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b0d7c495-c7d7-4e21-8692-81c4cd6872ef | a-recurrent-model-for-collective-entity | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/5367 | https://jqin.gitee.io/files/AAAI2020-zhou.pdf | A Recurrent Model for Collective Entity Linking with Adaptive Features | The vast amount of web data enables us to build knowledge bases with unprecedented quality and coverage. Named Entity Disambiguation (NED) is an important task that automatically resolves ambiguous mentions in free text to correct target entries in the knowledge base. Traditional machine learning based methods for NED ... | ['Jianbin Qin', 'Wei Wang', 'Yukai Miao', 'Xiaoling Zhou'] | 2020-04-03 | null | null | null | aaai-2020-4 | ['entity-disambiguation'] | ['natural-language-processing'] | [-3.51066709e-01 7.55498856e-02 -4.73299086e-01 -4.96175081e-01
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-1.45161599e-01 1.01510227e+00 4.07688618e-01 -4.85116512... | [9.492794036865234, 8.89466381072998] |
c9a6600a-f55b-4098-95e0-62286376fc6c | unsupervised-deep-persistent-monocular-visual | 2011.00341 | null | https://arxiv.org/abs/2011.00341v1 | https://arxiv.org/pdf/2011.00341v1.pdf | Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments | In recent years, unsupervised deep learning approaches have received significant attention to estimate the depth and visual odometry (VO) from unlabelled monocular image sequences. However, their performance is limited in challenging environments due to perceptual degradation, occlusions and rapid motions. Moreover, th... | ['Ali-akbar Agha-mohammadi', 'Benjamin Morrell', 'Angel Santamaria-Navarro', 'Yasin Almalioglu'] | 2020-10-31 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.96953926e-03 -2.86336988e-01 -3.59367132e-02 -3.38346481e-01
-6.72406256e-01 -6.07433915e-01 6.17827237e-01 -2.94450790e-01
-6.16184890e-01 8.55172396e-01 8.44864920e-02 2.99590826e-01
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7.46501908e-02 6.63210511e-01 3.83734167e-01 7.57235587... | [8.423195838928223, -2.2427845001220703] |
9c325236-5c28-4e30-ad8a-504fc2844d47 | communication-efficient-federated-learning-14 | 2202.02580 | null | https://arxiv.org/abs/2202.02580v1 | https://arxiv.org/pdf/2202.02580v1.pdf | Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting | The challenge of communication-efficient distributed optimization has attracted attention in recent years. In this paper, a communication efficient algorithm, called ordering-based alternating direction method of multipliers (OADMM) is devised in a general fully decentralized network setting where a worker can only exc... | ['Brian M. Sadler', 'Rick S. Blum', 'Yicheng Chen'] | 2022-02-05 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-6.08491674e-02 1.55460835e-01 -1.17465593e-01 -9.80613530e-02
-5.15046477e-01 -2.21818984e-01 4.06202942e-01 5.04956543e-01
-5.85052013e-01 1.13480091e+00 2.16578409e-01 -1.70966104e-01
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-6.17466927e-01 8.99471343e-01 -2.14145809e-01 4.42019068... | [6.244511127471924, 4.948222637176514] |
66cc12d6-6ce8-41e7-8276-1f74395f62f6 | bundletrack-6d-pose-tracking-for-novel | 2108.00516 | null | https://arxiv.org/abs/2108.00516v1 | https://arxiv.org/pdf/2108.00516v1.pdf | BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models | Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching. This work proposes BundleTrack, a general framework fo... | ['Kostas Bekris', 'Bowen Wen'] | 2021-08-01 | null | null | null | null | ['template-matching', 'real-time-visual-tracking', '6d-pose-estimation-using-rgbd', 'video-object-tracking', '3d-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.49889857e-01 -4.36672688e-01 -3.65811586e-01 -1.52983531e-01
-8.05016816e-01 -6.53824687e-01 4.13041621e-01 6.76955730e-02
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-7.99509324e-03 -4.07444328e-01 -1.06140673e+00 -4.58932161e-01
-2.11833879e-01 8.87184918e-01 6.74290776e-01 -6.99040517... | [6.890987873077393, -2.2801105976104736] |
