paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5c4cb815-bf72-437b-93fd-d59d05b4c7e3 | learnable-reconstruction-methods-from-rgb | 2106.15944 | null | https://arxiv.org/abs/2106.15944v2 | https://arxiv.org/pdf/2106.15944v2.pdf | A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging | Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods hav... | ['Yunfeng Nie', 'Felix Heide', 'Qiang Fu', 'Wenqi Ren', 'Runmu Su', 'Jingang Zhang'] | 2021-06-30 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 8.28603148e-01 -7.89180696e-01 -1.54297128e-01 -1.78287789e-01
-7.52189696e-01 -3.51710707e-01 1.25164896e-01 -4.05591220e-01
-3.01294774e-01 9.22371626e-01 -3.46766382e-01 -2.37151906e-01
-5.30264318e-01 -8.49554241e-01 -4.27578330e-01 -1.42596149e+00
1.86232522e-01 1.08625449e-01 -2.52312481e-01 -5.15873469... | [10.172677993774414, -2.096473455429077] |
474edd34-c408-4493-a0e0-0553aa432651 | channel-importance-matters-in-few-shot-image | 2206.08126 | null | https://arxiv.org/abs/2206.08126v2 | https://arxiv.org/pdf/2206.08126v2.pdf | Channel Importance Matters in Few-Shot Image Classification | Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this task distribution shift is central to FSL. In this paper, we show that a simple channel-wise feature transformation may be the key to unraveli... | ['Zenglin Xu', 'Jing Xu', 'Xu Luo'] | 2022-06-16 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 6.90134048e-01 -2.66641587e-01 -6.34579584e-02 -3.89240235e-01
-7.43810594e-01 -8.45241785e-01 6.72751307e-01 -1.91311866e-01
-4.44443703e-01 6.08417928e-01 5.56063801e-02 -3.37187231e-01
-2.97771990e-02 -5.57711184e-01 -9.74626899e-01 -5.98401785e-01
-7.95290992e-02 -9.51758325e-02 3.05900872e-01 -3.61413538... | [9.820462226867676, 2.71223521232605] |
b96eff5c-9e77-4611-b0cf-3c0b00c85574 | gamival-video-quality-prediction-on-mobile | 2305.02422 | null | https://arxiv.org/abs/2305.02422v2 | https://arxiv.org/pdf/2305.02422v2.pdf | GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content | The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Referen... | ['Ioannis Katsavounidis', 'Rahul Gowda', 'Alan C. Bovik', 'Xiaoming Wang', 'Bo Qiu', 'Chase Davis', 'Avinab Saha', 'Yu-Chih Chen'] | 2023-05-03 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [-3.18033136e-02 -8.11075807e-01 7.42831379e-02 -2.93198287e-01
-1.20281792e+00 -4.58256304e-01 2.19596401e-01 -5.56980133e-01
-3.35973613e-02 2.52103746e-01 2.00718999e-01 -2.33225599e-01
2.09015869e-02 -7.77750611e-01 -4.80628610e-01 -3.80470306e-01
-1.49241656e-01 7.09733143e-02 5.07126331e-01 -4.03093725... | [11.724225997924805, -1.819278597831726] |
b2bea6f5-f978-4327-af9e-24c89b23628d | fd-mar-fourier-dual-domain-network-for-ct | 2207.11678 | null | https://arxiv.org/abs/2207.11678v2 | https://arxiv.org/pdf/2207.11678v2.pdf | Quad-Net: Quad-domain Network for CT Metal Artifact Reduction | Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining met... | ['Hongming Shan', 'Ge Wang', 'Meiyun Wang', 'Junping Zhang', 'Chuang Niu', 'Yaping Wu', 'Qi Gao', 'Zilong Li'] | 2022-07-24 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 1.83135614e-01 1.00359591e-02 1.39852166e-01 -6.26021475e-02
-1.10100400e+00 -1.17165752e-01 6.14849143e-02 -1.93839446e-01
-1.83684245e-01 6.97923422e-01 3.16574097e-01 -2.08400577e-01
-3.33232284e-01 -8.38413060e-01 -7.85229504e-01 -8.30237210e-01
1.00481503e-01 2.00139731e-01 4.43141222e-01 -1.36030659... | [13.528361320495605, -2.5510525703430176] |
a5943c2b-c023-4ee3-9a08-c22d9afbc89c | toxicity-detection-for-indic-multilingual | 2201.00598 | null | https://arxiv.org/abs/2201.00598v1 | https://arxiv.org/pdf/2201.00598v1.pdf | Toxicity Detection for Indic Multilingual Social Media Content | Toxic content is one of the most critical issues for social media platforms today. India alone had 518 million social media users in 2020. In order to provide a good experience to content creators and their audience, it is crucial to flag toxic comments and the users who post that. But the big challenge is identifying ... | ['Harveen Singh Chadha', 'Devanshu Ramaiya', 'Manan Jhaveri'] | 2022-01-03 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-2.08508387e-01 -1.87313706e-01 5.38612083e-02 -8.75206515e-02
-1.17338598e+00 -7.75208712e-01 2.91584194e-01 4.42959696e-01
-5.48053563e-01 5.41917861e-01 2.49221414e-01 -4.66613561e-01
1.79837927e-01 -2.44518280e-01 -4.52968687e-01 -2.18989283e-01
1.19565070e-01 2.21522674e-01 1.22722335e-01 -5.68838716... | [8.975309371948242, 10.588798522949219] |
5e61da09-0368-4a70-a033-a1110c16085a | melm-data-augmentation-with-masked-entity | 2108.13655 | null | https://arxiv.org/abs/2108.13655v2 | https://arxiv.org/pdf/2108.13655v2.pdf | MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER | Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffer from token-label misalignment, which leads to unsatsifactory performance. In this work, we propose Masked Entity Language Modeling (MELM) ... | ['Xin Li', 'Chunyan Miao', 'Luo Si', 'Erik Cambria', 'Lidong Bing', 'Ruidan He', 'Ran Zhou'] | 2021-08-31 | null | https://aclanthology.org/2022.acl-long.160 | https://aclanthology.org/2022.acl-long.160.pdf | acl-2022-5 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.34768635e-01 -6.91947639e-02 -3.14856648e-01 -3.85832518e-01
-1.12044287e+00 -5.21638453e-01 3.71902674e-01 4.06364381e-01
-8.97609532e-01 8.74345541e-01 6.15753055e-01 -2.90957868e-01
6.23313665e-01 -5.66675723e-01 -6.24094963e-01 -3.10023248e-01
2.12356791e-01 1.20275654e-01 -4.55539733e-01 -2.09934399... | [9.870141983032227, 9.600290298461914] |
e397e449-5f25-4e84-ab5c-a8d5c29994e5 | dort-modeling-dynamic-objects-in-recurrent | 2303.16628 | null | https://arxiv.org/abs/2303.16628v2 | https://arxiv.org/pdf/2303.16628v2.pdf | DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking | Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis... | ['Jiangmiao Pang', 'Dahua Lin', 'Tai Wang', 'Qing Lian'] | 2023-03-29 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [-1.66698948e-01 -5.08992910e-01 -1.46770418e-01 -1.23243175e-01
-6.24281228e-01 -8.41070116e-01 5.09574652e-01 -2.61698544e-01
-3.37555677e-01 3.64519566e-01 5.24319634e-02 4.06870767e-02
7.50834271e-02 -5.86168587e-01 -6.18127584e-01 -8.74121487e-01
2.62204230e-01 -2.97840256e-02 9.77054119e-01 2.66898554... | [8.001287460327148, -1.9384799003601074] |
602cbf6d-182e-4e53-959b-3df8bc5e209f | godp-globally-optimized-dual-pathway-system | 1704.02402 | null | http://arxiv.org/abs/1704.02402v2 | http://arxiv.org/pdf/1704.02402v2.pdf | GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild | Facial landmark localization is a fundamental module for pose-invariant face
recognition. The most common approach for facial landmark detection is cascaded
regression, which is composed of two steps: feature extraction and facial shape
regression. Recent methods employ deep convolutional networks to extract robust
fea... | ['Ioannis A. Kakadiaris', 'Shishir K. Shah', 'Yuhang Wu'] | 2017-04-07 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 6.60574883e-02 -7.80257508e-02 -2.01363191e-01 -7.44184136e-01
-1.05054724e+00 -2.25996107e-01 5.15607238e-01 -3.42060596e-01
-7.95047045e-01 2.59895444e-01 -1.83155924e-01 -5.74159622e-02
1.40222982e-01 -3.63713920e-01 -7.89962888e-01 -8.16345870e-01
7.42910877e-02 5.90443790e-01 -1.63221464e-01 -1.34989068... | [13.50031566619873, 0.3829843997955322] |
ec9a827f-48ca-475e-845d-eaa75ccce8e1 | temporal-shift-module-for-efficient-video | 1811.08383 | null | https://arxiv.org/abs/1811.08383v3 | https://arxiv.org/pdf/1811.08383v3.pdf | TSM: Temporal Shift Module for Efficient Video Understanding | The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, mak... | ['Song Han', 'Ji Lin', 'Chuang Gan'] | 2018-11-20 | tsm-temporal-shift-module-for-efficient-video | http://openaccess.thecvf.com/content_ICCV_2019/html/Lin_TSM_Temporal_Shift_Module_for_Efficient_Video_Understanding_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_TSM_Temporal_Shift_Module_for_Efficient_Video_Understanding_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-object-tracking'] | ['computer-vision'] | [-3.34382772e-01 -5.40535390e-01 -3.83712709e-01 -2.25370854e-01
-4.92000252e-01 -4.91827190e-01 3.28793377e-01 -2.74328023e-01
-5.13167083e-01 2.68445481e-02 -2.63173312e-01 -5.93405366e-01
1.31997943e-01 -5.25605857e-01 -7.97149539e-01 -4.91900116e-01
-2.45366514e-01 1.43903196e-02 5.91325283e-01 6.08806685... | [8.975263595581055, 0.14004170894622803] |
77e04cc9-72be-41d4-aa7f-9f8cca0599de | label-information-enhanced-fraud-detection | 2302.10407 | null | https://arxiv.org/abs/2302.10407v1 | https://arxiv.org/pdf/2302.10407v1.pdf | Label Information Enhanced Fraud Detection against Low Homophily in Graphs | Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be sign... | ['Junzhou Luo', 'Beilun Wang', 'Jiahui Jin', 'Fang Dong', 'dianhai yu', 'Yu Sun', 'Ziheng Ma', 'Shikun Feng', 'Weibin Li', 'Zhengjie Huang', 'Jinghui Zhang', 'Yuchen Wang'] | 2023-02-21 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-2.00781114e-02 -1.16838552e-01 -2.85625786e-01 -3.68285209e-01
-2.80063957e-01 -2.22005874e-01 1.97993174e-01 4.75107759e-01
-9.17297974e-02 6.15588725e-01 -1.94619998e-01 -1.98725641e-01
-1.92721531e-01 -1.35748088e+00 -6.22076571e-01 -4.44342017e-01
-3.08739632e-01 3.86916071e-01 3.89432684e-02 -4.58418041... | [7.100923538208008, 5.965726852416992] |
60be8c6f-4cc9-47de-8871-595912266d69 | pretraining-without-wordpieces-learning-over | 2202.12142 | null | https://arxiv.org/abs/2202.12142v1 | https://arxiv.org/pdf/2202.12142v1.pdf | Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words | The standard BERT adopts subword-based tokenization, which may break a word into two or more wordpieces (e.g., converting "lossless" to "loss" and "less"). This will bring inconvenience in following situations: (1) what is the best way to obtain the contextual vector of a word that is divided into multiple wordpieces? ... | ['Shuming Shi', 'Yunbo Cao', 'Bing Qin', 'Xiaocheng Feng', 'Shuangzhi Wu', 'Junwei Liao', 'Cong Zhou', 'Duyu Tang', 'Zhangyin Feng'] | 2022-02-24 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [-1.18172124e-01 2.25027487e-01 -2.16814920e-01 -4.87678885e-01
-5.92415094e-01 -6.45676613e-01 2.90167063e-01 4.33092386e-01
-1.07737422e+00 7.53777921e-01 2.95245916e-01 -8.13034475e-01
1.71747789e-01 -8.89759183e-01 -6.98774695e-01 -2.14309171e-01
1.97619051e-01 6.63419485e-01 1.85319617e-01 -5.10630250... | [10.52548599243164, 9.135225296020508] |
91ebbd5b-b82f-45e7-9427-97100d693323 | correspondence-free-online-human-motion | 2302.00556 | null | https://arxiv.org/abs/2302.00556v1 | https://arxiv.org/pdf/2302.00556v1.pdf | Correspondence-free online human motion retargeting | We present a novel data-driven framework for unsupervised human motion retargeting which animates a target body shape with a source motion. This allows to retarget motions between different characters by animating a target subject with a motion of a source subject. Our method is correspondence-free,~\ie neither spatial... | ['Anne-Hélène Olivier', 'Jean-Sébastien Franco', 'Stefanie Wuhrer', 'Rim Rekik', 'Mathieu Marsot'] | 2023-02-01 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 3.26653957e-01 9.48043838e-02 -1.99293345e-01 -1.91345252e-02
-5.61696768e-01 -6.31060243e-01 6.75386786e-01 -2.91238546e-01
-5.08149028e-01 4.08597887e-01 2.28423327e-01 1.51725024e-01
2.72899389e-01 -6.98299944e-01 -8.27661574e-01 -5.35539091e-01
-1.53619482e-03 3.46709579e-01 8.06472540e-01 -2.20133394... | [7.404534339904785, -0.5631067752838135] |
d9e4607d-9d4c-4fad-9976-5ff6ec8cbdb9 | effects-of-locality-and-rule-language-on | 2302.06967 | null | https://arxiv.org/abs/2302.06967v1 | https://arxiv.org/pdf/2302.06967v1.pdf | Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings | Knowledge graphs (KGs) are key tools in many AI-related tasks such as reasoning or question answering. This has, in turn, propelled research in link prediction in KGs, the task of predicting missing relationships from the available knowledge. Solutions based on KG embeddings have shown promising results in this matter.... | ['Luis Galárraga'] | 2023-02-14 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.68858606e-01 6.36633039e-01 -8.01219463e-01 -3.85386229e-01
1.23017728e-01 -1.99567318e-01 7.57463813e-01 5.96773565e-01
-8.65842625e-02 7.85894513e-01 6.65221214e-01 -6.76419914e-01
-5.81757247e-01 -1.29590750e+00 -9.41736162e-01 -2.37071648e-01
-1.99715510e-01 4.47644413e-01 3.02464634e-01 -3.22729886... | [8.822402000427246, 7.8531718254089355] |
