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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2caf247d-2b30-4cf8-82d3-491e70a811c5 | open-world-story-generation-with-structured | 2212.04634 | null | https://arxiv.org/abs/2212.04634v2 | https://arxiv.org/pdf/2212.04634v2.pdf | Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey | Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has sh... | ['Börje F. Karlsson', 'Wei Hu', 'Zhiwei Yu', 'Jieru Lin', 'Yuxin Wang'] | 2022-12-09 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 2.18927816e-01 3.83920163e-01 -3.93108428e-01 -5.72699234e-02
-6.86002076e-01 -7.25541234e-01 9.64003801e-01 1.66795552e-02
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1.95519164e-01 3.34136099e-01 -7.71891922e-02 -3.86661053... | [11.647880554199219, 8.878547668457031] |
51c5c35d-37fb-4cee-96a2-f06a35bf9be8 | learning-action-effect-dynamics-for | 2212.03866 | null | https://arxiv.org/abs/2212.03866v1 | https://arxiv.org/pdf/2212.03866v1.pdf | Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task | 'Actions' play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning about Actions & Change' (RAC). This has been an important research direction in Artificial Intelligence (AI) in general, but the study of RA... | ['Chitta Baral', 'Yezhou Yang', 'Pratyay Banerjee', 'Shailaja Keyur Sampat'] | 2022-12-07 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [ 3.52776259e-01 2.24170133e-01 2.80314013e-02 -5.89323819e-01
-2.94445395e-01 -5.99015832e-01 1.17039585e+00 -3.43729034e-02
-6.68970287e-01 4.93021846e-01 6.95816934e-01 -6.08692288e-01
2.54489362e-01 -8.65227938e-01 -8.49077284e-01 -2.86551028e-01
3.69620353e-01 3.68000060e-01 3.97098094e-01 -4.17994380... | [10.756982803344727, 1.6076182126998901] |
597a16bf-e9c8-47ff-a374-652221213057 | a-single-camera-3d-scanning-velocimetry | 2102.05787 | null | https://arxiv.org/abs/2102.05787v1 | https://arxiv.org/pdf/2102.05787v1.pdf | A single-camera, 3D scanning velocimetry system for quantifying active particle aggregations | A three-dimensional (3D) scanning velocimetry system is developed to quantify the 3D configurations of particles and their surrounding volumetric, three-component velocity fields. The approach uses a translating laser sheet to rapidly scan through a volume of interest and sequentially illuminate slices of the flow cont... | ['John O. Dabiri', 'Isabel A. Houghton', 'Matt K. Fu'] | 2021-02-11 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [-9.59235337e-03 -5.78667819e-01 8.28372836e-01 3.16935956e-01
-9.19916332e-02 -7.61255383e-01 4.07955319e-01 1.80249229e-01
-1.04420233e+00 7.78190017e-01 -2.94149220e-01 -3.53039235e-01
-1.36565492e-01 -7.08525479e-01 -2.87219316e-01 -9.03352499e-01
-7.09918439e-01 6.26564205e-01 4.79688466e-01 -6.80690706... | [13.315694808959961, -3.0269155502319336] |
c390fdac-1bdc-4ff3-a263-4939c2a84819 | machine-learning-students-overfit-to | 2209.03032 | null | https://arxiv.org/abs/2209.03032v1 | https://arxiv.org/pdf/2209.03032v1.pdf | Machine Learning Students Overfit to Overfitting | Overfitting and generalization is an important concept in Machine Learning as only models that generalize are interesting for general applications. Yet some students have trouble learning this important concept through lectures and exercises. In this paper we describe common examples of students misunderstanding overfi... | ['Matthia Sabatelli', 'Matias Valdenegro-Toro'] | 2022-09-07 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-2.95269817e-01 2.52623975e-01 -1.04729503e-01 -7.00382352e-01
-4.33890581e-01 -4.72697258e-01 -3.50139588e-01 5.54179251e-01
-2.97192596e-02 7.80041575e-01 -1.23348042e-01 -9.02198493e-01
-2.78319448e-01 -8.35264742e-01 -9.15544748e-01 -3.84393632e-01
2.65794188e-01 1.66766951e-03 1.79009795e-01 -6.38079703... | [10.048091888427734, 7.334079742431641] |
995d635f-a6f5-432a-b302-04ba3669b3f0 | a-contrastive-knowledge-transfer-framework | 2303.07599 | null | https://arxiv.org/abs/2303.07599v1 | https://arxiv.org/pdf/2303.07599v1.pdf | A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning | Knowledge Transfer (KT) achieves competitive performance and is widely used for image classification tasks in model compression and transfer learning. Existing KT works transfer the information from a large model ("teacher") to train a small model ("student") by minimizing the difference of their conditionally independ... | ['Ming Zhao', 'Yitao Chen', 'Kaiqi Zhao'] | 2023-03-14 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.56913334e-01 8.72255638e-02 -6.18823290e-01 -3.44777584e-01
-6.38435245e-01 -2.74842918e-01 4.51886892e-01 -7.08794445e-02
-3.35043997e-01 8.84407163e-01 -1.67705119e-01 -3.28288555e-01
-2.81386763e-01 -8.36052418e-01 -1.03093398e+00 -8.45329404e-01
1.11362472e-01 5.31598508e-01 3.27137142e-01 6.60966486... | [9.492290496826172, 3.2946627140045166] |
9d573ec6-6b21-4b44-9b0d-9056998698f2 | union-subgraph-neural-networks | 2305.15747 | null | https://arxiv.org/abs/2305.15747v1 | https://arxiv.org/pdf/2305.15747v1.pdf | Union Subgraph Neural Networks | Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) test as they operate on rooted subtrees through iterative message passing. In this paper, we empower GNNs by injecting ... | ['Yiping Ke', 'Vijay Prakash Dwivedi', 'Qingtian Bian', 'Aihu Zhang', 'Jiaxing Xu'] | 2023-05-25 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [ 1.55373424e-01 3.13434064e-01 -7.10746884e-01 -2.14404076e-01
-3.05913985e-01 -4.92232978e-01 4.86866176e-01 4.71621871e-01
-1.55124009e-01 5.42696655e-01 -1.57901362e-01 -8.26901734e-01
-3.71363819e-01 -1.61246812e+00 -1.08724868e+00 -6.34744763e-01
-9.84496891e-01 3.05838346e-01 4.86866891e-01 -3.93082470... | [6.989717960357666, 6.210718631744385] |
a79d294c-ffc5-4008-9515-4a417174a0dd | addressing-class-imbalance-in-semi-supervised | 2209.00123 | null | https://arxiv.org/abs/2209.00123v1 | https://arxiv.org/pdf/2209.00123v1.pdf | Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI | Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate thi... | ['Ram Sarkar', 'Sagnik Ghosal', 'Hritam Basak'] | 2022-08-31 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.73066890e-01 1.30947419e-02 -3.16086531e-01 -6.41071737e-01
-8.17136347e-01 -3.09478521e-01 1.38378039e-01 7.24145710e-01
-4.55379933e-01 8.65117848e-01 -1.44733727e-01 -1.57597765e-01
-6.37656987e-01 -6.16411865e-01 -2.56547064e-01 -1.09179914e+00
9.07624327e-03 6.05540276e-01 3.99655491e-01 3.68229955... | [8.83029556274414, 4.110901355743408] |
16ebc5e4-552e-4551-8415-6a09cd5f9ebd | accurate-object-association-and-pose-updating | 2012.11368 | null | https://arxiv.org/abs/2012.11368v2 | https://arxiv.org/pdf/2012.11368v2.pdf | Accurate Object Association and Pose Updating for Semantic SLAM | Nowadays in the field of semantic SLAM, how to correctly use semantic information for data association is still a problem worthy of study. The key to solving this problem is to correctly associate multiple object measurements of one object landmark, and refine the pose of object landmark. However, different objects loc... | ['Zhenhua Wang', 'Jianhua Zhang', 'Jialing Liu', 'Kaiqi Chen'] | 2020-12-21 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 1.69377759e-01 -2.93694139e-01 1.35695368e-01 -3.92663121e-01
-7.20823109e-01 -3.08645785e-01 4.90603685e-01 7.31807828e-01
-6.28259778e-01 6.38509214e-01 -2.30109632e-01 3.90187323e-01
-7.32514441e-01 -5.53608060e-01 -6.91501498e-01 -7.39777088e-01
-2.88954586e-01 1.16441476e+00 9.62474704e-01 3.95799950... | [7.298513412475586, -2.248411178588867] |
f7b68b9c-6cc8-4b32-943e-f87f32ebf2f5 | transnet-category-level-transparent-object | 2208.10002 | null | https://arxiv.org/abs/2208.10002v1 | https://arxiv.org/pdf/2208.10002v1.pdf | TransNet: Category-Level Transparent Object Pose Estimation | Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even humans find certain transparent surfaces with little specular reflection or refraction, e.g. glass ... | ['Odest Chadwicke Jenkins', 'Zeren Yu', 'Jiyue Zhu', 'Xiaotong Chen', 'Anthony Opipari', 'Huijie Zhang'] | 2022-08-22 | null | null | null | null | ['transparent-objects', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 3.55474591e-01 1.80357829e-01 3.14760417e-01 -4.61921573e-01
-6.84214056e-01 -7.35115647e-01 3.59843194e-01 8.50816295e-02
-1.60516962e-01 2.33002052e-01 7.00268000e-02 1.32822758e-02
1.96383134e-01 -4.89576072e-01 -9.38181281e-01 -4.89700675e-01
-1.60014421e-01 4.03178871e-01 6.68816149e-01 2.10067838... | [7.178958415985107, -2.1280999183654785] |
03ae5097-fdaf-4dd5-b4d0-86d7f60fe4c9 | deep-audio-visual-speech-recognition | 1809.02108 | null | http://arxiv.org/abs/1809.02108v2 | http://arxiv.org/pdf/1809.02108v2.pdf | Deep Audio-Visual Speech Recognition | The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild ... | ['Triantafyllos Afouras', 'Joon Son Chung', 'Andrew Zisserman', 'Oriol Vinyals', 'Andrew Senior'] | 2018-09-06 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 6.73792422e-01 4.72792566e-01 -1.89521611e-01 -1.69541806e-01
-1.44413662e+00 -4.06947404e-01 8.08990538e-01 -4.38540757e-01
-3.25591743e-01 5.32339215e-01 7.88136363e-01 -3.33549023e-01
4.59253550e-01 4.52927575e-02 -9.67723012e-01 -6.49801493e-01
2.61937290e-01 2.13410854e-01 2.53237784e-01 7.86219090... | [14.343338966369629, 5.0409369468688965] |
38108657-2b23-4cf4-9ac0-f4262cd847c9 | distribution-level-battery-storage-valuation | 2106.07590 | null | https://arxiv.org/abs/2106.07590v3 | https://arxiv.org/pdf/2106.07590v3.pdf | Decision making under uncertainty for deploying battery storage as a non-wire alternative in distribution networks | The growing demand for electricity in emerging markets and developing economies (EMDE) such as India is causing loading and congestion problems on distribution networks, particularly in urban locations, that adversely impact sustainable development and economic growth. Electric utilities in these economies face unique ... | ['Robert Stoner', 'Dharik S. Mallapragada', 'Marc Barbar'] | 2021-06-14 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-8.00004721e-01 1.26796961e-01 -1.14019208e-01 6.41657487e-02
-2.02330127e-01 -8.60539436e-01 2.61412978e-01 3.35581243e-01
-8.53630248e-03 1.36548102e+00 5.45960665e-02 -1.09630787e+00
-6.42489195e-01 -1.21166492e+00 3.26383933e-02 -7.40830123e-01
-2.91090041e-01 5.19627631e-01 -2.46908247e-01 -2.53899127... | [5.689558506011963, 2.4507248401641846] |
b770634c-6ae9-4f10-a67e-fd49b4db6c72 | ellipsis-translation-for-a-medical-speech-to | null | null | https://aclanthology.org/2020.eamt-1.30 | https://aclanthology.org/2020.eamt-1.30.pdf | Ellipsis Translation for a Medical Speech to Speech Translation System | In diagnostic interviews, elliptical utterances allow doctors to question patients in a more efficient and economical way. However, literal translation of such incomplete utterances is rarely possible without affecting communication. Previous studies have focused on automatic ellipsis detection and resolution, but only... | ['Hervé Spechbach', 'Pierrette Bouillon', 'Johanna Gerlach', 'Jonathan Mutal'] | null | null | null | null | eamt-2020-11 | ['speech-to-speech-translation'] | ['speech'] | [ 3.39030594e-01 8.63034427e-01 1.19989529e-01 -5.05345106e-01
-1.11570001e+00 -3.37341189e-01 3.03326577e-01 5.17641962e-01
-5.40493846e-01 9.86985266e-01 6.99461460e-01 -6.40278339e-01
1.15008950e-01 -3.55599731e-01 4.04977985e-03 -2.40956411e-01
4.88131195e-01 1.15133703e+00 -5.81806339e-03 -5.17897785... | [11.280023574829102, 9.425070762634277] |
f520c38c-2ed0-4f1b-b9e1-da7b358f6409 | treasure-what-you-have-exploiting-similarity | 2305.06492 | null | https://arxiv.org/abs/2305.06492v1 | https://arxiv.org/pdf/2305.06492v1.pdf | Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing | Deep learning has enabled various Internet of Things (IoT) applications. Still, designing models with high accuracy and computational efficiency remains a significant challenge, especially in real-time video processing applications. Such applications exhibit high inter- and intra-frame redundancy, allowing further impr... | ['Smail Niar', 'Ozcan Ozturk', 'Hamza Ouarnoughi', 'Halima Bouzidi', 'Hadjer Benmeziane'] | 2023-05-10 | null | null | null | null | ['scene-parsing', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [ 5.60922027e-02 -3.86997849e-01 -3.67511034e-01 -7.60046601e-01
-3.34329635e-01 -1.04377829e-01 1.15505405e-01 3.21589977e-01
-7.10051239e-01 4.03749377e-01 -7.62827173e-02 -4.23404574e-01
-2.68051736e-02 -8.22209597e-01 -7.04044819e-01 -5.16862214e-01
-2.34062701e-01 -7.11822212e-02 6.20134056e-01 2.35578120... | [9.062188148498535, -0.266541451215744] |