2a53de3e-78ff-40c9-982a-1d34c7ead1a7 | connectivity-constrained-interactive-panoptic | 2212.06756 | null | https://arxiv.org/abs/2212.06756v1 | https://arxiv.org/pdf/2212.06756v1.pdf | Connectivity-constrained Interactive Panoptic Segmentation | We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce connectivity of each region, with a notable class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorith... | ['Thomas Guthier', 'Ismail Ben Ayed', 'Andrea Lodi', 'Bo Tang', 'Ruobing Shen'] | 2022-12-13 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 7.01440692e-01 5.87694407e-01 -6.78120792e-01 -4.56600994e-01
-3.60690832e-01 -1.20360076e+00 1.68434054e-01 -5.51389381e-02
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-1.44338414e-01 6.79167747e-01 4.65909809e-01 1.72083348... | [9.582569122314453, 0.3520773649215698] |
7e38f5c8-96ec-464f-b50c-037537599f83 | tracking-everything-everywhere-all-at-once | 2306.05422 | null | https://arxiv.org/abs/2306.05422v1 | https://arxiv.org/pdf/2306.05422v1.pdf | Tracking Everything Everywhere All at Once | We present a new test-time optimization method for estimating dense and long-range motion from a video sequence. Prior optical flow or particle video tracking algorithms typically operate within limited temporal windows, struggling to track through occlusions and maintain global consistency of estimated motion trajecto... | ['Noah Snavely', 'Aleksander Holynski', 'Bharath Hariharan', 'Zhengqi Li', 'Ruojin Cai', 'Yen-Yu Chang', 'Qianqian Wang'] | 2023-06-08 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [-4.59594578e-01 -8.16437304e-01 -3.27161640e-01 1.52492106e-01
-5.33323467e-01 -7.66468823e-01 6.01469040e-01 -3.85138005e-01
-4.47922021e-01 6.80175006e-01 9.15323049e-02 7.28937984e-02
2.39523500e-01 -3.46687883e-01 -7.93394327e-01 -5.66323459e-01
-4.74332124e-01 3.26152891e-01 6.73378587e-01 2.34927371... | [8.524796485900879, -1.807100534439087] |
0365ec85-a2ed-4974-942d-093c6c1eed6a | social-adaptive-module-for-weakly-supervised | 2007.09470 | null | https://arxiv.org/abs/2007.09470v1 | https://arxiv.org/pdf/2007.09470v1.pdf | Social Adaptive Module for Weakly-supervised Group Activity Recognition | This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data. This eases us to collect and annotate a large-scale NBA... | ['Xiangbo Shu', 'Jinhui Tang', 'Rui Yan', 'Qi Tian', 'Lingxi Xie'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/520_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530205.pdf | eccv-2020-8 | ['group-activity-recognition'] | ['computer-vision'] | [ 3.15642983e-01 4.68149222e-02 -4.65403438e-01 -5.70657015e-01
-8.85239899e-01 -4.19316262e-01 5.76183617e-01 6.09000884e-02
-6.24947190e-01 9.61232841e-01 7.59545743e-01 2.77459145e-01
-8.40363558e-03 -3.97873402e-01 -8.32639694e-01 -7.12982357e-01
-2.56719828e-01 3.42910022e-01 3.34691525e-01 4.11984622... | [8.357389450073242, 0.5873637199401855] |
b918e033-2645-45a9-9ea1-0fc238561e9b | unsupervised-3d-pose-estimation-for | 2109.09166 | null | https://arxiv.org/abs/2109.09166v1 | https://arxiv.org/pdf/2109.09166v1.pdf | Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition | Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dance genre). We propose a Hierarchical Dance Video Recognition framework (HDVR). HDVR estimates 2D pose sequences, tracks dancers, and then si... | ['Narendra Ahuja', 'Xiaodan Hu'] | 2021-09-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Hu_Unsupervised_3D_Pose_Estimation_for_Hierarchical_Dance_Video_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Hu_Unsupervised_3D_Pose_Estimation_for_Hierarchical_Dance_Video_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-pose-estimation', 'unsupervised-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.66164225e-02 -4.13940400e-01 -4.44938570e-01 -4.10206094e-02
-8.02107990e-01 -6.52853131e-01 2.18060404e-01 -7.72566497e-01
-3.86846155e-01 2.78490424e-01 3.41502160e-01 4.73668128e-01
-3.88706289e-03 -5.03717661e-01 -8.45123827e-01 -5.80192864e-01
-2.04015851e-01 7.38528132e-01 2.94278502e-01 -7.17042461... | [7.157917499542236, -0.7518528699874878] |