713116d7-6358-483f-9b68-b953759cf664 | sim-trans-structure-information-modeling | 2208.14607 | null | https://arxiv.org/abs/2208.14607v1 | https://arxiv.org/pdf/2208.14607v1.pdf | SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization | Fine-grained visual categorization (FGVC) aims at recognizing objects from similar subordinate categories, which is challenging and practical for human's accurate automatic recognition needs. Most FGVC approaches focus on the attention mechanism research for discriminative regions mining while neglecting their interdep... | ['Yuxin Peng', 'Xiangteng He', 'Hongbo Sun'] | 2022-08-31 | null | null | null | null | ['fine-grained-image-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 1.55596789e-02 -3.94988954e-01 -1.10989228e-01 -4.05042380e-01
-4.85596418e-01 -4.42279547e-01 5.41961610e-01 -6.57616481e-02
-2.18550369e-01 7.77508393e-02 1.99557751e-01 -1.34787649e-01
-7.92086869e-02 -7.73054123e-01 -5.90461969e-01 -8.52388501e-01
2.20899671e-01 -4.34176847e-02 4.60759401e-01 -6.42548082... | [9.658470153808594, 2.0048506259918213] |
b5518321-f24b-4f3b-ab05-613309b5a8e4 | fusion-in-t5-unifying-document-ranking | 2305.14685 | null | https://arxiv.org/abs/2305.14685v1 | https://arxiv.org/pdf/2305.14685v1.pdf | Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval | Common IR pipelines are typically cascade systems that may involve multiple rankers and/or fusion models to integrate different information step-by-step. In this paper, we propose a novel re-ranker named Fusion-in-T5 (FiT5), which integrates document text information, retrieval features, and global document information... | ['Zhenghao Liu', 'Zhiyuan Liu', 'David Jin', 'Chenyan Xiong', 'Chenghao Fan', 'Shi Yu'] | 2023-05-24 | null | null | null | null | ['document-ranking', 'passage-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [-2.03555033e-01 -5.11821806e-01 -1.49941593e-01 -4.67234105e-01
-1.67149866e+00 -9.92298186e-01 1.09830236e+00 4.64208275e-01
-4.57096666e-01 2.97861874e-01 7.49797761e-01 -2.54441023e-01
-5.78527510e-01 -2.14141741e-01 -5.03580868e-01 -1.66169740e-02
1.04909882e-01 6.07273102e-01 5.30761540e-01 -5.88703275... | [11.485848426818848, 7.611485481262207] |
51577bfc-5910-45f0-bbf5-c2f3fcdf684d | non-deterministic-oracles-for-unrestricted | null | null | https://aclanthology.org/W15-2210 | https://aclanthology.org/W15-2210.pdf | Non-Deterministic Oracles for Unrestricted Non-Projective Transition-Based Dependency Parsing | null | ['Anders Bj{\\"o}rkelund', 'Joakim Nivre'] | 2015-07-01 | null | null | null | ws-2015-7 | ['transition-based-dependency-parsing'] | ['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... | [-7.310624122619629, 3.7910807132720947] |
447479e5-6873-4792-a7f6-3da35e6823b5 | improving-robustness-of-facial-landmark | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Improving_Robustness_of_Facial_Landmark_Detection_by_Defending_Against_Adversarial_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Improving_Robustness_of_Facial_Landmark_Detection_by_Defending_Against_Adversarial_ICCV_2021_paper.pdf | Improving Robustness of Facial Landmark Detection by Defending Against Adversarial Attacks | Many recent developments in facial landmark detection have been driven by stacking model parameters or augmenting annotations. However, three subsequent challenges remain, including 1) an increase in computational overhead, 2) the risk of overfitting caused by increasing model parameters, and 3) the burden of labor... | ['Songmin Dai', 'Jide Li', 'Xiaoqiang Li', 'Congcong Zhu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['face-alignment'] | ['computer-vision'] | [ 3.04876000e-01 2.83336371e-01 9.63329524e-02 -3.33152145e-01
-1.00527108e+00 -7.42228925e-01 5.59238851e-01 -5.07250667e-01
-1.77773982e-01 1.42377630e-01 -6.83750659e-02 -1.81463942e-01
3.42909575e-01 -4.31995213e-01 -6.94589078e-01 -8.54046822e-01
-3.13547589e-02 6.50732964e-02 1.49046510e-01 -3.20092827... | [5.588517189025879, 7.860280990600586] |
d3c96493-f8a6-4ca2-af33-525d8848f911 | deep-colorization | 1605.00075 | null | http://arxiv.org/abs/1605.00075v1 | http://arxiv.org/pdf/1605.00075v1.pdf | Deep Colorization | This paper investigates into the colorization problem which converts a
grayscale image to a colorful version. This is a very difficult problem and
normally requires manual adjustment to achieve artifact-free quality. For
instance, it normally requires human-labelled color scribbles on the grayscale
target image or a ca... | ['Zezhou Cheng', 'Bin Sheng', 'Qingxiong Yang'] | 2016-04-30 | deep-colorization-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Cheng_Deep_Colorization_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Cheng_Deep_Colorization_ICCV_2015_paper.pdf | iccv-2015-12 | ['patch-matching'] | ['computer-vision'] | [ 2.56597757e-01 -6.81153536e-01 3.39067131e-01 -1.61044285e-01
-7.73461342e-01 -4.74900365e-01 2.22564057e-01 -3.14204425e-01
-5.78287005e-01 5.17116487e-01 -4.72775251e-01 -1.96756810e-01
2.93467846e-02 -9.30349350e-01 -6.46866143e-01 -9.05370414e-01
4.67176706e-01 8.18136334e-02 2.48551294e-01 -2.30940595... | [11.010205268859863, -1.614920973777771] |
d5b55c9a-2cf3-48b3-ac85-16e7ee446409 | clinical-text-summarization-with-syntax-based | 2003.00353 | null | https://arxiv.org/abs/2003.00353v1 | https://arxiv.org/pdf/2003.00353v1.pdf | Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification | In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can preserve important information in the informative but less organized, ill-structured clinical narrative texts. Instead of using pure statistical... | ['Yu-An Chung', 'Wei-Hung Weng', 'Schrasing Tong'] | 2020-02-29 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 5.03445566e-01 6.67989016e-01 -3.24171394e-01 -8.15195441e-02
-5.49597442e-01 -1.86498582e-01 -9.34037473e-03 1.19582403e+00
-3.52600604e-01 1.21294439e+00 9.56759930e-01 -1.68115929e-01
-5.56512535e-01 -4.84648883e-01 2.99612656e-02 -6.39660895e-01
-1.90684095e-01 4.85348225e-01 1.97105892e-02 -2.65021682... | [8.442699432373047, 8.61593246459961] |
44d9ddc0-0b22-4a0a-a598-d4c6354bdbe8 | bayesian-optimisation-for-constrained | 2105.13245 | null | https://arxiv.org/abs/2105.13245v1 | https://arxiv.org/pdf/2105.13245v1.pdf | Bayesian Optimisation for Constrained Problems | Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions. A popular approach to tackle such problems is Bayesian optimisation (BO), which builds a response surface model based on the data colle... | ['Juergen Branke', 'Juan Ungredda'] | 2021-05-27 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.06216896e-01 9.50715970e-03 -2.59319127e-01 -4.03178573e-01
-7.98156261e-01 -3.71603578e-01 5.75426638e-01 2.11928174e-01
-9.65504467e-01 1.16470611e+00 -1.39506429e-01 -4.22522753e-01
-8.67084861e-01 -5.13009667e-01 -7.43419290e-01 -1.00429165e+00
1.75841209e-02 9.26597118e-01 3.53744239e-01 -1.30658329... | [6.210909843444824, 3.7609386444091797] |
348e8c43-06ee-4110-b3f2-2b74f740e9e1 | perfect-match-improved-cross-modal-embeddings | 1809.08001 | null | http://arxiv.org/abs/1809.08001v2 | http://arxiv.org/pdf/1809.08001v2.pdf | Perfect match: Improved cross-modal embeddings for audio-visual synchronisation | This paper proposes a new strategy for learning powerful cross-modal
embeddings for audio-to-video synchronization. Here, we set up the problem as
one of cross-modal retrieval, where the objective is to find the most relevant
audio segment given a short video clip. The method builds on the recent
advances in learning r... | ['Hong-Goo Kang', 'Soo-Whan Chung', 'Joon Son Chung'] | 2018-09-21 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 1.34849623e-01 -7.94471055e-02 -3.95402849e-01 -2.81871557e-01
-1.62540460e+00 -5.53126037e-01 6.06414735e-01 7.60918036e-02
-3.95332038e-01 9.14886817e-02 7.13281751e-01 3.98891091e-01
-3.16029973e-02 -2.06081849e-02 -7.96032071e-01 -6.57018185e-01
-4.71379340e-01 2.56774426e-01 3.10804751e-02 -5.59939630... | [14.661617279052734, 4.891398906707764] |
abf1c3da-be71-494f-b17e-1ace42ea83f1 | a-robust-framework-for-moving-object | 1402.0289 | null | http://arxiv.org/abs/1402.0289v1 | http://arxiv.org/pdf/1402.0289v1.pdf | A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation | Intelligent machines require basic information such as moving-object
detection from videos in order to deduce higher-level semantic information. In
this paper, we propose a methodology that uses a texture measure to detect
moving objects in video. The methodology is computationally inexpensive,
requires minimal paramet... | ['Pranam Janney', 'Glenn Geers'] | 2014-02-03 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.07867479e-01 -4.02482688e-01 -1.99788436e-01 -2.78286964e-01
-3.50991607e-01 -6.43389598e-02 8.02731693e-01 4.61407239e-03
-6.09538853e-01 9.30795312e-01 -3.45566958e-01 -4.34289813e-01
-1.15972996e-01 -1.08902574e+00 -5.31540394e-01 -8.16266775e-01
-1.20120972e-01 6.61806941e-01 1.23976219e+00 2.14818478... | [8.835467338562012, -0.8569074869155884] |
20d0bedd-993d-4bf3-9e04-5e159a1f9fff | texture-synthesis-via-projection-onto | 2105.10825 | null | https://arxiv.org/abs/2105.10825v1 | https://arxiv.org/pdf/2105.10825v1.pdf | Texture synthesis via projection onto multiscale, multilayer statistics | We provide a new model for texture synthesis based on a multiscale, multilayer feature extractor. Within the model, textures are represented by a set of statistics computed from ReLU wavelet coefficients at different layers, scales and orientations. A new image is synthesized by matching the target statistics via an it... | ['Matthew Hirn', 'Jieqian He'] | 2021-05-22 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.84657866e-01 -1.05425701e-01 3.22837234e-02 -3.43661398e-01
-5.18392324e-01 -1.44756868e-01 6.96622193e-01 -3.00052524e-01
6.47668615e-02 5.06880343e-01 5.23300409e-01 4.01823252e-01
-7.06923231e-02 -1.06439781e+00 -6.64913893e-01 -8.87972295e-01
-1.10456593e-01 -1.07863143e-01 2.65032232e-01 -3.45631719... | [11.412171363830566, -0.49118563532829285] |
04d01856-16cc-4fda-b659-7638761ec386 | prompting-diffusion-representations-for-cross | 2307.02138 | null | https://arxiv.org/abs/2307.02138v1 | https://arxiv.org/pdf/2307.02138v1.pdf | Prompting Diffusion Representations for Cross-Domain Semantic Segmentation | While originally designed for image generation, diffusion models have recently shown to provide excellent pretrained feature representations for semantic segmentation. Intrigued by this result, we set out to explore how well diffusion-pretrained representations generalize to new domains, a crucial ability for any repre... | ['Luc van Gool', 'Julio Delgado Mangas', 'Han Sun', 'Martin Danelljan', 'Rui Gong'] | 2023-07-05 | null | null | null | null | ['image-generation', 'domain-generalization'] | ['computer-vision', 'methodology'] | [ 5.21121860e-01 5.59067801e-02 -2.17224091e-01 -5.42588055e-01
-8.34299386e-01 -8.78964722e-01 7.67949939e-01 -3.49372685e-01
-3.13372701e-01 7.22531915e-01 4.70910482e-02 -4.52452600e-01
6.86526000e-02 -8.74444246e-01 -8.34370375e-01 -6.92184329e-01
-1.44835114e-02 4.93371785e-01 3.61744195e-01 -2.70126283... | [9.753252983093262, 1.3535077571868896] |
6ad64128-6650-4334-bf73-e0cbbf5593b5 | crowdsourcing-pareto-optimal-object-finding | 1409.4161 | null | http://arxiv.org/abs/1409.4161v1 | http://arxiv.org/pdf/1409.4161v1.pdf | Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons | This is the first study on crowdsourcing Pareto-optimal object finding, which
has applications in public opinion collection, group decision making, and
information exploration. Departing from prior studies on crowdsourcing skyline
and ranking queries, it considers the case where objects do not have explicit
attributes ... | ['Gensheng Zhang', 'Chengkai Li', 'Naeemul Hassan', 'Gergely V. Zaruba', 'Abolfazl Asudeh'] | 2014-09-15 | null | null | null | null | ['question-selection'] | ['natural-language-processing'] | [-9.88788232e-02 1.36061147e-01 9.23432112e-02 -3.52869868e-01
-1.11400092e+00 -1.18480790e+00 2.52850652e-01 3.21046889e-01
-8.15623462e-01 8.74669075e-01 3.06732982e-01 -1.45626366e-01
-6.34030938e-01 -6.82052314e-01 -4.07225072e-01 -4.60756838e-01
-6.69633821e-02 1.03934133e+00 7.72443414e-01 -5.75493813... | [9.668664932250977, 4.793181419372559] |
6364e0c7-c253-4d82-9d67-c060720eec03 | multi-value-a-framework-for-cross-dialectal | 2212.08011 | null | https://arxiv.org/abs/2212.08011v3 | https://arxiv.org/pdf/2212.08011v3.pdf | Multi-VALUE: A Framework for Cross-Dialectal English NLP | Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current systems often fall... | ['Rahul Gupta', 'Jwala Dhamala', 'Diyi Yang', 'Jingfeng Yang', 'William Held', 'Caleb Ziems'] | 2022-12-15 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-1.33785427e-01 -3.37510139e-01 -2.96739340e-01 -8.31872165e-01