2ce7c12d-167b-45f9-8536-22e8ea29e283 | implicit-semantic-response-alignment-for | null | null | http://proceedings.neurips.cc/paper/2021/hash/731b03008e834f92a03085ef47061c4a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/731b03008e834f92a03085ef47061c4a-Paper.pdf | Implicit Semantic Response Alignment for Partial Domain Adaptation | Partial Domain Adaptation (PDA) addresses the unsupervised domain adaptation problem where the target label space is a subset of the source label space. Most state-of-art PDA methods tackle the inconsistent label space by assigning weights to classes or individual samples, in an attempt to discard the source data that ... | ['Hongfu Liu', 'Zhengming Ding', 'Wenxiao Xiao'] | 2021-12-01 | null | https://openreview.net/forum?id=LNXTIrMqyGz | https://openreview.net/pdf?id=LNXTIrMqyGz | neurips-2021-12 | ['partial-domain-adaptation'] | ['methodology'] | [ 5.02781570e-01 8.34284201e-02 -4.43908483e-01 -6.40201092e-01
-4.43822175e-01 -4.17747259e-01 4.33339566e-01 2.82737732e-01
-4.48975295e-01 5.45779824e-01 4.70538557e-01 3.90217334e-01
-1.72630936e-01 -8.49348843e-01 -3.43267113e-01 -9.16479945e-01
5.08282304e-01 4.94097978e-01 3.92029345e-01 -1.84160873... | [10.422688484191895, 3.0616345405578613] |
c04684b3-838d-4a19-b2aa-319eac8f457b | convex-aggregation-for-opinion-summarization | 2104.01371 | null | https://arxiv.org/abs/2104.01371v3 | https://arxiv.org/pdf/2104.01371v3.pdf | Convex Aggregation for Opinion Summarization | Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors. As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the ag... | ['Wang-Chiew Tan', 'Stefanos Angelidis', 'Yoshihiko Suhara', 'Xiaolan Wang', 'Hayate Iso'] | 2021-04-03 | null | https://aclanthology.org/2021.findings-emnlp.328 | https://aclanthology.org/2021.findings-emnlp.328.pdf | findings-emnlp-2021-11 | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 3.37368190e-01 1.88755661e-01 -5.35724498e-02 -2.79587448e-01
-8.67932796e-01 -4.65425253e-01 7.40825772e-01 2.78799951e-01
-8.35762694e-02 7.68012524e-01 8.61297727e-01 -5.32601476e-02
1.97185814e-01 -7.26719975e-01 -6.84135079e-01 -7.79551744e-01
4.12040383e-01 1.70464814e-01 -3.20392877e-01 -1.25642061... | [12.357246398925781, 9.34571647644043] |
fd68543e-2fe3-42d7-ab8d-725531d6254c | predict-and-use-latent-patterns-for-short | 2010.13982 | null | https://arxiv.org/abs/2010.13982v2 | https://arxiv.org/pdf/2010.13982v2.pdf | Predict and Use Latent Patterns for Short-Text Conversation | Many neural network models nowadays have achieved promising performances in Chit-chat settings. The majority of them rely on an encoder for understanding the post and a decoder for generating the response. Without given assigned semantics, the models lack the fine-grained control over responses as the semantic mapping ... | ['Wei-Yun Ma', 'Ta-Hsuan Chao', 'Yu-Chieh Chao', 'Hung-Ting Chen'] | 2020-10-27 | null | null | null | null | ['short-text-conversation'] | ['natural-language-processing'] | [ 1.59970984e-01 2.17262387e-01 -2.41908237e-01 -9.50259149e-01
-8.52433562e-01 -4.76856053e-01 8.45459819e-01 -1.42641798e-01
-2.33659476e-01 7.39176571e-01 9.34994161e-01 2.96848387e-01
2.53688358e-02 -8.57810318e-01 -4.09719467e-01 -3.85303885e-01
6.45068228e-01 9.30726230e-01 3.52723040e-02 -5.35191357... | [12.583917617797852, 8.320448875427246] |
00da4a23-a425-44d4-a379-8bbded32d619 | reinforced-multi-task-approach-for-multi-hop | 2004.02143 | null | https://arxiv.org/abs/2004.02143v4 | https://arxiv.org/pdf/2004.02143v4.pdf | Reinforced Multi-task Approach for Multi-hop Question Generation | Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. Fo... | ['Akella Ravi Tej', 'Pushpak Bhattacharyya', 'Hardik Chauhan', 'Deepak Gupta', 'Asif Ekbal'] | 2020-04-05 | null | https://aclanthology.org/2020.coling-main.249 | https://aclanthology.org/2020.coling-main.249.pdf | coling-2020-8 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.87275782e-01 6.98640525e-01 1.97849512e-01 -3.94442886e-01
-1.63721490e+00 -7.14500308e-01 7.93932080e-01 2.07478046e-01
-2.63097793e-01 1.18528533e+00 4.64199275e-01 -5.82016170e-01
-2.24726692e-01 -1.10571957e+00 -7.48674631e-01 1.02299280e-01
3.00413579e-01 8.85182381e-01 4.33345050e-01 -7.41418839... | [11.4198637008667, 8.12186336517334] |
58c740d6-be0b-44cb-9809-2a247a2e7fca | real-world-super-resolution-via-kernel | null | null | https://ieeexplore.ieee.org/document/9150628 | https://ieeexplore.ieee.org/document/9150628 | Real-World Super-Resolution via Kernel Estimation and Noise Injection | Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets regardless of blur and noise. However, these methods always fail in real-world image super-resolution, since most of them adopt simple bicubic downsampling from high-quality images to construct Low-Resolution (LR) an... | ['Feiyue Huang', 'Jilin Li', 'Chengjie Wang', 'Ying Tai', 'Yun Cao', 'Xiaozhong Ji'] | 2020-06-19 | null | null | null | cvprw-2020-6 | ['video-super-resolution'] | ['computer-vision'] | [ 2.50955194e-01 -5.79617560e-01 -5.06802369e-03 -1.50536299e-01
-9.71706808e-01 -1.52913198e-01 4.19128865e-01 -8.84857178e-01
-8.46778303e-02 1.03725731e+00 5.88395059e-01 3.29244137e-01
-7.92757422e-02 -7.01769769e-01 -6.53699994e-01 -6.91288292e-01
1.87513426e-01 -1.02019705e-01 5.69846809e-01 -5.17974913... | [11.101119041442871, -2.138272762298584] |
db900d1c-5eaf-49d9-809b-3de3bbc6faac | supervised-contrastive-learning-for-product | 2202.02098 | null | https://arxiv.org/abs/2202.02098v2 | https://arxiv.org/pdf/2202.02098v2.pdf | Supervised Contrastive Learning for Product Matching | Contrastive learning has moved the state of the art for many tasks in computer vision and information retrieval in recent years. This poster is the first work that applies supervised contrastive learning to the task of product matching in e-commerce using product offers from different e-shops. More specifically, we emp... | ['Christian Bizer', 'Ralph Peeters'] | 2022-02-04 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [ 4.28879291e-01 -1.85609668e-01 -5.50351083e-01 -5.85120976e-01
-9.24598873e-01 -6.05025351e-01 7.68181980e-01 4.42490816e-01
-5.63219786e-01 9.70617160e-02 -2.99299836e-01 -3.06962758e-01
-3.73556130e-02 -8.76984656e-01 -1.07027018e+00 -2.95061857e-01
-6.17340095e-02 8.06972980e-01 1.48426965e-01 -6.15274608... | [9.778712272644043, 8.315797805786133] |
726ef22e-eee7-422a-b68c-9b4f36f61e52 | a-deep-bag-of-features-model-for-music-auto | 1508.04999 | null | http://arxiv.org/abs/1508.04999v3 | http://arxiv.org/pdf/1508.04999v3.pdf | A Deep Bag-of-Features Model for Music Auto-Tagging | Feature learning and deep learning have drawn great attention in recent years
as a way of transforming input data into more effective representations using
learning algorithms. Such interest has grown in the area of music information
retrieval (MIR) as well, particularly in music audio classification tasks such
as auto... | ['Kyogu Lee', 'Juhan Nam', 'Jorge Herrera'] | 2015-08-20 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [ 2.70651996e-01 -3.11069936e-01 -1.75419465e-01 -4.61533070e-01
-1.11068392e+00 -6.70905948e-01 2.54341871e-01 1.61387041e-01
-2.17934653e-01 2.03830391e-01 5.17079651e-01 2.02412263e-01
-3.81510645e-01 -6.92582190e-01 -4.84435260e-01 -6.13061607e-01
-2.15855405e-01 4.87561256e-01 -4.36472781e-02 1.84964359... | [15.738375663757324, 5.219289302825928] |
88e62c17-9000-47ec-8f24-84e1154f29ac | glocal-energy-based-learning-for-few-shot | 2304.11855 | null | https://arxiv.org/abs/2304.11855v1 | https://arxiv.org/pdf/2304.11855v1.pdf | Glocal Energy-based Learning for Few-Shot Open-Set Recognition | Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-ba... | ['Yanning Zhang', 'Wei Wei', 'Lei Zhang', 'Peng Wang', 'Guansong Pang', 'Haoyu Wang'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Glocal_Energy-Based_Learning_for_Few-Shot_Open-Set_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Glocal_Energy-Based_Learning_for_Few-Shot_Open-Set_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-set-learning'] | ['miscellaneous'] | [ 5.50028443e-01 1.81048941e-02 -4.36160415e-01 -5.80807924e-01
-9.53108430e-01 -3.31038952e-01 5.21521568e-01 3.20935190e-01
-1.22814573e-01 3.18306088e-01 -1.60211712e-01 1.54207364e-01
-2.08087340e-01 -1.00410414e+00 -4.88221586e-01 -1.11590302e+00
6.19009323e-02 3.57783020e-01 3.75040293e-01 1.07182242... | [9.664225578308105, 2.1920089721679688] |
93fa1c5d-6cec-4186-94e8-a56b3f8ca745 | from-chaos-comes-order-ordering-event | 2304.13455 | null | https://arxiv.org/abs/2304.13455v2 | https://arxiv.org/pdf/2304.13455v2.pdf | From Chaos Comes Order: Ordering Event Representations for Object Detection | Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the... | ['Davide Scaramuzza', 'Mathias Gehrig', 'Daniel Gehrig', 'Nikola Zubić'] | 2023-04-26 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.31162214e-01 7.69610927e-02 -1.85636103e-01 -4.21116620e-01
-1.04786968e+00 -3.49313051e-01 7.51890004e-01 6.60230517e-01
-6.42232180e-01 7.66008675e-01 1.83574721e-01 -3.50971036e-02
-2.62411624e-01 -1.20217860e+00 -7.93009818e-01 -6.76297426e-01
-2.42046893e-01 8.56838703e-01 3.64562333e-01 2.08094995... | [9.845320701599121, 2.946431875228882] |
7d3f9653-77cd-4c68-9bc3-8755cb10fd21 | evimo2-an-event-camera-dataset-for-motion | 2205.03467 | null | https://arxiv.org/abs/2205.03467v1 | https://arxiv.org/pdf/2205.03467v1.pdf | EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms | A new event camera dataset, EVIMO2, is introduced that improves on the popular EVIMO dataset by providing more data, from better cameras, in more complex scenarios. As with its predecessor, EVIMO2 provides labels in the form of per-pixel ground truth depth and segmentation as well as camera and object poses. All sequen... | ['Yiannis Aloimonos', 'Cornelia Fermüller', 'Anton Mitrokhin', 'Levi Burner'] | 2022-05-06 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.56437978e-01 -3.76275539e-01 -1.59788355e-01 -1.67534307e-01
-7.17025459e-01 -8.52182925e-01 2.72448748e-01 -4.02302712e-01
-5.47799528e-01 6.71003520e-01 -2.05140397e-01 -1.06586918e-01
2.82589883e-01 -4.94459987e-01 -6.76379383e-01 -5.78574955e-01
1.12754166e-01 1.95546061e-01 4.91850823e-01 3.67608577... | [8.279294967651367, -1.8203102350234985] |
692b38fa-30ce-49a9-9116-2e773bd11dd6 | augment-features-beyond-color-for-domain | 2307.01703 | null | https://arxiv.org/abs/2307.01703v1 | https://arxiv.org/pdf/2307.01703v1.pdf | Augment Features Beyond Color for Domain Generalized Segmentation | Domain generalized semantic segmentation (DGSS) is an essential but highly challenging task, in which the model is trained only on source data and any target data is not available. Previous DGSS methods can be partitioned into augmentation-based and normalization-based ones. The former either introduces extra biased da... | ['Yang Tang', 'Michael Felsberg', 'Pavlo Melnyk', 'Qiyu Sun'] | 2023-07-04 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 3.91510785e-01 -3.85106094e-02 1.35798194e-02 -3.74538124e-01
-5.38080990e-01 -6.98166430e-01 7.00152814e-01 -3.01725745e-01
-4.51086611e-01 5.05981863e-01 -2.04164088e-01 -4.16138351e-01
4.50200588e-01 -1.19374037e+00 -6.89957261e-01 -9.22903955e-01
4.12246585e-01 2.39013106e-01 4.37077850e-01 -4.98332113... | [9.746969223022461, 1.1393702030181885] |
909bd7a8-b421-4f66-a9d7-1cb9b6c5fde8 | relevance-detection-in-cataract-surgery | 2104.14280 | null | https://arxiv.org/abs/2104.14280v1 | https://arxiv.org/pdf/2104.14280v1.pdf | Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization | In cataract surgery, the operation is performed with the help of a microscope. Since the microscope enables watching real-time surgery by up to two people only, a major part of surgical training is conducted using the recorded videos. To optimize the training procedure with the video content, the surgeons require an au... | ['Klaus Schoeffmann', 'Stephanie Sarny', 'Doris Putzgruber-Adamitsch', 'Mario Taschwer', 'Negin Ghamsarian'] | 2021-04-29 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 3.62773061e-01 -1.43234581e-02 -2.72769690e-01 3.22650746e-02
-5.00725627e-01 -1.96569487e-01 2.54017800e-01 1.41776547e-01
-7.20559835e-01 5.38670361e-01 2.14553520e-01 -9.36914012e-02
-3.46726298e-01 -4.40886468e-01 -2.70523280e-01 -8.91129851e-01
-1.21750928e-01 -1.55862287e-01 3.07277322e-01 -7.02754185... | [14.107080459594727, -3.333921432495117] |
