across incomes or ge... | ['Mark Ibrahim', 'Diane Bouchacourt', 'Caner Hazirbas', 'Melissa Hall', 'Megan Richards', 'Laura Gustafson'] | 2023-04-11 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.70170709e-01 -2.12381989e-01 -3.19976419e-01 -6.94169879e-01
-3.64790618e-01 -5.50282478e-01 5.46318889e-01 3.87903869e-01
-5.60901463e-01 3.51212710e-01 6.91248715e-01 -6.14786863e-01
-1.35209322e-01 -7.68851399e-01 -9.24988449e-01 -2.46970236e-01
1.35477901e-01 -1.04509190e-01 -3.41337055e-01 -2.11275786... | [12.897370338439941, 1.2204035520553589] |
8ca75246-951c-4ffa-bf20-593e7c6dd999 | learning-individual-speaking-styles-for | 2005.08209 | null | https://arxiv.org/abs/2005.08209v1 | https://arxiv.org/pdf/2005.08209v1.pdf | Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis | Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging the importance of con... | ['C. V. Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'K R Prajwal'] | 2020-05-17 | learning-individual-speaking-styles-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Prajwal_Learning_Individual_Speaking_Styles_for_Accurate_Lip_to_Speech_Synthesis_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Prajwal_Learning_Individual_Speaking_Styles_for_Accurate_Lip_to_Speech_Synthesis_CVPR_2020_paper.pdf | cvpr-2020-6 | ['speaker-specific-lip-to-speech-synthesis', 'lip-to-speech-synthesis'] | ['computer-vision', 'computer-vision'] | [ 1.88674718e-01 3.85155380e-01 -4.36846524e-01 -2.10711986e-01
-1.09214270e+00 -5.01209438e-01 5.68858087e-01 -5.65475464e-01
7.49507546e-02 7.91238427e-01 6.84047103e-01 -3.34714085e-01
4.27520603e-01 -2.21782811e-02 -5.69858551e-01 -4.83793855e-01
4.70868409e-01 2.42681086e-01 -9.11151916e-02 -7.88352117... | [14.312758445739746, 4.97475004196167] |
740fd5cc-5c6e-47a4-80ad-4ff130db65a7 | visual-relationship-detection-with-language-1 | 1904.07798 | null | http://arxiv.org/abs/1904.07798v1 | http://arxiv.org/pdf/1904.07798v1.pdf | Visual Relationship Detection with Language prior and Softmax | Visual relationship detection is an intermediate image understanding task
that detects two objects and classifies a predicate that explains the
relationship between two objects in an image. The three components are
linguistically and visually correlated (e.g. "wear" is related to "person" and
"shirt", while "laptop" is... | ['Jaewon Jung', 'Jongyoul Park'] | 2019-04-16 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.29175439e-01 -1.34545460e-01 -1.89813182e-01 -4.43072706e-01
-2.74182200e-01 -5.70168495e-01 7.25571692e-01 3.95983011e-01
-3.41159731e-01 4.11103487e-01 -4.00010534e-02 -5.10465920e-01
-7.88137838e-02 -5.55012405e-01 -9.23461378e-01 -3.96532983e-01
2.16900691e-01 3.95456046e-01 2.80717254e-01 -1.45065278... | [10.355036735534668, 1.604426383972168] |
6b904112-6f44-43ee-b71d-1f59abeb1be5 | visual-relationship-detection-based-on-guided | 1805.10802 | null | http://arxiv.org/abs/1805.10802v1 | http://arxiv.org/pdf/1805.10802v1.pdf | Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation | A thorough comprehension of image content demands a complex grasp of the
interactions that may occur in the natural world. One of the key issues is to
describe the visual relationships between objects. When dealing with real world
data, capturing these very diverse interactions is a difficult problem. It can
be allevia... | ['Françoise Prêteux', 'François Plesse', 'Bertrand Delezoide', 'Alexandru Ginsca'] | 2018-05-28 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.66060859e-01 3.69709805e-02 -4.39085588e-02 -5.52132249e-01
-9.29702595e-02 -6.29363477e-01 7.79869616e-01 4.99511361e-01
-5.89552343e-01 6.14964843e-01 2.22883239e-01 1.21263504e-01
-1.84682071e-01 -7.01134861e-01 -9.36514556e-01 -3.93727005e-01
6.02186769e-02 5.66286147e-01 3.87398154e-01 -2.75883287... | [10.456360816955566, 1.6993961334228516] |
4a6d3a7c-dad6-40b5-ae10-f91cac508f0a | towards-explainable-ai-for-channel-estimation | 2307.00952 | null | https://arxiv.org/abs/2307.00952v1 | https://arxiv.org/pdf/2307.00952v1.pdf | Towards Explainable AI for Channel Estimation in Wireless Communications | Research into 6G networks has been initiated to support a variety of critical artificial intelligence (AI) assisted applications such as autonomous driving. In such applications, AI-based decisions should be performed in a real-time manner. These decisions include resource allocation, localization, channel estimation, ... | ['Laurent Clavier', 'Ali J. Ghandour', 'Yahia Medjahdi', 'Abdul Karim Gizzini'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 3.64051700e-01 1.65310398e-01 -3.32309157e-01 -5.23021281e-01
-3.68970484e-01 -1.36673272e-01 4.59560841e-01 -3.86998989e-02
1.42912775e-01 1.12716079e+00 -1.11994475e-01 -8.95862460e-01
-5.13739705e-01 -7.20333278e-01 -6.83313668e-01 -9.37943876e-01
-4.03801292e-01 3.26963276e-01 -3.70247126e-01 -1.44442022... | [6.220977306365967, 1.4668123722076416] |
628ce173-8088-4eba-a0c7-41e727088db5 | layout-guided-indoor-panorama-inpainting-with | 2301.05624 | null | https://arxiv.org/abs/2301.05624v1 | https://arxiv.org/pdf/2301.05624v1.pdf | Layout-guided Indoor Panorama Inpainting with Plane-aware Normalization | We present an end-to-end deep learning framework for indoor panoramic image inpainting. Although previous inpainting methods have shown impressive performance on natural perspective images, most fail to handle panoramic images, particularly indoor scenes, which usually contain complex structure and texture content. To ... | ['Hung-Kuo Chu', 'Jheng-Wei Su', 'Cheng-Hsiu Chen', 'Chao-Chen Gao'] | 2023-01-13 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.73422015e-01 -1.48993835e-01 3.78293917e-02 -4.79053319e-01
-7.53699601e-01 -2.79604435e-01 3.05603713e-01 -2.96816945e-01
2.18291253e-01 6.49506211e-01 6.40001655e-01 5.46197034e-02
6.38866937e-03 -1.13301432e+00 -1.21153295e+00 -4.68684137e-01
2.51807958e-01 -6.71126246e-02 -1.43596798e-01 -2.46480271... | [10.196840286254883, -2.342907667160034] |
3037ca55-1647-473d-a578-6adfa9c89c33 | esta-an-esports-trajectory-and-action-dataset | 2209.09861 | null | https://arxiv.org/abs/2209.09861v1 | https://arxiv.org/pdf/2209.09861v1.pdf | ESTA: An Esports Trajectory and Action Dataset | Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models. However, data from conventional sports is often unsuitable for research use due to its size, veracity, and accessibility. To address these issues, we turn to esports, a growing domain that encom... | ['Claudio Silva', 'Peter Xenopoulos'] | 2022-09-20 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [-4.59674001e-01 -5.54481268e-01 -6.07514918e-01 5.52623868e-02
-1.14858532e+00 -8.22009683e-01 4.43361133e-01 4.85808961e-02
-5.59344649e-01 5.53600311e-01 6.21261120e-01 -1.33462831e-01
-2.27728814e-01 -1.05009234e+00 -6.91695094e-01 -1.56393990e-01
-1.17207669e-01 4.39447612e-01 6.57939196e-01 -5.35284162... | [6.61002779006958, 0.35257670283317566] |
c79b85a7-57ee-4296-b7d5-5d6f5e3b4fcc | modeling-transitions-of-focal-entities-for | null | null | https://aclanthology.org/2021.acl-long.255 | https://aclanthology.org/2021.acl-long.255.pdf | Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering | Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the convers... | ['Jing Jiang', 'Yunshi Lan'] | 2021-08-01 | null | null | null | acl-2021-5 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.45762697e-01 6.34603798e-01 7.24869072e-02 -5.66405952e-01
-7.01532364e-01 -5.09379268e-01 5.73204637e-01 2.47461259e-01
-2.02567503e-01 1.03323603e+00 7.69274294e-01 -3.76003951e-01
-1.33515298e-01 -1.01911485e+00 -5.99782407e-01 -1.17037885e-01
1.97519273e-01 8.84505570e-01 6.93792880e-01 -6.99620903... | [10.924246788024902, 7.8814849853515625] |
f416e60a-2d22-4f8e-b746-3ade273f06de | generation-of-radiology-findings-in-chest-x | 2306.10448 | null | https://arxiv.org/abs/2306.10448v1 | https://arxiv.org/pdf/2306.10448v1.pdf | Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge | Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient. The information included in Impression is also often covered in Findings. While Findings and Impression can be deduced by inspecting the imag... | ['Dorin Comaniciu', 'Oladimeji Farri', 'Sasa Grbic', 'Constantin Suciu', 'Lucian Mihai Itu', 'Florin Ghesu', 'Awais Mansoor', 'Bogdan Georgescu', 'Sanjeev Kumar Karn', 'George Marica', 'Manuela Daniela Danu'] | 2023-06-18 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 8.39222968e-01 6.34294212e-01 1.49741054e-01 -5.85534930e-01
-1.16461658e+00 -7.31137753e-01 6.24002457e-01 8.24045837e-01
-2.70530432e-01 5.61197996e-01 6.46807671e-01 -9.76568222e-01
-2.95018643e-01 -6.18099809e-01 -5.60190737e-01 -3.65685374e-01
2.86296397e-01 6.25239134e-01 -8.16996992e-02 3.77113849... | [15.045442581176758, -1.3982497453689575] |
4b36dc0d-8ecd-4b29-bf38-81136b8a9380 | memnet-a-persistent-memory-network-for-image | 1708.02209 | null | http://arxiv.org/abs/1708.02209v1 | http://arxiv.org/pdf/1708.02209v1.pdf | MemNet: A Persistent Memory Network for Image Restoration | Recently, very deep convolutional neural networks (CNNs) have been attracting
considerable attention in image restoration. However, as the depth grows, the
long-term dependency problem is rarely realized for these very deep models,
which results in the prior states/layers having little influence on the
subsequent ones.... | ['Ying Tai', 'Jian Yang', 'Xiaoming Liu', 'Chunyan Xu'] | 2017-08-07 | memnet-a-persistent-memory-network-for-image-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Tai_MemNet_A_Persistent_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Tai_MemNet_A_Persistent_ICCV_2017_paper.pdf | iccv-2017-10 | ['color-image-denoising', 'jpeg-artifact-correction'] | ['computer-vision', 'computer-vision'] | [-2.37435848e-02 -1.04498520e-01 -2.48537853e-01 -1.04766399e-01
4.20570858e-02 1.73990190e-01 4.17772233e-01 -3.34561914e-01
-3.46887887e-01 6.72180891e-01 5.52266777e-01 -1.86449394e-01
1.65739641e-01 -1.09937143e+00 -6.75807357e-01 -1.06697643e+00
2.47924119e-01 -2.45094612e-01 4.73599792e-01 -1.84550375... | [11.11847972869873, -2.0987372398376465] |
087c754a-0176-46e1-a445-56c59288bc51 | ppr10k-a-large-scale-portrait-photo | 2105.09180 | null | https://arxiv.org/abs/2105.09180v1 | https://arxiv.org/pdf/2105.09180v1.pdf | PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency | Different from general photo retouching tasks, portrait photo retouching (PPR), which aims to enhance the visual quality of a collection of flat-looking portrait photos, has its special and practical requirements such as human-region priority (HRP) and group-level consistency (GLC). HRP requires that more attention sho... | ['Lei Zhang', 'Xuansong Xie', 'Miaomiao Cui', 'Hui Zeng', 'Jie Liang'] | 2021-05-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.pdf | cvpr-2021-1 | ['photo-retouching'] | ['computer-vision'] | [ 4.46828008e-01 -1.32096961e-01 -3.43345463e-01 -1.92662105e-01
-8.24451387e-01 -4.32458013e-01 2.64167726e-01 -3.77179623e-01
-1.85231432e-01 6.09412909e-01 -8.38811994e-02 -6.49553835e-02
6.27248958e-02 -7.06814110e-01 -7.75949240e-01 -6.92346931e-01
4.05230254e-01 7.95765314e-03 3.99854273e-01 -2.30963126... | [11.243208885192871, -1.2784942388534546] |
6de4d837-4f82-492b-b1ca-ccffa05dddaf | ceil-generalized-contextual-imitation | 2306.14534 | null | https://arxiv.org/abs/2306.14534v1 | https://arxiv.org/pdf/2306.14534v1.pdf | CEIL: Generalized Contextual Imitation Learning | In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a ... | ['Huazhe Xu', 'Donglin Wang', 'Zifeng Zhuang', 'Yachen Kang', 'Li He', 'Jinxin Liu'] | 2023-06-26 | null | null | null | null | ['imitation-learning', 'd4rl'] | ['methodology', 'robots'] | [-3.85781899e-02 2.02550933e-01 -4.11483884e-01 -3.65679264e-01
-9.74017382e-01 -7.57306695e-01 6.20955527e-01 -2.44721219e-01
-9.52779531e-01 8.11100006e-01 1.35441601e-01 -6.40254915e-01
-2.57038146e-01 -1.14419714e-01 -1.28205931e+00 -4.49928999e-01
-9.69604701e-02 6.83904171e-01 -8.63307491e-02 -2.89869085... | [4.1255645751953125, 1.9578382968902588] |
8af4338e-39d4-48bb-ac38-d482d82e045e | meta-generative-attack-on-person | 2301.06286 | null | https://arxiv.org/abs/2301.06286v1 | https://arxiv.org/pdf/2301.06286v1.pdf | Meta Generative Attack on Person Reidentification | Adversarial attacks have been recently investigated in person re-identification. These attacks perform well under cross dataset or cross model setting. However, the challenges present in cross-dataset cross-model scenario does not allow these models to achieve similar accuracy. To this end, we propose our method with t... | ['A V Subramanyam'] | 2023-01-16 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-7.16413110e-02 -6.11377120e-01 -2.82655954e-01 -5.57512999e-01