-1.33578396e+00 -1.21199429e+00 5.47321677e-01 -1.01214588e-01
-4.44873899e-01 6.81563258e-01 4.22290653e-01 -8.35063398e-01
8.30784962e-02 -6.89136028e-01 -7.68395782e-01 1.06046677e-01
4.11553591e-01 7.97886908e-01 -1.35455847e-01 -1.00848722... | [11.214483261108398, 10.080942153930664] |
75c9a97a-6ba8-42ef-ab4d-5cacab2b3840 | open-vocabulary-object-detection-using-pseudo | 2303.13040 | null | https://arxiv.org/abs/2303.13040v1 | https://arxiv.org/pdf/2303.13040v1.pdf | Open-Vocabulary Object Detection using Pseudo Caption Labels | Recent open-vocabulary detection methods aim to detect novel objects by distilling knowledge from vision-language models (VLMs) trained on a vast amount of image-text pairs. To improve the effectiveness of these methods, researchers have utilized datasets with a large vocabulary that contains a large number of object c... | ['Byungseok Roh', 'Wooyoung Kang', 'Won Young Jhoo', 'Han-Cheol Cho'] | 2023-03-23 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 3.69497627e-01 3.03647459e-01 -2.55672365e-01 -4.29225534e-01
-6.50190830e-01 -5.87965906e-01 8.17774951e-01 -8.44224319e-02
-4.82936561e-01 6.69508636e-01 3.64109054e-02 -2.13354845e-02
4.08428609e-01 -5.83857238e-01 -1.01522124e+00 -4.77529377e-01
4.12139654e-01 5.77742159e-01 3.39128405e-01 -6.06280565... | [10.080842018127441, 1.5968714952468872] |
3a5ec7c8-a3e2-4a8e-9750-03c0620a4942 | self-configuring-nnu-nets-detect-clouds-in | 2210.13659 | null | https://arxiv.org/abs/2210.13659v1 | https://arxiv.org/pdf/2210.13659v1.pdf | Self-Configuring nnU-Nets Detect Clouds in Satellite Images | Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can help to reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquis... | ['Jakub Nalepa', 'Bertrand Le Saux', 'Nicolas Longépé', 'Michal Kawulok', 'Maciej Ziaja', 'Bartosz Grabowski'] | 2022-10-24 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.70132953e-01 1.21247554e-02 -1.13809772e-01 -2.32513011e-01
-7.01284170e-01 -7.98699021e-01 2.22862706e-01 9.56264958e-02
-7.11248159e-01 4.73618954e-01 -3.31763923e-01 -5.05988955e-01
-2.62535930e-01 -1.03981411e+00 -7.98306227e-01 -8.45355153e-01
-5.28704584e-01 4.78419453e-01 3.11199337e-01 -2.85309553... | [9.621465682983398, -1.5378224849700928] |
15addbe8-bddb-444f-9357-996e7703d49f | active-learning-framework-to-automate | 2211.08399 | null | https://arxiv.org/abs/2211.08399v1 | https://arxiv.org/pdf/2211.08399v1.pdf | Active Learning Framework to Automate NetworkTraffic Classification | Recent network traffic classification methods benefitfrom machine learning (ML) technology. However, there aremany challenges due to use of ML, such as: lack of high-qualityannotated datasets, data-drifts and other effects causing aging ofdatasets and ML models, high volumes of network traffic etc. Thispaper argues tha... | ['Tomáš Čejka', 'Dominik Soukup', 'Jaroslav Pešek'] | 2022-10-26 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.90443867e-01 -5.71712628e-02 -7.77822196e-01 -5.68192065e-01
-4.40127850e-01 -5.39962292e-01 4.36360836e-01 2.10242689e-01
-3.49346489e-01 1.25020742e+00 -6.52906477e-01 -6.86221600e-01
-4.54390526e-01 -7.81187236e-01 -4.06996727e-01 -7.04479635e-01
-3.41207802e-01 9.87914443e-01 9.02869403e-01 3.02640468... | [5.105746269226074, 7.202227592468262] |
e304622a-5749-4a04-93a0-e4cfb387d051 | trunk-branch-ensemble-convolutional-neural | 1607.05427 | null | http://arxiv.org/abs/1607.05427v2 | http://arxiv.org/pdf/1607.05427v2.pdf | Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition | Human faces in surveillance videos often suffer from severe image blur,
dramatic pose variations, and occlusion. In this paper, we propose a
comprehensive framework based on Convolutional Neural Networks (CNN) to
overcome challenges in video-based face recognition (VFR). First, to learn
blur-robust face representations... | ['DaCheng Tao', 'Changxing Ding'] | 2016-07-19 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 3.69888470e-02 -4.29683238e-01 5.85888922e-02 -6.48484528e-01
-3.65844995e-01 -3.50744337e-01 4.13658589e-01 -1.00298905e+00
-2.10815579e-01 6.79388762e-01 2.35707149e-01 1.72523677e-01
-3.94397490e-02 -4.41916227e-01 -8.72744560e-01 -6.33821726e-01
-2.42728386e-02 -3.73413354e-01 -2.76081055e-01 -1.34687126... | [13.18621826171875, 0.8224337697029114] |
51aaf8f5-a101-41d0-845a-bca497da748a | semi-supervised-lung-nodule-retrieval | 2005.01805 | null | https://arxiv.org/abs/2005.01805v1 | https://arxiv.org/pdf/2005.01805v1.pdf | Semi-supervised lung nodule retrieval | Content based image retrieval (CBIR) provides the clinician with visual information that can support, and hopefully improve, his or her decision making process. Given an input query image, a CBIR system provides as its output a set of images, ranked by similarity to the query image. Retrieved images may come with relev... | ['Hayit Greenspan', 'Mark Loyman'] | 2020-05-04 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 5.49966395e-01 1.89036369e-01 -4.46971923e-01 -5.46165586e-01
-1.28618801e+00 -5.59303463e-01 5.53682208e-01 7.86775053e-01
-5.23745477e-01 5.59175670e-01 2.60645807e-01 -2.00062618e-01
-5.38911104e-01 -4.90880787e-01 -7.76974559e-02 -8.55077982e-01
2.68299967e-01 6.10267997e-01 4.29341733e-01 1.02066323... | [14.45875358581543, -1.623732328414917] |
afea311f-9b72-4899-b81b-d0837a086eac | boosting-low-data-instance-segmentation-by | 2302.01171 | null | https://arxiv.org/abs/2302.01171v1 | https://arxiv.org/pdf/2302.01171v1.pdf | Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt | Recently, inspired by DETR variants, query-based end-to-end instance segmentation (QEIS) methods have outperformed CNN-based models on large-scale datasets. Yet they would lose efficacy when only a small amount of training data is available since it's hard for the crucial queries/kernels to learn localization and shape... | ['Junwei Han', 'Xinggang Wang', 'Chao Zhang', 'Yalun Dai', 'Lechao Cheng', 'Nian Liu', 'Dingwen Zhang', 'Hao Li'] | 2023-02-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Boosting_Low-Data_Instance_Segmentation_by_Unsupervised_Pre-Training_With_Saliency_Prompt_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Boosting_Low-Data_Instance_Segmentation_by_Unsupervised_Pre-Training_With_Saliency_Prompt_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-pre-training'] | ['methodology'] | [ 8.96429345e-02 2.28726700e-01 -5.64505517e-01 -4.31042403e-01
-8.50461841e-01 -3.49977940e-01 3.94084394e-01 9.70264748e-02
-5.58469474e-01 4.16634619e-01 -1.09891579e-01 -7.94721320e-02
-3.80613804e-02 -5.76592863e-01 -9.22810435e-01 -4.29957181e-01
1.84754938e-01 2.33590826e-01 1.03471804e+00 -7.01760426... | [9.5691499710083, 0.5273327827453613] |
dc9c524e-22fc-46dc-b4de-14e6b6e2c13e | i-code-studio-a-configurable-and-composable | 2305.13738 | null | https://arxiv.org/abs/2305.13738v1 | https://arxiv.org/pdf/2305.13738v1.pdf | i-Code Studio: A Configurable and Composable Framework for Integrative AI | Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Integrative AI is one important direction to approach AGI, through combining multiple models to tackle complex multimodal tasks. However, there... | ['Xuedong Huang', 'Michael Zeng', 'Lu Yuan', 'Takuya Yoshioka', 'Yao Qian', 'Yichong Xu', 'Reid Pryzant', 'ZiYi Yang', 'Chenguang Zhu', 'Mahmoud Khademi', 'Yuwei Fang'] | 2023-05-23 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [-3.86926010e-02 -1.61167219e-01 4.50952128e-02 -4.33667153e-01
-7.51659751e-01 -7.82450259e-01 7.94609070e-01 -2.73649424e-01
6.78484589e-02 -3.54512893e-02 3.34731154e-02 -1.40184432e-01
-1.62780300e-01 -4.16786939e-01 -3.80493492e-01 -2.79055178e-01
1.53880492e-01 7.66916573e-01 -1.41539618e-01 -5.30830681... | [10.865217208862305, 1.5214661359786987] |
33d5c348-ceec-4915-9edf-71ee6940e0db | the-ai-mechanic-acoustic-vehicle | 2205.09667 | null | https://arxiv.org/abs/2205.09667v1 | https://arxiv.org/pdf/2205.09667v1.pdf | The AI Mechanic: Acoustic Vehicle Characterization Neural Networks | In a world increasingly dependent on road-based transportation, it is essential to understand vehicles. We introduce the AI mechanic, an acoustic vehicle characterization deep learning system, as an integrated approach using sound captured from mobile devices to enhance transparency and understanding of vehicles and th... | ['Joshua E. Siegel', 'Adam M. Terwilliger'] | 2022-05-19 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.01029783e-01 -5.27401902e-02 1.70494542e-01 -5.22046387e-01
-1.28890765e+00 -4.38165009e-01 4.21936065e-01 -2.58602947e-02
5.36230840e-02 3.26729506e-01 1.37492701e-01 -6.57631814e-01
9.79439020e-02 -8.82356644e-01 -1.15418327e+00 -3.62510145e-01
-2.80355036e-01 8.31201226e-02 1.79637581e-01 -1.46354705... | [15.103267669677734, 5.219354629516602] |
ae007875-8bf3-48cb-a95a-6f09982847c4 | graph-based-neural-sentence-ordering | 1912.07225 | null | https://arxiv.org/abs/1912.07225v1 | https://arxiv.org/pdf/1912.07225v1.pdf | Graph-based Neural Sentence Ordering | Sentence ordering is to restore the original paragraph from a set of sentences. It involves capturing global dependencies among sentences regardless of their input order. In this paper, we propose a novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network \cite{Zhang:acl18} to... | ['Linfeng Song', 'Jiebo Luo', 'Jiali Zeng', 'Yongjing Yin', 'Jinsong Su', 'Chulun Zhou'] | 2019-12-16 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.10291654e-01 4.65750784e-01 -1.16878405e-01 -6.67931557e-01
-1.32516563e-01 -4.16171402e-01 3.34225655e-01 3.89232486e-01
-3.20249856e-01 7.34575212e-01 7.87432790e-01 -4.24077988e-01
-1.93752080e-01 -9.16388810e-01 -9.14927304e-01 7.81988055e-02
8.77642334e-02 3.02123547e-01 4.26953256e-01 -4.08697516... | [11.189460754394531, 8.672701835632324] |
2ea63c69-5223-4e2e-870a-05944a171249 | rgcl-at-semeval-2020-task-6-neural-approaches | 2010.06281 | null | https://arxiv.org/abs/2010.06281v1 | https://arxiv.org/pdf/2010.06281v1.pdf | RGCL at SemEval-2020 Task 6: Neural Approaches to Definition Extraction | This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels. It utilises state-of-the-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set. Ove... | ['Ruslan Mitkov', 'Constantin Orasan', 'Alistair Plum', 'Tharindu Ranasinghe'] | 2020-10-13 | null | null | null | null | ['definition-extraction'] | ['natural-language-processing'] | [ 1.97356388e-01 3.01427901e-01 -3.23143780e-01 -6.02629125e-01
-4.65688854e-01 -8.46577108e-01 8.77036273e-01 5.68835586e-02
-1.09669614e+00 9.33770359e-01 3.75215411e-01 -7.73154080e-01
-1.49494588e-01 -4.69800055e-01 -1.53345004e-01 1.81898475e-01
1.39302850e-01 7.09360778e-01 -3.19718122e-02 -5.69185793... | [10.135313987731934, 9.346162796020508] |
eadafbb8-8924-466e-bea7-eef5c6cd7690 | hyperspectral-unmixing-using-a-neural-network | null | null | https://ieeexplore.ieee.org/document/8322133 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8322133 | Hyperspectral Unmixing Using a Neural Network Autoencoder | In this paper, we present a deep learning based method for blind hyperspectral unmixing in the form of a neural network autoencoder. We show that the linear mixture model implicitly puts certain architectural constraints on the network, and it effectively performs blind hyperspectral unmixing. Several different archite... | ['Magnus O. Ulfarsson', 'Johannes R. Sveinsson', 'Jakob Sigurdsson', 'Burkni Palsson'] | 2018-03-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 2.21260861e-01 -4.18182820e-01 -6.98478520e-02 -1.75115064e-01
-4.46595371e-01 -5.62814057e-01 7.08600581e-01 -4.32970256e-01
-4.36613917e-01 7.51520753e-01 1.51176274e-01 -3.50517601e-01
-3.74469638e-01 -5.79150617e-01 -5.14309168e-01 -1.16088355e+00
5.37975989e-02 3.66096288e-01 -4.95890707e-01 -1.82343021... | [10.068490982055664, -2.0135788917541504] |
b24474d6-b8f0-4981-8be0-05742ba74f26 | towards-sustainable-self-supervised-learning | 2210.11016 | null | https://arxiv.org/abs/2210.11016v1 | https://arxiv.org/pdf/2210.11016v1.pdf | Towards Sustainable Self-supervised Learning | Although increasingly training-expensive, most self-supervised learning (SSL) models have repeatedly been trained from scratch but not fully utilized, since only a few SOTAs are employed for downstream tasks. In this work, we explore a sustainable SSL framework with two major challenges: i) learning a stronger new SSL ... | ['Shuicheng Yan', 'Ming-Ming Cheng', 'Pan Zhou', 'ShangHua Gao'] | 2022-10-20 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.37679118e-01 4.32936400e-01 -6.10982001e-01 -4.12424773e-01