f973730d-e9f1-4030-ba3e-14a34f5f5545 | learning-by-asking-questions-for-knowledge | 2210.05879 | null | https://arxiv.org/abs/2210.05879v1 | https://arxiv.org/pdf/2210.05879v1.pdf | Learning by Asking Questions for Knowledge-based Novel Object Recognition | In real-world object recognition, there are numerous object classes to be recognized. Conventional image recognition based on supervised learning can only recognize object classes that exist in the training data, and thus has limited applicability in the real world. On the other hand, humans can recognize novel objects... | ['Tatsuya Harada', 'Kohei Uehara'] | 2022-10-12 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 6.66801870e-01 3.46033543e-01 9.49383974e-02 -7.22309113e-01
-5.95362663e-01 -6.68633997e-01 5.97700298e-01 1.05519779e-01
-2.84622520e-01 5.65866172e-01 -3.77375335e-01 -2.47275427e-01
6.12344295e-02 -1.20000172e+00 -9.87745106e-01 -3.21998984e-01
4.17705804e-01 5.26238084e-01 6.75778985e-01 2.99502015... | [10.050262451171875, 1.903411626815796] |
952aafb7-e99d-445b-ab44-ca9f6673af39 | ovtrack-open-vocabulary-multiple-object | 2304.08408 | null | https://arxiv.org/abs/2304.08408v1 | https://arxiv.org/pdf/2304.08408v1.pdf | OVTrack: Open-Vocabulary Multiple Object Tracking | The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional multiple object tracking (MOT) benchmarks rely only on a few object categories that hardly represent the multitude of possible objects that a... | ['Fisher Yu', 'Martin Danelljan', 'Henghui Ding', 'Lei Ke', 'Tobias Fischer', 'Siyuan Li'] | 2023-04-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_OVTrack_Open-Vocabulary_Multiple_Object_Tracking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_OVTrack_Open-Vocabulary_Multiple_Object_Tracking_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multiple-object-tracking'] | ['computer-vision'] | [-1.00640237e-01 -4.91331011e-01 -2.65903771e-01 5.01008099e-03
-7.12711036e-01 -5.82435250e-01 8.53587627e-01 -7.77004883e-02
-2.43848845e-01 2.72048622e-01 -9.77589041e-02 1.06542110e-01
-1.39825836e-01 -3.67997319e-01 -8.21866989e-01 -7.36094892e-01
6.30779415e-02 6.96029484e-01 6.42630458e-01 -1.38869435... | [6.352503776550293, -2.0817127227783203] |
5a36b993-27f2-4ebf-9a35-f0a9c016e752 | learning-on-graphs-under-label-noise | 2306.08194 | null | https://arxiv.org/abs/2306.08194v1 | https://arxiv.org/pdf/2306.08194v1.pdf | Learning on Graphs under Label Noise | Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniques often presume that label information of nodes is accurate, which may not be th... | ['Ming Zhang', 'Wei Ju', 'Yusheng Zhao', 'Yifang Qin', 'Xiao Luo', 'Jingyang Yuan'] | 2023-06-14 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'anomaly-detection'] | ['computer-vision', 'methodology', 'methodology'] | [ 2.52480507e-01 1.98472768e-01 -3.01248461e-01 -4.86304998e-01
-1.66725263e-01 -3.77440244e-01 3.88118774e-01 3.46473902e-01
-4.70745750e-02 5.41112840e-01 -1.18471667e-01 -1.26724765e-01
-1.53874129e-01 -1.05733895e+00 -4.91600215e-01 -8.26043367e-01
-1.04767671e-02 2.75107443e-01 1.37810066e-01 1.90376583... | [7.303913116455078, 6.052811145782471] |
27b71a63-8ad0-439a-8fc4-75e531b13500 | exploring-optimal-granularity-for-extractive | 2209.10041 | null | https://arxiv.org/abs/2209.10041v2 | https://arxiv.org/pdf/2209.10041v2.pdf | Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan | Automated summarization of clinical texts can reduce the burden of medical professionals. "Discharge summaries" are one promising application of the summarization, because they can be generated from daily inpatient records. Our preliminary experiment suggests that 20-31% of the descriptions in discharge summaries overl... | ['Takashi Okumura', 'Yuji Matsumoto', 'Hiromasa Horiguchi', 'Mamoru Komachi', 'Kenichiro Ando'] | 2022-09-20 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.26738769e-01 5.71600199e-01 -1.72133520e-01 -2.92611003e-01
-1.30636132e+00 -5.42899907e-01 5.57002239e-02 1.05487013e+00
-4.69502687e-01 1.23728430e+00 9.93811250e-01 -2.17507899e-01
-2.37904742e-01 -5.59928179e-01 -2.14883298e-01 -5.62100828e-01
2.37204447e-01 7.20962048e-01 -1.20284133e-01 6.87652603... | [12.262003898620605, 9.507113456726074] |
5a545b9f-3a77-4b27-acb2-6e3bd5975a18 | mvm3det-a-novel-method-for-multi-view | 2109.10473 | null | https://arxiv.org/abs/2109.10473v1 | https://arxiv.org/pdf/2109.10473v1.pdf | MVM3Det: A Novel Method for Multi-view Monocular 3D Detection | Monocular 3D object detection encounters occlusion problems in many application scenarios, such as traffic monitoring, pedestrian monitoring, etc., which leads to serious false negative. Multi-view object detection effectively solves this problem by combining data from different perspectives. However, due to label conf... | ['Zhao Dongbin', 'Li Jiaqi', 'Chen Yaran', 'Ma Mingjun', 'Duan Zicheng', 'Li Haoran'] | 2021-09-22 | null | null | null | null | ['multiview-detection'] | ['computer-vision'] | [-2.49704391e-01 -5.89756429e-01 -2.41593406e-01 -1.98260903e-01
-6.06879354e-01 -4.56477553e-01 3.54768127e-01 -2.66436547e-01
-3.18997532e-01 2.61446267e-01 7.14982525e-02 2.52184384e-02
3.75618428e-01 -6.16598368e-01 -4.48104024e-01 -7.61266768e-01
6.35959029e-01 4.31515783e-01 9.55174804e-01 5.18274494... | [7.918281078338623, -2.2829999923706055] |
8f852868-8733-45bc-a378-12ab9a3fbdb6 | conceptbed-evaluating-concept-learning | 2306.04695 | null | https://arxiv.org/abs/2306.04695v1 | https://arxiv.org/pdf/2306.04695v1.pdf | ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models | The ability to understand visual concepts and replicate and compose these concepts from images is a central goal for computer vision. Recent advances in text-to-image (T2I) models have lead to high definition and realistic image quality generation by learning from large databases of images and their descriptions. Howev... | ['Yezhou Yang', 'Chitta Baral', 'Tejas Gokhale', 'Maitreya Patel'] | 2023-06-07 | null | null | null | null | ['concept-alignment'] | ['computer-vision'] | [ 4.03196216e-01 1.29088134e-01 2.00610638e-01 -4.12069559e-01
-5.42843640e-01 -8.77802253e-01 1.10449040e+00 2.87188530e-01
-1.02559596e-01 3.28248799e-01 2.11645693e-01 -1.53914243e-01
-6.32572472e-02 -5.74185610e-01 -8.41152847e-01 -3.20612043e-01
2.41375819e-01 6.36230707e-01 1.08040012e-02 -8.00781325... | [10.809503555297852, 1.4855324029922485] |
8da25666-7c39-4637-8bda-08c97ad561eb | empirical-effect-of-graph-embeddings-on-fraud | 1903.05976 | null | http://arxiv.org/abs/1903.05976v1 | http://arxiv.org/pdf/1903.05976v1.pdf | Empirical effect of graph embeddings on fraud detection/ risk mitigation | Graph embedding technics are studied with interest on public datasets, such
as BlogCatalog, with the common practice of maximizing scoring on graph
reconstruction, link prediction metrics etc. However, in the financial sector
the important metrics are often more business related, for example fraud
detection rates. With... | ['Sida Zhou'] | 2019-03-05 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-6.95075810e-01 6.55857563e-01 -3.23509902e-01 -9.32684392e-02
-1.45198554e-01 -2.79295027e-01 5.73579848e-01 9.85756099e-01
-4.18877631e-01 7.04831064e-01 4.83842254e-01 -3.92090112e-01
-3.17073464e-01 -1.20066953e+00 -2.93161541e-01 -3.53676051e-01
-6.11876607e-01 3.42544377e-01 4.02806669e-01 -5.06567955... | [6.972987651824951, 5.982858180999756] |
0dbb2958-4db7-4b05-8ab1-05ab00bc8c9b | face-parsing-with-roi-tanh-warping-1 | 1906.01342 | null | https://arxiv.org/abs/1906.01342v1 | https://arxiv.org/pdf/1906.01342v1.pdf | Face Parsing with RoI Tanh-Warping | Face parsing computes pixel-wise label maps for different semantic components (e.g., hair, mouth, eyes) from face images. Existing face parsing literature have illustrated significant advantages by focusing on individual regions of interest (RoIs) for faces and facial components. However, the traditional crop-and-resiz... | ['Lu Yuan', 'Fang Wen', 'Dong Chen', 'Hao Yang', 'Jinpeng Lin', 'Ming Zeng'] | 2019-06-04 | face-parsing-with-roi-tanh-warping | http://openaccess.thecvf.com/content_CVPR_2019/html/Lin_Face_Parsing_With_RoI_Tanh-Warping_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lin_Face_Parsing_With_RoI_Tanh-Warping_CVPR_2019_paper.pdf | cvpr-2019-6 | ['face-parsing'] | ['computer-vision'] | [ 1.59825593e-01 4.37694937e-01 1.99636109e-02 -7.82082915e-01
-3.36782485e-01 -4.27424490e-01 1.77746326e-01 -4.94912803e-01
-2.44520217e-01 3.61500353e-01 8.61671716e-02 1.70803413e-01
2.89809763e-01 -6.54915810e-01 -6.60719097e-01 -8.07559967e-01
2.64177293e-01 -9.43619013e-02 3.13482314e-01 8.03532824... | [13.450897216796875, 0.6330985426902771] |
1ff120ac-fa40-41bd-a09d-45e86dd04bd6 | switch-bert-learning-to-model-multimodal | 2306.14182 | null | https://arxiv.org/abs/2306.14182v1 | https://arxiv.org/pdf/2306.14182v1.pdf | Switch-BERT: Learning to Model Multimodal Interactions by Switching Attention and Input | The ability to model intra-modal and inter-modal interactions is fundamental in multimodal machine learning. The current state-of-the-art models usually adopt deep learning models with fixed structures. They can achieve exceptional performances on specific tasks, but face a particularly challenging problem of modality ... | ['Wei Chu', 'Kaisheng Yao', 'Qingpei Guo'] | 2023-06-25 | null | null | null | null | ['referring-expression', 'visual-question-answering-1', 'retrieval', 'question-answering'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing'] | [ 2.52823215e-02 1.11213356e-01 -1.71718583e-01 -4.16818112e-01
-8.78192723e-01 -4.95350152e-01 8.75006318e-01 -5.74560203e-02
-6.19454682e-01 4.38386351e-01 3.01069409e-01 -2.31423140e-01
-1.46019921e-01 -3.31023544e-01 -8.03896785e-01 -4.67663318e-01
6.22428916e-02 6.59585118e-01 2.19001602e-02 -4.09296840... | [10.846662521362305, 1.6249767541885376] |
f9cb513e-401a-4e4c-adc3-15ed1bfbef0e | attention-map-guided-transformer-pruning-for | 2304.01452 | null | https://arxiv.org/abs/2304.01452v1 | https://arxiv.org/pdf/2304.01452v1.pdf | Attention Map Guided Transformer Pruning for Edge Device | Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identification (Re-ID) tasks. However, the inherent problems of transformers such as the huge computational cost and memory footprint are still two unsol... | ['Heng-Tao Shen', 'Fumin Shen', 'Xingguo Huang', 'Zeren Sun', 'Yazhou Yao', 'Junzhu Mao'] | 2023-04-04 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 8.38134065e-02 -5.14473543e-02 1.19941972e-01 -3.77550274e-01
-6.46335363e-01 -1.24858953e-01 1.65082484e-01 2.36823633e-01
-7.74242163e-01 7.72484422e-01 1.52049989e-01 -1.24868378e-01
-3.15852225e-01 -8.42615664e-01 -5.52723825e-01 -5.10880113e-01
1.41175970e-01 3.91615480e-01 2.81666845e-01 9.01217908... | [14.68148422241211, 0.7670210599899292] |
99bf8787-d100-464b-abe0-4100fffb7355 | points-as-queries-weakly-semi-supervised | 2104.07434 | null | https://arxiv.org/abs/2104.07434v1 | https://arxiv.org/pdf/2104.07434v1.pdf | Points as Queries: Weakly Semi-supervised Object Detection by Points | We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points. It achieves a balance between tremendous annotation burden and detection performance. Based on this setting, we analyz... | ['Jian Sun', 'Wei zhang', 'Xiangyu Zhang', 'Tong Yang', 'Liangyu Chen'] | 2021-04-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.pdf | cvpr-2021-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-1.24225251e-01 2.76961774e-01 -4.01858389e-01 -1.09136447e-01
-1.27558780e+00 -6.55266941e-01 3.72890294e-01 -1.51308596e-01
-5.15082061e-01 3.39826554e-01 -7.63694048e-02 2.85898864e-01
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1.18883088e-01 3.74777198e-01 8.50171447e-01 -2.68150065... | [9.295876502990723, 1.142386555671692] |
f6107531-43f5-4abc-90f4-1cd7dea434d1 | visibility-aware-human-object-interaction | 2303.16479 | null | https://arxiv.org/abs/2303.16479v1 | https://arxiv.org/pdf/2303.16479v1.pdf | Visibility Aware Human-Object Interaction Tracking from Single RGB Camera | Capturing the interactions between humans and their environment in 3D is important for many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D human and object from a single RGB image do not have consistent relative translation across frames because they assume a fixed depth. Moreover, t... | ['Gerard Pons-Moll', 'Bharat Lal Bhatnagar', 'Xianghui Xie'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_Visibility_Aware_Human-Object_Interaction_Tracking_From_Single_RGB_Camera_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_Visibility_Aware_Human-Object_Interaction_Tracking_From_Single_RGB_Camera_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 3.09694782e-02 -1.70495555e-01 3.82499173e-02 -2.36706212e-01
-2.96165258e-01 -4.02712584e-01 5.40311873e-01 -4.04529989e-01
-3.05911481e-01 3.35459888e-01 9.84947290e-03 2.35848084e-01
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2.18200669e-01 5.91069102e-01 6.72691047e-01 1.18908331... | [7.39882755279541, -1.3946746587753296] |