-7.08926678e-01 -8.51547837e-01 9.02789593e-01 -1.35864586e-01
-5.20861626e-01 7.48984635e-01 -9.82639566e-03 -7.31157064e-02
-7.19787404e-02 -5.16825557e-01 -6.19774461e-01 -2.01329663e-01
-1.50112122e-01 3.80151093e-01 5.45618646e-02 -2.93089956... | [14.66025447845459, 1.0205045938491821] |
9c7aeba3-0e9b-4d1f-8628-fbda913650d1 | unifying-flow-stereo-and-depth-estimation | 2211.05783 | null | https://arxiv.org/abs/2211.05783v1 | https://arxiv.org/pdf/2211.05783v1.pdf | Unifying Flow, Stereo and Depth Estimation | We present a unified formulation and model for three motion and 3D perception tasks: optical flow, rectified stereo matching and unrectified stereo depth estimation from posed images. Unlike previous specialized architectures for each specific task, we formulate all three tasks as a unified dense correspondence matchin... | ['Andreas Geiger', 'DaCheng Tao', 'Fisher Yu', 'Hamid Rezatofighi', 'Jianfei Cai', 'Jing Zhang', 'Haofei Xu'] | 2022-11-10 | null | null | null | null | ['stereo-depth-estimation', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 9.36643332e-02 -5.76758757e-02 -1.32247671e-01 -4.26643848e-01
-6.43949151e-01 -5.46188831e-01 8.95329416e-01 -4.54405457e-01
-5.00380218e-01 5.74297130e-01 7.18934417e-01 1.36054412e-01
-4.28271368e-02 -4.99981284e-01 -6.86367691e-01 -3.54567349e-01
2.73862094e-01 5.22248507e-01 2.84013540e-01 -1.51778370... | [8.644646644592285, -1.946570873260498] |
5bfa3355-0f45-47c6-9da4-62797c30533d | model-unit-exploration-for-sequence-to | 1902.01955 | null | https://arxiv.org/abs/1902.01955v2 | https://arxiv.org/pdf/1902.01955v2.pdf | On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition | In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based ... | ['Antoine Bruguier', 'Anjuli Kannan', 'Rohit Prabhavalkar', 'Kazuki Irie', 'David Rybach', 'Patrick Nguyen'] | 2019-02-05 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 4.40897405e-01 2.02517107e-01 -1.32873967e-01 -3.16831201e-01
-1.50711989e+00 -6.47830844e-01 5.82097471e-01 1.12592448e-02
-7.44466364e-01 5.83310127e-01 6.11570477e-01 -8.11785340e-01
1.64182350e-01 -3.64668339e-01 -6.62386179e-01 -4.03900027e-01
5.26707232e-01 6.13769591e-01 2.00424954e-01 -2.82403916... | [14.335619926452637, 6.832771301269531] |
e9e5c0d7-3707-48ac-a330-ab8c6d062293 | learnable-online-graph-representations-for-3d | 2104.11747 | null | https://arxiv.org/abs/2104.11747v1 | https://arxiv.org/pdf/2104.11747v1.pdf | Learnable Online Graph Representations for 3D Multi-Object Tracking | Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality. Most recent approaches for 3D multi object tracking (MOT) from LIDAR use object dynamics together with a set of handcrafted features to match detec... | ['Luc van Gool', 'Martin Danelljan', 'Alexander Liniger', 'Dengxin Dai', 'Jan-Nico Zaech'] | 2021-04-23 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 1.08431496e-01 -4.20766085e-01 -1.52862072e-01 -1.94657862e-01
-5.05667984e-01 -7.01350868e-01 6.92488670e-01 3.66925657e-01
-5.42486966e-01 4.41964447e-01 -5.81673682e-01 -4.64417845e-01
-2.13780239e-01 -7.96112061e-01 -8.53394508e-01 -6.31814718e-01
-1.53458327e-01 7.97890782e-01 8.52738678e-01 -3.82956751... | [6.614516735076904, -2.2010135650634766] |
a9ca7751-c0c1-4639-9c88-e881cdce8081 | geometric-feature-learning-for-3d-meshes | 2112.01801 | null | https://arxiv.org/abs/2112.01801v3 | https://arxiv.org/pdf/2112.01801v3.pdf | Mesh Convolution with Continuous Filters for 3D Surface Parsing | Geometric feature learning for 3D surfaces is critical for many applications in computer graphics and 3D vision. However, deep learning currently lags in hierarchical modeling of 3D surfaces due to the lack of required operations and/or their efficient implementations. In this paper, we propose a series of modular oper... | ['Ajmal Mian', 'Mubarak Shah', 'Naveed Akhtar', 'Huan Lei'] | 2021-12-03 | null | null | null | null | ['scene-parsing', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.04392886e-01 -1.04643568e-01 2.77141392e-01 -3.55489850e-01
-6.23242378e-01 -4.41153079e-01 4.59622771e-01 4.74382311e-01
-1.78789109e-01 -1.12730920e-01 -2.54501492e-01 -4.11251247e-01
2.35939816e-01 -1.27930975e+00 -1.12561452e+00 -3.37031245e-01
-3.61024827e-01 5.39700031e-01 3.63506854e-01 -5.65764382... | [8.116226196289062, -3.6572461128234863] |
a56c4a8b-31e5-4749-b532-2e33076d81ac | convolutions-through-the-lens-of-tensor | 2307.02275 | null | https://arxiv.org/abs/2307.02275v1 | https://arxiv.org/pdf/2307.02275v1.pdf | Convolutions Through the Lens of Tensor Networks | Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the generalization of theoretical and algorithmic ideas. We provide a new perspective onto convolutions through tensor networks (TNs) which allow reasoning about the underlying tensor multiplications by drawing... | ['Felix Dangel'] | 2023-07-05 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.04073831e-01 5.61489128e-02 2.83770472e-01 -4.03636724e-01
6.43059090e-02 -8.29065859e-01 4.58203673e-01 1.38364151e-01
-6.09746397e-01 3.00172031e-01 3.42681557e-01 -8.34533155e-01
-2.48311728e-01 -8.75324309e-01 -8.47790420e-01 -4.69997436e-01
-6.59821212e-01 8.25976804e-02 6.57840222e-02 -4.61835414... | [6.120996475219727, 4.999866485595703] |
16ffce5f-26ef-49af-b920-06273c58a896 | on-device-model-fine-tuning-with-label | 2211.01163 | null | https://arxiv.org/abs/2211.01163v1 | https://arxiv.org/pdf/2211.01163v1.pdf | On-Device Model Fine-Tuning with Label Correction in Recommender Systems | To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices. To further deal with cross-device data heterogeneity, the offloaded models normally need to be fine-tuned with each individual ... | ['Guihai Chen', 'Chengfei Lyu', 'Shaojie Tang', 'Fan Wu', 'Chaoyue Niu', 'Yucheng Ding'] | 2022-10-21 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-7.75269866e-02 -5.93521953e-01 -5.41774392e-01 -5.27047992e-01
-7.77715087e-01 -5.89125991e-01 6.57798201e-02 -6.45190701e-02
-2.96231449e-01 5.71704805e-01 -3.78085613e-01 -4.81461376e-01
-2.47821689e-01 -6.37706220e-01 -8.01171482e-01 -6.48174167e-01
1.96022585e-01 7.41645634e-01 2.57883728e-01 3.17207128... | [5.925634860992432, 6.259609222412109] |
0ac0fe1a-d019-46d9-ae8e-e61d07268338 | learning-target-oriented-dual-attention-for | 1908.04441 | null | https://arxiv.org/abs/1908.04441v1 | https://arxiv.org/pdf/1908.04441v1.pdf | Learning Target-oriented Dual Attention for Robust RGB-T Tracking | RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a sub... | ['Xiao Wang', 'Rui Yang', 'Jin Tang', 'Yabin Zhu', 'Chenglong Li'] | 2019-08-12 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [-8.34158659e-02 -3.45698327e-01 -4.10232246e-01 -1.74546093e-01
-9.24209118e-01 -5.40737212e-01 4.75042075e-01 -6.49997711e-01
-4.60494131e-01 3.38363439e-01 9.98660251e-02 -1.75660159e-02
5.31821810e-02 -1.22225940e-01 -5.65391779e-01 -1.02372992e+00
6.15846992e-01 1.19735606e-01 5.24956286e-01 1.11853845... | [6.347821235656738, -2.194688320159912] |
21c6f58b-9fc2-4f40-a3db-e7a2a9e55e0f | skeletal-movement-to-color-map-a-novel | 1807.07033 | null | http://arxiv.org/abs/1807.07033v1 | http://arxiv.org/pdf/1807.07033v1.pdf | Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks | We propose a novel skeleton-based representation for 3D action recognition in
videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have
been addressed: First, how to construct a robust representation that easily
captures the spatial-temporal evolutions of motions from skeleton sequences.
Second, how... | ['Alain Crouzil', 'Sergio A. Velastin', 'Pablo Zegers', 'Louahdi Khoudour', 'Huy Hieu Pham'] | 2018-07-18 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.85394484e-01 -2.48457208e-01 -3.95019174e-01 -2.38349885e-01
-2.30133235e-01 1.13155821e-03 5.12808442e-01 -5.47981977e-01
-2.91044623e-01 3.11772794e-01 4.40349311e-01 2.00107738e-01
-1.49051607e-01 -5.58583915e-01 -7.38798976e-01 -7.18387604e-01
-2.24891946e-01 2.56799962e-02 3.92347991e-01 -2.30652705... | [7.839609146118164, 0.377939373254776] |
1da21ea4-5a54-4dba-8d0e-75e53caf23e9 | video-relation-detection-via-tracklet-based | 2108.08669 | null | https://arxiv.org/abs/2108.08669v1 | https://arxiv.org/pdf/2108.08669v1.pdf | Video Relation Detection via Tracklet based Visual Transformer | Video Visual Relation Detection (VidVRD), has received significant attention of our community over recent years. In this paper, we apply the state-of-the-art video object tracklet detection pipeline MEGA and deepSORT to generate tracklet proposals. Then we perform VidVRD in a tracklet-based manner without any pre-cutti... | ['Jun Xiao', 'Yifeng Huang', 'Long Chen', 'Kaifeng Gao'] | 2021-08-19 | null | null | null | null | ['video-visual-relation-detection'] | ['computer-vision'] | [-2.29647189e-01 -1.23407096e-02 -5.71907103e-01 -1.64996028e-01
-8.98444295e-01 -4.61283922e-01 7.35297501e-01 1.01166531e-01
-1.44473597e-01 2.88680941e-01 4.22389358e-01 -3.60560954e-01
1.25059724e-01 -5.04711151e-01 -1.10162890e+00 -1.29467845e-01
-7.99442232e-02 5.74835181e-01 8.82602453e-01 -1.30643705... | [9.383708953857422, 0.7387280464172363] |
acf287cd-207f-4b68-9f29-9d5f8a2d5383 | breaking-the-representation-bottleneck-of | 2211.12781 | null | https://arxiv.org/abs/2211.12781v1 | https://arxiv.org/pdf/2211.12781v1.pdf | Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling | Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning bottleneck, the learning cannot benefit from its rich internal features (e.g., radicals and strokes); and 2) Parameter bottleneck, each indiv... | ['Min Zhang', 'Xuebo Liu', 'Zhijun Wang'] | 2022-11-23 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.36853436e-01 -3.51968586e-01 -4.82967824e-01 -1.72719121e-01
-8.21234643e-01 -6.96982145e-01 6.85330153e-01 -2.79029518e-01
-6.56032383e-01 7.09524632e-01 5.41652799e-01 -8.05858374e-01
6.54376209e-01 -6.60233855e-01 -7.34964311e-01 -7.15397656e-01
5.53261220e-01 4.12724495e-01 -2.72675931e-01 -2.98959792... | [10.45827579498291, 10.251154899597168] |
726c6a73-2b01-4be7-8385-9ac47f2eb3de | variational-inference-for-bayesian-neural | 2305.00934 | null | https://arxiv.org/abs/2305.00934v1 | https://arxiv.org/pdf/2305.00934v1.pdf | Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty | Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a Bayesian approach: Parameter and prediction uncertainties become easily available,... | ['Geir Storvik', 'Aliaksandr Hubin'] | 2023-05-01 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.10913841e-02 3.62525731e-02 -1.46009356e-01 -6.98774099e-01
-7.63394177e-01 -1.92505240e-01 7.74962008e-01 -1.92699850e-01
-4.26928103e-01 1.15835416e+00 4.45714258e-02 -2.38716915e-01
-5.33823431e-01 -6.96551025e-01 -7.82067597e-01 -8.22025776e-01
1.01028524e-01 6.79020047e-01 3.84698540e-01 3.82891893... | [7.1942949295043945, 3.8685572147369385] |
d0305c51-3ce9-4d06-bd10-489a37e39903 | 3d-medical-point-transformer-introducing | 2112.04863 | null | https://arxiv.org/abs/2112.04863v2 | https://arxiv.org/pdf/2112.04863v2.pdf | 3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis | General point clouds have been increasingly investigated for different tasks, and recently Transformer-based networks are proposed for point cloud analysis. However, there are barely related works for medical point clouds, which are important for disease detection and treatment. In this work, we propose an attention-ba... | ['Weidong Cai', 'Dongnan Liu', 'Tiange Xiang', 'Yang song', 'Dingxin Zhang', 'Heng Wang', 'Chaoyi Zhang', 'Jianhui Yu'] | 2021-12-09 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [-1.65114179e-01 5.95280454e-02 -1.18471779e-01 -2.84259290e-01
-5.65962017e-01 -1.90832898e-01 2.73320884e-01 5.95884204e-01
6.33272007e-02 4.44859684e-01 1.32148638e-01 -1.58058345e-01
-4.12145406e-01 -9.71576333e-01 -8.60366464e-01 -8.07385027e-01
-3.26593965e-01 5.54753721e-01 2.96575457e-01 -3.39863211... | [7.960484504699707, -3.6002018451690674] |
5129c75a-f27b-4778-9f11-fc916b294417 | deep-multi-task-learning-for-anomalous | 1907.00749 | null | https://arxiv.org/abs/1907.00749v1 | https://arxiv.org/pdf/1907.00749v1.pdf | Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data | Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection conside... | ['Teruhisa Misu', 'Dario Pompili', 'Vidyasagar Sadhu'] | 2019-06-28 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.76941037e-01 -6.59235269e-02 -1.61511943e-01 -5.80267191e-01