8f83d3f8-7144-41b7-a573-065dd8dfab93 | learning-what-to-learn-for-video-object | 2003.11540 | null | https://arxiv.org/abs/2003.11540v2 | https://arxiv.org/pdf/2003.11540v2.pdf | Learning What to Learn for Video Object Segmentation | Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined during inference with a given first-frame reference mask. The problem of how to capture and utilize this limited target information remains a fundamental research question. We address this by introducing an end-to-e... | ['Luc van Gool', 'Martin Danelljan', 'Felix Järemo Lawin', 'Radu Timofte', 'Goutam Bhat', 'Andreas Robinson', 'Michael Felsberg'] | 2020-03-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4440_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470766.pdf | eccv-2020-8 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.89773464e-01 1.72373146e-01 -6.01336718e-01 -2.88971126e-01
-1.05096436e+00 -3.43131989e-01 2.76234478e-01 -4.30442929e-01
-4.41549242e-01 4.28570271e-01 1.68730672e-02 2.05321819e-01
3.70328277e-01 -4.34281141e-01 -1.01059878e+00 -5.06570935e-01
2.11013392e-01 4.45042312e-01 8.92684639e-01 2.36684620... | [9.173091888427734, 0.021023431792855263] |
f91a1cac-6476-40eb-a528-10752a181739 | learning-local-global-contextual-adaptation | 2109.03622 | null | https://arxiv.org/abs/2109.03622v2 | https://arxiv.org/pdf/2109.03622v2.pdf | Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation | This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-... | ['Liangpei Zhang', 'Gui-Song Xia', 'Tianfu Wu', 'Nan Xue'] | 2021-09-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xue_Learning_Local-Global_Contextual_Adaptation_for_Multi-Person_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xue_Learning_Local-Global_Contextual_Adaptation_for_Multi-Person_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['multi-person-pose-estimation'] | ['computer-vision'] | [-1.20226882e-01 -6.97709396e-02 -2.30609532e-02 -3.39808166e-01
-1.27498233e+00 -4.97637838e-01 4.93669361e-01 4.37197275e-02
-8.41308951e-01 5.78393638e-01 4.59041834e-01 6.85036242e-01
-4.82753180e-02 -1.28554136e-01 -1.04668343e+00 -3.89980972e-01
3.91357467e-02 8.66937339e-01 2.01236159e-01 -3.01781923... | [7.14972448348999, -0.8363513946533203] |
060b55f9-92b0-453e-a0c4-7f8d284469ee | note-rcnn-noise-tolerant-ensemble-rcnn-for | 1812.00124 | null | http://arxiv.org/abs/1812.00124v1 | http://arxiv.org/pdf/1812.00124v1.pdf | NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection | The labeling cost of large number of bounding boxes is one of the main
challenges for training modern object detectors. To reduce the dependence on
expensive bounding box annotations, we propose a new semi-supervised object
detection formulation, in which a few seed box level annotations and a large
scale of image leve... | ['Li-Jia Li', 'JIyang Gao', 'Ram Nevatia', 'Shengyang Dai', 'Jiang Wang'] | 2018-12-01 | note-rcnn-noise-tolerant-ensemble-rcnn-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Gao_NOTE-RCNN_NOise_Tolerant_Ensemble_RCNN_for_Semi-Supervised_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Gao_NOTE-RCNN_NOise_Tolerant_Ensemble_RCNN_for_Semi-Supervised_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 2.12056279e-01 1.44586787e-01 -1.03356726e-01 -5.15993774e-01
-7.15087354e-01 -3.37374926e-01 3.17041963e-01 2.34470651e-01
-6.72620416e-01 6.14872634e-01 -3.02590668e-01 -1.61948770e-01
1.49485424e-01 -7.74959564e-01 -7.07556129e-01 -6.65704191e-01
5.58316670e-02 4.12280887e-01 8.44812274e-01 4.64057773... | [9.156103134155273, 1.1982804536819458] |
71d4a067-8033-4fe1-b743-64a464d480f0 | analyzing-multiple-choice-reading-and | 2307.01076 | null | https://arxiv.org/abs/2307.01076v1 | https://arxiv.org/pdf/2307.01076v1.pdf | Analyzing Multiple-Choice Reading and Listening Comprehension Tests | Multiple-choice reading and listening comprehension tests are an important part of language assessment. Content creators for standard educational tests need to carefully curate questions that assess the comprehension abilities of candidates taking the tests. However, recent work has shown that a large number of questio... | ['Mark Gales', 'Adian Liusie', 'Vatsal Raina'] | 2023-07-03 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 4.68980104e-01 5.30401409e-01 1.26486123e-01 -5.40291786e-01