d27f09e3-849f-4b9b-8ba6-975cd6d8da8e | generative-models-for-spear-phishing-posts-on | 1802.05196 | null | http://arxiv.org/abs/1802.05196v1 | http://arxiv.org/pdf/1802.05196v1.pdf | Generative Models for Spear Phishing Posts on Social Media | Historically, machine learning in computer security has prioritized defense:
think intrusion detection systems, malware classification, and botnet traffic
identification. Offense can benefit from data just as well. Social networks,
with their access to extensive personal data, bot-friendly APIs, colloquial
syntax, and ... | ['Philip Tully', 'John Seymour'] | 2018-02-14 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-9.08614621e-02 8.88857245e-02 -4.50309068e-01 -1.51936889e-01
-2.87166655e-01 -7.98729599e-01 1.05442679e+00 3.53174955e-02
-5.12917459e-01 4.49941725e-01 1.70189947e-01 -1.01265526e+00
-1.70699090e-01 -9.47399020e-01 -3.47087294e-01 -2.60782957e-01
-5.69857061e-01 6.97512746e-01 3.95087302e-01 -3.31056565... | [8.07491397857666, 10.0950288772583] |
31e23a75-6bdb-47af-ac59-c18d21b05d82 | expansion-via-prediction-of-importance-with | 2004.14245 | null | https://arxiv.org/abs/2004.14245v2 | https://arxiv.org/pdf/2004.14245v2.pdf | Expansion via Prediction of Importance with Contextualization | The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that: (1) explicitly models the importance of each term using a contextualized language model; (2) performs passage expansion by propagating the i... | ['Nicola Tonellotto', 'Raffaele Perego', 'Nazli Goharian', 'Franco Maria Nardini', 'Sean MacAvaney', 'Ophir Frieder'] | 2020-04-29 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.50722647e-01 -2.73183495e-01 -5.70565999e-01 1.29796630e-02
-1.72203720e+00 -7.33623266e-01 6.39268935e-01 8.99367511e-01
-6.48813128e-01 5.72756052e-01 9.10524547e-01 -5.45326471e-01
-2.63930976e-01 -5.15363753e-01 -4.56766844e-01 1.04216494e-01
-3.02317590e-01 6.84484839e-01 4.63793755e-01 -5.43971479... | [11.543680191040039, 7.657987117767334] |
972886e5-9b5b-4cf9-abad-5a4d9b90ec27 | resfpn-residual-skip-connections-in-multi | 2006.12235 | null | https://arxiv.org/abs/2006.12235v1 | https://arxiv.org/pdf/2006.12235v1.pdf | ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching | Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a suitable feature extractor for CNN-based dense matching tasks. FPN generates well localized and semantically strong features at multiple scale... | ['Oliver Wasenmüller', 'René Schuster', 'Ramy Battrawy', 'Didier Stricker', 'Rishav'] | 2020-06-22 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-3.14862505e-02 -4.22934413e-01 -1.62472963e-01 -2.57271886e-01
-5.87982535e-01 -2.29461566e-01 5.82909048e-01 -2.96285599e-01
-3.78556728e-01 6.59635067e-01 4.71146464e-01 1.74910963e-01
-6.10447153e-02 -8.56545031e-01 -5.90746462e-01 -2.54428387e-01
2.33705938e-02 -8.61612037e-02 5.80948472e-01 -2.95838088... | [8.8681640625, -2.210829734802246] |
70fa5b58-be0d-44c0-a622-fbbf5bb61099 | multi-channel-nuclear-norm-minus-frobenius | 2209.08094 | null | https://arxiv.org/abs/2209.08094v1 | https://arxiv.org/pdf/2209.08094v1.pdf | Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising | Color image denoising is frequently encountered in various image processing and computer vision tasks. One traditional strategy is to convert the RGB image to a less correlated color space and denoise each channel of the new space separately. However, such a strategy can not fully exploit the correlated information bet... | ['Tao Jia', 'Zhi Wang', 'Dong Hu', 'Yiwen Shan'] | 2022-09-16 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 4.34608519e-01 -6.50553584e-01 2.21863762e-01 5.72522450e-03
-6.78311348e-01 -1.31769747e-01 3.70363854e-02 -3.49734902e-01
-5.57803512e-01 5.33360124e-01 -1.99903682e-01 8.27815756e-02
-1.71342924e-01 -6.24530971e-01 -3.88307601e-01 -1.32231462e+00
3.66264492e-01 -4.76376295e-01 -8.13687369e-02 -1.19176239... | [11.234712600708008, -2.453395366668701] |
bedffe38-3ab9-41b3-b51d-91ab620ea184 | code2seq-generating-sequences-from-structured | 1808.01400 | null | http://arxiv.org/abs/1808.01400v6 | http://arxiv.org/pdf/1808.01400v6.pdf | code2seq: Generating Sequences from Structured Representations of Code | The ability to generate natural language sequences from source code snippets
has a variety of applications such as code summarization, documentation, and
retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine
translation (NMT), have achieved state-of-the-art performance on these tasks by
treating... | ['Uri Alon', 'Eran Yahav', 'Shaked Brody', 'Omer Levy'] | 2018-08-04 | code2seq-generating-sequences-from-structured-1 | https://openreview.net/forum?id=H1gKYo09tX | https://openreview.net/pdf?id=H1gKYo09tX | iclr-2019-5 | ['code-summarization'] | ['computer-code'] | [ 4.93474811e-01 8.52460414e-02 -4.76140708e-01 -4.40328121e-01
-1.47537315e+00 -8.33427608e-01 3.41260225e-01 1.72475323e-01
1.29491478e-01 3.92538100e-01 4.29963350e-01 -8.82955015e-01
3.90871406e-01 -4.72196966e-01 -9.97958660e-01 1.13423757e-01
-1.09048992e-01 -2.00474216e-03 4.18683626e-02 -3.18723202... | [7.661342620849609, 7.895580768585205] |
fb0839b4-7c1c-4c0a-8c3c-7a7761c5e7cc | learning-nuclei-representations-with-masked | 2306.17116 | null | https://arxiv.org/abs/2306.17116v1 | https://arxiv.org/pdf/2306.17116v1.pdf | Learning Nuclei Representations with Masked Image Modelling | Masked image modelling (MIM) is a powerful self-supervised representation learning paradigm, whose potential has not been widely demonstrated in medical image analysis. In this work, we show the capacity of MIM to capture rich semantic representations of Haemotoxylin & Eosin (H&E)-stained images at the nuclear level. I... | ['Katarzyna Bożek', 'Reinhard Büttner', 'Adrian Simon', 'Hussein Naji', 'Piotr Wójcik'] | 2023-06-29 | null | null | null | null | ['whole-slide-images', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.07026839e-01 6.20002329e-01 -1.87171564e-01 -1.48703635e-01
-1.05018330e+00 -5.19259751e-01 3.99005830e-01 4.66589391e-01
-3.40183258e-01 7.49744296e-01 -4.74007428e-02 -1.74691230e-01
2.08007663e-01 -9.60833311e-01 -7.87342489e-01 -1.14562345e+00
1.87506557e-01 7.70799398e-01 3.59236419e-01 1.04840621... | [14.96348762512207, -2.8942995071411133] |
d559ed2a-2832-4849-b2fe-9dfaa67e1dac | neural-fourier-filter-bank | 2212.01735 | null | https://arxiv.org/abs/2212.01735v3 | https://arxiv.org/pdf/2212.01735v3.pdf | Neural Fourier Filter Bank | We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to b... | ['Kwang Moo Yi', 'Yuhe Jin', 'Zhijie Wu'] | 2022-12-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Neural_Fourier_Filter_Bank_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Neural_Fourier_Filter_Bank_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 1.72561482e-01 -4.48597223e-01 1.53031409e-01 -2.57606685e-01
-8.54104459e-01 -3.75062436e-01 5.04110038e-01 6.52370751e-02
-1.35281801e-01 5.43180406e-01 4.45309341e-01 -1.65951729e-01
-2.75169283e-01 -9.78195131e-01 -9.84273553e-01 -8.86625290e-01
-4.23330218e-01 -6.71590790e-02 -1.22784898e-01 2.83627734... | [11.015636444091797, -1.9761295318603516] |
a181f5bf-8c8e-4b9e-9abe-474f3ab1fb42 | meta-learning-by-hallucinating-useful | null | null | https://openreview.net/forum?id=rJx8I1rFwr | https://openreview.net/pdf?id=rJx8I1rFwr | Meta-Learning by Hallucinating Useful Examples | Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks, which aim to learn novel concepts from very few examples. The hallucination process, however, is still far from generating effective samples for learning. In this work, we investigate two imp... | ['Karteek Alahari', 'Martial Hebert', 'Yuki Uchiyama', 'Yu-Xiong Wang'] | 2019-09-25 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.2302118e-01 1.8980050e-01 -8.9161754e-02 -2.5295949e-01
-8.9963931e-01 2.9025576e-01 8.8495344e-01 3.8060263e-02
-3.0911702e-01 8.6158043e-01 2.2402653e-01 3.6697146e-01
6.2920004e-02 -5.9703583e-01 -6.6177630e-01 -7.0655328e-01
-1.1834326e-01 4.4700670e-01 3.1614760e-01 -3.3703613e-01
1.8137611e-01... | [9.957225799560547, 2.869290351867676] |
424997a4-76a4-4941-8abc-3dd546b72d68 | dual-task-learning-by-leveraging-both-dense | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Park_Dual_Task_Learning_by_Leveraging_Both_Dense_Correspondence_and_Mis-Correspondence_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Park_Dual_Task_Learning_by_Leveraging_Both_Dense_Correspondence_and_Mis-Correspondence_CVPR_2022_paper.pdf | Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches | Accurate change detection enables a wide range of tasks in visual surveillance, anomaly detection and mobile robotics. However, contemporary change detection approaches assume an ideal matching between the current and stored scenes, whereas only coarse matching is possible in real-world scenarios. Thus, contemporar... | ['Jong-Hwan Kim', 'Seon-Hoon Lee', 'Ue-Hwan Kim', 'Jin-Man Park'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 3.66001487e-01 -4.52492595e-01 -1.08555883e-01 -2.15326294e-01
-1.19548924e-01 -6.18896544e-01 9.33308482e-01 -1.84807759e-02
-4.96545047e-01 3.20051998e-01 -2.06556857e-01 -4.16431457e-01
2.18122318e-01 -7.65512943e-01 -7.22202241e-01 -4.19768333e-01
-2.49964193e-01 6.78592697e-02 9.96113122e-01 -2.89477080... | [8.581045150756836, -1.2941335439682007] |
a110d0e6-4c00-4707-ad89-ee3c1fa8f276 | from-pixels-to-ui-actions-learning-to-follow | 2306.00245 | null | https://arxiv.org/abs/2306.00245v1 | https://arxiv.org/pdf/2306.00245v1.pdf | From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces | Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available. These input representations have been often coupled with custom, task-specific action spaces. This ... | ['Kristina Toutanova', 'Kenton Lee', 'Urvashi Khandelwal', 'Hexiang Hu', 'Panupong Pasupat', 'Jonathan Berant', 'James Cohan', 'Mandar Joshi', 'Peter Shaw'] | 2023-05-31 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 5.38383961e-01 3.11147392e-01 1.32514626e-01 -4.94000822e-01
-5.26233673e-01 -6.82212651e-01 8.87841940e-01 2.05850735e-01
-7.48988211e-01 6.17780089e-01 3.47083330e-01 -8.77522647e-01
-1.11183479e-01 -7.50667274e-01 -5.22148788e-01 -1.46149814e-01
1.96152061e-01 6.21648192e-01 5.62567651e-01 -5.60634673... | [4.277980804443359, 0.9585561156272888] |
729ebca6-7490-468f-a16e-9e5685cb80f9 | quickest-change-detection-for-unnormalized | 2302.00250 | null | https://arxiv.org/abs/2302.00250v1 | https://arxiv.org/pdf/2302.00250v1.pdf | Quickest Change Detection for Unnormalized Statistical Models | Classical quickest change detection algorithms require modeling pre-change and post-change distributions. Such an approach may not be feasible for various machine learning models because of the complexity of computing the explicit distributions. Additionally, these methods may suffer from a lack of robustness to model ... | ['Vahid Tarokh', 'Jie Ding', 'Taposh Banerjee', 'Enmao Diao', 'Suya Wu'] | 2023-02-01 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.94638187e-01 -4.40372318e-01 1.08090080e-01 -1.75344065e-01
-6.90577686e-01 -4.40609008e-01 4.00849015e-01 5.13505638e-01
-3.30810398e-01 8.97569299e-01 -6.99729860e-01 -4.32708591e-01
-3.11355501e-01 -5.59866250e-01 -3.96237582e-01 -8.93115103e-01
-2.45932594e-01 6.97879270e-02 3.71318132e-01 1.93724334... | [7.051922798156738, 3.8587937355041504] |
ec067307-174f-4f8d-835d-18e447aee2c5 | spa-gcn-efficient-and-flexible-gcn | 2111.05936 | null | https://arxiv.org/abs/2111.05936v1 | https://arxiv.org/pdf/2111.05936v1.pdf | SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation | While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving graphs. The unique characteristics of graphs, such as the irregular memory access and dynamic parallelism, impose several challenges when th... | ['Jason Cong', 'Yuze Chi', 'Atefeh Sohrabizadeh'] | 2021-11-10 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.88659981e-01 -9.53705013e-02 -5.07144518e-02 -2.16081023e-01
1.26183107e-01 -1.09574415e-01 3.57386976e-01 3.32256883e-01
-4.79488611e-01 -4.41424660e-02 -1.21390246e-01 -8.62379789e-01
-3.83199267e-02 -1.19382966e+00 -7.17488945e-01 -6.79522514e-01
-3.59036833e-01 1.41494378e-01 2.35272110e-01 -1.76534951... | [7.013814926147461, 5.6533427238464355] |