-6.11609995e-01 -4.79466677e-01 6.42619967e-01 7.01892793e-01
-8.81554782e-02 4.92766649e-01 -7.87072331e-02 -9.09649611e-01
-1.18251085e-01 -5.86565733e-01 -7.67041743e-01 -4.36166734e-01
-1.64040849e-01 5.83512187e-01 4.62392181e-01 -6.97212875... | [7.49802827835083, 2.4420418739318848] |
ea093bf1-124e-465b-9045-6808fa20a529 | sparkly-a-simple-yet-surprisingly-strong-tf | null | null | https://dl.acm.org/doi/abs/10.14778/3583140.3583163 | https://dl.acm.org/doi/pdf/10.14778/3583140.3583163 | Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching | Blocking is a major task in entity matching. Numerous blocking solutions have been developed, but as far as we can tell, blocking using the well-known tf/idf measure has received virtually no attention. Yet, when we experimented with tf/idf blocking using Lucene, we found it did quite well. So in this paper we examine ... | ['AnHai Doan', 'Yash Govind', 'Derek Paulsen'] | 2023-04-20 | null | null | null | proceedings-of-the-vldb-endowment-2023-4 | ['blocking'] | ['natural-language-processing'] | [-5.93155265e-01 -4.46546137e-01 -4.18732613e-01 -7.23925829e-01
-8.53441834e-01 -6.26167715e-01 5.22189736e-01 5.31334043e-01
-7.27785051e-01 3.66274834e-01 6.18788183e-01 -7.30993390e-01
-1.22942194e-01 -9.11597967e-01 -7.59312391e-01 -3.92549396e-01
-2.01041207e-01 6.13694847e-01 5.05397379e-01 -2.81783879... | [9.413714408874512, 8.423264503479004] |
2b05f893-e400-4c1f-9e71-f2cfca1ca7f7 | on-computing-universal-plans-for-partially | 2305.16203 | null | https://arxiv.org/abs/2305.16203v2 | https://arxiv.org/pdf/2305.16203v2.pdf | On Computing Universal Plans for Partially Observable Multi-Agent Path Finding | Multi-agent routing problems have drawn significant attention nowadays due to their broad industrial applications in, e.g., warehouse robots, logistics automation, and traffic control. Conventionally, they are modelled as classical planning problems. In this paper, we argue that it is beneficial to formulate them as un... | ['Fangzhen Lin', 'Fengming Zhu'] | 2023-05-25 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.38842794e-02 5.04659355e-01 -2.05347076e-01 -5.15928388e-01
-3.02647799e-01 -7.99632788e-01 3.51051599e-01 5.04803240e-01
-4.52443480e-01 9.30799961e-01 -4.40780111e-02 -3.73243988e-01
-6.42157912e-01 -1.23666096e+00 -4.42932546e-01 -6.41757309e-01
-4.13807482e-01 1.09680402e+00 5.61522007e-01 -5.02758026... | [4.908859729766846, 1.6509590148925781] |
0ea369e4-ed3f-4700-9282-3d27187a2b2d | super-resolution-and-image-re-projection-for | 2210.11129 | null | https://arxiv.org/abs/2210.11129v1 | https://arxiv.org/pdf/2210.11129v1.pdf | Super-Resolution and Image Re-projection for Iris Recognition | Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine... | ['Fernando Alonso-Fernandez', 'Andreas Uhl', 'Eduardo Ribeiro'] | 2022-10-20 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 2.63498336e-01 1.02030829e-01 1.43729514e-02 -4.90945727e-01
-6.58285677e-01 -9.14816260e-02 5.78900099e-01 -4.38551664e-01
-1.67650670e-01 7.85114288e-01 4.98311937e-01 2.46509522e-01
-4.20032322e-01 -7.39198625e-01 -5.54117620e-01 -6.77400410e-01
1.87272370e-01 4.25767899e-01 2.93424409e-02 -3.62165868... | [3.767946243286133, -3.624748706817627] |
1c9adb6e-6a9c-4576-991e-97b3db92a4b0 | improving-action-quality-assessment-using | 2102.10555 | null | https://arxiv.org/abs/2102.10555v2 | https://arxiv.org/pdf/2102.10555v2.pdf | Improving Action Quality Assessment using Weighted Aggregation | Action quality assessment (AQA) aims at automatically judging human action based on a video of the said action and assigning a performance score to it. The majority of works in the existing literature on AQA divide RGB videos into short clips, transform these clips to higher-level representations using Convolutional 3D... | ['Md. Bakhtiar Hasan', 'Hasibul Himel', 'Moshiur Farazi', 'Md. Hasanul Kabir', 'Fakhruddin Gazzali', 'Shafkat Farabi'] | 2021-02-21 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 2.38925576e-01 -1.45598650e-01 -9.15088579e-02 -4.38401163e-01
-7.15001225e-01 -2.28035539e-01 3.97238374e-01 1.33487806e-01
-5.87992251e-01 3.98845315e-01 4.52494711e-01 1.30524442e-01
-2.36698642e-01 -8.15274477e-01 -4.23765153e-01 -5.03398955e-01
-3.24464679e-01 -2.38335222e-01 5.43208599e-01 -8.63026232... | [8.017416954040527, 0.6206842064857483] |
18ff5f17-4c45-4d41-99c0-c2f44694d5a5 | single-shot-freestyle-dance-reenactment | 2012.01158 | null | https://arxiv.org/abs/2012.01158v2 | https://arxiv.org/pdf/2012.01158v2.pdf | Single-Shot Freestyle Dance Reenactment | The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer. In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unse... | ['Lior Wolf', 'Oron Ashual', 'Oran Gafni'] | 2020-12-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Gafni_Single-Shot_Freestyle_Dance_Reenactment_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gafni_Single-Shot_Freestyle_Dance_Reenactment_CVPR_2021_paper.pdf | cvpr-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 5.81898928e-01 1.85554594e-01 3.93346816e-01 -2.00386405e-01
-4.12103087e-01 -5.07390320e-01 5.76016486e-01 -6.73493385e-01
-4.87367779e-01 8.54391634e-01 -2.36054540e-01 1.99865192e-01
4.69460815e-01 -6.15776122e-01 -8.71831417e-01 -6.31876826e-01
2.31057018e-01 7.41737485e-01 4.95859593e-01 -2.62476414... | [11.016790390014648, -0.8139723539352417] |
e99e9975-a0e1-4346-8efb-e94dd5f6638e | on-the-stability-and-generalization-of-1 | 2302.09815 | null | https://arxiv.org/abs/2302.09815v1 | https://arxiv.org/pdf/2302.09815v1.pdf | On the Stability and Generalization of Triplet Learning | Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the th... | ['Feng Zheng', 'Tieliang Gong', 'Weifu Li', 'Bin Gu', 'Xue Jiang', 'Hong Chen', 'Jun Chen'] | 2023-02-20 | null | null | null | null | ['person-re-identification', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.19288006e-01 4.18433435e-02 1.03215210e-01 -6.43476784e-01
-1.18183351e+00 -3.82163018e-01 9.58430246e-02 8.34591463e-02
-5.22060037e-01 8.91034365e-01 -4.85467881e-01 -4.93354410e-01
-5.63930392e-01 -3.87786597e-01 -6.93489134e-01 -1.00796664e+00
-2.01739132e-01 2.61927783e-01 -2.26685911e-01 1.41834691... | [7.639455795288086, 4.147721290588379] |
0ff99deb-4459-45d3-b968-5ea74fcc0ba2 | an-arabic-tweets-sentiment-analysis-dataset | null | null | https://aclanthology.org/2020.osact-1.1 | https://aclanthology.org/2020.osact-1.1.pdf | An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training | As the number of social media users increases, they express their thoughts, needs, socialise and publish their opinions reviews. For good social media sentiment analysis, good quality resources are needed, and the lack of these resources is particularly evident for languages other than English, in particular Arabic. Th... | ['Richard Johansson', 'Stergios Chatzikyriakidis', 'Simon Dobnik', 'Kathrein Abu Kwaik', 'Motaz Saad'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.13652393e-01 3.16670537e-01 -3.18033338e-01 -5.72237074e-01
-5.54966450e-01 -7.66049325e-01 5.99020422e-01 6.33356631e-01
-7.84516096e-01 7.37154007e-01 2.70336270e-01 -1.11188479e-01
5.97986519e-01 -6.30436540e-01 -2.25827798e-01 -4.54245180e-01
3.56724739e-01 3.33235562e-01 1.00483805e-01 -6.91257000... | [11.129498481750488, 6.933278560638428] |
ab2fee0d-2021-4b0c-a230-ad46f779546c | modeling-heterogeneous-hierarchies-with | 2110.14923 | null | https://arxiv.org/abs/2110.14923v2 | https://arxiv.org/pdf/2110.14923v2.pdf | Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones | Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they induce a partial ordering over the entities, which needs to be modeled in order to allow for hierarchical reasoning. However, current KG embed... | ['Jure Leskovec', 'Hongyu Ren', 'Rex Ying', 'Yushi Bai'] | 2021-10-28 | null | http://proceedings.neurips.cc/paper/2021/hash/662a2e96162905620397b19c9d249781-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/662a2e96162905620397b19c9d249781-Paper.pdf | neurips-2021-12 | ['ancestor-descendant-prediction'] | ['graphs'] | [-4.16377753e-01 8.21265399e-01 -3.96533638e-01 -6.85499683e-02
-2.53807575e-01 -7.65658557e-01 4.27391440e-01 5.49239039e-01
-9.03781876e-02 4.67141330e-01 6.99520946e-01 -2.44195163e-01
-7.00635493e-01 -1.29003561e+00 -7.54801154e-01 -3.39286476e-01
-3.30716103e-01 1.04435420e+00 6.24253094e-01 -4.21736807... | [8.77641773223877, 7.830065727233887] |
026867bf-a75d-4f6d-81a6-22711aac75fb | localization-of-ice-rink-for-broadcast-hockey | 2104.10847 | null | https://arxiv.org/abs/2104.10847v1 | https://arxiv.org/pdf/2104.10847v1.pdf | Localization of Ice-Rink for Broadcast Hockey Videos | In this work, an automatic and simple framework for hockey ice-rink localization from broadcast videos is introduced. First, video is broken into video-shots by a hierarchical partitioning of the video frames, and thresholding based on their histograms. To localize the frames on the ice-rink model, a ResNet18-based reg... | ['Alexander Wong', 'John Zelek', 'David A. Clausi', 'Pascale Berunelle Walters', 'Mehrnaz Fani'] | 2021-04-22 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.62721902e-01 -3.14170659e-01 -6.66512921e-03 -1.73973545e-01
-5.87432623e-01 -3.85703206e-01 3.62800986e-01 1.95309650e-02
-5.31155586e-01 3.58976364e-01 1.33415470e-02 4.13185179e-01
-1.38436958e-01 -4.26061064e-01 -9.35317814e-01 -8.54455352e-01
-4.37842727e-01 1.08611673e-01 5.21627128e-01 -9.67385396... | [8.310474395751953, 0.01546634454280138] |
735d189a-71d8-4828-bb12-37402f0363b5 | autonomous-driving-on-curvy-roads-without | null | null | https://ieeexplore.ieee.org/document/9703250 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9703250 | Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method | Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on t... | ['Youmin Zhang', 'Li Li', 'Yakun Ouyang', 'Bai Li'] | 2022-02-03 | null | null | null | ieee-transactions-on-intelligent-8 | ['trajectory-planning'] | ['robots'] | [-2.09029123e-01 3.02074160e-02 -3.54252219e-01 -1.14799216e-01
-3.97184938e-01 -5.09589136e-01 4.27959681e-01 -1.92827642e-01
-5.53690195e-01 9.06462908e-01 -2.20622912e-01 -8.65235686e-01
-4.60176438e-01 -9.88597631e-01 -5.85432589e-01 -7.75145054e-01
1.39013501e-02 3.17364603e-01 2.31467828e-01 -6.27165139... | [5.246136665344238, 1.643401026725769] |
ae180025-ea7b-4302-a916-539217e1e030 | smart-parking-space-detection-under-hazy | 2201.05858 | null | https://arxiv.org/abs/2201.05858v1 | https://arxiv.org/pdf/2201.05858v1.pdf | Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach | Limited urban parking space combined with urbanization has necessitated the development of smart parking systems that can communicate the availability of parking slots to the end users. Towards this, various deep learning based solutions using convolutional neural networks have been proposed for parking space occupatio... | ['Rajendra Kumar Roul', 'Jajati Keshari Sahoo', 'Gaurav Satyanath'] | 2022-01-15 | null | null | null | null | ['parking-space-occupancy'] | ['computer-vision'] | [-2.62966305e-01 -7.97773227e-02 3.49067718e-01 -2.86695004e-01
-2.90276140e-01 1.64456457e-01 8.18432689e-01 -3.40390682e-01
-6.73453987e-01 8.42536569e-01 -1.15066119e-01 -6.17658556e-01
1.34792268e-01 -1.11829197e+00 -4.78472352e-01 -7.96815574e-01
1.79602280e-01 4.02872086e-01 5.20104468e-01 -6.02162302... | [8.082958221435547, -1.1061218976974487] |
13928fa1-9939-4621-87da-b82478e21379 | multi-label-ranking-mining-multi-label-and | 2101.00583 | null | https://arxiv.org/abs/2101.00583v1 | https://arxiv.org/pdf/2101.00583v1.pdf | Multi-label Ranking: Mining Multi-label and Label Ranking Data | We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a... | ['Lihi Dery'] | 2021-01-03 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 6.19414151e-01 -2.11854056e-01 -5.79481363e-01 -8.63290966e-01
-1.17646468e+00 -5.56935310e-01 3.99497598e-01 5.05296350e-01
-4.66766834e-01 7.98178315e-01 5.38478419e-02 2.83338457e-01
-3.69013250e-01 -3.36752683e-01 -2.26836696e-01 -5.51639736e-01
1.87981918e-01 8.59402418e-01 -4.56931531e-01 -4.50240448... | [9.599181175231934, 4.311259746551514] |
ef6304e3-0714-44bd-9eea-9d2c897cfb91 | sentence-level-adaptation-for-low-resource | null | null | https://aclanthology.org/W19-6807 | https://aclanthology.org/W19-6807.pdf | Sentence-Level Adaptation for Low-Resource Neural Machine Translation | null | ['Yash Kumar Lal', 'Aaron Mueller'] | 2019-08-01 | null | null | null | ws-2019-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.509810924530029, 3.680128335952759] |