-1.24280894e+00 -1.23976898e+00 3.90952796e-01 8.32332194e-01
-3.87523443e-01 5.36766589e-01 5.38904071e-01 -1.09420836e+00
-5.83719492e-01 -1.04638088e+00 -4.72283244e-01 1.78111225e-01
5.87805092e-01 5.41253507e-01 4.53953832e-01 -6.06002390... | [11.453063011169434, 8.064873695373535] |
c5d855e3-e91f-4bb7-a60d-cc472a156e86 | abcde-approximating-betweenness-centrality | null | null | https://peerj.com/articles/cs-699/ | https://peerj.com/articles/cs-699.pdf | ABCDE: Approximating Betweenness-Centrality ranking with progressive-DropEdge | Betweenness-centrality is a popular measure in network analysis that aims to describe the importance of nodes in a graph. It accounts for the fraction of shortest paths passing through that node and is a key measure in many applications including community detection and network dismantling. The computation of betweenne... | ['Mirakyan Martin'] | 2021-09-06 | null | null | null | peerj-computer-science-2021-9 | ['approximating-betweenness-centrality-ranking'] | ['graphs'] | [-2.90558279e-01 1.85656488e-01 -2.06565578e-02 -2.06883118e-01
-1.15983590e-01 -3.74811798e-01 3.09984118e-01 7.82939017e-01
-3.60892206e-01 6.36750817e-01 -1.29934505e-01 -3.87295425e-01
-5.10146439e-01 -1.32701826e+00 -5.00100076e-01 -6.13963187e-01
-6.65023267e-01 6.73729122e-01 3.89417440e-01 -1.98927253... | [7.0471343994140625, 6.1504974365234375] |
afc54c68-f669-41a8-967f-1dedf14ecff6 | weakly-supervised-instance-segmentation-via | 2104.01526 | null | https://arxiv.org/abs/2104.01526v1 | https://arxiv.org/pdf/2104.01526v1.pdf | Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images | Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using bo... | ['Wenyu Liu', 'Xiaoxin Chen', 'Longjin Ran', 'Qi Ding', 'Bin Hu', 'Jiapei Feng', 'Xinggang Wang'] | 2021-04-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.pdf | cvpr-2021-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.10476393e-01 6.70360267e-01 -4.35589880e-01 -7.38932610e-01
-1.19376731e+00 -7.16191232e-01 3.43666136e-01 6.67267740e-02
-3.13123673e-01 5.28453767e-01 -1.12225942e-01 1.65643796e-01
6.92480728e-02 -3.17872584e-01 -9.20503139e-01 -4.51967657e-01
2.10443705e-01 9.63766217e-01 7.28053093e-01 -6.72933832... | [9.565367698669434, 0.5585986971855164] |
38891514-b022-4acc-853b-23b87ee0310e | iterative-deep-homography-estimation | 2203.15982 | null | https://arxiv.org/abs/2203.15982v1 | https://arxiv.org/pdf/2203.15982v1.pdf | Iterative Deep Homography Estimation | We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator, the iterator of IHN has tied weights and is completely trainable. IHN achieves state-of-the-art accuracy... | ['Hui-Liang Shen', 'Zehua Sheng', 'Jianxin Hu', 'Si-Yuan Cao'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cao_Iterative_Deep_Homography_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cao_Iterative_Deep_Homography_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['homography-estimation'] | ['computer-vision'] | [-7.58319423e-02 5.51466942e-02 -9.37116891e-02 -7.69020021e-02
-6.86345696e-01 -3.75787437e-01 3.09671909e-01 -7.64346182e-01
-4.23126221e-01 4.76802438e-01 1.68537453e-01 -1.29870266e-01
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1.77174121e-01 4.33965981e-01 6.26208544e-01 -1.09501630... | [8.643437385559082, -2.158951759338379] |
06336d1f-ae9f-4386-a62a-e2396d405ff5 | aspect-and-sentiment-aware-abstractive-review | null | null | https://aclanthology.org/C18-1095 | https://aclanthology.org/C18-1095.pdf | Aspect and Sentiment Aware Abstractive Review Summarization | Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is towards the abstractive review summarization that is essential for business organizations and individual consumers to make informed decisions. This work takes the lead to study the asp... | ['Qiang Qu', 'Qiao Liu', 'Min Yang', 'Jia Zhu', 'Ying Shen', 'Wei Zhao'] | 2018-08-01 | aspect-and-sentiment-aware-abstractive-review-1 | https://aclanthology.org/C18-1095 | https://aclanthology.org/C18-1095.pdf | coling-2018-8 | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.01982570e-01 1.26102358e-01 -5.59997499e-01 -6.60748959e-01