ce40110f-2ca4-4755-94ab-1d6e52ce98b2 | reward-gaming-in-conditional-text-generation | 2211.08714 | null | https://arxiv.org/abs/2211.08714v3 | https://arxiv.org/pdf/2211.08714v3.pdf | Reward Gaming in Conditional Text Generation | To align conditional text generation model outputs with desired behaviors, there has been an increasing focus on training the model using reinforcement learning (RL) with reward functions learned from human annotations. Under this framework, we identify three common cases where high rewards are incorrectly assigned to ... | ['He He', 'Ankur P. Parikh', 'Thibault Sellam', 'Vishakh Padmakumar', 'Richard Yuanzhe Pang'] | 2022-11-16 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 4.86785710e-01 9.14366722e-01 -1.49815485e-01 -4.24465388e-01
-1.08005285e+00 -5.76953530e-01 7.75518298e-01 1.98887922e-02
-1.76639602e-01 1.06623530e+00 6.82774246e-01 -3.25995475e-01
7.50157088e-02 -6.34437203e-01 -7.39157021e-01 -3.17803830e-01
-8.03020690e-03 3.96485776e-01 -3.97819668e-01 -1.53536901... | [11.710933685302734, 8.932479858398438] |
e6179fd1-0447-4730-bb41-60b25b82dcc1 | combining-state-of-the-art-models-with | 2211.10808 | null | https://arxiv.org/abs/2211.10808v1 | https://arxiv.org/pdf/2211.10808v1.pdf | Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization | In Natural Language Processing, multi-document summarization (MDS) poses many challenges to researchers above those posed by single-document summarization (SDS). These challenges include the increased search space and greater potential for the inclusion of redundant information. While advancements in deep learning appr... | ['Yllias Chali', 'Gandharv Suri', 'David Adams'] | 2022-11-19 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 4.58800465e-01 9.74802375e-02 -2.88985908e-01 -1.77178264e-01
-1.31040549e+00 -2.93610901e-01 9.24314737e-01 7.37474203e-01
-4.19137239e-01 7.74950981e-01 9.20083523e-01 -5.21338545e-02
-2.59087205e-01 -6.01009250e-01 -3.38590115e-01 -3.63624990e-01
7.02096671e-02 5.65650225e-01 2.87633598e-01 -6.12105906... | [12.40308666229248, 9.439666748046875] |
6fcb840a-de3e-4dd6-b067-af45874e8cc2 | video-representation-learning-by-dense | 1909.04656 | null | https://arxiv.org/abs/1909.04656v3 | https://arxiv.org/pdf/1909.04656v3.pdf | Video Representation Learning by Dense Predictive Coding | The objective of this paper is self-supervised learning of spatio-temporal embeddings from video, suitable for human action recognition. We make three contributions: First, we introduce the Dense Predictive Coding (DPC) framework for self-supervised representation learning on videos. This learns a dense encoding of spa... | ['Weidi Xie', 'Tengda Han', 'Andrew Zisserman'] | 2019-09-10 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 5.35054088e-01 2.50994712e-01 -4.89516258e-01 -3.43257576e-01
-5.42024374e-01 -1.92699686e-01 9.10931766e-01 -6.08620942e-02
-5.33499241e-01 5.16658664e-01 7.76189864e-01 1.58432975e-01
7.12488368e-02 -4.89949793e-01 -1.03105366e+00 -5.80426812e-01
-6.02016211e-01 2.30774611e-01 5.41625082e-01 -4.52261120... | [8.647808074951172, 0.8116270899772644] |
a7c9aea5-d353-4f46-988b-cb6a9e0bbd86 | a-label-attention-model-for-icd-coding-from | 2007.06351 | null | https://arxiv.org/abs/2007.06351v1 | https://arxiv.org/pdf/2007.06351v1.pdf | A Label Attention Model for ICD Coding from Clinical Text | ICD coding is a process of assigning the International Classification of Disease diagnosis codes to clinical/medical notes documented by health professionals (e.g. clinicians). This process requires significant human resources, and thus is costly and prone to error. To handle the problem, machine learning has been util... | ['Anthony Nguyen', 'Thanh Vu', 'Dat Quoc Nguyen'] | 2020-07-13 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [-9.35724820e-04 3.24549526e-02 -5.41777074e-01 -4.03149247e-01
-6.23074055e-01 -2.20185354e-01 -1.14854895e-01 8.36918652e-01
-2.10223466e-01 4.31796104e-01 3.29273641e-01 -4.82778281e-01
-1.64295331e-01 -7.34795272e-01 -2.67529845e-01 -5.04806280e-01
-7.32602999e-02 8.46111417e-01 -5.22072949e-02 1.12104207... | [8.008199691772461, 6.852161884307861] |
8761c3bd-c267-457c-a38f-daa501d89b5b | unpaired-image-to-image-translation-with-2 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.pdf | Unpaired Image-to-Image Translation With Shortest Path Regularization | Unpaired image-to-image translation aims to learn proper mappings that can map images from one domain to another domain while preserving the content of the input image. However, with large enough capacities, the network can learn to map the inputs to any random permutation of images in another domain. Existing meth... | ['Kun Zhang', 'Mingming Gong', 'Yanwu Xu', 'Shaoan Xie'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 7.15270817e-01 3.51222217e-01 -2.98106968e-01 -3.51463795e-01
-2.12710410e-01 -8.46722245e-01 4.87158120e-01 -4.29116368e-01
-3.11545193e-01 7.46234357e-01 -3.49656008e-02 -2.62385577e-01
-1.14040107e-01 -1.05135822e+00 -1.09203744e+00 -6.98021710e-01
2.65677840e-01 3.00274014e-01 1.57801002e-01 -8.29466954... | [11.648972511291504, -0.39622703194618225] |
277a2318-4d58-4c05-b049-d5816c97fe24 | distributional-reinforcement-learning-with | 1902.03149 | null | http://arxiv.org/abs/1902.03149v1 | http://arxiv.org/pdf/1902.03149v1.pdf | Distributional reinforcement learning with linear function approximation | Despite many algorithmic advances, our theoretical understanding of practical
distributional reinforcement learning methods remains limited. One exception is
Rowland et al. (2018)'s analysis of the C51 algorithm in terms of the Cram\'er
distance, but their results only apply to the tabular setting and ignore C51's
use ... | ['Subhodeep Moitra', 'Marc G. Bellemare', 'Pablo Samuel Castro', 'Nicolas Le Roux'] | 2019-02-08 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.20462531e-02 3.36466521e-01 -4.59073722e-01 -3.12975556e-01
-9.06881511e-01 -8.72095048e-01 6.07536435e-01 2.91970015e-01
-7.45448291e-01 1.05243146e+00 1.02596499e-01 -9.27808583e-01
-4.35225576e-01 -7.33124197e-01 -7.71707475e-01 -8.91819358e-01
-4.22350541e-02 6.84293628e-01 6.26695305e-02 -2.31936976... | [4.099818706512451, 2.6142802238464355] |
1d10f85f-b36f-45f1-a7fb-c280ff6e5518 | branchconnect-large-scale-visual-recognition | 1704.06010 | null | http://arxiv.org/abs/1704.06010v3 | http://arxiv.org/pdf/1704.06010v3.pdf | BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections | We introduce an architecture for large-scale image categorization that
enables the end-to-end learning of separate visual features for the different
classes to distinguish. The proposed model consists of a deep CNN shaped like a
tree. The stem of the tree includes a sequence of convolutional layers common
to all classe... | ['Karim Ahmed', 'Lorenzo Torresani'] | 2017-04-20 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 5.87268770e-02 2.18699947e-01 -2.65118271e-01 -6.49849772e-01
-4.40863788e-01 -6.53090000e-01 4.31109309e-01 2.26601288e-01
-5.07866144e-01 4.48032320e-01 -1.46203250e-01 -2.53735960e-01
-2.25131400e-02 -8.64281774e-01 -6.96493208e-01 -8.50730062e-01
-3.37315381e-01 1.81473032e-01 3.80006224e-01 -6.85303509... | [9.410053253173828, 2.2724761962890625] |
479f2592-d004-41c2-a898-022dacc2740d | the-genea-challenge-2022-a-large-evaluation | 2208.10441 | null | https://arxiv.org/abs/2208.10441v1 | https://arxiv.org/pdf/2208.10441v1.pdf | The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation | This paper reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and e... | ['Gustav Eje Henter', 'Mihail Tsakov', 'Teodor Nikolov', 'Carla Viegas', 'Taras Kucherenko', 'Pieter Wolfert', 'Youngwoo Yoon'] | 2022-08-22 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 2.93783154e-02 1.67163521e-01 2.05131933e-01 -9.84784588e-02
-9.18195069e-01 -7.85296619e-01 1.10727620e+00 -7.27968633e-01
-3.65129471e-01 5.13528943e-01 1.00525737e+00 3.35385948e-02
2.94329256e-01 -5.29037751e-02 -3.54716867e-01 -4.50415283e-01
1.11867920e-01 4.45531845e-01 2.18239039e-01 -3.62983406... | [5.60507869720459, -0.08735744655132294] |
fde3f535-8fad-475a-9f4d-cced5de9759a | seedformer-patch-seeds-based-point-cloud | 2207.10315 | null | https://arxiv.org/abs/2207.10315v1 | https://arxiv.org/pdf/2207.10315v1.pdf | SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer | Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation. In this paper, we propose a novel SeedFormer to improve the ability of detail preservation and rec... | ['Chengjie Wang', 'Ying Tai', 'Tong Lu', 'Junwei Zhu', 'Wenqing Chu', 'Yun Cao', 'Haoran Zhou'] | 2022-07-21 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.06078550e-01 -2.80277818e-01 7.24623203e-02 -2.78015465e-01
-7.83566952e-01 -6.93750381e-01 6.18071139e-01 -3.10246330e-02
5.10213263e-02 3.64612162e-01 1.56991169e-01 4.93262662e-03
-1.66031718e-02 -9.91327703e-01 -8.73809099e-01 -4.26292390e-01
2.67933369e-01 4.41806078e-01 1.53563678e-01 -1.86624214... | [8.33443546295166, -3.5887677669525146] |
d8ee1f24-087b-45d8-b439-c933c0fd864d | sparsity-based-convolutional-kernel-network | 1807.05648 | null | https://arxiv.org/abs/1807.05648v4 | https://arxiv.org/pdf/1807.05648v4.pdf | Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis | The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain w... | ['Euijoon Ahn', 'Jinman Kim', 'Michael Fulham', 'Dagan Feng', 'Ashnil Kumar'] | 2018-07-16 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 5.41741729e-01 -4.11153547e-02 -2.07168445e-01 -4.75305259e-01
-1.16862679e+00 -5.23813605e-01 4.17988628e-01 5.75681329e-01
-6.19478524e-01 2.30064258e-01 3.80020142e-01 -2.29510479e-02
-3.56099904e-01 -3.87677491e-01 -6.15637302e-01 -7.26854205e-01
-4.46784496e-01 1.03359692e-01 3.14578742e-01 1.93012834... | [14.907489776611328, -2.4762940406799316] |
0e188fde-032e-4332-8e90-149fc882e98c | reconstructing-the-mind-s-eye-fmri-to-image | 2305.18274 | null | https://arxiv.org/abs/2305.18274v1 | https://arxiv.org/pdf/2305.18274v1.pdf | Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors | We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized for retrieval (using contrastive learning) and reconstruction (using a diffusion prior). MindEye can map fMRI brain activity to any high dime... | ['Tanishq Mathew Abraham', 'Kenneth A. Norman', 'David Weisberg', 'Elad Yundler', 'Nathalie Verlinde', 'Aidan J. Dempster', 'Ethan Cohen', 'Alex Nguyen', 'Stepan Shabalin', 'Jimmie Goode', 'Atmadeep Banerjee', 'Paul S. Scotti'] | 2023-05-29 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 2.59124395e-02 -1.40405998e-01 -7.83831701e-02 -2.67452359e-01
-9.62324023e-01 -5.35488188e-01 7.40436792e-01 -2.96641558e-01
-6.66819513e-01 4.87850636e-01 5.86538017e-01 2.50304729e-01
-1.14700496e-01 -5.18187761e-01 -7.88706124e-01 -8.03600192e-01
-6.73026443e-02 4.45274174e-01 -1.25712365e-01 2.86142170... | [10.743611335754395, 2.4993081092834473] |
2ff35440-d2d1-4faa-b7ed-22838ec2edfe | mcua-multi-level-context-and-uncertainty | 2108.10709 | null | https://arxiv.org/abs/2108.10709v1 | https://arxiv.org/pdf/2108.10709v1.pdf | MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification | Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability... | ['Saeid Nahavandi', 'Abbas Khosravi', 'U Rajendra Acharya', 'Moloud Abdar', 'Mohamed Medhat Gaber', 'Mohammed M. Abdelsamea', 'Zakaria Senousy'] | 2021-08-24 | null | null | null | null | ['breast-cancer-histology-image-classification'] | ['medical'] | [ 4.91594672e-02 -1.48460850e-01 5.52560203e-02 -4.40852553e-01
-1.00056279e+00 -1.71683535e-01 4.30691510e-01 5.21197796e-01
-5.03927767e-01 7.31119335e-01 -1.95979461e-01 -2.29114935e-01
-4.56618071e-01 -7.23642409e-01 -5.92551947e-01 -1.35005319e+00
-1.59179792e-01 2.49182522e-01 2.06911445e-01 1.02656998... | [15.07246208190918, -2.918498992919922] |
198e0339-8248-41d7-81ef-affc4109ad2b | pyramid-diffusion-models-for-low-light-image | 2305.10028 | null | https://arxiv.org/abs/2305.10028v1 | https://arxiv.org/pdf/2305.10028v1.pdf | Pyramid Diffusion Models For Low-light Image Enhancement | Recovering noise-covered details from low-light images is challenging, and the results given by previous methods leave room for improvement. Recent diffusion models show realistic and detailed image generation through a sequence of denoising refinements and motivate us to introduce them to low-light image enhancement f... | ['Yi Yang', 'Zongxin Yang', 'Dewei Zhou'] | 2023-05-17 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 2.39282787e-01 -3.71569723e-01 2.26384684e-01 -4.74475659e-02
-5.44626176e-01 -1.56983435e-01 3.50109249e-01 -3.01913172e-01
-3.37194413e-01 6.84380710e-01 3.11065525e-01 9.39828753e-02
2.32843935e-01 -1.09533405e+00 -5.71896434e-01 -1.05782819e+00
4.07906294e-01 -2.28978559e-01 7.92659342e-01 -2.77522892... | [11.022858619689941, -2.3973701000213623] |