3e8432f1-fc35-4a38-a18c-b3aeea86ec14 | fine-grained-visual-classification-with-high-1 | 2303.06442 | null | https://arxiv.org/abs/2303.06442v2 | https://arxiv.org/pdf/2303.06442v2.pdf | Fine-grained Visual Classification with High-temperature Refinement and Background Suppression | Fine-grained visual classification is a challenging task due to the high similarity between categories and distinct differences among data within one single category. To address the challenges, previous strategies have focused on localizing subtle discrepancies between categories and enhencing the discriminative featur... | ['Cheng-Hung Lin', 'Yu-Yung Kao', 'Po-Yung Chou'] | 2023-03-11 | fine-grained-visual-classification-with-high | https://arxiv.org/abs/2303.06442 | https://arxiv.org/pdf/2303.06442.pdf | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 0.15352783 -0.7279219 -0.14267574 -0.43234104 -0.49737298 -0.446844
0.55630195 0.2203819 -0.29622158 0.7013547 0.12639724 -0.0292681
-0.14779297 -0.7537077 -0.41238534 -1.1145238 0.18517976 -0.08505777
0.78984684 -0.02668878 0.23263443 0.6656298 -1.8918871 0.6461259
0.9376809 1.4451182 0.2134... | [9.606197357177734, 2.0029964447021484] |
c7512df7-c086-46d5-9fd9-b2cf520b6753 | window-transformer-for-dialogue-document-a | null | null | https://link.springer.com/article/10.1007/s13042-023-01792-y | https://link.springer.com/article/10.1007/s13042-023-01792-y | Window transformer for dialogue document: a joint framework for causal emotion entailment | The Causal Emotion Entailment (CEE) task aims to extract all potential pairs of emotions and corresponding causes from the unannotated emotion document in the conversational context. Most existing methods to solve CEE task follow a two-stage pipeline framework, in which the first stage is to identify emotional clauses ... | ['Geng Tu & Runguo Wei', 'Hao liu', 'Dazhi Jiang'] | 2023-02-24 | null | null | null | international-journal-of-machine-learning-and-2 | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 2.80949622e-01 1.19620115e-01 -1.77724913e-01 -9.34354961e-01
-7.42423534e-01 -5.33971190e-01 5.92913210e-01 1.63694009e-01
-2.52896130e-01 3.18602949e-01 6.96992517e-01 1.23673163e-01
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-1.25837162e-01 1.65254593e-01 -2.36202374e-01 -2.32648775... | [12.61863899230957, 6.216686248779297] |
ee91e3eb-68a4-483e-b23f-8ded137b01f4 | brain-tumor-classification-by-cascaded | 2112.14320 | null | https://arxiv.org/abs/2112.14320v1 | https://arxiv.org/pdf/2112.14320v1.pdf | Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation | Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. Diagnosis and evaluation of brain tumors in the early stages increase the probability of successful treatment. However, the complexity and variety of tumors, shapes, and locations make their segmentation an... | ['Shadrokh Samavi', 'Pejman Khadivi', 'Nader Karimi', 'Zahra Sobhaninia'] | 2021-12-28 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 1.91042915e-01 -1.61431313e-01 -3.03380370e-01 -3.21511775e-01
-6.11360431e-01 -6.76127672e-02 3.50447476e-01 4.06582981e-01
-7.36475408e-01 6.10059142e-01 -1.63092345e-01 -2.16107801e-01
-1.75278366e-01 -5.66908777e-01 1.69333011e-01 -9.91888225e-01
7.25622894e-03 6.39207006e-01 4.71362472e-01 2.12646246... | [14.698792457580566, -2.5222814083099365] |
7bd2ba27-e22e-4e9b-a35e-e68696065b92 | collective-wisdom-improving-low-resource | 2010.05445 | null | https://arxiv.org/abs/2010.05445v1 | https://arxiv.org/pdf/2010.05445v1.pdf | Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation | Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios. A standard approach is transfer learning, which involves taking a model trained on a high-resource language-pair and fine-tuning it on the data of the l... | ['Gholamreza Haffari', 'Wray Buntine', 'Fahimeh Saleh'] | 2020-10-12 | null | https://aclanthology.org/2020.coling-main.302 | https://aclanthology.org/2020.coling-main.302.pdf | coling-2020-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.30020851e-01 5.67058921e-02 -2.14118227e-01 -3.82503510e-01
-1.40409732e+00 -7.45625198e-01 5.01670063e-01 -1.56128123e-01
-6.93423510e-01 1.12517130e+00 2.51235098e-01 -6.88670278e-01
1.78523600e-01 -4.31129456e-01 -1.04806149e+00 -6.24287307e-01
3.96220684e-01 1.00584948e+00 6.24304228e-02 -4.80558723... | [11.639171600341797, 10.224075317382812] |
bf95a9bc-dee8-457b-8990-80de5fb6e2c1 | hr-crime-human-related-anomaly-detection-in | 2108.00246 | null | https://arxiv.org/abs/2108.00246v1 | https://arxiv.org/pdf/2108.00246v1.pdf | HR-Crime: Human-Related Anomaly Detection in Surveillance Videos | The automatic detection of anomalies captured by surveillance settings is essential for speeding the otherwise laborious approach. To date, UCF-Crime is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-... | ['Estefanía Talavera', 'Maya Aghaei', 'Alina Matei', 'Kayleigh Boekhoudt'] | 2021-07-31 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 1.76002190e-01 -2.76108354e-01 5.91838777e-01 -2.27201968e-01
-4.74291682e-01 -5.79579771e-01 8.83249819e-01 8.03351760e-01
-3.45772773e-01 3.44001576e-02 1.86300635e-01 -3.75840098e-01
-1.69117935e-02 -7.82500923e-01 -2.42676169e-01 -3.00028473e-01
-5.44888437e-01 1.12023756e-01 5.07091641e-01 -1.04796536... | [7.876350402832031, 1.4772660732269287] |
83ed47d6-ef02-404d-933e-08d0a9ab4703 | unsupervised-high-fidelity-facial-texture | 2110.04760 | null | https://arxiv.org/abs/2110.04760v1 | https://arxiv.org/pdf/2110.04760v1.pdf | Unsupervised High-Fidelity Facial Texture Generation and Reconstruction | Many methods have been proposed over the years to tackle the task of facial 3D geometry and texture recovery from a single image. Such methods often fail to provide high-fidelity texture without relying on 3D facial scans during training. In contrast, the complementary task of 3D facial generation has not received as m... | ['Ron Kimmel', 'Ibrahim Jubran', 'Ron Slossberg'] | 2021-10-10 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.82979620e-01 5.42979181e-01 3.21960390e-01 -2.93947875e-01
-9.61545706e-01 -2.67325908e-01 9.06952202e-01 -3.70532334e-01
1.07688554e-01 5.25621712e-01 -1.01181015e-01 2.91663408e-02
2.22465098e-01 -9.92921889e-01 -8.94597054e-01 -7.06766903e-01
3.16742420e-01 7.68015563e-01 4.54240069e-02 -3.88069749... | [12.709869384765625, -0.3193938136100769] |
f51671e9-0254-48cd-a236-1b8d03135fb8 | carigan-caricature-generation-through-weakly | 1811.00445 | null | http://arxiv.org/abs/1811.00445v2 | http://arxiv.org/pdf/1811.00445v2.pdf | CariGAN: Caricature Generation through Weakly Paired Adversarial Learning | Caricature generation is an interesting yet challenging task. The primary
goal is to generate plausible caricatures with reasonable exaggerations given
face images. Conventional caricature generation approaches mainly use low-level
geometric transformations such as image warping to generate exaggerated images,
which la... | ['Yang Gao', 'Wenbin Li', 'Jiebo Luo', 'Jing Huo', 'Wei Xiong', 'Haofu Liao'] | 2018-11-01 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 2.11351305e-01 3.87328982e-01 2.41955873e-02 -3.56877387e-01
-5.13719559e-01 -3.85596246e-01 6.25836015e-01 -6.76658511e-01
7.46101961e-02 8.04852188e-01 1.07574709e-01 2.45411605e-01
3.12464178e-01 -9.62875247e-01 -8.50435913e-01 -7.27541447e-01
4.88841981e-01 2.00670704e-01 -1.53033450e-01 -5.13161838... | [12.161757469177246, -0.35785606503486633] |
1a8df427-9afa-4e9c-9982-6ebe4fe5bebd | robust-face-recognition-with-structural | 1506.00481 | null | http://arxiv.org/abs/1506.00481v1 | http://arxiv.org/pdf/1506.00481v1.pdf | Robust Face Recognition with Structural Binary Gradient Patterns | This paper presents a computationally efficient yet powerful binary framework
for robust facial representation based on image gradients. It is termed as
structural binary gradient patterns (SBGP). To discover underlying local
structures in the gradient domain, we compute image gradients from multiple
directions and sim... | ['Weilin Huang', 'Hujun Yin'] | 2015-06-01 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.22583884e-01 -5.52063346e-01 -4.41402912e-01 -5.44217587e-01
-3.24824065e-01 -2.68544644e-01 5.34657896e-01 -8.45023170e-02
-2.37028226e-01 5.08228898e-01 -1.62604507e-02 -1.27430514e-01
-4.53194678e-01 -9.10718143e-01 -2.55891740e-01 -1.05190778e+00
-4.73372221e-01 -3.18652064e-01 3.51806939e-01 -3.04472804... | [10.55929183959961, -0.35410141944885254] |
d99a84d7-9202-47d6-9ffc-4c47611fdcb1 | interpretability-then-what-editing-machine | 2206.15465 | null | https://arxiv.org/abs/2206.15465v1 | https://arxiv.org/pdf/2206.15465v1.pdf | Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values | Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However, how to take action to address these patterns is not always clear. In a collaboration between ML and human-computer interaction researchers,... | ['Rich Caruana', 'Jennifer Wortman Vaughan', 'Mihaela Vorvoreanu', 'Duen Horng Chau', 'Mark E. Nunnally', 'Peter Stella', 'Harsha Nori', 'Alex Kale', 'Zijie J. Wang'] | 2022-06-30 | null | null | null | null | ['additive-models', 'model-editing'] | ['methodology', 'natural-language-processing'] | [ 1.21871345e-01 4.80227888e-01 -2.09908739e-01 -4.84673172e-01
-3.82724226e-01 -5.12956202e-01 -9.92394686e-02 6.34687841e-01
-1.42044425e-01 3.01957250e-01 4.67812061e-01 -1.02967870e+00
-2.50721157e-01 -4.93398517e-01 -3.43417078e-01 7.54738674e-02
2.20403925e-01 7.39793837e-01 -2.83163100e-01 5.69275804... | [8.642718315124512, 6.030359745025635] |
ad8b4abf-fd6b-4fcd-bbce-89e50dfcec45 | functional2structural-cross-modality-brain | 2205.07854 | null | https://arxiv.org/abs/2205.07854v1 | https://arxiv.org/pdf/2205.07854v1.pdf | Functional2Structural: Cross-Modality Brain Networks Representation Learning | MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, such as brain development and disease. Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes... | ['Liang Zhan', 'Heng Huang', 'Paul Thompson', 'Alex Leow', 'Olusola Ajilore', 'Scott Mackin', 'Yalin Wang', 'Lei Guo', 'Xiyao Fu', 'Haoteng Tang'] | 2022-05-06 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 2.70518601e-01 1.81930482e-01 -1.50695518e-01 -4.80098486e-01
1.83536157e-01 -4.13855702e-01 4.51728076e-01 -1.23447031e-01
-1.24041677e-01 6.51920795e-01 2.14379713e-01 -1.06000558e-01
-5.09254873e-01 -7.94276714e-01 -5.85760057e-01 -7.50810087e-01
-5.10398388e-01 4.10352826e-01 2.83354968e-01 5.25299460... | [12.398632049560547, 3.3807077407836914] |
be021dab-928e-4cff-b912-c8b90b63b5ec | numerical-methods-for-convex-multistage | 2303.15672 | null | https://arxiv.org/abs/2303.15672v1 | https://arxiv.org/pdf/2303.15672v1.pdf | Numerical Methods for Convex Multistage Stochastic Optimization | Optimization problems involving sequential decisions in a stochastic environment were studied in Stochastic Programming (SP), Stochastic Optimal Control (SOC) and Markov Decision Processes (MDP). In this paper we mainly concentrate on SP and SOC modelling approaches. In these frameworks there are natural situations whe... | ['Alexander Shapiro', 'Guanghui Lan'] | 2023-03-28 | null | null | null | null | ['stochastic-optimization', 'type'] | ['methodology', 'speech'] | [ 2.55996212e-02 1.45285614e-02 -1.04927614e-01 1.13657461e-02
-5.35651505e-01 -6.24988019e-01 5.68275213e-01 2.75374264e-01
-5.90835273e-01 9.56293106e-01 -3.37077916e-01 -3.70163649e-01
-5.85695326e-01 -6.18561566e-01 -3.73842686e-01 -1.11020577e+00
-7.48334303e-02 8.31988394e-01 3.20827633e-01 -2.89669245... | [4.636254787445068, 2.725917339324951] |
0c5f4870-a606-4476-a4cc-4f6cd2e13718 | neural-sign-language-translation | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Camgoz_Neural_Sign_Language_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Camgoz_Neural_Sign_Language_CVPR_2018_paper.pdf | Neural Sign Language Translation | Sign Language Recognition (SLR) has been an active research field for the last two decades. However, most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign langu... | ['Richard Bowden', 'Oscar Koller', 'Hermann Ney', 'Simon Hadfield', 'Necati Cihan Camgoz'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['sign-language-translation'] | ['computer-vision'] | [ 3.87535781e-01 -1.24795541e-01 -2.38539934e-01 -5.65320373e-01
-1.14938474e+00 -8.23782563e-01 9.45093751e-01 -8.27321231e-01
-8.80568981e-01 4.09584463e-01 7.42524385e-01 -2.62503922e-01
1.43914029e-01 -2.77051270e-01 -7.39743412e-01 -6.69309795e-01
-6.71783164e-02 3.38129371e-01 2.22933609e-02 -1.68154806... | [9.194832801818848, -6.520578384399414] |