-1.01016986e+00 -3.15092653e-01 7.14492261e-01 2.81642586e-01
-2.27722988e-01 5.27302623e-01 9.84128594e-01 -1.37724310e-01
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5.15996993e-01 1.56885266e-01 -2.70514011e-01 -3.75073582... | [11.473560333251953, 6.758363246917725] |
09da6b80-a7c3-4ffa-b060-82497f1f1a80 | efficient-remote-photoplethysmography-with | 2203.10882 | null | https://arxiv.org/abs/2203.10882v2 | https://arxiv.org/pdf/2203.10882v2.pdf | Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss | We present a lightweight neural model for remote heart rate estimation focused on the efficient spatio-temporal learning of facial photoplethysmography (PPG) based on i) modelling of PPG dynamics by combinations of multiple convolutional derivatives, and ii) increased flexibility of the model to learn possible offsets ... | ['Federico Sukno', 'Adria Ruiz', 'Joaquim Comas'] | 2022-03-21 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 8.88703242e-02 3.77482712e-01 -8.86861701e-03 -3.20203990e-01
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-5.71810126e-01 8.12614337e-02 6.77043572e-02 2.51133651... | [13.896750450134277, 2.788062572479248] |
011c177e-25f5-41a1-9a15-a22257ed184e | on-the-use-of-learning-based-forecasting | 2211.04798 | null | https://arxiv.org/abs/2211.04798v1 | https://arxiv.org/pdf/2211.04798v1.pdf | On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper | The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year. A plethora of business processes are involved in this large-scale industry, but due to the generally short life-cycle of clothing items, supply-chain managemen... | ['Marco Cristani', 'Matteo Denitto', 'Christian Joppi', 'Geri Skenderi'] | 2022-11-09 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 6.39922619e-02 -3.05604577e-01 -3.04602176e-01 -4.94342655e-01
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-1.56256687e-02 6.68321490e-01 -2.44896084e-01 -5.94467342... | [9.281397819519043, 5.8457560539245605] |
ce43e06f-740a-4b14-8be4-59a520072a72 | adapting-unsupervised-syntactic-parsing | null | null | https://aclanthology.org/2021.acl-long.449 | https://aclanthology.org/2021.acl-long.449.pdf | Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing | One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing. Fortunately, unsupervised syntacti... | ['Kewei Tu', 'Wenjuan Han', 'Ge Wang', 'Liwen Zhang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.93813556e-01 7.09161162e-01 -4.07932371e-01 -4.41515177e-01
-9.11957979e-01 -7.39649415e-01 4.42446738e-01 3.89906526e-01
-4.40825105e-01 1.00008321e+00 6.30706012e-01 -5.08061886e-01
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8.81203562e-02 4.20814902e-01 5.16653538e-01 -1.93594635... | [10.638288497924805, 9.54377269744873] |
0bc5251b-fb71-470c-8166-669d37ad4b2b | behavioral-analysis-of-vision-and-language | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Behavioral_Analysis_of_Vision-and-Language_Navigation_Agents_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Behavioral_Analysis_of_Vision-and-Language_Navigation_Agents_CVPR_2023_paper.pdf | Behavioral Analysis of Vision-and-Language Navigation Agents | To be successful, Vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis -- examining how well existing agents ground instructions about stopping, turning, and mo... | ['Stefan Lee', 'Arjun Majumdar', 'Zijiao Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['vision-and-language-navigation'] | ['robots'] | [-1.23131806e-02 1.91119537e-01 3.28563005e-02 -2.58909255e-01
-2.97212124e-01 -5.97541034e-01 1.08410370e+00 1.92656681e-01
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-1.87682703e-01 8.39790881e-01 3.68371516e-01 -7.59202838... | [4.28153133392334, 0.8116440773010254] |