58f93127-af64-487d-926c-b7fa273acbf0 | domain-adversarial-spatial-temporal-network-a | 2202.03630 | null | https://arxiv.org/abs/2202.03630v2 | https://arxiv.org/pdf/2202.03630v2.pdf | Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities | Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becom... | ['Wei Ma', 'S. C. Wong', 'William H. K. Lam', 'Andy H. F. Chow', 'Ao Qu', 'Yihong Tang'] | 2022-02-08 | null | null | null | null | ['time-series-regression', 'spatio-temporal-forecasting'] | ['time-series', 'time-series'] | [-1.84045862e-02 -2.67212149e-02 -4.92205977e-01 -3.70705247e-01
-4.93521452e-01 -2.66980231e-01 8.15650642e-01 -4.44723904e-01
-9.95066836e-02 8.93652737e-01 2.70039201e-01 -8.52363527e-01
-1.47599205e-01 -1.25523055e+00 -5.62421083e-01 -6.50579810e-01
7.88149983e-02 7.46426523e-01 6.22258663e-01 -6.98189020... | [6.47177267074585, 2.0692965984344482] |
6b8ea2c9-0e42-4861-bd0a-1516651b7535 | benchmarking-zero-shot-and-few-shot | 2208.01814 | null | https://arxiv.org/abs/2208.01814v2 | https://arxiv.org/pdf/2208.01814v2.pdf | Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text | The grammatical analysis of texts in any written language typically involves a number of basic processing tasks, such as tokenization, morphological tagging, and dependency parsing. State-of-the-art systems can achieve high accuracy on these tasks for languages with large datasets, but yield poor results for languages ... | ['Franz de Leon', 'Angelina Aquino'] | 2022-08-03 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [-1.58833891e-01 1.80163443e-01 -2.48553529e-01 -6.38427138e-01
-1.25970352e+00 -7.94708252e-01 6.04059458e-01 7.10548341e-01
-7.61915982e-01 5.93648255e-01 3.35225642e-01 -4.53459382e-01
4.58150655e-01 -6.63767040e-01 -4.99494940e-01 -2.18185946e-01
8.96249991e-03 6.86868489e-01 2.75110096e-01 -2.64051139... | [10.43354606628418, 9.85705852508545] |
e8da2db8-a1de-413b-8042-b78194e01a44 | when-do-you-need-chain-of-thought-prompting | 2304.03262 | null | https://arxiv.org/abs/2304.03262v2 | https://arxiv.org/pdf/2304.03262v2.pdf | When do you need Chain-of-Thought Prompting for ChatGPT? | Chain-of-Thought (CoT) prompting can effectively elicit complex multi-step reasoning from Large Language Models~(LLMs). For example, by simply adding CoT instruction ``Let's think step-by-step'' to each input query of MultiArith dataset, GPT-3's accuracy can be improved from 17.7\% to 78.7\%. However, it is not clear w... | ['Tianyi Zhou', 'Heng Huang', 'Lichang Chen', 'Jiuhai Chen'] | 2023-04-06 | null | null | null | null | ['memorization', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 2.15306699e-01 3.05730581e-01 -2.32141986e-02 -4.10271198e-01
-9.95612264e-01 -7.74941325e-01 3.43098640e-01 3.80284518e-01
-5.38314402e-01 5.60820699e-01 1.96954787e-01 -1.18527102e+00
8.62448066e-02 -9.68845904e-01 -1.06317079e+00 -2.75319606e-01
2.79059142e-01 6.23413682e-01 2.77380377e-01 -5.19619226... | [9.739147186279297, 7.435482025146484] |
0d9c24cd-2651-48b0-9ead-1cde330c2d00 | socially-aware-robot-crowd-navigation-with | 2203.01821 | null | https://arxiv.org/abs/2203.01821v4 | https://arxiv.org/pdf/2203.01821v4.pdf | Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph | We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. To learn a safe and e... | ['Katherine Driggs-Campbell', 'D. Livingston McPherson', 'Weihang Liang', 'Kaiwen Hong', 'Junyi Geng', 'Neeloy Chakraborty', 'Zhe Huang', 'Peixin Chang', 'Shuijing Liu'] | 2022-03-03 | null | null | null | null | ['social-navigation'] | ['robots'] | [-3.83726090e-01 5.34855425e-01 2.29608119e-01 -8.38196427e-02
-2.50589073e-01 -3.56292129e-01 5.04855216e-01 -2.65229821e-01
-6.58722341e-01 9.94351864e-01 2.82429218e-01 -2.45401889e-01
1.94612786e-01 -7.21217215e-01 -7.76708663e-01 -6.38875365e-01
-5.92181146e-01 7.20532954e-01 4.71175641e-01 -6.22329950... | [4.765652179718018, 1.0544382333755493] |
343e5e3a-9ce3-4cbc-8d50-de2731835265 | repairing-adversarial-texts-through | 2201.02504 | null | https://arxiv.org/abs/2201.02504v1 | https://arxiv.org/pdf/2201.02504v1.pdf | Repairing Adversarial Texts through Perturbation | It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predictions. Furthermore, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation m... | ['Jin Song Dong', 'Jie Shi', 'Ting Dai', 'Xinyu Wang', 'Sudipta Chattopadhyay', 'Jun Sun', 'Jingyi Wang', 'Guoliang Dong'] | 2021-12-29 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 7.54421890e-01 4.33370382e-01 3.67899150e-01 -1.59731865e-01
-6.14332318e-01 -1.18732762e+00 5.86299241e-01 2.65961528e-01
-2.22140566e-01 6.58864796e-01 -2.39370808e-01 -4.93085414e-01
4.51565146e-01 -1.10960495e+00 -1.24758697e+00 -6.15884006e-01
3.79688084e-01 3.77895594e-01 3.53802651e-01 -3.20132524... | [5.903046131134033, 8.01942253112793] |
f455c3e7-3873-4be7-aec2-84acde5cfd63 | hallucinated-adversarial-control-for | 2303.01076 | null | https://arxiv.org/abs/2303.01076v2 | https://arxiv.org/pdf/2303.01076v2.pdf | Hallucinated Adversarial Control for Conservative Offline Policy Evaluation | We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, collected by other agents, we seek to obtain a (tight) lower bound on a policy's performance. This is crucial when deciding whether a given policy satisfies certain minimal performance/safety cr... | ['Andreas Krause', 'Parnian Kassraie', 'Tobias Birchler', 'Bhavya Sukhija', 'Jonas Rothfuss'] | 2023-03-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.84922934e-01 3.87925148e-01 -3.17281634e-01 9.02520120e-02
-1.00156379e+00 -9.37340498e-01 5.63102722e-01 4.00141418e-01
-5.30044436e-01 1.05857539e+00 -1.39072955e-01 -6.59287810e-01
-4.56718355e-01 -6.37472868e-01 -1.01522243e+00 -7.67460704e-01
-6.05954230e-01 6.80940390e-01 2.86078066e-01 1.31118387... | [4.3677802085876465, 2.4467296600341797] |
340335e6-b664-4f7d-b8d5-1e08f65957c4 | secoda-sense-complexity-dataset | null | null | https://aclanthology.org/2020.lrec-1.730 | https://aclanthology.org/2020.lrec-1.730.pdf | SeCoDa: Sense Complexity Dataset | The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of wo... | ['Shiva Taslimipoor', 'David Strohmaier', 'Sian Gooding', 'Ekaterina Kochmar'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['complex-word-identification'] | ['natural-language-processing'] | [-3.14476527e-02 8.77648592e-02 -1.71545163e-01 -9.89747420e-02
-4.38726127e-01 -1.03402567e+00 6.91744685e-01 1.13476086e+00
-1.17995787e+00 6.20123565e-01 7.09559321e-01 -4.64153767e-01
-2.54585803e-01 -7.34676898e-01 4.28000301e-01 -2.19428912e-01
8.68590996e-02 4.87530828e-01 1.77197248e-01 -8.24136257... | [10.177331924438477, 9.194952964782715] |
87bbac80-d006-436f-8bcb-847d0d6bba04 | a-framework-for-information-extraction-from | 1902.10031 | null | http://arxiv.org/abs/1902.10031v1 | http://arxiv.org/pdf/1902.10031v1.pdf | A framework for information extraction from tables in biomedical literature | The scientific literature is growing exponentially, and professionals are no
more able to cope with the current amount of publications. Text mining provided
in the past methods to retrieve and extract information from text; however,
most of these approaches ignored tables and figures. The research done in
mining table ... | ['Robert Hernandez', 'Goran Nenadic', 'Nikola Milosevic', 'Cassie Gregson'] | 2019-02-26 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 4.27278817e-01 3.13637972e-01 -2.19599888e-01 -3.24427128e-01
-6.25473499e-01 -5.69176555e-01 2.67397970e-01 1.42494702e+00
-4.30560410e-01 9.78422344e-01 6.14829242e-01 -6.44686401e-01
-2.96433151e-01 -6.30503416e-01 -1.36200666e-01 -1.20696455e-01
9.20160487e-02 6.92676365e-01 2.22478360e-01 1.19457029... | [8.537771224975586, 8.692288398742676] |
2594ace5-7ab0-4738-b2d2-de3466083f63 | recognizing-disguised-faces-in-the-wild | 1811.08837 | null | http://arxiv.org/abs/1811.08837v1 | http://arxiv.org/pdf/1811.08837v1.pdf | Recognizing Disguised Faces in the Wild | Research in face recognition has seen tremendous growth over the past couple
of decades. Beginning from algorithms capable of performing recognition in
constrained environments, the current face recognition systems achieve very
high accuracies on large-scale unconstrained face datasets. While upcoming
algorithms contin... | ['Nalini Ratha', 'Mayank Vatsa', 'Maneet Singh', 'Richa Singh', 'Rama Chellappa'] | 2018-11-21 | null | null | null | null | ['disguised-face-verification'] | ['computer-vision'] | [ 3.96327376e-01 -7.68292025e-02 2.66606003e-01 -5.78429341e-01
-1.75964221e-01 -9.34789300e-01 9.83245850e-01 -7.13747084e-01
-3.24456662e-01 5.32012105e-01 -1.24322481e-01 -1.66542530e-01
-9.39587206e-02 -5.23617983e-01 -6.71704352e-01 -8.38381827e-01
-2.69468963e-01 2.86109000e-01 -4.04184818e-01 -2.02025875... | [12.984569549560547, 1.0391535758972168] |
e1119827-0c8c-4494-a4fc-8fbdd12e1e6f | towards-accurate-ground-plane-normal | 2212.04224 | null | https://arxiv.org/abs/2212.04224v1 | https://arxiv.org/pdf/2212.04224v1.pdf | Towards Accurate Ground Plane Normal Estimation from Ego-Motion | In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground pl... | ['Cong Yang', 'Tao Chen', 'Qian Zhang', 'Wei Sui', 'Jiaxin Zhang'] | 2022-12-08 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.64054886e-01 -2.61582229e-02 -3.05881083e-01 -3.26931983e-01
-4.61550891e-01 -4.16573465e-01 4.88363624e-01 -1.57770678e-01
-3.15163344e-01 4.48974103e-01 -3.44915748e-01 -3.92469645e-01
8.92523453e-02 -1.00487220e+00 -9.00854766e-01 -7.77364671e-01
2.00652376e-01 3.00966620e-01 5.17250180e-01 -5.68839312... | [7.523440837860107, -2.0063624382019043] |
01736970-5076-4626-aa35-37a796bca6f1 | a-comparative-study-on-application-of-class | 2206.09752 | null | https://arxiv.org/abs/2206.09752v1 | https://arxiv.org/pdf/2206.09752v1.pdf | A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization | In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization. We extracted multiple features from the data, and selected "hospitalization or not" as the target for classif... | ['Tong Jia', 'Zhengke Sun', 'Ning Chen'] | 2022-06-20 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-3.02640408e-01 -4.14021879e-01 -8.03344190e-01 -7.51034915e-01
-6.25402212e-01 -2.65549988e-01 -3.28597724e-01 9.42373991e-01
-1.99468791e-01 6.78523362e-01 2.60473073e-01 -6.22415543e-01
-3.75806451e-01 -1.13375890e+00 -5.69645643e-01 -4.52562124e-01
3.74261435e-04 7.40548790e-01 2.25109890e-01 -8.91285613... | [8.293390274047852, 5.376340389251709] |
e23bb3c5-f9b0-47c4-8913-9dd1aa586cb0 | physics-informed-computer-vision-a-review-and | 2305.18035 | null | https://arxiv.org/abs/2305.18035v2 | https://arxiv.org/pdf/2305.18035v2.pdf | Physics-Informed Computer Vision: A Review and Perspectives | Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting a... | ['George Karniadakis', 'Clinton Fookes', 'Kien Nguyen', 'Chayan Banerjee'] | 2023-05-29 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 4.58632767e-01 8.90999734e-02 -5.34683406e-01 -3.41964275e-01
-2.42665276e-01 -4.57762718e-01 1.08144379e+00 3.16321328e-02
-2.66867220e-01 4.88966465e-01 1.57221053e-02 -6.24485075e-01
-6.86383963e-01 -5.38749516e-01 -8.23802710e-01 -9.80227053e-01
1.11262709e-01 2.42323592e-01 3.15008342e-01 -2.69011455... | [6.526979923248291, 3.6115894317626953] |
7cf4bd05-883d-470b-8cf0-c85b3849d1bb | sentence-level-propaganda-detection-in-news | null | null | https://aclanthology.org/D19-5022 | https://aclanthology.org/D19-5022.pdf | Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model | In recent years, the need for communication increased in online social media. Propaganda is a mechanism which was used throughout history to influence public opinion and it is gaining a new dimension with the rising interest of online social media. This paper presents our submission to NLP4IF-2019 Shared Task SLC: Sent... | ['Dumitru-Clementin Cercel', 'Cristian Onose', 'Mircea-Adrian Tanase', 'ru', 'George-Alex Vlad'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [-9.37438309e-02 1.66100696e-01 -7.00183287e-02 -2.85563260e-01
-7.57019639e-01 -3.25613588e-01 8.01080346e-01 5.00464916e-01
-7.41342962e-01 7.37941682e-01 4.93618339e-01 -3.55975807e-01
4.75593895e-01 -5.70480525e-01 -5.66347182e-01 -6.81108892e-01
2.74512112e-01 9.87447873e-02 -1.91593871e-01 -5.34059227... | [8.494525909423828, 10.654776573181152] |
af894ebc-39d5-4574-979f-ad0fea584211 | multiple-discrimination-and-pairwise-cnn-for | 2002.11977 | null | https://arxiv.org/abs/2002.11977v1 | https://arxiv.org/pdf/2002.11977v1.pdf | Multiple Discrimination and Pairwise CNN for View-based 3D Object Retrieval | With the rapid development and wide application of computer, camera device, network and hardware technology, 3D object (or model) retrieval has attracted widespread attention and it has become a hot research topic in the computer vision domain. Deep learning features already available in 3D object retrieval have been p... | ['Z. Gao', 'K. X Xue', 'S. H Wan'] | 2020-02-27 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [-3.39796305e-01 -8.04370522e-01 -9.38938260e-02 -4.31211114e-01