630f9dda-0caa-4f98-8503-ec8e5ece1f3d | identifying-consistent-statements-about | 1701.07696 | null | http://arxiv.org/abs/1701.07696v2 | http://arxiv.org/pdf/1701.07696v2.pdf | Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery | Existing algorithms for subgroup discovery with numerical targets do not
optimize the error or target variable dispersion of the groups they find. This
often leads to unreliable or inconsistent statements about the data, rendering
practical applications, especially in scientific domains, futile. Therefore, we
here exte... | ['Luca M. Ghiringhelli', 'Mario Boley', 'Bryan R. Goldsmith', 'Jilles Vreeken'] | 2017-01-26 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 2.69740224e-01 3.76992039e-02 -6.52936757e-01 -4.37954843e-01
-8.90012562e-01 -7.20069349e-01 5.11515319e-01 6.06740773e-01
-4.38508451e-01 1.21998191e+00 5.24198636e-02 -4.30403978e-01
-5.87052345e-01 -7.31087923e-01 -5.58020175e-01 -9.97282743e-01
-3.01820844e-01 7.55057096e-01 2.60890305e-01 1.79693967... | [7.598612308502197, 4.6164984703063965] |
e204fb6b-8e68-4b31-9dbb-ac13851c2680 | eeg-based-emotion-recognition-using-genetic | null | null | https://ieeexplore.ieee.org/document/9588702/ | https://ieeexplore.ieee.org/document/9588702/ | EEG-Based Emotion Recognition Using Genetic Algorithm Optimized Multi-Layer Perceptron | Emotion Recognition is an important problem within Affective Computing and Human Computer Interaction. In recent years, various machine learning models have provided significant progress in the field of emotion recognition. This paper proposes a framework for EEG-based emotion recognition using Multi Layer Perceptron (... | ['Shyam Marjit'] | 2021-11-04 | null | null | null | 2021-international-symposium-of-asian-control | ['eeg', 'eeg-emotion-recognition', 'eeg'] | ['methodology', 'miscellaneous', 'time-series'] | [-6.47110939e-02 -7.62410164e-02 1.62184596e-01 -4.30963963e-01
-1.82369381e-01 -4.40565169e-01 2.77029932e-01 6.61747336e-01
-4.03433621e-01 1.05617356e+00 2.69973911e-02 2.89338857e-01
-2.97865212e-01 -5.69904566e-01 -2.00257838e-01 -8.33717644e-01
-1.87544554e-01 2.51929387e-02 -3.97614807e-01 1.12145543... | [13.300179481506348, 3.275223970413208] |
8936be60-f2e0-4959-9c56-ba6531fff391 | comprehensive-dataset-of-face-manipulations | 2208.11776 | null | https://arxiv.org/abs/2208.11776v2 | https://arxiv.org/pdf/2208.11776v2.pdf | Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools | Digital media (e.g., photographs, video) can be easily created, edited, and shared. Tools for editing digital media are capable of doing so while also maintaining a high degree of photo-realism. While many types of edits to digital media are generally benign, others can also be applied for malicious purposes. State-of-... | ['Kirill Trapeznikov', 'Brian DeCann'] | 2022-08-24 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 4.00922000e-01 2.91349608e-02 1.58437952e-01 -1.69540927e-01
-4.85476106e-01 -8.98718476e-01 5.07772982e-01 -4.12227921e-02
-1.76014483e-01 5.64840376e-01 -2.72613823e-01 -3.30528319e-01
1.70606166e-01 -7.03329980e-01 -6.97756469e-01 -3.77561241e-01
1.22027509e-01 5.90836592e-02 1.79343313e-01 1.10491529... | [12.524069786071777, 1.0587319135665894] |
c2ca5e44-82b5-4974-a216-11683042675f | nonconvex-matrix-factorization-from-rank-one | 1802.06286 | null | http://arxiv.org/abs/1802.06286v2 | http://arxiv.org/pdf/1802.06286v2.pdf | Nonconvex Matrix Factorization from Rank-One Measurements | We consider the problem of recovering low-rank matrices from random rank-one
measurements, which spans numerous applications including covariance sketching,
phase retrieval, quantum state tomography, and learning shallow polynomial
neural networks, among others. Our approach is to directly estimate the
low-rank factor ... | ['Yuxin Chen', 'Yuanxin Li', 'Yuejie Chi', 'Cong Ma'] | 2018-02-17 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.69846314e-01 2.49594584e-01 -1.41709492e-01 -7.82285444e-03
-1.15142035e+00 -6.74244165e-01 4.40085709e-01 2.03928594e-02
-4.92352307e-01 9.11057830e-01 3.59364264e-02 -3.07841212e-01
-4.75575715e-01 -4.31931376e-01 -6.73697174e-01 -9.69639778e-01
-1.60399780e-01 6.34368002e-01 -2.70037234e-01 -9.19982269... | [6.545301914215088, 4.62223482131958] |
afebb6f5-3341-429d-95fc-721a64dd1299 | optimizing-human-interpretable-dialog | 1605.03915 | null | http://arxiv.org/abs/1605.03915v2 | http://arxiv.org/pdf/1605.03915v2.pdf | Optimizing human-interpretable dialog management policy using Genetic Algorithm | Automatic optimization of spoken dialog management policies that are robust
to environmental noise has long been the goal for both academia and industry.
Approaches based on reinforcement learning have been proved to be effective.
However, the numerical representation of dialog policy is
human-incomprehensible and diff... | ['Yonghong Yan', 'Weiqun Xu', 'Hang Ren'] | 2016-05-12 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-1.66398883e-01 2.56429583e-01 8.05392787e-02 -6.19595826e-01
-2.24032700e-01 -7.23998189e-01 6.37210190e-01 9.53388661e-02
-5.99538863e-01 1.24439907e+00 2.47889966e-01 -5.03861129e-01
-2.81919539e-01 -6.01731777e-01 2.58618891e-02 -5.46876669e-01
1.64502487e-01 6.14895642e-01 1.89733699e-01 -6.19003177... | [13.043426513671875, 7.974075794219971] |
0bdfa056-185e-4bf6-8862-07764977c770 | category-level-global-camera-pose-estimation | 2209.14419 | null | https://arxiv.org/abs/2209.14419v1 | https://arxiv.org/pdf/2209.14419v1.pdf | Category-Level Global Camera Pose Estimation with Multi-Hypothesis Point Cloud Correspondences | Correspondence search is an essential step in rigid point cloud registration algorithms. Most methods maintain a single correspondence at each step and gradually remove wrong correspondances. However, building one-to-one correspondence with hard assignments is extremely difficult, especially when matching two point clo... | ['Volkan Isler', 'Nicolai Häni', 'Selim Engin', 'Jun-Jee Chao'] | 2022-09-28 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 4.78733405e-02 -2.07443327e-01 1.46644250e-01 -3.88260067e-01
-1.01455545e+00 -5.65120220e-01 6.16108179e-01 3.30909342e-01
-3.59666348e-01 2.27911398e-01 -2.61964798e-01 4.23263133e-01
-2.86810666e-01 -6.86902285e-01 -8.37852716e-01 -6.30753040e-01
2.74139792e-01 1.24294376e+00 5.98077893e-01 -1.52992129... | [7.7400803565979, -2.877607583999634] |
9cbc9f11-49f4-4fef-9635-9c5c7dcd3efe | lipschitzness-effect-of-a-loss-function-on | 2303.16464 | null | https://arxiv.org/abs/2303.16464v1 | https://arxiv.org/pdf/2303.16464v1.pdf | Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers | The generalization performance of deep neural networks with regard to the optimization algorithm is one of the major concerns in machine learning. This performance can be affected by various factors. In this paper, we theoretically prove that the Lipschitz constant of a loss function is an important factor to diminish ... | ['Amin Gheibi', 'Mohammad Lashkari'] | 2023-03-29 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.85850286e-01 1.70726016e-01 -3.24207127e-01 -5.63174069e-01
-1.49483129e-01 -6.50215968e-02 1.44357830e-01 1.11894928e-01
-8.35509121e-01 8.78716350e-01 -2.74605274e-01 -2.31673151e-01
-3.65836799e-01 -7.70411015e-01 -7.41672456e-01 -1.02382946e+00
2.54109725e-02 2.45428190e-01 4.93832398e-03 2.14281175... | [7.918297290802002, 3.7010858058929443] |
4b941bd3-694d-4377-9a4a-9b4aeb6c61eb | pre-train-self-train-distill-a-simple-recipe | 2204.03642 | null | https://arxiv.org/abs/2204.03642v1 | https://arxiv.org/pdf/2204.03642v1.pdf | Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction | Our work learns a unified model for single-view 3D reconstruction of objects from hundreds of semantic categories. As a scalable alternative to direct 3D supervision, our work relies on segmented image collections for learning 3D of generic categories. Unlike prior works that use similar supervision but learn independe... | ['Shubham Tulsiani', 'Abhinav Gupta', 'Kalyan Vasudev Alwala'] | 2022-04-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Alwala_Pre-Train_Self-Train_Distill_A_Simple_Recipe_for_Supersizing_3D_Reconstruction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Alwala_Pre-Train_Self-Train_Distill_A_Simple_Recipe_for_Supersizing_3D_Reconstruction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 7.75352716e-02 2.33066782e-01 -3.21215272e-01 -7.88984478e-01
-9.54480410e-01 -8.14554453e-01 8.66150796e-01 -3.04700345e-01
3.93692078e-03 4.80828732e-02 4.07704622e-01 -5.95783331e-02
2.13055655e-01 -6.36041284e-01 -1.09747398e+00 -3.06000322e-01
3.09795290e-01 9.59124982e-01 4.50322270e-01 2.31486067... | [8.372900009155273, -3.1174840927124023] |
5cdfc668-9e35-4895-b1fc-87704db3a546 | mix-n-match-ensemble-and-compositional | 2003.07329 | null | https://arxiv.org/abs/2003.07329v2 | https://arxiv.org/pdf/2003.07329v2.pdf | Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning | This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show that none of the existing methods satisfy all three requirements, and demonstrate... | ['T. Yong-Jin Han', 'Jize Zhang', 'Bhavya Kailkhura'] | 2020-03-16 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.43716797e-01 -2.69987851e-01 -3.42083752e-01 -7.83589423e-01
-1.46721697e+00 -8.22046936e-01 4.85373288e-01 2.69542336e-01
-4.50948775e-01 9.82387900e-01 -3.42937112e-01 -6.23785436e-01
-3.67294133e-01 -5.67383230e-01 -8.62906933e-01 -1.01718760e+00
2.00180590e-01 5.83567619e-01 2.23020673e-01 1.42831862... | [8.366475105285645, 4.191654682159424] |
3c831c9a-f10a-4db6-a05c-f303d8ccf673 | from-audio-to-symbolic-encoding | 2302.13401 | null | https://arxiv.org/abs/2302.13401v1 | https://arxiv.org/pdf/2302.13401v1.pdf | From Audio to Symbolic Encoding | Automatic music transcription (AMT) aims to convert raw audio to symbolic music representation. As a fundamental problem of music information retrieval (MIR), AMT is considered a difficult task even for trained human experts due to overlap of multiple harmonics in the acoustic signal. On the other hand, speech recognit... | ['Jiushuang Guo', 'Lingjie Kong', 'Shenli Yuan'] | 2023-02-26 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 5.09686589e-01 2.07563341e-01 2.55852222e-01 -5.75514212e-02
-8.39233339e-01 -3.40615302e-01 7.08626151e-01 -7.49534816e-02
-4.69832152e-01 4.17161614e-01 2.55712748e-01 -1.35617331e-01
-2.91927636e-01 -5.87837338e-01 -6.30967796e-01 -4.28815633e-01
5.85759804e-02 6.63108885e-01 1.77908793e-01 -3.03608447... | [15.790192604064941, 5.361970901489258] |
5932c7db-d1bd-4cc7-b092-c1dc1b4fd1b0 | improving-neural-abstractive-document-1 | null | null | https://aclanthology.org/D18-1441 | https://aclanthology.org/D18-1441.pdf | Improving Neural Abstractive Document Summarization with Structural Regularization | Recent neural sequence-to-sequence models have shown significant progress on short text summarization. However, for document summarization, they fail to capture the long-term structure of both documents and multi-sentence summaries, resulting in information loss and repetitions. In this paper, we propose to leverage th... | ['Xinyan Xiao', 'Yajuan Lyu', 'Wei Li', 'Yuanzhuo Wang'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 4.83782947e-01 1.06439739e-01 -5.09552956e-01 -2.12293029e-01
-1.01401401e+00 -3.89365911e-01 4.64853406e-01 6.98396504e-01
-2.03280702e-01 9.54503000e-01 1.13901234e+00 -5.60455844e-02
2.95724981e-02 -5.43730319e-01 -6.10992908e-01 -3.47229332e-01
9.13313404e-02 1.94356844e-01 -6.13392331e-02 2.68164556... | [12.552239418029785, 9.518001556396484] |
9baf64e8-8472-4e1c-9203-a850caf72cfd | one-comment-from-one-perspective-an-effective | null | null | https://aclanthology.org/2020.coling-main.259 | https://aclanthology.org/2020.coling-main.259.pdf | One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment | The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform{'}s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical pe... | ['Jie zhou', 'Jinchao Zhang', 'Zhiqiang Liu', 'Tengfei Huo'] | 2020-12-01 | null | null | null | coling-2020-8 | ['comment-generation'] | ['natural-language-processing'] | [ 1.74340140e-02 -2.16417477e-01 -1.24702774e-01 -1.11257516e-01
-8.40014338e-01 -8.53133261e-01 5.93188822e-01 -5.21223247e-02
2.08839715e-01 6.28252983e-01 9.62248802e-01 2.15610266e-01
1.37136266e-01 -5.34845173e-01 -4.97532897e-02 -7.27732658e-01
6.32296860e-01 2.67100275e-01 -9.24777053e-03 -5.60145855... | [15.726689338684082, 5.7285075187683105] |