-6.32564843e-01 -3.04655939e-01 4.77672070e-01 -2.02926517e-01
-3.71167868e-01 8.53696823e-01 3.95676792e-02 -2.71772206e-01
-8.37788656e-02 -7.11719573e-01 -1.01727962e+00 -5.97789049e-01
3.15292895e-01 4.63920414e-01 7.94337571e-01 -3.38120162... | [9.496569633483887, 3.2047581672668457] |
e3c00993-d1a6-430c-a000-a06f2de51313 | differentiable-supervector-extraction-for | 1812.09484 | null | http://arxiv.org/abs/1812.09484v1 | http://arxiv.org/pdf/1812.09484v1.pdf | Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification | In this paper, we propose a new differentiable neural network alignment
mechanism for text-dependent speaker verification which uses alignment models
to produce a supervector representation of an utterance. Unlike previous works
with similar approaches, we do not extract the embedding of an utterance from
the mean redu... | ['Alfonso Ortega', 'Victoria Mingote', 'Eduardo Lleida', 'Antonio Miguel'] | 2018-12-22 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.27670884e-01 1.47923380e-01 1.07535072e-01 -7.66735911e-01
-6.35966539e-01 -4.63124961e-01 7.02777147e-01 5.88042289e-02
-7.72805035e-01 2.09514573e-01 3.13404322e-01 -3.15704077e-01
-1.45705016e-02 -4.27932382e-01 -5.42672276e-01 -9.91006613e-01
-1.35661494e-02 3.17634195e-01 -2.84808189e-01 -3.35634261... | [14.332198143005371, 6.086800575256348] |
87c77338-285c-478d-9e25-c5bad8bdb190 | recouple-event-field-via-probabilistic-bias | 2305.11498 | null | https://arxiv.org/abs/2305.11498v1 | https://arxiv.org/pdf/2305.11498v1.pdf | Recouple Event Field via Probabilistic Bias for Event Extraction | Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PLM-based methods ignore the information of trigger/argument fields, which is crucial for understanding event schemas. To this end, we propose... | ['Yujiu Yang', 'Weigang Guo', 'Qi Ju', 'Weijie Liu', 'Jiayi Li', 'Xuefeng Yang', 'Zhe Zhao', 'Han Guo', 'Taiqiang Wu', 'Xingyu Bai'] | 2023-05-19 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.28055066e-01 1.29721999e-01 -2.50278622e-01 -4.61951554e-01
-6.01910591e-01 -7.55902946e-01 1.00485837e+00 8.59663546e-01
-3.58293563e-01 9.04528856e-01 9.14720833e-01 -1.34927258e-01
-4.37751085e-01 -1.07662928e+00 -7.05842316e-01 -7.10716367e-01
-8.09265375e-02 1.64769411e-01 3.34440917e-01 -4.58274549... | [9.046899795532227, 9.139142036437988] |
74dfd0a9-31a0-4a6f-bccb-269d27fa9449 | clutrr-a-diagnostic-benchmark-for-inductive | 1908.06177 | null | https://arxiv.org/abs/1908.06177v2 | https://arxiv.org/pdf/1908.06177v2.pdf | CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text | The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systemati... | ['William L. Hamilton', 'Shagun Sodhani', 'Koustuv Sinha', 'Jin Dong', 'Joelle Pineau'] | 2019-08-16 | clutrr-a-diagnostic-benchmark-for-inductive-1 | https://aclanthology.org/D19-1458 | https://aclanthology.org/D19-1458.pdf | ijcnlp-2019-11 | ['systematic-generalization'] | ['reasoning'] | [ 2.14870751e-01 6.59069300e-01 -3.80386889e-01 -3.70204687e-01
-2.19059512e-01 -7.10237801e-01 5.95545709e-01 6.05905414e-01
1.66066185e-01 7.16489196e-01 2.81817526e-01 -8.41947675e-01
-4.98430371e-01 -1.38555360e+00 -1.04597366e+00 1.68253854e-01
-4.21861917e-01 6.78440392e-01 2.20197037e-01 -4.51616079... | [9.237570762634277, 7.54093074798584] |
d78d3b25-f311-42d9-b235-c444cb0be1de | pandas-abusive-comment-detection-in-tamil | null | null | https://aclanthology.org/2022.dravidianlangtech-1.18 | https://aclanthology.org/2022.dravidianlangtech-1.18.pdf | PANDAS@Abusive Comment Detection in Tamil Code-Mixed Data Using Custom Embeddings with LaBSE | Abusive language has lately been prevalent in comments on various social media platforms. The increasing hostility observed on the internet calls for the creation of a system that can identify and flag such acerbic content, to prevent conflict and mental distress. This task becomes more challenging when low-resource la... | ['Bharathi B', 'Thenmozhi Durairaj', 'Gayathri G L', 'Divyasri K', 'Krithika Swaminathan'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['abusive-language'] | ['natural-language-processing'] | [-2.12071508e-01 -6.38776496e-02 -6.18002675e-02 -2.64858425e-01
-3.22540671e-01 -4.27187979e-01 7.27241457e-01 7.29825258e-01
-6.03454232e-01 7.42211759e-01 3.60328645e-01 -4.99289989e-01
6.95628077e-02 -5.16168594e-01 2.37039804e-01 -4.19256836e-01
-4.98221666e-02 8.15174654e-02 3.21863964e-02 -5.81153274... | [8.782768249511719, 10.508416175842285] |
4379fcda-5b4d-46ff-8ef4-e57212904ed7 | autoencoders-for-unsupervised-anomaly-1 | 2104.09051 | null | https://arxiv.org/abs/2104.09051v1 | https://arxiv.org/pdf/2104.09051v1.pdf | Autoencoders for unsupervised anomaly detection in high energy physics | Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction l... | ['Ivan Oleksiyuk', 'Alexander Mück', 'Alessandro Morandini', 'Michael Krämer', 'Thorben Finke'] | 2021-04-19 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-3.51655841e-01 6.74072504e-02 4.72095758e-01 -3.68865728e-01
-2.02284530e-01 -5.62690377e-01 7.93179333e-01 4.41981882e-01
-5.92363656e-01 2.26678312e-01 -6.15123250e-02 -2.50653327e-01
-1.71051383e-01 -8.45259666e-01 -6.48560762e-01 -9.87066388e-01
-6.90401644e-02 1.29753792e+00 6.61714911e-01 -4.48291928... | [15.685892105102539, 2.922760486602783] |
3581a206-18c4-48ac-85f8-98f3398ba532 | tensor-basis-gaussian-process-models-of | 1912.10872 | null | https://arxiv.org/abs/1912.10872v1 | https://arxiv.org/pdf/1912.10872v1.pdf | Tensor Basis Gaussian Process Models of Hyperelastic Materials | In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this a... | ['Reese Jones', 'Laura Swiler', 'Ari Frankel'] | 2019-12-23 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.23255381e-01 3.05748731e-01 1.22815460e-01 -2.36581951e-01
-6.19671762e-01 -3.40482980e-01 5.01068890e-01 1.87164154e-02
-2.41904423e-01 3.30951750e-01 -1.21107832e-01 -3.00852284e-02
-4.84645039e-01 -7.18125224e-01 -8.48283648e-01 -1.14456844e+00
1.11849792e-01 8.06126416e-01 3.47906083e-01 -1.24436855... | [6.419939994812012, 3.453782320022583] |
c9880c94-da30-4858-813f-7010ea0fa588 | open-source-iris-recognition-hardware-and | 2008.08220 | null | https://arxiv.org/abs/2008.08220v1 | https://arxiv.org/pdf/2008.08220v1.pdf | Open Source Iris Recognition Hardware and Software with Presentation Attack Detection | This paper proposes the first known to us open source hardware and software iris recognition system with presentation attack detection (PAD), which can be easily assembled for about 75 USD using Raspberry Pi board and a few peripherals. The primary goal of this work is to offer a low-cost baseline for spoof-resistant i... | ['Zhaoyuan Fang', 'Adam Czajka'] | 2020-08-19 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.66201347e-01 -3.72020245e-01 -7.85742253e-02 -3.73462170e-01
-1.38261482e-01 -6.03542984e-01 3.71336550e-01 3.75603810e-02
-5.13092875e-01 2.20787451e-01 -1.80400833e-01 -9.14022207e-01
-2.24254936e-01 -6.56596899e-01 -2.88860857e-01 -8.25903773e-01
-5.57818860e-02 1.65317804e-01 -2.05405459e-01 -6.39905110... | [3.7475852966308594, -3.6271414756774902] |
bf1cd79d-a5ae-41cf-be95-f1a28e99b07f | semeval-2015-task-3-answer-selection-in-1 | 1911.11403 | null | https://arxiv.org/abs/1911.11403v1 | https://arxiv.org/pdf/1911.11403v1.pdf | SemEval-2015 Task 3: Answer Selection in Community Question Answering | Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on "Answer Selection in cQA", which included two... | ['Lluís Màrquez', 'Walid Magdy', 'James Glass', 'Preslav Nakov', 'Bilal Randeree', 'Alessandro Moschitti'] | 2019-11-26 | semeval-2015-task-3-answer-selection-in | https://aclanthology.org/S15-2047 | https://aclanthology.org/S15-2047.pdf | semeval-2015-6 | ['answer-selection'] | ['natural-language-processing'] | [-3.22013587e-01 2.39509344e-02 5.37424743e-01 -3.97609919e-01
-1.40071762e+00 -1.16363931e+00 4.92575616e-01 6.11046433e-01
-7.00670302e-01 7.56463349e-01 4.15425658e-01 -5.77169240e-01
1.36547023e-02 -7.60342240e-01 -3.93628597e-01 -4.21751767e-01
3.51153910e-01 8.85731101e-01 3.34545135e-01 -8.92342091... | [11.406464576721191, 8.07149600982666] |
1bd9414c-eb0f-4990-b35b-7b84d675b7b7 | counterfactual-analysis-of-the-impact-of-the | 2202.09391 | null | https://arxiv.org/abs/2202.09391v1 | https://arxiv.org/pdf/2202.09391v1.pdf | Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows | This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribution, scholars showed that normalizing flows carry certain properties, making them particularly suitable for causal and counterfactual analy... | ['Adel Daoud', 'Jose M. Peña', 'Sourabh Balgi'] | 2022-02-17 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-4.13847715e-02 4.29121017e-01 -8.76102209e-01 -1.82419807e-01
-1.20182164e-01 -1.04208484e-01 7.83762693e-01 3.13232660e-01
-4.60076362e-01 1.18784165e+00 1.00127053e+00 -9.19484079e-01
-6.04510367e-01 -1.52769899e+00 -1.11882484e+00 -6.38767838e-01
-6.22274995e-01 2.20582634e-01 -5.20290911e-01 -2.12348804... | [8.114216804504395, 5.408175468444824] |
f2abafc3-d788-489b-82c3-902b58667efc | qualitative-analysis-of-a-graph-transformer | 2301.10871 | null | https://arxiv.org/abs/2301.10871v3 | https://arxiv.org/pdf/2301.10871v3.pdf | Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content | Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when hateful discourse may arise. Using graph transformer networks, coupled with modelling attention and BERT-level natural language processing, o... | ['Lukasz Golab', 'Robin Cohen', 'Hong Yi Chen', 'Liam Hebert'] | 2023-01-25 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 3.22003603e-01 6.71962559e-01 -1.61932975e-01 8.89900252e-02
-2.54063785e-01 -5.79516172e-01 9.35761094e-01 6.34907186e-01
-2.25305289e-01 3.32968444e-01 1.13248599e+00 -3.89244527e-01
-2.41061822e-01 -4.59931552e-01 -3.91359878e-04 -3.22459131e-01
-1.93492204e-01 5.94372191e-02 -1.49614979e-02 -8.01740825... | [8.678915977478027, 10.440394401550293] |
2f041f04-aec0-4ad8-8df6-64e9d16f6d9b | hohonet-360-indoor-holistic-understanding | 2011.11498 | null | https://arxiv.org/abs/2011.11498v3 | https://arxiv.org/pdf/2011.11498v3.pdf | HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features | We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHo... | ['Hwann-Tzong Chen', 'Min Sun', 'Cheng Sun'] | 2020-11-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 3.55351150e-01 2.20444277e-01 2.25555345e-01 -4.27300513e-01
-7.78311968e-01 -3.39470148e-01 1.29904926e-01 -1.31786108e-01
-3.09290481e-03 2.58177638e-01 4.77299958e-01 -3.04708958e-01
2.69346666e-02 -1.03469753e+00 -9.17522609e-01 -2.97723413e-01
2.69383758e-01 4.33168888e-01 3.30937058e-01 3.11250817... | [8.724772453308105, -2.839165687561035] |
df1bd839-6093-4bb9-91a7-f43b534c20fc | understanding-the-lexical-simplification | null | null | https://aclanthology.org/C16-1069 | https://aclanthology.org/C16-1069.pdf | Understanding the Lexical Simplification Needs of Non-Native Speakers of English | We report three user studies in which the Lexical Simplification needs of non-native English speakers are investigated. Our analyses feature valuable new insight on the relationship between the non-natives{'} notion of complexity and various morphological, semantic and lexical word properties. Some of our findings cont... | ['Lucia Specia', 'Gustavo Paetzold'] | 2016-12-01 | understanding-the-lexical-simplification-1 | https://aclanthology.org/C16-1069 | https://aclanthology.org/C16-1069.pdf | coling-2016-12 | ['misconceptions', 'complex-word-identification'] | ['miscellaneous', 'natural-language-processing'] | [ 5.07815974e-03 4.91910189e-01 -5.07076569e-02 -4.93193150e-01
-3.22227895e-01 -6.10799730e-01 2.98790991e-01 6.69515729e-01
-1.00252914e+00 2.99845070e-01 8.53298843e-01 -7.29245484e-01
1.17576867e-01 -4.50480312e-01 -3.73508781e-02 2.12838426e-01
9.09619927e-02 5.49767315e-02 -8.51334829e-04 -7.54711568... | [10.888312339782715, 10.337677955627441] |
87cd585e-980f-4a07-a242-ec3dc8b0303f | an-open-source-gloss-based-baseline-for | 2305.17714 | null | https://arxiv.org/abs/2305.17714v1 | https://arxiv.org/pdf/2305.17714v1.pdf | An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translation | Sign language translation systems are complex and require many components. As a result, it is very hard to compare methods across publications. We present an open-source implementation of a text-to-gloss-to-pose-to-video pipeline approach, demonstrating conversion from German to Swiss German Sign Language, French to Fr... | ['Sarah Ebling', 'Yoav Goldberg', 'Zifan Jiang', 'Anne Göhring', 'Mathias Müller', 'Amit Moryossef'] | 2023-05-28 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 5.69553375e-01 -2.06206739e-01 -4.47344296e-02 -6.79100811e-01