-8.43219995e-01 -4.16307420e-01 4.93777752e-01 -2.40207035e-02
-4.73356068e-01 1.54388383e-01 -3.24792802e-01 2.34857291e-01
-4.68219161e-01 -6.50164723e-01 -3.26124787e-01 -8.67335439e-01
2.18465164e-01 5.77612877e-01 2.69025683e-01 1.71760216... | [8.18588638305664, -3.880969762802124] |
44359e65-802e-4b07-ade7-bb40124a7d5f | maven-multi-agent-variational-exploration | 1910.07483 | null | https://arxiv.org/abs/1910.07483v2 | https://arxiv.org/pdf/1910.07483v2.pdf | MAVEN: Multi-Agent Variational Exploration | Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in com... | ['Shimon Whiteson', 'Tabish Rashid', 'Mikayel Samvelyan', 'Anuj Mahajan'] | 2019-10-16 | maven-multi-agent-variational-exploration-1 | http://papers.nips.cc/paper/8978-maven-multi-agent-variational-exploration | http://papers.nips.cc/paper/8978-maven-multi-agent-variational-exploration.pdf | neurips-2019-12 | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-9.02584195e-02 2.32189104e-01 -4.30467308e-01 1.68401629e-01
-8.53753507e-01 -3.94583225e-01 8.37488472e-01 3.74320149e-02
-8.19080293e-01 1.31746018e+00 6.39724254e-04 -1.61967263e-01
-5.75093746e-01 -4.90693003e-01 -6.96685493e-01 -1.15185308e+00
-8.29415798e-01 8.40670586e-01 7.20083341e-02 -3.79651278... | [3.8386924266815186, 1.874169945716858] |
904f342c-fca5-42f0-bf12-4e8abe941996 | boundary-smoothing-for-named-entity-1 | 2204.12031 | null | https://arxiv.org/abs/2204.12031v1 | https://arxiv.org/pdf/2204.12031v1.pdf | Boundary Smoothing for Named Entity Recognition | Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration. Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boundary smoothing as a regularization technique for span-based neura... | ['Jinpeng Li', 'Enwei Zhu'] | 2022-04-26 | null | https://aclanthology.org/2022.acl-long.490 | https://aclanthology.org/2022.acl-long.490.pdf | acl-2022-5 | ['nested-named-entity-recognition', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.38906762e-01 3.50373030e-01 -2.54869640e-01 -6.43190920e-01
-8.73596311e-01 -5.63033521e-01 2.01450154e-01 2.84089327e-01
-9.92730319e-01 8.30747724e-01 5.28831542e-01 -1.09836847e-01
1.36574477e-01 -6.25280797e-01 -6.88005805e-01 -2.07762092e-01
-6.92218542e-02 1.69635743e-01 2.66233295e-01 1.61346525... | [9.55909252166748, 9.378582000732422] |
851be997-198d-4201-a77c-6f1b5fc63f3d | prompter-zero-shot-adaptive-prefixes-for | 2306.04724 | null | https://arxiv.org/abs/2306.04724v1 | https://arxiv.org/pdf/2306.04724v1.pdf | Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation | A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data, zero-shot domain adaptation. Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenar... | ['Nancy F. Chen', 'Min-Yen Kan', 'Taha Aksu'] | 2023-06-07 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.91271120e-01 4.73298371e-01 -4.08853978e-01 -4.03401405e-01
-8.13300073e-01 -6.74011528e-01 1.03454828e+00 1.12530164e-01
-5.33141434e-01 9.52491045e-01 8.27520370e-01 -2.75165230e-01
1.24466509e-01 -5.81920922e-01 -1.79861009e-01 -2.40753442e-01
1.00009805e-02 9.84012544e-01 4.50347066e-01 -8.78767014... | [12.773920059204102, 7.893133640289307] |
1c990ab0-f5eb-4079-bed0-26ccb5d0ba8f | prompt-federated-learning-for-weather | 2301.09152 | null | https://arxiv.org/abs/2301.09152v2 | https://arxiv.org/pdf/2301.09152v2.pdf | Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data | To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urgency, heterogeneous meteorological sensors across countries and regions, inevitably causing multivariate heterogeneity and data exposure, bec... | ['Jing Jiang', 'Tao Shen', 'Guodong Long', 'Shengchao Chen'] | 2023-01-22 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-2.55351990e-01 -3.97498280e-01 6.88442215e-02 -6.56618595e-01
-5.41674852e-01 -6.88915968e-01 6.65418744e-01 3.11906040e-01
-1.78467795e-01 8.82155120e-01 3.44784945e-01 -4.19499844e-01
-4.82182771e-01 -1.16044271e+00 -3.80569875e-01 -7.86137998e-01
-5.63399017e-01 -1.56216174e-01 -1.29053026e-01 -3.46535206... | [6.725500583648682, 2.8042893409729004] |
6602ea92-1df5-4b2a-b943-d6806a50556c | making-person-search-enjoy-the-merits-of | 2108.10536 | null | https://arxiv.org/abs/2108.10536v2 | https://arxiv.org/pdf/2108.10536v2.pdf | Making Person Search Enjoy the Merits of Person Re-identification | Person search is an extended task of person re-identification (Re-ID). However, most existing one-step person search works have not studied how to employ existing advanced Re-ID models to boost the one-step person search performance due to the integration of person detection and Re-ID. To address this issue, we propose... | ['Shibao Zheng', 'Qin Zhou', 'Hua Yang', 'Chuang Liu'] | 2021-08-24 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.21423358e-01 -2.71406054e-01 -1.93263546e-01 -2.64330387e-01
-3.97084475e-01 -3.73598844e-01 9.65369403e-01 -3.07536006e-01
-8.65721226e-01 4.07466501e-01 4.06482488e-01 1.71042047e-02
-5.22399545e-01 -6.55977190e-01 -2.59398520e-01 -3.87390912e-01
6.45935535e-01 9.74619627e-01 4.31249261e-01 -2.57578701... | [14.820977210998535, 0.798926055431366] |
a75da425-4d89-4474-90ae-ee81ac9680c2 | ptt-point-track-transformer-module-for-3d | 2108.06455 | null | https://arxiv.org/abs/2108.06455v3 | https://arxiv.org/pdf/2108.06455v3.pdf | PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds | 3D single object tracking is a key issue for robotics. In this paper, we propose a transformer module called Point-Track-Transformer (PTT) for point cloud-based 3D single object tracking. PTT module contains three blocks for feature embedding, position encoding, and self-attention feature computation. Feature embedding... | ['Yubo Cui', 'Zheng Fang', 'Sifan Zhou', 'Jiayao Shan'] | 2021-08-14 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.65637571e-01 -1.87637240e-01 8.01149532e-02 -6.72867522e-02
-6.20123267e-01 -3.94941807e-01 4.68366504e-01 7.07577094e-02
-3.32903653e-01 1.82705373e-02 -1.19758314e-02 -1.54193074e-01
9.51250829e-03 -7.49453723e-01 -1.16415203e+00 -4.88472074e-01
-2.10735753e-01 2.94959277e-01 4.94917691e-01 -1.61711693... | [6.714693069458008, -2.4302709102630615] |
3969aac5-0ad8-4483-a656-22f0d215628e | time-out-of-mind-generating-emotionally | 2301.12331 | null | https://arxiv.org/abs/2301.12331v2 | https://arxiv.org/pdf/2301.12331v2.pdf | Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker | Voice synthesis has seen significant improvements in the past decade resulting in highly intelligible voices. Further investigations have resulted in models that can produce variable speech, including conditional emotional expression. The problem lies, however, in a focus on phrase-level modifications and prosodic voca... | ['Paige Tuttosi', 'Navjot Kaur'] | 2023-01-29 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 2.55826294e-01 7.22881377e-01 1.02866642e-01 -6.01489604e-01
-1.00781631e+00 -4.82526034e-01 7.88461328e-01 -4.61682260e-01
-6.22709766e-02 9.27948236e-01 6.36412144e-01 -4.09076223e-03
3.51235092e-01 -4.39184725e-01 -2.17003390e-01 -7.07237959e-01
1.02859087e-01 5.00399590e-01 -2.74924994e-01 -3.71879905... | [14.991663932800293, 6.506266117095947] |
72672991-2bc8-48c0-a444-50cac090dd3f | design-and-implementation-of-real-time-1 | 2112.04839 | null | https://arxiv.org/abs/2112.04839v1 | https://arxiv.org/pdf/2112.04839v1.pdf | Design and Implementation of Real-Time Localization System (RTLS) based on UWB and TDoA Algorithm | Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and high cost. So the universal positioning technologies... | ['Hao Li', 'Shuang-Hua Yang', 'Yulong Ding', 'Yuhuan Liu', 'Li Yang', 'Fengyun Zhang'] | 2021-12-09 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-1.87413380e-01 -5.57274044e-01 -2.27397159e-01 -1.44841865e-01
-7.08238900e-01 -6.03839815e-01 1.63962469e-01 1.57993451e-01
-4.49900120e-01 1.14826608e+00 -3.53197128e-01 -5.58623910e-01
-5.74747622e-01 -7.65864253e-01 -1.09279357e-01 -1.10169971e+00
-5.21518469e-01 -2.21976787e-01 4.04456854e-01 -1.13064587... | [6.31514835357666, 1.0585548877716064] |
1e9c30fe-bede-4aad-9883-b3a84a907884 | faster-r-cnn-features-for-instance-search | 1604.08893 | null | http://arxiv.org/abs/1604.08893v1 | http://arxiv.org/pdf/1604.08893v1.pdf | Faster R-CNN Features for Instance Search | Image representations derived from pre-trained Convolutional Neural Networks
(CNNs) have become the new state of the art in computer vision tasks such as
instance retrieval. This work explores the suitability for instance retrieval
of image- and region-wise representations pooled from an object detection CNN
such as Fa... | ["Shin'ichi Satoh", 'Xavier Giro-i-Nieto', 'Ferran Marques', 'Amaia Salvador'] | 2016-04-29 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [-3.21460254e-02 5.42515032e-02 -1.62414983e-01 -5.46796322e-01
-1.09601569e+00 -5.38165748e-01 1.23929310e+00 5.28414667e-01
-9.22172785e-01 4.90054458e-01 2.64112711e-01 7.51421824e-02
-6.39615953e-01 -8.51597548e-01 -8.60199630e-01 -3.14115316e-01
-2.61652261e-01 7.43041217e-01 5.58504105e-01 -2.83820003... | [10.645170211791992, 0.6165434122085571] |
a53a92ab-6a1c-420e-908e-86246a14f172 | a-comparative-study-between-full-parameter | 2304.08109 | null | https://arxiv.org/abs/2304.08109v2 | https://arxiv.org/pdf/2304.08109v2.pdf | A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model | Recently, the instruction-tuning of large language models is a crucial area of research in the field of natural language processing. Due to resource and cost limitations, several researchers have employed parameter-efficient tuning techniques, such as LoRA, for instruction tuning, and have obtained encouraging results ... | ['Xiangang Li', 'Baochang Ma', 'Yunjie Ji', 'Xianghui Sun'] | 2023-04-17 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-3.45922917e-01 -5.44515371e-01 -8.29564333e-01 -6.15476370e-01
-7.53649771e-01 -4.49272126e-01 1.21264949e-01 2.73105502e-01
-6.71899796e-01 6.25751078e-01 -1.43877591e-03 -9.22828019e-01
6.12011105e-02 -7.41550744e-01 -5.85170329e-01 -3.78315151e-01
-3.62099186e-02 2.44292051e-01 3.78837377e-01 -2.11329058... | [10.703971862792969, 8.361773490905762] |
10faa59d-d2fe-425b-8057-03a27f54904d | do-we-actually-need-dense-over | 2102.02887 | null | https://arxiv.org/abs/2102.02887v3 | https://arxiv.org/pdf/2102.02887v3.pdf | Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training | In this paper, we introduce a new perspective on training deep neural networks capable of state-of-the-art performance without the need for the expensive over-parameterization by proposing the concept of In-Time Over-Parameterization (ITOP) in sparse training. By starting from a random sparse network and continuously e... | ['Mykola Pechenizkiy', 'Decebal Constantin Mocanu', 'Lu Yin', 'Shiwei Liu'] | 2021-02-04 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-1.20572396e-01 2.80324996e-01 -2.02752769e-01 -2.40929395e-01
-5.60396433e-01 -3.06552291e-01 4.18515086e-01 -3.44071716e-01
-4.26274866e-01 5.99138618e-01 1.99614108e-01 -2.28690043e-01
-2.84094602e-01 -6.29585385e-01 -1.06974268e+00 -6.97622061e-01
-5.37373424e-01 5.00511348e-01 8.78343061e-02 -1.21558771... | [8.526068687438965, 3.406461000442505] |
869cde5c-fa77-4b43-9051-2f389a103843 | ranking-news-feed-updates-on-social-media-a | null | null | https://www.researchgate.net/publication/339043426_Ranking_news_feed_updates_on_social_media_A_comparative_study_of_supervised_models | https://www.researchgate.net/publication/339043426_Ranking_news_feed_updates_on_social_media_A_comparative_study_of_supervised_models | Ranking news feed updates on social media: A comparative study of supervised models | Social media users are overwhelmed by a large number of updates displayed chronologically in their news feed. Moreover, most updates are irrelevant. Ranking news feed updates by relevance has been proposed to help users catch up with the content they may find interesting. For this matter, supervised learning models hav... | ['Omar Boussaid', 'Kamel Boukhalfa', 'Sami Belkacem'] | 2020-01-01 | null | null | null | conference-on-knowledge-extraction-and | ['social-media-popularity-prediction', 'social-media-popularity-prediction'] | ['miscellaneous', 'time-series'] | [-2.41014376e-01 -6.55793399e-02 -4.83573586e-01 -2.27871016e-01
-2.67610729e-01 -2.38528207e-01 8.87823641e-01 1.18853748e+00
-5.79565465e-01 1.06550419e+00 2.96382308e-01 1.16554208e-01
-3.27235222e-01 -8.20052445e-01 -2.34553635e-01 -1.25738621e-01