b1578979-b3e3-4376-92a1-54f6d9beb2ab | active-learning-driven-surrogate-modeling-for | 2306.06174 | null | https://arxiv.org/abs/2306.06174v1 | https://arxiv.org/pdf/2306.06174v1.pdf | Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems | When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on model order reduction are desired. In absence of the governing equations describing the dynamics, we need to construct the parametric reduced-order surrogate model in a n... | ['Peter Benner', 'Lihong Feng', 'Harshit Kapadia'] | 2023-06-09 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [-1.38343140e-01 -2.26107508e-01 1.12163253e-01 3.48204076e-01
-7.76857615e-01 -3.56235951e-01 4.06991988e-01 8.48262832e-02
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-5.34891009e-01 -5.76632857e-01 -5.31481922e-01 -1.32289231e+00
-3.07735682e-01 3.83416921e-01 -6.23820797e-02 -2.24356279... | [6.504958152770996, 3.4567759037017822] |
2761d020-45a0-4d60-a180-40d510f65465 | gamnet-robust-feature-matching-via-graph | null | null | https://dl.acm.org/doi/abs/10.1145/3474085.3475669 | https://dl.acm.org/doi/pdf/10.1145/3474085.3475669 | GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network | Recently, deep graph matching (GM) methods have gained increasing attention. These methods integrate graph nodes¡¯s embedding, node/edges¡¯s affinity learning and final correspondence solver together in an end-to-end manner. For deep graph matching problem, one main issue is how to generate consensus node's embeddings ... | ['Bin Luo', 'Jin Tang', 'Ziyan Zhang', 'Pengfei Sun', 'Bo Jiang'] | 2021-10-17 | null | null | null | mm-2021-10 | ['graph-matching'] | ['graphs'] | [-2.23402351e-01 2.82877356e-01 -2.54959557e-02 -3.36283058e-01
-6.53095782e-01 -4.94970292e-01 3.43096644e-01 -8.94502848e-02
-6.57390729e-02 3.40747058e-01 -9.03037749e-03 -9.21006873e-02
-9.65913087e-02 -1.06069863e+00 -8.21431577e-01 -4.33078706e-01
1.07521834e-02 8.09517205e-01 1.23969555e-01 -2.84323305... | [7.115074157714844, 6.40888786315918] |
511e0aae-3e70-4f10-ad29-b9db41d7bf97 | development-of-a-hand-pose-recognition-system-1 | null | null | https://ieeexplore.ieee.org/document/8853573 | https://dennishnf.bitbucket.io/research/2019%20-%20intercon%202019%20-%20hand%20pose%20on%20embedded%20computer%20using%20ai.pdf | Development of a hand pose recognition system on an embedded computer using Artificial Intelligence | The recognition of hand gestures is a very interesting research topic due to the growing demand in recent years in robotics, virtual reality, autonomous driving systems, human-machine interfaces and in other new technologies. Despite several approaches for a robust recognition system, gesture recognition based on visua... | ['Dennis Núñez-Fernández'] | 2019-10-03 | null | null | null | ieee-international-conference-on-electronics | ['hand-detection'] | ['computer-vision'] | [-2.42874157e-02 -5.12766123e-01 -3.27964127e-01 -1.97705805e-01
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2.92328924e-01 1.43107310e-01 6.02543831e-01 8.41582939... | [6.494182586669922, -0.2504022717475891] |
c21d182d-22d9-4fd3-8a8d-bf07669256f1 | affordances-in-grounded-language-learning | null | null | https://aclanthology.org/W18-2806 | https://aclanthology.org/W18-2806.pdf | Affordances in Grounded Language Learning | We present a novel methodology involving mappings between different modes of semantic representation. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, moti... | ['Kyungtae Lim', 'Stephen McGregor'] | 2018-07-01 | null | null | null | ws-2018-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [-1.41592026e-02 3.13579649e-01 -8.19685087e-02 -3.27281892e-01
2.42398754e-01 -6.85219407e-01 1.22327042e+00 4.70876217e-01
-5.22158384e-01 1.55028775e-01 7.57773519e-01 -6.85261846e-01
-5.73009670e-01 -1.24048698e+00 -6.08806133e-01 -9.75348353e-02
-4.18532528e-02 1.54450119e-01 2.48143658e-01 -8.03887904... | [9.350427627563477, 6.896485328674316] |
40950347-708a-4e77-8812-790c05460826 | pix2struct-screenshot-parsing-as-pretraining | 2210.03347 | null | https://arxiv.org/abs/2210.03347v2 | https://arxiv.org/pdf/2210.03347v2.pdf | Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding | Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures... | ['Kristina Toutanova', 'Ming-Wei Chang', 'Peter Shaw', 'Urvashi Khandelwal', 'Julian Eisenschlos', 'Fangyu Liu', 'Hexiang Hu', 'Iulia Turc', 'Mandar Joshi', 'Kenton Lee'] | 2022-10-07 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 5.76329827e-01 2.36634701e-01 -3.45081389e-02 -3.29403311e-01