-1.41237676e+00 -9.42147136e-01 5.34937978e-01 -8.28906119e-01
-4.45188910e-01 5.94106853e-01 5.97956419e-01 -1.38721317e-01
1.64913163e-01 -3.18385929e-01 -7.40355074e-01 -4.07323241e-01
4.74755257e-01 8.82707357e-01 3.34104270e-01 -2.50825167... | [9.196379661560059, -6.524487495422363] |
0b7b4f2f-fffb-4b04-90aa-209083c34f51 | fire-burns-sword-cuts-commonsense-inductive | null | null | https://aclanthology.org/2022.acl-short.56 | https://aclanthology.org/2022.acl-short.56.pdf | Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games | Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces. In these games, the agent learns to explore the environment via natural language interactions with the game simulator. A fundamental challenge in TGs i... | ['Reza Haf', 'Shirui Pan', 'Yunqiu Xu', 'Meng Fang', 'Ehsan Shareghi', 'Dongwon Ryu'] | null | null | null | null | acl-2022-5 | ['text-based-games'] | ['playing-games'] | [-2.26759613e-01 3.47391665e-01 2.13389143e-01 1.13969296e-01
-2.77207285e-01 -8.29168320e-01 8.22977006e-01 -2.48129696e-01
-8.06606591e-01 9.81753707e-01 1.64797097e-01 -5.99346220e-01
-3.84586230e-02 -1.00049174e+00 -6.28514767e-01 -3.03985178e-01
-3.42101306e-01 9.30787861e-01 1.90404788e-01 -9.78720307... | [3.827516555786133, 1.4408315420150757] |
4d632a9f-df03-4715-9ddc-0837c1f26253 | a-deep-learning-based-and-fully-automated | 2302.10634 | null | https://arxiv.org/abs/2302.10634v1 | https://arxiv.org/pdf/2302.10634v1.pdf | A Deep Learning-Based and Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis from 3D Echocardiography | 3D transesophageal echocardiography (3DTEE), is the recommended method for diagnosing mitral regurgitation (MR). 3DTEE provides a high-quality 3D image of the mitral valve (MV), allowing for precise segmentation and measurement of the regurgitant valve anatomy. However, manual TEE segmentations are time-consuming and p... | ['Emiliano Votta', 'Alberto Redaelli', 'Eustachio Agricola', 'Francesco Maisano', 'Paolo Denti', 'Giacomo Ingallina', 'Simone Saitta', 'Riccardo Munafò'] | 2023-02-21 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-4.19925302e-01 2.00540960e-01 3.45081776e-01 2.30834018e-02
-7.51445472e-01 -1.11423326e+00 -1.23390198e-01 1.14971295e-01
-3.91932577e-01 4.62207854e-01 -2.37884179e-01 -7.75469720e-01
3.04404851e-02 -4.91939217e-01 -3.15522045e-01 -5.03856719e-01
-6.01033688e-01 6.93249285e-01 2.11821899e-01 2.78006643... | [14.142598152160645, -2.504316568374634] |
a4a1191b-0d6f-4902-b8b5-3049ba81d107 | improving-text-to-sql-semantic-parsing-with | 2209.14415 | null | https://arxiv.org/abs/2209.14415v1 | https://arxiv.org/pdf/2209.14415v1.pdf | Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding | Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance... | ['Sudipta Sengupta', 'Bing Xiang', 'Ramesh Nallapati', 'Zhiguo Wang', 'Jiarong Jiang', 'Alexander Hanbo Li', 'Patrick Ng', 'Jun Wang'] | 2022-09-28 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 5.19564040e-02 4.28760916e-01 -4.60551441e-01 -7.92703629e-01
-1.04604876e+00 -1.02290452e+00 6.94396487e-03 4.97884989e-01
-2.80010015e-01 4.88920063e-01 2.20560312e-01 -6.62997305e-01
2.09759563e-01 -1.54740548e+00 -1.25292814e+00 5.03379285e-01
3.42593729e-01 6.64323807e-01 7.06546962e-01 -4.13151860... | [9.970898628234863, 7.800204277038574] |
803a9952-2a9a-4055-9fee-8ab15bb7b6b7 | buffered-streaming-graph-partitioning | 2102.09384 | null | https://arxiv.org/abs/2102.09384v2 | https://arxiv.org/pdf/2102.09384v2.pdf | Buffered Streaming Graph Partitioning | Partitioning graphs into blocks of roughly equal size is widely used when processing large graphs. Currently there is a gap in the space of available partitioning algorithms. On the one hand, there are streaming algorithms that have been adopted to partition massive graph data on small machines. In the streaming model,... | ['Christian Schulz', 'Marcelo Fonseca Faraj'] | 2021-02-18 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 3.80757377e-02 2.67136067e-01 4.91057523e-02 2.68793292e-02
-6.55989766e-01 -5.54625690e-01 7.11954907e-02 8.88177097e-01
-3.11603725e-01 5.02068043e-01 -1.60573050e-01 -2.95013219e-01
-2.60874867e-01 -1.45793402e+00 -8.54310930e-01 -6.53913498e-01
-3.19791824e-01 1.13024914e+00 8.24325979e-01 -1.07542418... | [6.9958930015563965, 5.195828914642334] |
03045dd4-988c-488b-b00b-328349c711fe | dreamix-video-diffusion-models-are-general | 2302.01329 | null | https://arxiv.org/abs/2302.01329v1 | https://arxiv.org/pdf/2302.01329v1.pdf | Dreamix: Video Diffusion Models are General Video Editors | Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the first diffusion-based method that is able to perform text-based motion and appearan... | ['Yedid Hoshen', 'Yaniv Leviathan', 'Yael Pritch', 'Yossi Matias', 'Alex Rav Acha', 'Dani Valevski', 'Eliahu Horwitz', 'Eyal Molad'] | 2023-02-02 | null | null | null | null | ['text-to-video-editing', 'video-generation', 'subject-driven-video-generation', 'image-to-video', 'image-animation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.55657208e-01 -2.91769486e-02 1.66141838e-01 1.39007634e-02
-3.12614560e-01 -5.23403585e-01 8.99397790e-01 -4.05324370e-01
-3.04829925e-01 6.36305630e-01 2.99452007e-01 -1.21191174e-01
1.24264643e-01 -5.68689644e-01 -7.88837731e-01 -5.77573121e-01
6.37023002e-02 1.62368730e-01 4.57190454e-01 -3.53428602... | [10.915070533752441, -0.7643884420394897] |
58182101-cb26-4feb-9c7e-b44a06c10b1c | clipvg-text-guided-image-manipulation-using | 2212.02122 | null | https://arxiv.org/abs/2212.02122v2 | https://arxiv.org/pdf/2212.02122v2.pdf | CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics | Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, because CLIP alone cannot provide enough guidance information for fine-s... | ['Xuning Shao', 'Zhongliang Jing', 'Minzhe Li', 'Weidong Zhang', 'Kang Chen', 'Yiren Song'] | 2022-12-05 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 5.31291246e-01 -2.12277725e-01 2.74554789e-02 -2.22164497e-01
-5.94918132e-01 -4.95172977e-01 7.97242582e-01 -4.28649634e-01
7.60414079e-02 4.83336419e-01 -4.92651202e-02 -4.37364340e-01
1.47773564e-01 -8.05769324e-01 -7.77184606e-01 -6.15171373e-01
3.94338310e-01 6.03176169e-02 3.20219755e-01 -5.10431170... | [11.401976585388184, -0.46513187885284424] |
aeedc158-d8dd-4848-84bb-7468daf20130 | a-comparative-study-of-fingerprint-image | 2111.07432 | null | https://arxiv.org/abs/2111.07432v1 | https://arxiv.org/pdf/2111.07432v1.pdf | A Comparative Study of Fingerprint Image-Quality Estimation Methods | One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and v... | ['Josef Bigun', 'Klaus Kollreider', 'Hartwig Fronthaler', 'Joaquin Gonzalez-Rodriguez', 'Javier Ortega-Garcia', 'Julian Fierrez', 'Fernando Alonso-Fernandez'] | 2021-11-14 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 6.87630057e-01 -4.04035777e-01 -2.05407515e-01 -6.86457157e-01
-4.52171445e-01 -7.33288765e-01 4.12080407e-01 1.20047629e-01
-3.91037852e-01 6.65337622e-01 -3.83602321e-01 6.78245649e-02
-4.88633662e-01 -8.59191298e-01 -7.60967076e-01 -6.37216330e-01
-2.62431353e-01 2.95122325e-01 -1.36626363e-01 1.83773205... | [12.967262268066406, 0.9896544218063354] |
8501c845-5d1e-4f6d-b28d-cfcc34d80f59 | dnf-decouple-and-feedback-network-for-seeing | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_DNF_Decouple_and_Feedback_Network_for_Seeing_in_the_Dark_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_DNF_Decouple_and_Feedback_Network_for_Seeing_in_the_Dark_CVPR_2023_paper.pdf | DNF: Decouple and Feedback Network for Seeing in the Dark | The exclusive properties of RAW data have shown great potential for low-light image enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of existing architectures in both single-stage and multi-stage methods. Mixed mapping across two different domains, noise-to-clean and RAW-to-sRG... | ['Chongyi Li', 'Zhi Chai', 'Chun-Le Guo', 'Zhen Li', 'Ling-Hao Han', 'Xin Jin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.30181527e-01 -2.90642858e-01 2.05768440e-02 -2.71288306e-01
-6.80539429e-01 -2.60329306e-01 2.84441829e-01 -3.59815449e-01
-4.49325383e-01 8.80189061e-01 6.16390035e-02 -1.93851903e-01
1.62310079e-02 -6.98529780e-01 -5.68574190e-01 -9.34033751e-01
3.78082275e-01 -6.56282783e-01 5.90017200e-01 -5.47316134... | [10.822454452514648, -2.457315683364868] |
989a4fde-d093-4180-9880-b486ff57b6aa | seasonal-contrast-unsupervised-pre-training | 2103.16607 | null | https://arxiv.org/abs/2103.16607v2 | https://arxiv.org/pdf/2103.16607v2.pdf | Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data | Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change. Although there exist vast amounts of remote sensing data, most of it remains unlabeled and thus inaccessible for supervised learning algorithms. Transfer l... | ['Pau Rodriguez', 'David Vazquez', 'Xavier Giro-i-Nieto', 'Alexandre Lacoste', 'Oscar Mañas'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Manas_Seasonal_Contrast_Unsupervised_Pre-Training_From_Uncurated_Remote_Sensing_Data_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Manas_Seasonal_Contrast_Unsupervised_Pre-Training_From_Uncurated_Remote_Sensing_Data_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-pre-training'] | ['methodology'] | [ 2.86679685e-01 -3.24158549e-01 -2.96219349e-01 -6.68261945e-01
-9.17507887e-01 -8.16284716e-01 5.93146503e-01 2.11216775e-05
-4.57050800e-01 7.84861684e-01 4.23106831e-03 -7.70231247e-01
-2.32256745e-04 -1.11760008e+00 -7.38237679e-01 -5.78464925e-01
-6.07678235e-01 3.34954917e-01 -2.06290856e-02 -3.50709260... | [9.623419761657715, -1.4597957134246826] |
9069ead2-2479-41db-97a4-29aee8ab67f7 | towards-faster-training-of-global-covariance | 1712.01034 | null | http://arxiv.org/abs/1712.01034v2 | http://arxiv.org/pdf/1712.01034v2.pdf | Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization | Global covariance pooling in convolutional neural networks has achieved
impressive improvement over the classical first-order pooling. Recent works
have shown matrix square root normalization plays a central role in achieving
state-of-the-art performance. However, existing methods depend heavily on
eigendecomposition (... | ['Zilin Gao', 'Peihua Li', 'Jiangtao Xie', 'Qilong Wang'] | 2017-12-04 | towards-faster-training-of-global-covariance-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Towards_Faster_Training_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Towards_Faster_Training_CVPR_2018_paper.pdf | cvpr-2018-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.33469462e-01 -2.68118829e-01 2.35648647e-01 -4.20744270e-01
-2.43111178e-01 -3.38513017e-01 3.46236467e-01 -1.08349144e-01
-9.75183308e-01 2.88206577e-01 -8.77232254e-02 -5.24596870e-01
-9.21585262e-02 -7.30617106e-01 -8.01554680e-01 -8.20259392e-01
-2.82610748e-02 -1.31682575e-01 4.05970871e-01 -2.60382771... | [8.997037887573242, 2.302155017852783] |
f499c63d-2d62-4c09-a313-23e5b1dd5261 | tuning-traditional-language-processing | 2305.03737 | null | https://arxiv.org/abs/2305.03737v1 | https://arxiv.org/pdf/2305.03737v1.pdf | Tuning Traditional Language Processing Approaches for Pashto Text Classification | Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The... | ['Nematullah Hassanzada', 'Mohammad Zarif Joya', 'Mursal Dawodi', 'Jawid Ahmad Baktash'] | 2023-05-04 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 3.58839668e-02 -2.66255200e-01 -5.17041028e-01 -4.32988614e-01
-9.54021467e-04 -2.93504924e-01 7.66929626e-01 8.82397294e-01
-7.48381793e-01 1.01153982e+00 2.80659467e-01 -6.55292511e-01
-4.85009581e-01 -9.59708929e-01 3.30189645e-01 -5.51979363e-01
2.47927174e-01 5.77531874e-01 -1.34474128e-01 -2.01962620... | [10.601959228515625, 7.274409770965576] |
59d547a1-425b-444f-b36a-588ba61cc434 | a-review-of-multi-objective-deep-learning | 2003.12108 | null | http://arxiv.org/abs/2003.12108v1 | http://arxiv.org/pdf/2003.12108v1.pdf | A Review of Multi-Objective Deep Learning Speech Denoising Methods | This paper presents a review of multi-objective deep learning methods that
have been introduced in the literature for speech denoising. After stating an
overview of conventional, single objective deep learning, and hybrid or
combined conventional and deep learning methods, a review of the mathematical
framework of the ... | [] | 2020-03-26 | null | null | null | null | ['speech-denoising'] | ['speech'] | [-2.52275646e-01 -1.37693033e-01 1.87480077e-01 -4.67208117e-01
-1.29169405e+00 -4.22541723e-02 1.89656034e-01 4.92526144e-02
-6.42323196e-01 8.29148769e-01 4.58455712e-01 1.04195021e-01
-5.39528012e-01 -5.17617702e-01 -2.88377434e-01 -1.23978353e+00