e2929f9f-0a36-45f9-87aa-9ae621699ef8 | bilinear-factor-matrix-norm-minimization-for | 1810.05186 | null | http://arxiv.org/abs/1810.05186v1 | http://arxiv.org/pdf/1810.05186v1.pdf | Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications | The heavy-tailed distributions of corrupted outliers and singular values of
all channels in low-level vision have proven effective priors for many
applications such as background modeling, photometric stereo and image
alignment. And they can be well modeled by a hyper-Laplacian. However, the use
of such distributions g... | ['Zhi-Quan Luo', 'Fanhua Shang', 'Zhouchen Lin', 'Yuanyuan Liu', 'James Cheng'] | 2018-10-11 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 2.67465472e-01 -3.79328579e-01 1.14226751e-02 -9.86343920e-02
-9.79775250e-01 -4.00128424e-01 4.04282957e-01 -2.76742786e-01
-3.67630720e-01 6.89292908e-01 5.69280311e-02 4.29822840e-02
-3.96197885e-02 -1.43039584e-01 -9.07704294e-01 -1.12091398e+00
2.26435751e-01 1.09817691e-01 1.73940673e-01 7.03981444... | [7.677067279815674, 4.30270528793335] |
412d5010-4463-400c-887c-046b71c7ce71 | thai-nested-named-entity-recognition-corpus | null | null | https://aclanthology.org/2022.findings-acl.116 | https://aclanthology.org/2022.findings-acl.116.pdf | Thai Nested Named Entity Recognition Corpus | This paper presents the first Thai Nested Named Entity Recognition (N-NER) dataset. Thai N-NER consists of 264,798 mentions, 104 classes, and a maximum depth of 8 layers obtained from 4,894 documents in the domains of news articles and restaurant reviews. Our work, to the best of our knowledge, presents the largest non... | ['Sarana Nutanong', 'Attapol Rutherford', 'Peerat Limkonchotiwat', 'Can Udomcharoenchaikit', 'Weerayut Buaphet'] | null | null | null | null | findings-acl-2022-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.08325696e-01 -2.97592618e-02 -2.50808865e-01 -4.45791811e-01
-8.24250400e-01 -5.68633378e-01 4.22956973e-01 1.46849483e-01
-1.04956734e+00 9.12547708e-01 6.98732078e-01 -4.64823246e-01
1.87067628e-01 -5.62350214e-01 -5.27212322e-01 -2.34672993e-01
-6.57608034e-03 4.64839131e-01 1.98934004e-01 -3.37543964... | [9.89746379852295, 9.726642608642578] |
43b0eca4-a102-4b93-89ec-0d8662aeac2d | language-agnostic-bert-sentence-embedding | 2007.01852 | null | https://arxiv.org/abs/2007.01852v2 | https://arxiv.org/pdf/2007.01852v2.pdf | Language-agnostic BERT Sentence Embedding | While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embedd... | ['Yinfei Yang', 'Daniel Cer', 'Naveen Arivazhagan', 'Fangxiaoyu Feng', 'Wei Wang'] | 2020-07-03 | null | https://aclanthology.org/2022.acl-long.62 | https://aclanthology.org/2022.acl-long.62.pdf | acl-2022-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-3.06127906e-01 -7.78246075e-02 -4.44350451e-01 -2.15984672e-01
-1.49697983e+00 -7.86576331e-01 1.01169419e+00 3.53683859e-01
-8.43872607e-01 7.49803722e-01 4.83291566e-01 -8.37619960e-01
2.28963777e-01 -4.64822650e-01 -9.86753702e-01 -1.43160731e-01
6.89267814e-02 5.69015265e-01 -2.30716914e-01 -4.66654480... | [11.200488090515137, 9.751641273498535] |
9885f038-13c9-46ac-81a3-6818ddd34c0a | assessing-the-efficacy-of-large-language | 2307.04274 | null | https://arxiv.org/abs/2307.04274v1 | https://arxiv.org/pdf/2307.04274v1.pdf | Assessing the efficacy of large language models in generating accurate teacher responses | (Tack et al., 2023) organized the shared task hosted by the 18th Workshop on Innovative Use of NLP for Building Educational Applications on generation of teacher language in educational dialogues. Following the structure of the shared task, in this study, we attempt to assess the generative abilities of large language ... | ['Tushaar Gangavarapu', 'Wentao Guo', 'Abhishek Masand', 'Yann Hicke'] | 2023-07-09 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-1.91565007e-01 7.02258945e-01 1.15728177e-01 -2.99910814e-01
-9.57314193e-01 -8.98198247e-01 9.79288101e-01 -2.82112323e-02
-2.29690135e-01 1.06128025e+00 6.38677299e-01 -5.02750456e-01
-3.01833481e-01 -5.97762048e-01 -3.05582374e-01 -2.60204673e-01
1.19908527e-01 8.99426341e-01 3.30001563e-01 -6.08217478... | [12.559064865112305, 8.104456901550293] |
cf9d9302-3d15-433a-9c2c-39f393610dd8 | bridging-continuous-and-discrete-spaces | 2305.14599 | null | https://arxiv.org/abs/2305.14599v1 | https://arxiv.org/pdf/2305.14599v1.pdf | Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations | Traditional sentence embedding models encode sentences into vector representations to capture useful properties such as the semantic similarity between sentences. However, in addition to similarity, sentence semantics can also be interpreted via compositional operations such as sentence fusion or difference. It is uncl... | ['Dong Yu', 'Muhao Chen', 'Hongming Zhang', 'Kaiqiang Song', 'Wenlin Yao', 'James Y. Huang'] | 2023-05-24 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity', 'semantic-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 7.15752602e-01 5.41629374e-01 -1.07671268e-01 -9.33623791e-01
-4.64875609e-01 -6.62864387e-01 8.50754261e-01 7.18434930e-01
-3.48195821e-01 3.07752699e-01 1.21718025e+00 -6.94190025e-01
1.46438971e-01 -8.15313756e-01 -4.72398162e-01 -8.85182712e-03
1.33369744e-01 3.25409204e-01 -2.62706906e-01 -5.23431480... | [10.774727821350098, 8.798331260681152] |
bda1fafb-455c-4b3a-874b-34016e3d1f7e | introducing-the-prague-discourse-treebank-10 | null | null | https://aclanthology.org/I13-1011 | https://aclanthology.org/I13-1011.pdf | Introducing the Prague Discourse Treebank 1.0 | null | ["Eva Haji{\\v{c}}ov{\\'a}", "{\\v{S}}{\\'a}rka Zik{\\'a}nov{\\'a}", "Pavl{\\'\\i}na J{\\'\\i}nov{\\'a}", "Ji{\\v{r}}{\\'\\i} M{\\'\\i}rovsk{\\'y}", "Lucie Pol{\\'a}kov{\\'a}", 'Anna Nedoluzhko'] | 2013-10-01 | introducing-the-prague-discourse-treebank-10-1 | https://aclanthology.org/I13-1011 | https://aclanthology.org/I13-1011.pdf | ijcnlp-2013-10 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.290279388427734, 3.7589826583862305] |
d33f743a-5ca1-4a8d-94b5-c42f12893b5d | source-summary-entity-aggregation-in | null | null | https://aclanthology.org/2022.coling-1.526 | https://aclanthology.org/2022.coling-1.526.pdf | Source-summary Entity Aggregation in Abstractive Summarization | In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, Celine Dion and Justin Bieber can be referred to by Canadian singers or celebrities. In this work, we study this phenomenon in the context of summarization, where entities from a source text are gener... | ['Jackie Chi Kit Cheung', 'Annie Louis', 'José Ángel González'] | null | null | null | null | coling-2022-10 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.20364839e-01 7.49584496e-01 -2.22351447e-01 -3.79346699e-01
-1.11458743e+00 -9.40318286e-01 1.01186562e+00 7.59349108e-01
-2.44053185e-01 1.19231439e+00 1.19796252e+00 -7.64562637e-02
1.06834896e-01 -6.71708882e-01 -7.40507722e-01 -1.32661819e-01
1.10098921e-01 5.02750278e-01 2.33854771e-01 -4.40919816... | [12.34532356262207, 9.366759300231934] |
7687646f-48c9-4710-9cc9-1f1d9df31487 | speechocean762-an-open-source-non-native | 2104.01378 | null | https://arxiv.org/abs/2104.01378v2 | https://arxiv.org/pdf/2104.01378v2.pdf | speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment | This paper introduces a new open-source speech corpus named "speechocean762" designed for pronunciation assessment use, consisting of 5000 English utterances from 250 non-native speakers, where half of the speakers are children. Five experts annotated each of the utterances at sentence-level, word-level and phoneme-lev... | ['Yujun Wang', 'Daniel Povey', 'Ke Li', 'YuKai Huang', 'Qiong Song', 'Zhiyong Yan', 'Yongqing Wang', 'Zhiwen Zhang', 'Junbo Zhang'] | 2021-04-03 | null | null | null | null | ['phone-level-pronunciation-scoring'] | ['speech'] | [-1.71152025e-01 2.90952474e-01 2.80694783e-01 -5.68894684e-01
-1.23542428e+00 -7.48752296e-01 1.11083075e-01 8.16899464e-02
-5.09600222e-01 4.94868279e-01 5.94171464e-01 -6.67443693e-01
4.98583555e-01 -3.54378283e-01 -2.82932609e-01 -2.92447567e-01
1.32207006e-01 5.81830263e-01 -2.36750040e-02 -3.31920683... | [14.246417045593262, 6.908626079559326] |
2282a592-3a8d-4936-9be6-2182509f1205 | weakly-supervised-audio-visual-sound-source | 2104.02606 | null | https://arxiv.org/abs/2104.02606v1 | https://arxiv.org/pdf/2104.02606v1.pdf | Weakly-supervised Audio-visual Sound Source Detection and Separation | Learning how to localize and separate individual object sounds in the audio channel of the video is a difficult task. Current state-of-the-art methods predict audio masks from artificially mixed spectrograms, known as Mix-and-Separate framework. We propose an audio-visual co-segmentation, where the network learns both ... | ['Leonid Sigal', 'Tanzila Rahman'] | 2021-03-25 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 6.28957272e-01 -1.21351242e-01 1.30633652e-01 -2.73098856e-01
-1.23523188e+00 -8.71774793e-01 2.46993944e-01 -7.04050735e-02
-2.07758486e-01 1.94689184e-01 3.36844504e-01 2.21945256e-01
-1.06020346e-01 -6.86102435e-02 -1.02771091e+00 -7.26899266e-01
-1.00840971e-01 7.01578557e-02 2.90097058e-01 3.29504579... | [14.863975524902344, 4.959963321685791] |
aad06732-35e5-4d6d-8868-425edbe834c4 | deep-virtual-stereo-odometry-leveraging-deep | 1807.02570 | null | http://arxiv.org/abs/1807.02570v2 | http://arxiv.org/pdf/1807.02570v2.pdf | Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry | Monocular visual odometry approaches that purely rely on geometric cues are
prone to scale drift and require sufficient motion parallax in successive
frames for motion estimation and 3D reconstruction. In this paper, we propose
to leverage deep monocular depth prediction to overcome limitations of
geometry-based monocu... | ['Jörg Stückler', 'Rui Wang', 'Nan Yang', 'Daniel Cremers'] | 2018-07-06 | deep-virtual-stereo-odometry-leveraging-deep-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Nan_Yang_Deep_Virtual_Stereo_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Nan_Yang_Deep_Virtual_Stereo_ECCV_2018_paper.pdf | eccv-2018-9 | ['monocular-visual-odometry'] | ['robots'] | [-1.60826176e-01 8.40326995e-02 -1.47078931e-01 -3.98407608e-01
-4.19716358e-01 -5.00124156e-01 9.09722328e-01 -4.30899024e-01
-3.30658764e-01 7.29607463e-01 4.35592115e-01 -9.20844451e-02
4.73248959e-01 -6.77429259e-01 -9.52069759e-01 -2.89341211e-01
4.78969991e-01 8.43158722e-01 4.10965413e-01 -7.38340542... | [8.164275169372559, -2.3116939067840576] |
f43d2c64-2f1f-45a2-95e0-314e96498d0a | long-term-photometric-consistent-novel-view | 2304.10700 | null | https://arxiv.org/abs/2304.10700v1 | https://arxiv.org/pdf/2304.10700v1.pdf | Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models | Novel view synthesis from a single input image is a challenging task, where the goal is to generate a new view of a scene from a desired camera pose that may be separated by a large motion. The highly uncertain nature of this synthesis task due to unobserved elements within the scene (i.e., occlusion) and outside the f... | ['Marcus A. Brubaker', 'Konstantinos G. Derpanis', 'Fereshteh Forghani', 'Jason J. Yu'] | 2023-04-21 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 5.27834237e-01 9.87567455e-02 3.28767091e-01 -3.26384962e-01
-6.41164184e-01 -9.56972301e-01 9.84260261e-01 -6.81550801e-01
1.58208922e-01 6.66341305e-01 1.13940306e-01 4.59490269e-02
8.61086845e-02 -5.90682089e-01 -1.00867736e+00 -6.13591373e-01
4.91148144e-01 3.46939594e-01 2.66501874e-01 1.57517985... | [9.319002151489258, -2.701500654220581] |
db97c229-8a97-499d-8816-13055d6682c9 | multi-step-ahead-stock-price-prediction-using | 2212.14687 | null | https://arxiv.org/abs/2212.14687v1 | https://arxiv.org/pdf/2212.14687v1.pdf | Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition | Financial time series prediction, a growing research topic, has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-based methods approximate a single function, which is insufficient for ob... | ['Mohammad Mehdi Ebadzadeh', 'Hamid Nasiri'] | 2022-12-24 | null | null | null | null | ['time-series-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-5.18959224e-01 -8.15658152e-01 -9.01133940e-02 -5.58982827e-02
-2.12546736e-01 -2.87683636e-01 3.31620365e-01 -4.06694591e-01
-8.21911320e-02 6.47550821e-01 2.39885584e-01 -3.94786537e-01
-1.55695170e-01 -9.16553855e-01 -1.05842531e-01 -9.36076403e-01
-2.43998915e-02 -6.56476244e-02 1.25063211e-01 -3.49901348... | [4.735313892364502, 4.066105842590332] |