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2.98855096e-01 2.21978799e-01 -5.52558117e-02 -1.47821561... | [11.009029388427734, 1.8727617263793945] |
b2d27483-cb58-42b9-b713-91c71bd8630c | multi-view-non-negative-matrix-factorization | 2201.04726 | null | https://arxiv.org/abs/2201.04726v1 | https://arxiv.org/pdf/2201.04726v1.pdf | Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss | Multi-view learning accomplishes the task objectives of classification by leverag-ing the relationships between different views of the same object. Most existing methods usually focus on consistency and complementarity between multiple views. But not all of this information is useful for classification tasks. Instead, ... | ['Xionglin Luo', 'Run-kun Lu', 'Yuan-Fang Wang', 'Jian-wei Liu'] | 2022-01-08 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-3.67256939e-01 -5.60250878e-01 -4.96177912e-01 -4.51054007e-01
-4.23528939e-01 -5.73216498e-01 2.88919121e-01 -2.88094670e-01
-9.80830044e-02 4.79686975e-01 4.18620378e-01 2.68896490e-01
-2.63779402e-01 -5.88571012e-01 -2.76642829e-01 -8.21734488e-01
2.20416874e-01 2.68898934e-01 1.52870387e-01 -1.98277518... | [8.485538482666016, 4.5124192237854] |
24a654af-de1d-49f2-8238-b24788bb5605 | counterfactual-recipe-generation-exploring | 2210.11431 | null | https://arxiv.org/abs/2210.11431v1 | https://arxiv.org/pdf/2210.11431v1.pdf | Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario | People can acquire knowledge in an unsupervised manner by reading, and compose the knowledge to make novel combinations. In this paper, we investigate whether pretrained language models can perform compositional generalization in a realistic setting: recipe generation. We design the counterfactual recipe generation tas... | ['Dongyan Zhao', 'Chengang Hu', 'Jizhi Tang', 'Yansong Feng', 'Xiao Liu'] | 2022-10-20 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 4.01004761e-01 3.01621765e-01 -2.52408832e-01 -4.31711972e-01
-5.34358263e-01 -1.05159247e+00 8.11864793e-01 8.71635824e-02
-2.09723473e-01 8.97461772e-01 9.13554311e-01 -2.48893321e-01
3.35040808e-01 -1.23361981e+00 -1.27819717e+00 -3.08589995e-01
3.96019548e-01 4.48713869e-01 -3.28046918e-01 -6.13698304... | [11.512299537658691, 4.579752445220947] |
4a6841cb-af14-4106-b912-3558ea470827 | mutual-learning-to-adapt-for-joint-human | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xuecheng_Nie_Mutual_Learning_to_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xuecheng_Nie_Mutual_Learning_to_ECCV_2018_paper.pdf | Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation | This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing and pose estimation. It effectively exploits mutual benefits from both tasks and simultaneously boosts their performance. Different from existing post-processing or multi-task learning based methods, MuLA predicts dynamic task-sp... | ['Xuecheng Nie', 'Jiashi Feng', 'Shuicheng Yan'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['human-parsing'] | ['computer-vision'] | [ 4.91678715e-03 -4.90952358e-02 -3.85514766e-01 -5.88519812e-01
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2.08208948e-01 5.20621419e-01 3.59645098e-01 1.22782700... | [7.554295539855957, -0.5628314018249512] |
075d1b17-6b9a-497e-9005-785fdaa846cb | experimental-analysis-regarding-the-influence | 2211.05507 | null | https://arxiv.org/abs/2211.05507v1 | https://arxiv.org/pdf/2211.05507v1.pdf | Experimental analysis regarding the influence of iris segmentation on the recognition rate | In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of t... | ['Andreas Uhl', 'Josef Bigun', 'Fernando Alonso-Fernandez', 'Heinz Hofbauer'] | 2022-11-10 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-7.26394430e-02 1.11717626e-01 -1.04596511e-01 -4.97571640e-02
1.59828156e-01 -5.66312790e-01 3.01954240e-01 1.54494241e-01