-9.04416516e-02 9.96965766e-02 -2.46901780e-01 -3.64794940... | [15.102602005004883, 5.90800142288208] |
f1504c0a-0b49-4941-b7af-a0b012e30571 | pearl-preprocessing-enhanced-adversarial | 2305.15709 | null | https://arxiv.org/abs/2305.15709v1 | https://arxiv.org/pdf/2305.15709v1.pdf | PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation | In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of segmentation models against natural degradation factors (e.g., rain streaks) or artificially attack factors (e.g., adversarial attack). Whe... | ['Xin Fan', 'Risheng Liu', 'Xinyuan Chu', 'Jiaxin Gao', 'Yaohua Liu', 'Xianghao Jiao'] | 2023-05-25 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 6.90610945e-01 -7.98885226e-02 3.90178174e-01 -9.13846716e-02
-5.67594528e-01 -1.05748987e+00 6.43541157e-01 -4.52821255e-02
-2.90089041e-01 3.78296167e-01 -1.97682977e-01 -2.69581944e-01
2.23889574e-02 -8.71335566e-01 -9.04792547e-01 -9.33821499e-01
9.13785100e-02 -7.56386071e-02 5.58872521e-01 -4.27049011... | [5.52023983001709, 7.957825660705566] |
6378acc3-477b-4c26-b319-9fcea939f268 | relation-based-generalized-zero-shot | null | null | https://openreview.net/forum?id=BJl8ZlHFwr | https://openreview.net/pdf?id=BJl8ZlHFwr | Relation-based Generalized Zero-shot Classification with the Domain Discriminator on the shared representation | Generalized zero-shot learning (GZSL) is the task of predicting a test image from seen or unseen classes using pre-defined class-attributes and images from the seen classes. Typical ZSL models assign the class corresponding to the most relevant attribute as the predicted label of the test image based on the learned re... | ['Yutaka Matsuo', 'Masahiro Suzuki'] | 2019-09-25 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 6.01302087e-01 4.23995465e-01 -2.04144746e-01 -4.54569250e-01
-7.64096081e-01 -3.56244087e-01 6.31516576e-01 -1.78755894e-01
-1.66475341e-01 7.67056286e-01 -2.27404878e-01 1.74612805e-01
-2.98786610e-01 -1.07131124e+00 -8.77789080e-01 -1.06065774e+00
4.97754991e-01 7.25923121e-01 4.18801367e-01 -1.28874555... | [9.921894073486328, 2.7798068523406982] |
00276683-bea0-482b-8c4c-276e5d625c0b | s3cnet-a-sparse-semantic-scene-completion | 2012.09242 | null | https://arxiv.org/abs/2012.09242v1 | https://arxiv.org/pdf/2012.09242v1.pdf | S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds | With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike. Prior attempts on the challenging Semantic Scene Completion task - which entails the inference of dense 3... | ['Liu Bingbing', 'Xinhai Li', 'Yuan Ren', 'Christopher Agia', 'Ran Cheng'] | 2020-12-16 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 3.80513966e-01 7.96387941e-02 1.07715711e-01 -6.47608936e-01
-5.99338531e-01 -7.02314019e-01 4.87851918e-01 -1.07897101e-02
-4.24462527e-01 3.01272869e-01 -1.61689833e-01 -1.98764086e-01
-1.00337014e-01 -8.27685893e-01 -9.30252731e-01 -1.64310843e-01
1.53153002e-01 9.75221574e-01 3.93940240e-01 -1.25062570... | [8.225261688232422, -2.846393346786499] |
10b78fc9-759d-4548-81a4-1f5d44b8cd3f | utilising-knowledge-graph-embeddings-for-data | null | null | https://aclanthology.org/2020.webnlg-1.6 | https://aclanthology.org/2020.webnlg-1.6.pdf | Utilising Knowledge Graph Embeddings for Data-to-Text Generation | Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their utility for end-to-end approaches in a data-to-text generation task. Our experiments show that usi... | ['Paul Buitelaar', 'Mihael Arcan', 'Nivranshu Pasricha'] | null | null | null | null | acl-webnlg-inlg-2020-12 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'data-to-text-generation'] | ['graphs', 'methodology', 'natural-language-processing'] | [ 1.72868118e-01 9.10329044e-01 -2.92132460e-02 -4.44588721e-01
-9.41503346e-01 -6.15536273e-01 1.21896791e+00 3.49144220e-01
-2.96771854e-01 6.75522625e-01 8.48153472e-01 -4.00092989e-01
8.85825884e-03 -1.00169563e+00 -4.40517604e-01 7.31722265e-03
2.41133988e-01 1.07332838e+00 -2.53165532e-02 -4.96219873... | [11.398269653320312, 8.920001029968262] |
d53c4267-1431-4343-8e57-6f1babbd5482 | neural-rgb-d-sensing-depth-and-uncertainty | 1901.02571 | null | http://arxiv.org/abs/1901.02571v1 | http://arxiv.org/pdf/1901.02571v1.pdf | Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera | Depth sensing is crucial for 3D reconstruction and scene understanding.
Active depth sensors provide dense metric measurements, but often suffer from
limitations such as restricted operating ranges, low spatial resolution, sensor
interference, and high power consumption. In this paper, we propose a deep
learning (DL) m... | ['Kihwan Kim', 'Chao Liu', 'Jan Kautz', 'Srinivasa Narasimhan', 'Jinwei Gu'] | 2019-01-09 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.74203467e-01 -2.00251658e-02 -9.02076624e-03 -5.54722309e-01
-7.40406334e-01 -2.55401552e-01 3.07545751e-01 7.36294836e-02
-6.50275707e-01 5.29102445e-01 2.70721829e-03 -3.97430882e-02
2.18211133e-02 -1.08269143e+00 -7.54943848e-01 -6.94053710e-01
2.56959617e-01 3.83946478e-01 6.97641075e-01 7.20048845... | [8.88271427154541, -2.550079107284546] |
bb317c20-f259-4801-add9-6bbd7f94dcd1 | classifying-variable-length-audio-files-with | 1607.02857 | null | http://arxiv.org/abs/1607.02857v1 | http://arxiv.org/pdf/1607.02857v1.pdf | Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling | We trained a deep all-convolutional neural network with masked global pooling
to perform single-label classification for acoustic scene classification and
multi-label classification for domestic audio tagging in the DCASE-2016
contest. Our network achieved an average accuracy of 84.5% on the four-fold
cross-validation ... | ['Alfred Mertins', 'Huy Phan', 'Lars Hertel'] | 2016-07-11 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 4.15392727e-01 2.59373784e-01 2.43363619e-01 -5.60257673e-01
-1.52252591e+00 -6.05833769e-01 1.49388298e-01 -8.93539935e-02
-7.58406401e-01 9.44388807e-02 4.31082100e-02 3.81915905e-02
4.76319224e-01 -5.14203787e-01 -6.00827098e-01 -8.68447304e-01
-2.79636323e-01 -2.75349855e-01 2.15105727e-01 3.08886051... | [15.211390495300293, 5.099597930908203] |
e5a48aac-09c4-47ae-b9cb-86505704655f | towards-integration-of-discriminability-and | 2304.00824 | null | https://arxiv.org/abs/2304.00824v1 | https://arxiv.org/pdf/2304.00824v1.pdf | Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction | Document-level relation extraction (DocRE) predicts relations for entity pairs that rely on long-range context-dependent reasoning in a document. As a typical multi-label classification problem, DocRE faces the challenge of effectively distinguishing a small set of positive relations from the majority of negative ones.... | ['Lidong Bing', 'Stanley Kok', 'Jia Guo'] | 2023-04-03 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.67688662e-01 1.53401211e-01 -3.93136710e-01 -4.51367706e-01
-1.04698575e+00 -4.93977755e-01 5.62740207e-01 3.75703394e-01
-3.54825646e-01 1.16409481e+00 -1.16045177e-01 -8.79816711e-02
-6.57720566e-01 -8.05655181e-01 -4.39168304e-01 -7.53210902e-01
-2.77841967e-02 6.76852882e-01 4.95737493e-02 -1.15517870... | [9.182272911071777, 8.61283016204834] |
f098a9b5-6d1e-422f-821b-0d5dffff560a | a-survey-on-aspect-based-sentiment-analysis | 2203.01054 | null | https://arxiv.org/abs/2203.01054v2 | https://arxiv.org/pdf/2203.01054v2.pdf | A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges | As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing differe... | ['Wai Lam', 'Lidong Bing', 'Yang Deng', 'Xin Li', 'Wenxuan Zhang'] | 2022-03-02 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-6.29758462e-02 -3.00169975e-01 -3.29777360e-01 -7.87377775e-01
-7.64059544e-01 -7.89102316e-01 6.56216860e-01 6.02278233e-01
-2.30769485e-01 5.15556991e-01 2.73799360e-01 -2.98732489e-01
-7.43941497e-03 -8.65247846e-01 -4.55136001e-02 -5.97813845e-01
3.02856743e-01 3.91698182e-01 -2.54729360e-01 -9.21775281... | [11.348712921142578, 6.751372337341309] |
eedddcb8-1c79-4f28-972d-d195d7a70925 | bertic-the-transformer-language-model-for-1 | null | null | https://aclanthology.org/2021.bsnlp-1.5 | https://aclanthology.org/2021.bsnlp-1.5.pdf | BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian | In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-speech tagging, named-entity-recognition, geo-location prediction and commonsense causal reasoning, sho... | ['Davor Lauc', 'Nikola Ljubešić'] | null | null | null | null | eacl-bsnlp-2021-4 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [-1.86761335e-01 5.04606724e-01 -3.71372342e-01 -4.49562788e-01
-9.46364701e-01 -6.14634454e-01 1.21620929e+00 3.37776989e-01
-4.92917091e-01 1.15907705e+00 8.51777256e-01 -6.50476813e-01
-3.10909599e-01 -8.73472273e-01 -6.39251411e-01 -1.87584326e-01
1.46743162e-02 1.02357411e+00 2.79361218e-01 -5.94075143... | [9.87158489227295, 8.195725440979004] |
541506fd-5ddd-48e8-8d76-9b7832fc551d | learning-action-completeness-from-points-for | 2108.05029 | null | https://arxiv.org/abs/2108.05029v1 | https://arxiv.org/pdf/2108.05029v1.pdf | Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization | We tackle the problem of localizing temporal intervals of actions with only a single frame label for each action instance for training. Owing to label sparsity, existing work fails to learn action completeness, resulting in fragmentary action predictions. In this paper, we propose a novel framework, where dense pseudo-... | ['Hyeran Byun', 'Pilhyeon Lee'] | 2021-08-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lee_Learning_Action_Completeness_From_Points_for_Weakly-Supervised_Temporal_Action_Localization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lee_Learning_Action_Completeness_From_Points_for_Weakly-Supervised_Temporal_Action_Localization_ICCV_2021_paper.pdf | iccv-2021-1 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 5.18629670e-01 6.98659867e-02 -7.72460938e-01 -2.21150637e-01
-9.36165333e-01 -3.79365474e-01 4.20369834e-01 5.32607660e-02
-3.25158954e-01 8.16279590e-01 4.92640555e-01 1.69883355e-01
1.05774784e-02 -3.49571735e-01 -7.67066598e-01 -6.63806498e-01
-1.36316419e-01 3.77714813e-01 5.44164777e-01 1.72749326... | [8.46605110168457, 0.5815978646278381] |
0b9d628e-5fa7-4e1f-b268-986dd76a653a | exploring-object-centric-temporal-modeling | 2303.11926 | null | https://arxiv.org/abs/2303.11926v2 | https://arxiv.org/pdf/2303.11926v2.pdf | Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection | In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection. Built upon the sparse query design in the PETR series, we systematically develop an object-centric temporal mechanism. The model is performed in an online manner and the long-term historical information i... | ['Xiangyu Zhang', 'Ying Li', 'Tiancai Wang', 'Yingfei Liu', 'Shihao Wang'] | 2023-03-21 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-1.61338910e-01 -4.26092446e-01 -2.64224023e-01 -1.97114274e-01
-1.04562128e+00 -6.81395829e-01 6.98047340e-01 1.05182491e-02
-5.48383474e-01 2.52093405e-01 4.37719189e-03 1.77096222e-02
2.75092125e-01 -7.63463259e-01 -8.45398366e-01 -4.00740385e-01
-3.75087070e-03 5.76177001e-01 1.03712606e+00 -2.69017704... | [8.090742111206055, -1.2632662057876587] |
9bc873ea-bae2-413c-8e03-2246bed392c8 | graph-contrastive-pre-training-for-effective | 2108.10821 | null | https://arxiv.org/abs/2108.10821v1 | https://arxiv.org/pdf/2108.10821v1.pdf | Graph Contrastive Pre-training for Effective Theorem Reasoning | Interactive theorem proving is a challenging and tedious process, which requires non-trivial expertise and detailed low-level instructions (or tactics) from human experts. Tactic prediction is a natural way to automate this process. Existing methods show promising results on tactic prediction by learning a deep neural ... | ['Xujie Si', 'Binghong Chen', 'Zhaoyu Li'] | 2021-08-24 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.97242254e-01 5.57625115e-01 -3.76328081e-01 -1.17638864e-01
-7.82985538e-02 -7.67158985e-01 7.48555541e-01 3.68470438e-02
1.79189697e-01 4.20538664e-01 1.07544139e-02 -1.22112274e+00
-1.70463085e-01 -1.11293340e+00 -1.12653816e+00 2.83661991e-01
-1.74721971e-01 6.20526791e-01 -5.68600604e-03 -4.83955115... | [8.979100227355957, 7.105759620666504] |
d32a3d8e-55bd-493b-a2a8-317cd8457fc8 | partmix-regularization-strategy-to-learn-part | 2304.01537 | null | https://arxiv.org/abs/2304.01537v1 | https://arxiv.org/pdf/2304.01537v1.pdf | PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification | Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored. In this pa... | ['Kwanghoon Sohn', 'Seongheon Park', 'Jungin Park', 'Seungryong Kim', 'Minsu Kim'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_PartMix_Regularization_Strategy_To_Learn_Part_Discovery_for_Visible-Infrared_Person_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_PartMix_Regularization_Strategy_To_Learn_Part_Discovery_for_Visible-Infrared_Person_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification'] | ['computer-vision'] | [ 3.58108670e-01 6.14832006e-02 -2.74845690e-01 -5.45329392e-01