de8cbea3-d4c8-4be9-aa62-d0e3a40fcd3d | trimap-guided-feature-mining-and-fusion | 2112.00510 | null | https://arxiv.org/abs/2112.00510v3 | https://arxiv.org/pdf/2112.00510v3.pdf | Trimap-guided Feature Mining and Fusion Network for Natural Image Matting | Utilizing trimap guidance and fusing multi-level features are two important issues for trimap-based matting with pixel-level prediction. To utilize trimap guidance, most existing approaches simply concatenate trimaps and images together to feed a deep network or apply an extra network to extract more trimap guidance, w... | ['Hongtao Lu', 'Zehuan Yuan', 'Yaoyi Li', 'Zhaozhi Xie', 'Dongdong Yu', 'Weihao Jiang'] | 2021-12-01 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 1.66617244e-01 -2.91790366e-01 -2.74886936e-01 -5.69042802e-01
-8.53457510e-01 -1.82290599e-02 1.92938626e-01 -1.17232122e-01
-1.82037175e-01 3.75001460e-01 1.91641569e-01 1.16726279e-01
-3.32720056e-02 -9.58336771e-01 -9.58155036e-01 -7.32904136e-01
-2.84841433e-02 7.28154182e-02 5.27428746e-01 -2.13304266... | [10.619043350219727, -0.8748883605003357] |
d87e9177-98a8-472e-990a-4f7bc8a50679 | coreference-resolution-for-polish | null | null | https://aclanthology.org/2022.crac-mcr.2 | https://aclanthology.org/2022.crac-mcr.2.pdf | Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task | The paper presents our system for coreference resolution in Polish. We compare the system with previous works for the Polish language as well as with the multilingual approach in the CRAC 2022 Shared Task on Multilingual Coreference Resolution thanks to a universal, multilingual data format and evaluation tool. We disc... | ['Karol Saputa'] | null | null | null | null | crac-acl-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [-3.41956466e-01 3.34300339e-01 -9.48518589e-02 -3.01136136e-01
-1.54373348e+00 -7.85414577e-01 9.27964211e-01 1.29092112e-01
-1.01097369e+00 1.26751661e+00 9.29406524e-01 -1.45510480e-01
-5.64174473e-01 -3.75361085e-01 -1.54474273e-01 -3.56750101e-01
2.27898851e-01 1.58020329e+00 3.77907723e-01 -1.00433636... | [9.327295303344727, 9.614083290100098] |
642128a3-ad34-4907-bd1c-26109edbc47b | approximate-conditional-coverage-via-neural | 2205.14310 | null | https://arxiv.org/abs/2205.14310v3 | https://arxiv.org/pdf/2205.14310v3.pdf | Approximate Conditional Coverage & Calibration via Neural Model Approximations | A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration. That is, such sets should map to the output of well-calibrated selective classifiers, matching the observed frequencies of similar instances. Recent works proposing adapt... | ['Danielle Rasooly', 'Allen Schmaltz'] | 2022-05-28 | null | null | null | null | ['protein-secondary-structure-prediction', 'grammatical-error-detection'] | ['medical', 'natural-language-processing'] | [ 3.51069570e-01 5.67482412e-01 -5.30483067e-01 -1.03306031e+00
-8.07707608e-01 -6.47758543e-01 9.62536395e-01 4.66976941e-01
-3.28049272e-01 9.93948817e-01 3.09446752e-01 9.69258696e-03
-8.37193668e-01 -8.79976928e-01 -8.13067675e-01 -1.00594246e+00
-1.07699715e-01 1.06232083e+00 3.10069650e-01 1.83141068... | [8.01925277709961, 4.102053165435791] |
35956758-8d5e-4ce5-9ef6-a21b6389e941 | domainmix-learning-generalizable-person-re | 2011.11953 | null | https://arxiv.org/abs/2011.11953v3 | https://arxiv.org/pdf/2011.11953v3.pdf | DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations | Existing person re-identification models often have low generalizability, which is mostly due to limited availability of large-scale labeled data in training. However, labeling large-scale training data is very expensive and time-consuming, while large-scale synthetic dataset shows promising value in learning generaliz... | ['Ling Shao', 'Cuicui Kang', 'Fang Zhao', 'Shengcai Liao', 'Wenhao Wang'] | 2020-11-24 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 8.86304602e-02 -3.49232674e-01 -3.11814368e-01 -5.13040602e-01
-6.16667628e-01 -5.82435787e-01 5.36109209e-01 -6.83873072e-02
-6.84709489e-01 8.91800344e-01 1.40116308e-02 2.90733755e-01
-3.13870073e-03 -6.44080460e-01 -3.86856943e-01 -6.99029624e-01
5.63611984e-01 6.89519823e-01 7.92281777e-02 -2.51217615... | [14.779193878173828, 1.1138030290603638] |
c1a2c32b-beef-4915-856c-f38efcea2032 | investigating-the-reordering-capability-in | 2105.04840 | null | https://arxiv.org/abs/2105.04840v1 | https://arxiv.org/pdf/2105.04840v1.pdf | Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation | We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech recognition as an auxiliary task to improve the performance. CTC's success on translation is counter-intuitive due to its monotonicity assump... | ['Hung-Yi Lee', 'Chih-Chiang Chang', 'Yung-Sung Chuang', 'Shun-Po Chuang'] | 2021-05-11 | null | https://aclanthology.org/2021.findings-acl.92 | https://aclanthology.org/2021.findings-acl.92.pdf | findings-acl-2021-8 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.73035994e-01 1.52682126e-01 -5.02422810e-01 -4.56025898e-01
-6.71053410e-01 -5.86044431e-01 9.78789032e-01 -2.92518228e-01
-1.75335094e-01 5.60921609e-01 6.78733945e-01 -1.19211078e+00
8.43361989e-02 -4.36226726e-01 -4.29919988e-01 -4.93221849e-01
-1.59939155e-01 4.76113379e-01 1.28242835e-01 -5.68361163... | [14.421964645385742, 7.203676223754883] |
c787d2ee-5be6-4063-a463-23d3fd17c058 | re-benchmarking-pool-based-active-learning | 2306.08954 | null | https://arxiv.org/abs/2306.08954v1 | https://arxiv.org/pdf/2306.08954v1.pdf | Re-Benchmarking Pool-Based Active Learning for Binary Classification | Active learning is a paradigm that significantly enhances the performance of machine learning models when acquiring labeled data is expensive. While several benchmarks exist for evaluating active learning strategies, their findings exhibit some misalignment. This discrepancy motivates us to develop a transparent and re... | ['Hsuan-Tien Lin', 'Chun-Liang Li', 'Po-Yi Lu'] | 2023-06-15 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.53326276e-02 4.20990795e-01 -7.54832685e-01 -5.79504251e-01
-1.38334823e+00 -7.53372252e-01 6.49117053e-01 3.54980230e-01
-5.53922296e-01 8.61559331e-01 3.10005903e-01 -5.44944882e-01
-2.14624226e-01 -3.51984382e-01 -6.80490017e-01 -5.26021421e-01
2.15208933e-01 4.52491164e-01 9.42712128e-02 3.98220271... | [9.61316204071045, 4.370011806488037] |
8ab5969d-26fb-4fdb-bcc9-c0faebda1174 | horae-an-annotated-dataset-of-books-of-hours | 2012.00351 | null | https://arxiv.org/abs/2012.00351v1 | https://arxiv.org/pdf/2012.00351v1.pdf | HORAE: an annotated dataset of books of hours | We introduce in this paper a new dataset of annotated pages from books of hours, a type of handwritten prayer books owned and used by rich lay people in the late middle ages. The dataset was created for conducting historical research on the evolution of the religious mindset in Europe at this period since the book of h... | ['Christopher Kermorvant', 'Dominique Stutzmann', 'Marie-Laurence Bonhomme', 'Mélodie Boillet'] | 2020-12-01 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-3.25430483e-01 3.07515562e-01 -4.96689528e-01 -5.26410379e-02
-2.99983293e-01 -7.13688135e-01 1.24446952e+00 3.42534065e-01
-4.05880481e-01 8.15134466e-01 7.58249938e-01 2.21041679e-01
-7.75894970e-02 -8.05166960e-01 -2.05697492e-01 -3.53735745e-01
2.87384391e-02 1.01888585e+00 5.08355677e-01 -5.92596233... | [10.154866218566895, 10.307100296020508] |
2019234a-7722-453b-bc4b-9e71900a2446 | estimating-the-performance-of-entity | 2210.01230 | null | https://arxiv.org/abs/2210.01230v2 | https://arxiv.org/pdf/2210.01230v2.pdf | Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org | This paper introduces a novel evaluation methodology for entity resolution algorithms. It is motivated by PatentsView.org, a U.S. Patents and Trademarks Office patent data exploration tool that disambiguates patent inventors using an entity resolution algorithm. We provide a data collection methodology and tailored per... | ['Christina Jones', 'Sarvo Madhavan', 'Youngsoo Baek', 'Emma Hickerson', 'Sokhna A York', 'Olivier Binette'] | 2022-10-03 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [ 2.81511635e-01 2.70334244e-01 -9.15642262e-01 -3.12356874e-02
-8.41799080e-01 -1.06055188e+00 7.60477364e-01 4.17970330e-01
-2.92506933e-01 1.14516973e+00 1.47419363e-01 -1.04185760e+00
-9.47712421e-01 -5.22802889e-01 -3.85465294e-01 -1.92323774e-01
4.44074534e-02 5.78332424e-01 -8.02905336e-02 2.85758048... | [9.722464561462402, 8.252643585205078] |
174d00b3-76d0-4944-b32a-e566f5ddd2d1 | bag-of-tricks-and-a-strong-baseline-for-fgvc | null | null | http://star.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-3180/paper-182.pdf | http://star.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-3180/paper-182.pdf | Bag of Tricks and a Strong Baseline for FGVC | Fine-grained visual classification (FGVC), as a subclass classification task under the superclass, brings
more challenges. However, in addition to fine-grained features, the FungiCLEF 2022 dataset is also
characterized by imbalance and rich meta-information. This motivates us to explore the impact of different
metho... | ['Feng Shuang', 'Fang Gao', 'Zepeng Liu', 'Zhihong Wei', 'Shenshen Du', 'Zhongpeng Cai', 'Liwen Zhang', 'Guochen Xie', 'Keda Lu', 'Hao Chang', 'Jun Yu'] | 2022-09-05 | null | null | null | conference-and-labs-of-the-evaluation-forum | ['fine-grained-image-classification'] | ['computer-vision'] | [-3.36287677e-01 -5.04077971e-01 -3.25201899e-01 -4.98222023e-01
-9.64502394e-01 -1.01286304e+00 9.32964683e-01 2.46139407e-01
-2.84695387e-01 9.84416366e-01 4.25757855e-01 -1.21556140e-01
-4.70767729e-02 -5.42727590e-01 -6.70921683e-01 -6.42357945e-01
2.01591447e-01 3.75383079e-01 3.97886932e-02 3.26207072... | [9.587434768676758, 2.2905683517456055] |
a5829321-4fde-4fc7-b607-7c72b65f8e76 | the-open-catalyst-2022-oc22-dataset-and | 2206.08917 | null | https://arxiv.org/abs/2206.08917v3 | https://arxiv.org/pdf/2206.08917v3.pdf | The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts | The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of OER catalysts. To address this, we devel... | ['C. Lawrence Zitnick', 'Edward H. Sargent', 'Oleksandr Voznyy', 'Jehad Abed', 'Felix Therrien', 'Brandon M. Wood', 'Zachary Ulissi', 'Anuroop Sriram', 'Nima Shoghi', 'Ammar Rizvi', 'Adeesh Kolluru', 'Javier Heras-Domingo', 'Abhishek Das', 'Siddharth Goyal', 'Muhammed Shuaibi', 'Janice Lan', 'Richard Tran'] | 2022-06-17 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 3.75514865e-01 2.50914302e-02 -3.80161613e-01 -1.09590411e-01
-8.72810364e-01 -2.97107965e-01 4.86952454e-01 3.81299824e-01
-3.20312321e-01 9.89012480e-01 1.38903260e-01 -5.85381925e-01
-2.77106851e-01 -1.00334954e+00 -9.65631247e-01 -8.25342178e-01
-1.65334389e-01 5.07331967e-01 2.33566225e-01 -6.95833623... | [5.234614372253418, 5.498034954071045] |
782734ab-dddd-43d7-bab9-0ac19fd78213 | perceptual-based-deep-learning-denoiser-as-a | 2107.05222 | null | https://arxiv.org/abs/2107.05222v1 | https://arxiv.org/pdf/2107.05222v1.pdf | Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems | In this paper we investigate speech denoising as a defense against adversarial attacks on automatic speech recognition (ASR) systems. Adversarial attacks attempt to force misclassification by adding small perturbations to the original speech signal. We propose to counteract this by employing a neural-network based deno... | ['Shrikanth Narayanan', 'Dillon Knox', 'Raghuveer Peri', 'Nicholas Mehlman', 'Anirudh Sreeram'] | 2021-07-12 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 6.34589553e-01 3.69912177e-01 6.29997730e-01 -3.09882909e-01
-1.20330203e+00 -8.75498891e-01 5.87530315e-01 -1.40220791e-01
-4.18557703e-01 2.52338946e-01 4.29215401e-01 -9.43866193e-01
2.73862869e-01 -3.54880393e-01 -5.49293399e-01 -6.43091559e-01
2.28417162e-02 -1.08940750e-01 9.69192609e-02 -6.30762160... | [14.115492820739746, 5.849719047546387] |
f3ecfb51-3246-42e3-8bde-7cf3fae1d645 | self-supervised-amodal-video-object | 2210.12733 | null | https://arxiv.org/abs/2210.12733v1 | https://arxiv.org/pdf/2210.12733v1.pdf | Self-supervised Amodal Video Object Segmentation | Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervi... | ['Zheng Zhang', 'Yanwei Fu', 'David Wipf', 'Francesco Locatello', 'Tong He', 'Tianjun Xiao', 'Chiyu Wang', 'Yuxin Hong', 'Jian Yao'] | 2022-10-23 | null | null | null | null | ['video-object-segmentation', 'temporal-sequences'] | ['computer-vision', 'reasoning'] | [ 3.12584460e-01 3.37457448e-01 -1.62753090e-01 -3.40341508e-01
-4.73507673e-01 -7.67814934e-01 3.68568003e-01 -2.00320646e-01
-3.04168254e-01 7.45931149e-01 -2.34497339e-01 4.47717980e-02
2.82003403e-01 -5.10227025e-01 -1.46752405e+00 -1.01274943e+00
3.51739042e-02 5.29176891e-01 6.70565009e-01 -2.22749323... | [9.299084663391113, 0.05589378625154495] |