-1.72015548e-01 4.27811623e-01 2.71381199e-01 -7.32701540... | [10.197062492370605, 6.156817436218262] |
cf957b2b-9d86-4517-86cd-290bd0e9a78e | ca-centripetalnet-a-novel-anchor-free-deep | 2307.04103 | null | https://arxiv.org/abs/2307.04103v1 | https://arxiv.org/pdf/2307.04103v1.pdf | CA-CentripetalNet: A novel anchor-free deep learning framework for hardhat wearing detection | Automatic hardhat wearing detection can strengthen the safety management in construction sites, which is still challenging due to complicated video surveillance scenes. To deal with the poor generalization of previous deep learning based methods, a novel anchor-free deep learning framework called CA-CentripetalNet is p... | ['Han Wang', 'Nili Tian', 'Chengbin Zhang', 'Wensheng Ouyang', 'Nian Cai', 'Zhijian Liu'] | 2023-07-09 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.00449391e-01 1.09740056e-01 8.66235420e-03 2.33811815e-03
-5.73782384e-01 2.38706544e-01 4.59590964e-02 -8.96904692e-02
-3.49017709e-01 4.94334400e-01 1.83525309e-01 5.39720641e-04
-2.53336370e-01 -8.79194617e-01 -6.73384070e-01 -1.16964197e+00
-1.13514662e-01 -1.40555441e-01 6.62022650e-01 -4.54857051... | [8.796307563781738, -0.5515021681785583] |
ef0018d8-5882-42b2-aa03-99af38c34050 | cross-individual-recognition-of-emotions-by-a | 2009.12525 | null | https://arxiv.org/abs/2009.12525v2 | https://arxiv.org/pdf/2009.12525v2.pdf | Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features | Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions. However, the type of EEG data constitutes an obstacle for cross-individual EEG feature modelling and classification. To address this issue, we propose a deep-learning framewo... | ['Zhong Yin', 'Xiaolong Zhong'] | 2020-09-26 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-1.86475351e-01 -2.60620415e-01 4.00978029e-01 -5.94874382e-01
-1.05261400e-01 -1.45985886e-01 4.53883380e-01 1.74078062e-01
-3.98271680e-01 9.30373609e-01 -3.71010927e-03 3.93083662e-01
-5.13650775e-01 -4.85214233e-01 -4.38929319e-01 -8.31477940e-01
-6.27070367e-01 3.27096768e-02 -3.25657547e-01 -2.41422772... | [13.167445182800293, 3.4370369911193848] |
401652a4-5614-4469-84e3-848ed5474afd | affinity-attention-graph-neural-network-for | 2106.04054 | null | https://arxiv.org/abs/2106.04054v1 | https://arxiv.org/pdf/2106.04054v1.pdf | Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation | Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bounding box annotations as supervision. To this end, we propose Affinity A... | ['Yao Zhao', 'Yunchao Wei', 'Jianbo Jiao', 'Jimin Xiao', 'Bingfeng Zhang'] | 2021-06-08 | null | null | null | null | ['box-supervised-instance-segmentation'] | ['computer-vision'] | [ 3.40102464e-01 5.07777870e-01 -3.60639274e-01 -6.12594485e-01
-8.22820723e-01 -5.00898600e-01 1.71353802e-01 5.35203293e-02
-4.71654266e-01 6.46229506e-01 -4.22910511e-01 -2.00894237e-01
8.50530863e-02 -8.27767491e-01 -1.02746856e+00 -7.18717575e-01
2.34965384e-01 4.64035034e-01 5.54965377e-01 -5.08716479... | [9.534878730773926, 0.5471881628036499] |
11064626-bdd5-470e-84c6-78f84572734b | language-conditioned-goal-generation-a-new-1 | null | null | https://openreview.net/forum?id=OeLMp3kWT8y | https://openreview.net/pdf?id=OeLMp3kWT8y | Language-Conditioned Goal Generation: a New Approach to Language Grounding in RL | In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world. The notion of Language Grounding questions the interactions between language and embodiment: how do learning agents connect or ground linguistic representations to the physical world ? This question has recently ... | ['Olivier Sigaud', 'Mohamed Chetouani', 'Pierre-Yves Oudeyer', 'Ahmed Akakzia', 'Cédric Colas'] | 2020-06-12 | null | null | null | icml-workshop-larel-2020-7 | ['language-acquisition'] | ['natural-language-processing'] | [ 1.78721473e-01 4.76726413e-01 -7.86761567e-02 -2.58968603e-02
-1.04900785e-01 -7.99672663e-01 1.03828216e+00 1.78386003e-01
-6.94114149e-01 7.74338126e-01 2.41698116e-01 -3.16457063e-01
-8.23628306e-02 -1.17309153e+00 -6.21436357e-01 -6.52174532e-01
-2.29931369e-01 2.24213183e-01 3.54402438e-02 -5.01610398... | [4.2674407958984375, 1.2729872465133667] |
42c79ea1-3b17-4477-81f7-0b9cdcce141e | proposalclip-unsupervised-open-category | 2201.06696 | null | https://arxiv.org/abs/2201.06696v1 | https://arxiv.org/pdf/2201.06696v1.pdf | ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues | Object proposal generation is an important and fundamental task in computer vision. In this paper, we propose ProposalCLIP, a method towards unsupervised open-category object proposal generation. Unlike previous works which require a large number of bounding box annotations and/or can only generate proposals for limite... | ['Jianfei Cai', 'Yicheng Wu', 'Munawar Hayat', 'Hengcan Shi'] | 2022-01-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_ProposalCLIP_Unsupervised_Open-Category_Object_Proposal_Generation_via_Exploiting_CLIP_Cues_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_ProposalCLIP_Unsupervised_Open-Category_Object_Proposal_Generation_via_Exploiting_CLIP_Cues_CVPR_2022_paper.pdf | cvpr-2022-1 | ['object-proposal-generation'] | ['computer-vision'] | [ 2.16123790e-01 4.12090659e-01 -1.57310665e-01 -5.22709727e-01
-8.18009555e-01 -5.90496659e-01 5.85733175e-01 4.46463943e-01
-4.23813522e-01 2.17890888e-01 -8.80623609e-02 -1.45330891e-01
1.78924456e-01 -6.85835958e-01 -7.83490539e-01 -2.33319089e-01
1.52704865e-01 6.76120579e-01 1.04154730e+00 -3.40438187... | [9.311633110046387, 0.701696515083313] |
6fd6113f-762e-4873-a7c7-e50e0eb7381a | evaluating-mixed-initiative-conversational | 2204.08046 | null | https://arxiv.org/abs/2204.08046v2 | https://arxiv.org/pdf/2204.08046v2.pdf | Evaluating Mixed-initiative Conversational Search Systems via User Simulation | Clarifying the underlying user information need by asking clarifying questions is an important feature of modern conversational search system. However, evaluation of such systems through answering prompted clarifying questions requires significant human effort, which can be time-consuming and expensive. In this paper, ... | ['Fabio Crestani', 'Mohammad Aliannejadi', 'Ivan Sekulić'] | 2022-04-17 | null | null | null | null | ['user-simulation', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.40499203e-02 3.47240180e-01 3.18370104e-01 -4.88479257e-01
-1.12810373e+00 -1.14370394e+00 8.41788888e-01 1.16208918e-01
-2.86723584e-01 8.52442622e-01 3.08358163e-01 -5.11182189e-01
-2.03669779e-02 -3.14315289e-01 -3.12804341e-01 -9.05070975e-02
4.79539990e-01 1.04422510e+00 1.99190393e-01 -7.28981197... | [12.09553337097168, 7.888439655303955] |
6a07780e-163f-4c95-adf0-2f94d9f54490 | long-term-person-re-identification-a | 2105.14685 | null | https://arxiv.org/abs/2105.14685v4 | https://arxiv.org/pdf/2105.14685v4.pdf | DeepChange: A Large Long-Term Person Re-Identification Benchmark with Clothes Change | Existing person re-identification (re-id) works mostly consider short-term application scenarios without clothes change. In real-world, however, we often dress differently across space and time. To solve this contrast, a few recent attempts have been made on long-term re-id with clothes change. Currently, one of the mo... | ['Xiatian Zhu', 'Peng Xu'] | 2021-05-31 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-9.42335576e-02 -8.85557234e-01 9.39994454e-02 -4.28555608e-01
-6.20548986e-02 -6.32407427e-01 5.93044937e-01 -3.44454885e-01
-3.41442406e-01 8.35421324e-01 2.79614002e-01 4.62297022e-01
1.46121472e-01 -5.50564170e-01 -6.42079890e-01 -5.40716112e-01
4.38728631e-02 3.58521760e-01 -1.01314239e-01 -5.06679952... | [14.621301651000977, 0.9557335376739502] |
ce096816-cce9-4e84-bc81-6b22624df7b6 | clovacall-korean-goal-oriented-dialog-speech | 2004.09367 | null | https://arxiv.org/abs/2004.09367v2 | https://arxiv.org/pdf/2004.09367v2.pdf | ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers | Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services. Despite the advancement of ASR, however, most publicly available call-based speech corpora such as Switchboard are old-fashioned. Also, most existing call corpora are in English and mainly... | ['Sunghun Kim', 'Kyoungtae Doh', 'Sang-Woo Lee', 'Jung-Woo Ha', 'Soojin Kim', 'Hyunhoon Jung', 'Eunmi Kim', 'Kihyun Nam', 'Hyeji Kim', 'Sohee Yang', 'Nako Sung', 'Jingu Kang', 'Hyun Ah Kim', 'Chan Kyu Lee'] | 2020-04-20 | null | null | null | null | ['goal-oriented-dialog', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-0.33612233 -0.07518198 0.04552832 -0.70384085 -1.3514099 -0.7051899
0.4340225 -0.22042365 -0.34681004 0.72759885 0.7571383 -0.5508018
0.48802057 -0.3182082 -0.10283002 -0.22762619 0.2867657 0.80350906
0.17289315 -0.86525464 -0.1639951 0.14195043 -0.73932403 0.39753973
0.70731044 0.6960069 0.42... | [14.18829345703125, 6.907529354095459] |
eb0b058d-989e-4f1b-a798-8f2ba5da3f21 | rethinking-the-evaluation-of-unbiased-scene | 2208.01909 | null | https://arxiv.org/abs/2208.01909v2 | https://arxiv.org/pdf/2208.01909v2.pdf | Rethinking the Evaluation of Unbiased Scene Graph Generation | Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates. To improve the robustness of SGG models on different predicate categories, recent research has focused on unbiased SGG and adopted mean Reca... | ['Jun Xiao', 'Songyang Zhang', 'Shaoning Xiao', 'Jian Shao', 'Long Chen', 'Xingchen Li'] | 2022-08-03 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 3.37124407e-01 4.41378474e-01 -4.19271648e-01 -4.42780584e-01
-6.21099234e-01 -5.64654827e-01 7.79357791e-01 2.31232300e-01
6.13022521e-02 8.70341003e-01 4.05293763e-01 -2.14046732e-01
-4.32397097e-01 -1.04620290e+00 -5.65637827e-01 -6.19294226e-01
8.44928175e-02 6.67779326e-01 5.02600610e-01 -1.89837635... | [10.299129486083984, 1.7773667573928833] |
33b702e9-958c-4dde-9f22-3f75907a84d5 | an-encoder-decoder-based-audio-captioning | 2108.02752 | null | https://arxiv.org/abs/2108.02752v1 | https://arxiv.org/pdf/2108.02752v1.pdf | An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning | Automated audio captioning aims to use natural language to describe the content of audio data. This paper presents an audio captioning system with an encoder-decoder architecture, where the decoder predicts words based on audio features extracted by the encoder. To improve the proposed system, transfer learning from ei... | ['Wenwu Wang', 'Mark D. Plumbley', 'Xi Shao', 'H Lilian Tang', 'Tom Ko', 'Shengchen Li', 'Jinzheng Zhao', 'Yusong Wu', 'Jingqian Wu', 'Gengyun Chen', 'Xubo Liu', 'Qiushi Huang', 'Xinhao Mei'] | 2021-08-05 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.75914419e-01 4.98915076e-01 1.20389633e-01 -3.86415303e-01
-1.42528725e+00 -2.16396376e-01 3.08227897e-01 4.32652831e-02
-3.70388985e-01 7.71426082e-01 7.04862833e-01 1.91970468e-01
1.30755156e-01 -3.88530910e-01 -8.84649754e-01 -4.97139215e-01
-4.07550372e-02 1.65346652e-01 6.98871762e-02 -1.02894366... | [15.268620491027832, 4.900907039642334] |
fbddfd8e-3e4d-4579-b10b-53bdc9438993 | retrieve-and-refine-exemplar-based-neural | 2010.04459 | null | https://arxiv.org/abs/2010.04459v1 | https://arxiv.org/pdf/2010.04459v1.pdf | Retrieve and Refine: Exemplar-based Neural Comment Generation | Code comment generation which aims to automatically generate natural language descriptions for source code, is a crucial task in the field of automatic software development. Traditional comment generation methods use manually-crafted templates or information retrieval (IR) techniques to generate summaries for source co... | ['Zhi Jin', 'Xin Xia', 'Ge Li', 'Yongmin Li', 'Bolin Wei'] | 2020-10-09 | null | null | null | null | ['code-comment-generation', 'comment-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.22595114e-01 2.50997603e-01 -2.43101522e-01 -2.94357359e-01
-1.03361368e+00 -4.50360805e-01 5.32736123e-01 3.32250088e-01
1.88047767e-01 3.84951204e-01 6.66739225e-01 -4.23637599e-01
3.39047402e-01 -7.18845308e-01 -6.84907794e-01 -1.48550808e-01
1.20771199e-01 -3.23667843e-03 -2.81314994e-03 -3.84059966... | [7.667696952819824, 7.9518280029296875] |
0d54634f-3eff-450a-b9fc-32f8434ed046 | understanding-engagement-with-insurgents | null | null | https://aclanthology.org/U15-1015 | https://aclanthology.org/U15-1015.pdf | Understanding engagement with insurgents through retweet rhetoric | null | ['Timothy Baldwin', 'Joel Nothman', 'Christoph Breidbach', 'Atif Ahmad', 'David Malet'] | 2015-12-01 | understanding-engagement-with-insurgents-1 | https://aclanthology.org/U15-1015 | https://aclanthology.org/U15-1015.pdf | alta-2015-12 | ['dialogue-act-classification'] | ['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... | [-7.536954879760742, 3.546748638153076] |
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