-5.29542208e-01 -2.11797252e-01 7.82855332e-01 -2.45785519e-01
-3.66621584e-01 6.36418998e-01 3.23266089e-01 1.13555916e-01
6.83223307e-02 -4.42274421e-01 -6.37717366e-01 -7.26848304e-01
2.68472254e-01 3.15556973e-01 -4.38519865e-01 -3.02349087... | [14.74648666381836, 1.0236868858337402] |
bbd1f69c-1246-4990-b14f-b1e5fd135664 | depth-aware-multi-grid-deep-homography | 2107.02524 | null | https://arxiv.org/abs/2107.02524v2 | https://arxiv.org/pdf/2107.02524v2.pdf | Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation | Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration. Traditional homography estimation methods heavily depend on the quantity and distribution of feature correspondences, leading to poor robustness in low-texture scenes. The le... | ['Yao Zhao', 'Shuaicheng Liu', 'Kang Liao', 'Chunyu Lin', 'Lang Nie'] | 2021-07-06 | null | null | null | null | ['image-stitching', 'video-stabilization', 'homography-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.30766770e-02 -4.18009013e-01 -1.14660896e-02 -1.73948184e-01
-5.11377156e-01 -3.21454167e-01 4.59373176e-01 -4.03881758e-01
-1.37152761e-01 4.43166316e-01 2.10558530e-02 1.79639995e-01
-1.68515906e-01 -7.73007751e-01 -7.05554962e-01 -9.29923475e-01
4.91158754e-01 2.24332497e-01 2.84836441e-01 -1.92484796... | [8.874505996704102, -2.3209385871887207] |
8b32c978-7dd6-4056-aa28-3c4532bb7fcd | evaluating-paraphrastic-robustness-in-textual | 2306.16722 | null | https://arxiv.org/abs/2306.16722v1 | https://arxiv.org/pdf/2306.16722v1.pdf | Evaluating Paraphrastic Robustness in Textual Entailment Models | We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine ... | ['Adam Poliak', 'Benjamin Van Durme', 'Shreyashee Sinha', 'Yash Kumar Lal', 'Dhruv Verma'] | 2023-06-29 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 4.19082701e-01 1.54592857e-01 -3.84727806e-01 -9.88019466e-01
-6.45783305e-01 -1.00199187e+00 6.82731271e-01 2.30369031e-01
-2.87637591e-01 6.30861759e-01 6.82636738e-01 -8.22912872e-01
1.73481211e-01 -5.68448603e-01 -1.10017061e+00 4.58345562e-01
4.10847545e-01 2.39836633e-01 3.01228493e-01 -5.62916338... | [11.281116485595703, 9.05488395690918] |
c2e78f74-e089-46b1-a8e8-e21f43f48a62 | emergent-agentic-transformer-from-chain-of | 2305.16554 | null | https://arxiv.org/abs/2305.16554v1 | https://arxiv.org/pdf/2305.16554v1.pdf | Emergent Agentic Transformer from Chain of Hindsight Experience | Large transformer models powered by diverse data and model scale have dominated natural language modeling and computer vision and pushed the frontier of multiple AI areas. In reinforcement learning (RL), despite many efforts into transformer-based policies, a key limitation, however, is that current transformer-based p... | ['Pieter Abbeel', 'Hao liu'] | 2023-05-26 | null | null | null | null | ['d4rl'] | ['robots'] | [-9.22743157e-02 -6.97174519e-02 -4.78677005e-01 -1.80163234e-01
-9.28552389e-01 -7.51159012e-01 1.03924656e+00 7.50397816e-02
-9.22171116e-01 9.51444328e-01 2.91372061e-01 -3.52188230e-01
-3.01110744e-01 -5.05897582e-01 -9.19638276e-01 -6.16850197e-01
-2.90941715e-01 9.32541430e-01 2.57916570e-01 -2.79483229... | [4.131809234619141, 1.7108391523361206] |
972a94d7-344e-472a-8301-2a5eed6712b4 | combining-deep-and-unsupervised-features-for | null | null | https://link.springer.com/chapter/10.1007%2F978-3-030-68790-8_10 | https://link.springer.com/content/pdf/10.1007%2F978-3-030-68790-8_10.pdf | Combining deep and unsupervised features for multilingual speech emotion recognition | In this paper we present a Convolutional Neural Network for multilingual emotion recognition from spoken sentences. The purpose of this work was to build a model capable of recognising emotions combining textual and acoustic information compatible with multiple languages. The model we derive has an end-to-end deep arch... | ['Roberto Tedesco', 'Licia Sbattella', 'Federico Galati', 'Vincenzo Scotti'] | 2021-01-10 | null | null | null | international-workshop-on-pattern-recognition | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-2.94110805e-01 7.60628879e-02 4.71698612e-01 -7.03628540e-01
-9.54353988e-01 -3.50529760e-01 4.45732951e-01 3.02478135e-01
-9.10203934e-01 5.47992349e-01 1.08306989e-01 1.05169296e-01
3.03646531e-02 -2.65826225e-01 -4.54468697e-01 -3.70279968e-01
-3.46494138e-01 3.09940577e-01 -3.70010734e-01 -4.19381738... | [13.568646430969238, 5.752387523651123] |
cbcf119f-9318-46fd-b127-f3c9322389d3 | audio-source-separation-with-discriminative | 1412.7022 | null | http://arxiv.org/abs/1412.7022v3 | http://arxiv.org/pdf/1412.7022v3.pdf | Audio Source Separation with Discriminative Scattering Networks | In this report we describe an ongoing line of research for solving
single-channel source separation problems. Many monaural signal decomposition
techniques proposed in the literature operate on a feature space consisting of
a time-frequency representation of the input data. A challenge faced by these
approaches is to e... | ['Yann Lecun', 'Joan Bruna', 'Pablo Sprechmann'] | 2014-12-22 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.67440933e-01 -4.43369180e-01 2.48536497e-01 -2.57460266e-01
-1.29631865e+00 -5.88834107e-01 4.49410051e-01 1.41028631e-02
-5.16078234e-01 5.97629249e-01 2.94196665e-01 1.00022681e-01
-6.62897170e-01 -5.13355136e-01 -5.42973280e-01 -9.48058367e-01
-1.78812519e-01 -8.90958533e-02 1.40809685e-01 -3.47127259... | [15.420822143554688, 5.560679912567139] |
98b196fc-1f0d-4541-a276-0150b94cc136 | a-fast-ilp-based-heuristic-for-the-robust | 1704.04640 | null | http://arxiv.org/abs/1704.04640v1 | http://arxiv.org/pdf/1704.04640v1.pdf | A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks | We consider the problem of optimally designing a body wireless sensor
network, while taking into account the uncertainty of data generation of
biosensors. Since the related min-max robustness Integer Linear Programming
(ILP) problem can be difficult to solve even for state-of-the-art commercial
optimization solvers, we... | ["Fabio D'Andreagiovanni", 'Antonella Nardin', 'Enrico Natalizio'] | 2017-04-15 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 6.19185627e-01 9.96393025e-01 -5.54649234e-01 -3.48327532e-02
-9.41441476e-01 -6.11125410e-01 -3.22784364e-01 3.58526915e-01
-3.07246536e-01 1.34607840e+00 -1.40367493e-01 -9.95487943e-02
-8.92054617e-01 -7.66766071e-01 -1.10568273e+00 -8.23513329e-01
-4.26629335e-01 7.36252010e-01 -5.79398312e-02 -2.33646366... | [5.6157402992248535, 3.4734861850738525] |
3bdc17c0-0e69-4312-9778-f577992cf4e3 | machine-learning-for-clouds-and-climate | null | null | https://www.semanticscholar.org/paper/Machine-Learning-for-Clouds-and-Climate-Beucler-Ebert%E2%80%90Uphoff/ea0ccde42b1f4517e87ee2e3f77fb6c06671666b | https://www.essoar.org/doi/abs/10.1002/essoar.10506925.1 | Machine Learning for Clouds and Climate | Machine learning (ML) algorithms are powerful tools to build models of clouds and climate that are more faithful to the rapidly-increasing volumes of Earth system data than commonly-used semiempirical models. Here, we review ML tools, including interpretable and physics-guided ML, and outline how they can be applied to... | ['P. Gentine', 'M. Pritchard', 'S. Rasp', 'I. Ebert‐Uphoff', 'Tom Beucler'] | 2020-01-01 | null | null | null | open-access-2020-1 | ['cloud-detection'] | ['computer-vision'] | [-3.30943972e-01 -7.04076111e-01 -1.32569626e-01 -3.95627350e-01
-1.10811353e-01 -8.18769991e-01 8.60281527e-01 3.18383425e-01
1.29425660e-01 9.80437338e-01 -3.95330131e-01 -1.04875350e+00
2.75824189e-01 -8.70572686e-01 -1.18908718e-01 -8.81146252e-01
-2.75544107e-01 6.32582843e-01 -2.41138395e-02 -2.11161047... | [6.503835678100586, 3.0646414756774902] |
9f2826f9-b64c-4971-8dee-d891d24836d0 | zero-shot-domain-adaptation-of-anomalous | 2304.02221 | null | https://arxiv.org/abs/2304.02221v1 | https://arxiv.org/pdf/2304.02221v1.pdf | Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection | Semi-supervised anomaly detection~(SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer adapting to domain shifts, since anomalous data are unlikely to be available for the target domain in the training phase. To solve this pro... | ['Yohei Kawaguchi', 'Takashi Endo', 'Tomoya Nishida'] | 2023-04-05 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.11828345e-01 1.20503977e-01 -1.46611109e-01 -5.55415213e-01
-7.76673615e-01 -3.10364246e-01 3.74269843e-01 -2.62057662e-01
-2.24165499e-01 1.05252981e+00 1.71060916e-02 -1.91010848e-01
3.48909587e-01 -6.44277036e-01 -8.02603722e-01 -6.97341383e-01
1.36576310e-01 5.76081157e-01 2.59780109e-01 -1.11847773... | [10.313961029052734, 3.1481270790100098] |
1f4bffdb-150d-4462-9531-03541143560e | provably-efficient-representation-learning | 2306.12356 | null | https://arxiv.org/abs/2306.12356v1 | https://arxiv.org/pdf/2306.12356v1.pdf | Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP | In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning. We focus our attention on the... | ['Xuezhou Zhang', 'Zhuoran Yang', 'Mengdi Wang', 'Huazheng Wang', 'Zihao Li', 'Jiacheng Guo'] | 2023-06-21 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 4.72659975e-01 8.76561284e-01 -3.10944766e-01 -1.75707236e-01
-1.50270498e+00 -6.12063169e-01 6.18230700e-01 1.62708834e-01
-7.19388902e-01 1.17612672e+00 3.62418801e-01 -6.71552718e-01
-5.09251535e-01 -6.92629576e-01 -6.35153413e-01 -9.50331509e-01
-6.58445776e-01 1.22784340e+00 1.06474943e-02 2.65641063... | [4.295647144317627, 2.1631722450256348] |
b286b307-d559-4db8-bfd5-cff5e88cc900 | a-permutable-hybrid-network-for-volumetric | 2303.13111 | null | https://arxiv.org/abs/2303.13111v2 | https://arxiv.org/pdf/2303.13111v2.pdf | A Permutable Hybrid Network for Volumetric Medical Image Segmentation | The advent of Vision Transformer (ViT) has brought substantial advancements in 3D volumetric benchmarks, particularly in 3D medical image segmentation. Concurrently, Multi-Layer Perceptron (MLP) networks have regained popularity among researchers due to their comparable results to ViT, albeit with the exclusion of the ... | ['Hao Chen', 'Kwang-Ting Cheng', 'Dong Zhang', 'Xiao Fang', 'Yi Lin'] | 2023-03-23 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 2.09447816e-01 3.03364992e-01 -3.25560153e-01 -2.48284966e-01
-5.49003899e-01 -6.71112016e-02 2.97352105e-01 -2.74273027e-02
-6.49518311e-01 6.02440834e-01 2.28667915e-01 -2.91255534e-01
1.70646217e-02 -7.09339559e-01 -8.15181077e-01 -7.78604150e-01
1.14571229e-02 3.75710100e-01 3.65063906e-01 2.55672168... | [14.574784278869629, -2.556323289871216] |
e4fd2a34-8fd5-4341-8bcf-0784ee826617 | deep-generative-modeling-of-lidar-data | 1812.01180 | null | https://arxiv.org/abs/1812.01180v4 | https://arxiv.org/pdf/1812.01180v4.pdf | Deep Generative Modeling of LiDAR Data | Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep ... | ['Lucas Caccia', 'Herke van Hoof', 'Joelle Pineau', 'Aaron Courville'] | 2018-12-04 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 4.35765564e-01 4.32516903e-01 -5.45713715e-02 -6.08540297e-01
-1.02045703e+00 -7.34958112e-01 9.22527075e-01 -2.31929362e-01
-8.82759914e-02 7.50181615e-01 2.23750934e-01 -1.93315804e-01
-4.76980209e-03 -1.28253102e+00 -1.21268725e+00 -4.92300570e-01
2.50331044e-01 1.30509615e+00 1.13854900e-01 -1.22928642... | [8.782988548278809, -3.6472926139831543] |
a4df0654-d43a-4222-b2d7-56f991c80a7f | worst-case-matters-for-few-shot-recognition | 2203.06574 | null | https://arxiv.org/abs/2203.06574v2 | https://arxiv.org/pdf/2203.06574v2.pdf | Worst Case Matters for Few-Shot Recognition | Few-shot recognition learns a recognition model with very few (e.g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over many episodes. We argue that in real-world applications we may often only try one episode instead of many, and hence maximizing the worst-c... | ['Jianxin Wu', 'Yun-Hao Cao', 'Minghao Fu'] | 2022-03-13 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.20305502e-01 -2.21008450e-01 -3.26998651e-01 -4.57471102e-01
-9.94724572e-01 -3.04081976e-01 4.87331718e-01 -8.11084509e-02
-4.17920172e-01 6.91610873e-01 -1.38094902e-01 3.76318432e-02
-2.96760261e-01 -6.56521380e-01 -5.97604334e-01 -9.16521132e-01
2.77483881e-01 2.74990618e-01 3.95822197e-01 3.17904353... | [9.896740913391113, 3.2702767848968506] |