fd6f4cf6-764b-4327-8db6-45a64dde197e | a-structural-causal-model-for-mr-images-of | 2103.03158 | null | https://arxiv.org/abs/2103.03158v3 | https://arxiv.org/pdf/2103.03158v3.pdf | A Structural Causal Model for MR Images of Multiple Sclerosis | Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM th... | ['Jerry L. Prince', 'Aaron Carass', 'Jacob C. Reinhold'] | 2021-03-04 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.39235175e-01 4.71660703e-01 -4.88639534e-01 -6.22117162e-01
-1.85634762e-01 -8.78092200e-02 7.90600538e-01 1.07171677e-01
-5.52172065e-01 1.16252196e+00 8.63843858e-01 -8.53057265e-01
-3.56915712e-01 -8.49467754e-01 -9.18746948e-01 -5.69705188e-01
-4.67676103e-01 6.07229829e-01 -2.99719155e-01 1.93272904... | [8.030915260314941, 5.460659980773926] |
09ebd734-4acf-4289-9c4e-24a958ca9f02 | exploring-the-value-of-multi-view-learning | null | null | https://openreview.net/forum?id=05JLuxoSbX5 | https://openreview.net/pdf?id=05JLuxoSbX5 | Exploring the Value of Multi-View Learning for Session-Aware Query Representation | Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user’... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['multi-view-learning', 'document-ranking'] | ['computer-vision', 'natural-language-processing'] | [-2.93080751e-02 -5.20337880e-01 -7.10079670e-01 -4.76598710e-01
-9.44779038e-01 -8.89817357e-01 1.23057067e+00 5.63487947e-01
-5.14784157e-01 -6.75216243e-02 7.34977186e-01 -6.69702590e-02
-7.69691110e-01 -6.31116331e-01 -2.80870140e-01 -4.11660880e-01
-1.37059778e-01 7.09067106e-01 2.13215783e-01 -5.62697768... | [11.550670623779297, 7.608453750610352] |
9c19629c-cf12-42a0-b384-05ebdb6d543d | identity-driven-three-player-generative | 2305.00358 | null | https://arxiv.org/abs/2305.00358v1 | https://arxiv.org/pdf/2305.00358v1.pdf | Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition | Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have... | ['Naser Damer', 'Arjan Kuijper', 'Fadi Boutros', 'Jurek Elliesen', 'Tim Rieber', 'Jan Niklas Kolf'] | 2023-04-30 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 4.17747229e-01 2.66973555e-01 2.49350339e-01 -4.39436674e-01
-6.08149290e-01 -6.62090659e-01 1.01177871e+00 -7.99134016e-01
-1.91278830e-01 8.51824701e-01 -1.61486566e-01 -4.06163484e-02
8.82873386e-02 -1.00881529e+00 -6.15738928e-01 -7.33104408e-01
2.29578048e-01 6.55096769e-01 -3.62205058e-01 -2.41795853... | [12.84754467010498, 0.5794481039047241] |
fa088547-71e5-4d6b-ad45-fa38e931a81e | hyspecnet-11k-a-large-scale-hyperspectral | 2306.00385 | null | https://arxiv.org/abs/2306.00385v2 | https://arxiv.org/pdf/2306.00385v2.pdf | HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods | The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to optimize all parameters and reach a high compression performance. However, existing hyperspectral d... | ['Begüm Demir', 'Martin Hermann Paul Fuchs'] | 2023-06-01 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 8.01259398e-01 -4.59303886e-01 1.53255656e-01 -2.68296063e-01
-5.67520022e-01 -3.34674031e-01 2.91811764e-01 1.04492478e-01
-2.53628612e-01 6.14327788e-01 -1.37253478e-01 -2.78389901e-01
-6.09982014e-01 -1.23233593e+00 -6.35592341e-01 -1.06665146e+00
-3.31647158e-01 1.94035426e-01 -4.78537291e-01 -7.12608546... | [10.072078704833984, -1.9165961742401123] |
f91cf2e6-e606-45e3-bf45-f237171763fc | few-shot-in-context-learning-for-knowledge | 2305.01750 | null | https://arxiv.org/abs/2305.01750v2 | https://arxiv.org/pdf/2305.01750v2.pdf | Few-shot In-context Learning for Knowledge Base Question Answering | Question answering over knowledge bases is considered a difficult problem due to the challenge of generalizing to a wide variety of possible natural language questions. Additionally, the heterogeneity of knowledge base schema items between different knowledge bases often necessitates specialized training for different ... | ['Wenhu Chen', 'Yu Su', 'Yu Gu', 'Alex Zhuang', 'Xueguang Ma', 'Tianle Li'] | 2023-05-02 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.72631437e-01 3.05510670e-01 -1.09331027e-01 -1.94595799e-01
-1.55095196e+00 -8.70944262e-01 2.88814545e-01 2.74773203e-02
-8.42337906e-02 8.50008428e-01 1.90763801e-01 -7.26924181e-01
-1.18587010e-01 -1.09687364e+00 -1.15774751e+00 1.92322750e-02
2.84571558e-01 8.53170872e-01 8.03542495e-01 -9.35416877... | [10.795254707336426, 7.960903167724609] |
c1b5aa77-0330-4aba-9625-64f04882c2cb | prophnet-efficient-agent-centric-motion | 2303.12071 | null | https://arxiv.org/abs/2303.12071v3 | https://arxiv.org/pdf/2303.12071v3.pdf | ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals | Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric mo... | ['Xiaodong Yang', 'Fang Da', 'Tong Su', 'Xishun Wang'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_ProphNet_Efficient_Agent-Centric_Motion_Forecasting_With_Anchor-Informed_Proposals_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_ProphNet_Efficient_Agent-Centric_Motion_Forecasting_With_Anchor-Informed_Proposals_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction', 'motion-forecasting'] | ['computer-vision', 'computer-vision'] | [-2.79003456e-02 -9.72717777e-02 -6.20918214e-01 -4.28435415e-01
-8.38575602e-01 -6.49809301e-01 9.86101091e-01 -6.28879517e-02
-3.40701073e-01 7.16894984e-01 4.68871564e-01 -2.69159645e-01
-8.12858343e-03 -6.95248127e-01 -7.09479690e-01 -4.05487567e-01
-2.39507362e-01 6.49056911e-01 4.02447581e-01 -5.94923079... | [5.870617866516113, 0.7825969457626343] |
e7d17bfe-1299-487e-8593-0571ca161e03 | extracting-motion-and-appearance-via-inter | 2303.00440 | null | https://arxiv.org/abs/2303.00440v2 | https://arxiv.org/pdf/2303.00440v2.pdf | Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation | Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or elaborate separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this pap... | ['LiMin Wang', 'Gangshan Wu', 'Youxin Chen', 'Haonan Wang', 'Yuhan Zhu', 'Guozhen Zhang'] | 2023-03-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-frame-interpolation'] | ['computer-vision'] | [-9.77332518e-03 -2.91804612e-01 -1.83958590e-01 -3.96428287e-01
-6.49791241e-01 -3.02648574e-01 3.87323081e-01 -2.07821161e-01
-3.08338135e-01 5.21436393e-01 2.30629638e-01 -2.02238604e-01
2.25334316e-01 -7.50327051e-01 -7.31005847e-01 -6.14454865e-01
2.95828849e-01 -3.35480541e-01 3.79153341e-01 -7.80606791... | [10.787790298461914, -1.425649881362915] |
40f4a435-7a9c-4d8d-9efc-aa693bdacfae | teamuncc-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.20 | https://aclanthology.org/2021.ltedi-1.20.pdf | TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with Transformers | In this paper, we describe our approach towards utilizing pre-trained models for the task of hope speech detection. We participated in Task 2: Hope Speech Detection for Equality, Diversity and Inclusion at LT-EDI-2021 @ EACL2021. The goal of this task is to predict the presence of hope speech, along with the presence o... | ['Samira Shaikh', 'Erfan Al-Hossami', 'Khyati Mahajan'] | 2021-04-19 | null | null | null | null | ['hope-speech-detection-for-tamil', 'hope-speech-detection-for-malayalam', 'hope-speech-detection', 'hope-speech-detection-for-english'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-8.81831795e-02 1.08727597e-01 -2.49689117e-01 -1.23702332e-01
-1.63036108e+00 -5.70475221e-01 1.00456643e+00 2.39050135e-01
-4.19818282e-01 6.36857629e-01 9.65620279e-01 -7.60228992e-01
-5.83728217e-02 -5.02360404e-01 -2.32513085e-01 -2.86233902e-01
2.17728689e-01 5.24427414e-01 -3.63713689e-02 -4.81245786... | [9.367219924926758, 10.718904495239258] |
e17f119c-97ec-462b-af96-e40618f8d093 | deep-region-and-multi-label-learning-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhao_Deep_Region_and_CVPR_2016_paper.pdf | Deep Region and Multi-Label Learning for Facial Action Unit Detection | Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection. Knowing that AUs are active on sparse facial regions, RL aims to identify these regions for a better specificity. On the other hand, a strong statistical evidence of AU cor... | ['Wen-Sheng Chu', 'Kaili Zhao', 'Honggang Zhang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 1.88127175e-01 2.01583520e-01 -5.06931365e-01 -3.86946917e-01
-6.76453948e-01 -3.63360256e-01 5.90989411e-01 -3.27358872e-01
-3.28554362e-01 4.58029509e-01 1.76651463e-01 2.03433231e-01
1.79559484e-01 -6.59469724e-01 -8.21126521e-01 -8.73347223e-01
3.90672982e-02 8.53503309e-03 1.59496903e-01 -4.99341711... | [13.599496841430664, 1.526954174041748] |
08d96658-db7d-4e2c-9f6a-d857bcf6ae42 | hybrid-precoder-and-combiner-designs-for | 2306.14301 | null | https://arxiv.org/abs/2306.14301v1 | https://arxiv.org/pdf/2306.14301v1.pdf | Hybrid Precoder and Combiner Designs for Decentralized Parameter Estimation in mmWave MIMO Wireless Sensor Networks | Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mea... | ['Lajos Hanzo', 'Aditya K. Jagannatham', 'Naveen K. D. Venkategowda', 'Kunwar Pritiraj Rajput', 'Suraj Srivastava', 'Priyanka Maity'] | 2023-06-25 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 5.91650605e-01 4.93172944e-01 1.68240860e-01 -2.72102118e-01
-8.60378861e-01 -3.27940613e-01 2.73057997e-01 -1.68434739e-01
-4.72783923e-01 5.78525484e-01 1.71431735e-01 -3.64319772e-01
-6.85485184e-01 -5.17998993e-01 -4.25117612e-01 -1.36481714e+00
-4.53968793e-01 -1.19457453e-01 -6.14443123e-01 1.32134512... | [6.283343315124512, 1.2648673057556152] |
376ed628-6192-47e5-8b83-587b834d794e | automatic-prosody-prediction-for-chinese | 1511.00360 | null | http://arxiv.org/abs/1511.00360v1 | http://arxiv.org/pdf/1511.00360v1.pdf | Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features | Prosody affects the naturalness and intelligibility of speech. However,
automatic prosody prediction from text for Chinese speech synthesis is still a
great challenge and the traditional conditional random fields (CRF) based
method always heavily relies on feature engineering. In this paper, we propose
to use neural ne... | ['Chuang Ding', 'Yang Liu', 'Weini Zhang', 'Lei Xie', 'Jie Yan'] | 2015-11-02 | null | null | null | null | ['prosody-prediction'] | ['natural-language-processing'] | [-9.31326523e-02 -3.96179333e-02 -2.89139569e-01 -5.54273248e-01
-6.12904489e-01 -8.20138082e-02 8.41489285e-02 -3.58035356e-01
-3.79070759e-01 8.38174820e-01 7.50632644e-01 -4.95736003e-01
6.30140245e-01 -6.35972440e-01 -3.01751763e-01 -5.00164032e-01
4.82496560e-01 -9.92765278e-02 1.06994890e-01 -2.04599306... | [14.728447914123535, 6.77061128616333] |
32de6110-7818-4a13-8253-7ccd70554abb | e2gan-end-to-end-generative-adversarial | null | null | https://doi.org/10.24963/ijcai.2019/429 | https://www.ijcai.org/proceedings/2019/0429.pdf | E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation | The missing values, appear in most of multivariate time series, prevent advanced analysis of multivariate time series data. Existing imputation approaches try to deal with missing values by deletion, statistical imputation, machine learning based imputation and generative imputation. However, these methods are either i... | ['Xiangrui Cai', 'Yonghong Luo', 'Xiaojie Yuan', 'Ying Zhang'] | 2019-08-10 | null | null | null | proceedings-of-the-twenty-eighth | ['multivariate-time-series-imputation'] | ['time-series'] | [-1.37936631e-02 -1.25798047e-01 -4.36038196e-01 -4.96771365e-01
-1.13364875e+00 -3.22143376e-01 4.10508454e-01 -3.19835424e-01
-1.61538050e-01 1.37150466e+00 3.13064545e-01 -2.65054107e-01
-2.51343518e-01 -6.77448571e-01 -9.71349895e-01 -7.84487069e-01
-2.20276907e-01 4.42706615e-01 -7.71427751e-01 4.50012051... | [7.098151206970215, 3.3685669898986816] |
d52a508d-658b-4d30-8936-8734090512b7 | multilingual-denoising-pre-training-for | 2001.08210 | null | https://arxiv.org/abs/2001.08210v2 | https://arxiv.org/pdf/2001.08210v2.pdf | Multilingual Denoising Pre-training for Neural Machine Translation | This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -- a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mB... | ['Xi-An Li', 'Mike Lewis', 'Luke Zettlemoyer', 'Jiatao Gu', 'Sergey Edunov', 'Yinhan Liu', 'Naman Goyal', 'Marjan Ghazvininejad'] | 2020-01-22 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.11276311e-01 3.75767238e-02 -1.12906896e-01 -3.91959876e-01
-1.79340577e+00 -8.43918562e-01 7.89438307e-01 -1.99524805e-01
-5.56313813e-01 9.31115210e-01 4.53742892e-01 -7.67826617e-01
5.90006530e-01 -9.36196819e-02 -1.07457972e+00 -4.07611132e-01
3.62245411e-01 9.34440255e-01 -2.53220528e-01 -6.08119905... | [11.633651733398438, 10.271897315979004] |
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