b1853d05-9f4c-4f4f-928f-41ae42d4cb39 | traffic-state-data-imputation-an-efficient | null | null | https://ieeexplore.ieee.org/abstract/document/9582618 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9582618 | Traffic state data imputation: An efficient generating method based on the graph aggregator | Road traffic state estimation is an essential component
of intelligent transportation systems (ITSs). However,
road traffic state data collected by traffic detectors are often
incomplete, which can cause problems across a variety of
transportation applications, such as traffic state prediction and
pattern recognit... | ['Haijian Li', 'Xuetian Shang', 'Chenchen Wei', 'Hang Peng', 'Dongwei Xu'] | 2022-08-12 | null | null | null | ieee-transactions-on-intelligent-11 | ['traffic-data-imputation'] | ['time-series'] | [ 2.76548773e-01 1.01089664e-01 -3.20024997e-01 -3.80807668e-01
-4.48524684e-01 -2.60644406e-01 5.01103699e-01 -5.36439121e-01
1.48396194e-01 1.06911433e+00 2.52045214e-01 -8.70483160e-01
-2.24907720e-03 -1.44243360e+00 -8.57120097e-01 -7.46760428e-01
2.97573805e-01 4.74984288e-01 4.58586849e-02 -2.37550721... | [6.492879390716553, 2.0614700317382812] |
358bddab-f2fc-4e29-8197-0a727e635ab0 | seeing-a-rose-in-five-thousand-ways | 2212.04965 | null | https://arxiv.org/abs/2212.04965v1 | https://arxiv.org/pdf/2212.04965v1.pdf | Seeing a Rose in Five Thousand Ways | What is a rose, visually? A rose comprises its intrinsics, including the distribution of geometry, texture, and material specific to its object category. With knowledge of these intrinsic properties, we may render roses of different sizes and shapes, in different poses, and under different lighting conditions. In this ... | ['Jiajun Wu', 'Noah Snavely', 'Shangzhe Wu', 'Yunzhi Zhang'] | 2022-12-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.pdf | cvpr-2023-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.04880047e-01 -2.22033963e-01 2.67375708e-01 -2.25460112e-01
-6.06876373e-01 -9.90114450e-01 5.90169489e-01 -1.77916184e-01
4.60296750e-01 3.32636625e-01 1.60615906e-01 4.31062639e-01
1.47386700e-01 -1.03569973e+00 -1.05139184e+00 -7.55747437e-01
3.06681782e-01 6.31829798e-01 9.17939395e-02 -1.59275338... | [9.260847091674805, -3.1264350414276123] |
26392994-82b2-48d2-bebd-2d747c28a3c8 | machine-learning-with-membership-privacy | 1807.05852 | null | http://arxiv.org/abs/1807.05852v1 | http://arxiv.org/pdf/1807.05852v1.pdf | Machine Learning with Membership Privacy using Adversarial Regularization | Machine learning models leak information about the datasets on which they are
trained. An adversary can build an algorithm to trace the individual members of
a model's training dataset. As a fundamental inference attack, he aims to
distinguish between data points that were part of the model's training set and
any other... | ['Amir Houmansadr', 'Milad Nasr', 'Reza Shokri'] | 2018-07-16 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.49359176e-01 5.88616848e-01 -2.51946151e-01 -2.62615889e-01
-6.94605708e-01 -1.37396002e+00 3.23318750e-01 -4.64178808e-02
-5.10682166e-01 5.48358560e-01 -6.61952972e-01 -7.04692841e-01
-2.45357230e-01 -1.35277426e+00 -1.22700477e+00 -1.02372992e+00
-1.81463942e-01 6.31018758e-01 -5.66342622e-02 1.37529820... | [5.93508243560791, 7.1097187995910645] |
f239f221-3802-4d92-9e62-33551c50e633 | perspective-melanoma-diagnosis-and-monitoring | 1607.06720 | null | http://arxiv.org/abs/1607.06720v2 | http://arxiv.org/pdf/1607.06720v2.pdf | Perspective: Melanoma diagnosis and monitoring: Sunrise for melanoma therapy but early detection remains in the shade | Melanoma is one of the most dangerous forms of cancer. The five-year survival
rate is 98% if it is detected early. However, this rate plummets to 63% for
regional disease and 17% when tumors have metastasized, that is, spread to
distant sites. Furthermore, the incidence of melanoma has been rising by about
3% per year,... | [] | 2016-07-25 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 4.86066163e-01 -4.00206372e-02 -5.89047134e-01 3.38484049e-01
-9.94626045e-01 -4.19051737e-01 5.48781574e-01 1.10433483e+00
-8.98419797e-01 1.00592279e+00 4.14066901e-03 -4.76201594e-01
2.44160779e-02 -6.51716948e-01 7.70108402e-02 -1.20596611e+00
2.13721275e-01 4.32333827e-01 3.49049985e-01 -1.22730337... | [15.174018859863281, -3.0888946056365967] |
070f0b8e-8f6d-4567-a2fb-a918baccf1f4 | l3dor-lifelong-3d-object-recognition | 1912.06135 | null | https://arxiv.org/abs/1912.06135v2 | https://arxiv.org/pdf/1912.06135v2.pdf | L3DOC: Lifelong 3D Object Classification | 3D object classification has been widely-applied into both academic and industrial scenarios. However, most state-of-the-art algorithms are facing with a fixed 3D object classification task set, which cannot well tackle the new coming data with incremental tasks as human ourselves. Meanwhile, the performance of most st... | ['Yang Cong', 'Gan Sun', 'Yuyang Liu'] | 2019-12-12 | null | null | null | null | ['3d-object-classification', '3d-object-recognition'] | ['computer-vision', 'computer-vision'] | [-3.70214075e-01 -3.20264280e-01 -2.34889984e-03 -2.56560892e-01
7.55422842e-03 -3.72363955e-01 3.04041773e-01 -6.50471300e-02
-2.32325286e-01 3.45517755e-01 -2.00585529e-01 -2.54416406e-01
-2.37767383e-01 -7.92397439e-01 -1.07290363e+00 -6.33341312e-01
-2.34470498e-02 4.80017900e-01 5.53125799e-01 -2.63138413... | [7.923289775848389, -3.3732266426086426] |
6d6d18bd-5002-41a7-a85b-58e327381613 | large-scale-multi-granular-concept-extraction | 2208.14139 | null | https://arxiv.org/abs/2208.14139v1 | https://arxiv.org/pdf/2208.14139v1.pdf | Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension | The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor coverage of concepts, especially fine-grained concepts. In order to supply existing KGs with more fine-grained and new concepts, we propose... | ['Rui Xie', 'Yanghua Xiao', 'Kaiyan Cao', 'Jingyue Huang', 'Jilun Sun', 'Jiaqing Liang', 'Deqing Yang', 'Siyu Yuan'] | 2022-08-30 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [-5.19437730e-01 4.68780339e-01 -3.43330413e-01 -2.35204458e-01
-1.83206245e-01 -4.42289799e-01 3.46273422e-01 6.18628919e-01
-4.77172285e-01 1.09809721e+00 8.49977434e-02 -3.50740880e-01
-3.85263413e-01 -1.53332424e+00 -7.17052102e-01 -1.08726442e-01
-1.60409093e-01 6.49133146e-01 4.50209290e-01 -4.76077318... | [9.22546100616455, 8.414558410644531] |
731cbe4d-9b3c-494e-9ada-e05d4f6bc486 | boosting-algorithms-for-uplift-modeling | 1807.07909 | null | http://arxiv.org/abs/1807.07909v1 | http://arxiv.org/pdf/1807.07909v1.pdf | Boosting algorithms for uplift modeling | Uplift modeling is an area of machine learning which aims at predicting the
causal effect of some action on a given individual. The action may be a medical
procedure, marketing campaign, or any other circumstance controlled by the
experimenter. Building an uplift model requires two training sets: the
treatment group, w... | ['Michał Sołtys', 'Szymon Jaroszewicz'] | 2018-07-20 | null | null | null | null | ['medical-procedure'] | ['medical'] | [ 5.90405464e-01 5.66758774e-02 -7.63132632e-01 -5.51724076e-01
-2.99865931e-01 -9.19826552e-02 8.80828202e-01 5.37156641e-01
-4.21005100e-01 1.05900729e+00 6.99293315e-02 -5.95402122e-01
-3.48634154e-01 -9.43918705e-01 -9.10988212e-01 -1.16147792e+00
8.53961408e-02 6.34047270e-01 -8.66806731e-02 3.74494493... | [8.324538230895996, 5.358263969421387] |
d157818d-9794-4ab2-8bce-7fafe504ac41 | feature-fused-ssd-fast-detection-for-small | 1709.05054 | null | http://arxiv.org/abs/1709.05054v3 | http://arxiv.org/pdf/1709.05054v3.pdf | Feature-Fused SSD: Fast Detection for Small Objects | Small objects detection is a challenging task in computer vision due to its
limited resolution and information. In order to solve this problem, the
majority of existing methods sacrifice speed for improvement in accuracy. In
this paper, we aim to detect small objects at a fast speed, using the best
object detector Sing... | ['Guimei Cao', 'Xuemei Xie', 'Jinjian Wu', 'Guangming Shi', 'Wenzhe Yang', 'Quan Liao'] | 2017-09-15 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-6.82245195e-02 -3.94760579e-01 3.59582394e-01 -2.21302509e-01
-7.61207938e-01 -2.86125779e-01 4.27208692e-01 3.25049721e-02
-7.64499187e-01 2.80010253e-01 -1.80982068e-01 1.02615416e-01
1.90124929e-01 -5.19931674e-01 -7.87191868e-01 -7.50764847e-01
3.40811312e-01 7.12286755e-02 1.30227315e+00 -1.93842366... | [8.642452239990234, -0.5582991242408752] |
e72d11d5-5f3d-4d08-b5bc-1bcc469b1d41 | plmcl-partial-label-momentum-curriculum | 2208.09999 | null | https://arxiv.org/abs/2208.09999v1 | https://arxiv.org/pdf/2208.09999v1.pdf | PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification | Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each trai... | ['Song Wang', 'XiaoFeng Wang', 'Xinyi Wu', 'Zhenyao Wu', 'Xin Zhang', 'Rabab Abdelfattah'] | 2022-08-22 | null | null | null | null | ['multi-label-image-classification', 'partial-label-learning'] | ['computer-vision', 'methodology'] | [ 5.68503737e-01 1.45042285e-01 -5.57948947e-01 -6.35623813e-01
-8.02351654e-01 -6.31277978e-01 1.80003807e-01 2.54036784e-01
-6.67901218e-01 7.81164229e-01 -6.90030038e-01 -8.57061967e-02
1.36833349e-02 -3.68746012e-01 -7.28646398e-01 -1.06485665e+00
4.28877503e-01 6.61626458e-01 1.12660430e-01 5.33982992... | [9.53879451751709, 4.0780463218688965] |
e40677e9-41ab-4d2a-aa1c-0b2d66099558 | lit01-196-a-metabolically-stable-apelin-17 | 2211.04115 | null | https://arxiv.org/abs/2211.04115v1 | https://arxiv.org/pdf/2211.04115v1.pdf | LIT01-196, a Metabolically Stable Apelin-17 Analog, Normalizes Blood Pressure in Hypertensive DOCA-Salt Rats via a NO Synthase-dependent Mechanism | Apelin is a neuro-vasoactive peptide that plays a major role in the control of cardiovascular functions and water balance, but has an in-vivo half-life in the minute range, limiting its therapeutic use. We previously developed LIT01-196, a systemically active metabolically stable apelin-17 analog, produced by chemical ... | ['Catherine Llorens-Cortes', 'Dominique Bonnet', 'Nadia de Mota', 'Lucie Esteoulle', 'Pierre-Emmanuel Girault-Sotias', 'Mathilde Keck', 'Adrien Flahault'] | 2022-11-08 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 1.75171793e-01 -2.85259694e-01 -4.92080271e-01 -2.29219154e-01
1.51686355e-01 -7.24093735e-01 2.29611069e-01 7.82229662e-01
-9.32785809e-01 9.33481455e-01 1.71033040e-01 -4.85933423e-01
2.24631995e-01 -7.27067888e-01 -3.54132771e-01 -4.45998937e-01
-3.38031322e-01 5.42075753e-01 -8.32709018e-03 1.65150344... | [13.993151664733887, 2.897386074066162] |
a807340c-552d-4638-a340-0b731c77ba98 | steady-state-analysis-of-networked-epidemic | 2305.19023 | null | https://arxiv.org/abs/2305.19023v1 | https://arxiv.org/pdf/2305.19023v1.pdf | Steady-state analysis of networked epidemic models | Compartmental epidemic models with dynamics that evolve over a graph network have gained considerable importance in recent years but analysis of these models is in general difficult due to their complexity. In this paper, we develop two positive feedback frameworks that are applicable to the study of steady-state value... | ['Lanlan Su', 'Sei Zhen Khong'] | 2023-05-30 | null | null | null | null | ['unity'] | ['computer-vision'] | [ 1.89962953e-01 2.34842598e-01 2.36952409e-01 5.37077665e-01
5.89991212e-01 -6.85551047e-01 6.31820560e-01 3.96568745e-01
-4.35472101e-01 9.35663223e-01 -1.89681917e-01 -4.36077505e-01
-7.05134451e-01 -8.13377023e-01 -3.68393391e-01 -1.18647838e+00
-4.51905340e-01 5.16742229e-01 5.63174665e-01 -6.27026737... | [5.934700012207031, 4.352358341217041] |
c4f33969-125b-4c37-a369-53bdfe9da13a | contrastive-loss-is-all-you-need-to-recover | 2306.08221 | null | https://arxiv.org/abs/2306.08221v1 | https://arxiv.org/pdf/2306.08221v1.pdf | Contrastive Loss is All You Need to Recover Analogies as Parallel Lines | While static word embedding models are known to represent linguistic analogies as parallel lines in high-dimensional space, the underlying mechanism as to why they result in such geometric structures remains obscure. We find that an elementary contrastive-style method employed over distributional information performs c... | ['Nakul Verma', 'Fei-Tzin Lee', 'Narutatsu Ri'] | 2023-06-14 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-2.17968449e-01 -4.20709029e-02 -4.25459236e-01 -2.28471160e-01
-4.32855785e-01 -6.98072791e-01 9.20918763e-01 6.94873333e-01
-6.70499325e-01 2.63892710e-01 7.66343474e-01 -6.50545597e-01
-2.28072539e-01 -7.97021091e-01 -5.47820807e-01 -4.25058931e-01
-3.18617672e-01 5.08422434e-01 -9.25073698e-02 -4.74193990... | [10.34595775604248, 8.711685180664062] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.