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4.22497690e-01 3.96646559e-01 -7.11931363e-02 1.60249829... | [3.748403787612915, -3.6240618228912354] |
1ed691c8-12d8-4f3e-9206-f587d196b18f | gradient-scarcity-with-bilevel-optimization | 2303.13964 | null | https://arxiv.org/abs/2303.13964v1 | https://arxiv.org/pdf/2303.13964v1.pdf | Gradient scarcity with Bilevel Optimization for Graph Learning | A common issue in graph learning under the semi-supervised setting is referred to as gradient scarcity. That is, learning graphs by minimizing a loss on a subset of nodes causes edges between unlabelled nodes that are far from labelled ones to receive zero gradients. The phenomenon was first described when optimizing t... | ['Nicolas Keriven', 'Samuel Vaiter', 'Hashem Ghanem'] | 2023-03-24 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 3.65266472e-01 8.17339838e-01 -8.08529109e-02 -1.00309409e-01
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-3.50439042e-01 3.30688775e-01 7.35785142e-02 -1.28667608... | [6.990577220916748, 5.475196361541748] |
00273fc3-98b8-4c90-b610-bc897f7dcace | abcnet-real-time-scene-text-spotting-with | 2002.10200 | null | https://arxiv.org/abs/2002.10200v2 | https://arxiv.org/pdf/2002.10200v2.pdf | ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network | Scene text detection and recognition has received increasing research attention. Existing methods can be roughly categorized into two groups: character-based and segmentation-based. These methods either are costly for character annotation or need to maintain a complex pipeline, which is often not suitable for real-time... | ['Chunhua Shen', 'Yuliang Liu', 'Lianwen Jin', 'Hao Chen', 'Tong He', 'Liangwei Wang'] | 2020-02-24 | abcnet-real-time-scene-text-spotting-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_ABCNet_Real-Time_Scene_Text_Spotting_With_Adaptive_Bezier-Curve_Network_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_ABCNet_Real-Time_Scene_Text_Spotting_With_Adaptive_Bezier-Curve_Network_CVPR_2020_paper.pdf | cvpr-2020-6 | ['text-spotting'] | ['computer-vision'] | [ 1.28707707e-01 -6.76416397e-01 6.12945855e-02 -2.33533427e-01
-6.22044504e-01 -6.84385061e-01 3.06326717e-01 2.01350585e-01
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5.39338768e-01 3.22187096e-01 7.45782495e-01 -1.07211128... | [12.047886848449707, 2.2474300861358643] |
99f15c65-397f-422a-8577-1f17c25ea168 | an-incremental-negative-sequence-admittance | 2205.02962 | null | https://arxiv.org/abs/2205.02962v1 | https://arxiv.org/pdf/2205.02962v1.pdf | An Incremental Negative Sequence Admittance Method for Fault Detection in Inverter-Based Microgrids | Superimposed sequence quantities have been relied on for years to provide pure fault components for various applications in protection schemes. In more recent times, they have been employed in various solutions to the challenges introduced by integration of distributed generation in distribution systems. To improve the... | ['Aleksandar Dimitrovski', 'Subash Pokharel', 'Kwasi Opoku'] | 2022-05-05 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.00483993e-01 -1.74928367e-01 3.48692656e-01 -2.81076059e-02
1.89765003e-02 -9.91100848e-01 5.02262473e-01 5.61055303e-01
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-6.56830668e-01 -8.40276539e-01 1.32241398e-01 -8.20969045e-01
-6.70450211e-01 1.56634197e-01 3.84978265e-01 -7.58847713... | [5.9120402336120605, 2.5434539318084717] |
51bf8564-76f1-4fad-a7ab-2c1781d1aaaa | scanpaths-in-reading-are-informative-about | null | null | https://aclanthology.org/W12-4904 | https://aclanthology.org/W12-4904.pdf | Scanpaths in reading are informative about sentence processing | null | ['Reinhold Kliegl', 'Titus von der Malsburg', 'Shravan Vasishth'] | 2012-12-01 | scanpaths-in-reading-are-informative-about-1 | https://aclanthology.org/W12-4904 | https://aclanthology.org/W12-4904.pdf | ws-2012-12 | ['human-parsing'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.341312408447266, 3.7039685249328613] |
5587994e-dd04-4bb8-b1b0-ae47f3688589 | robust-object-detection-with-multi-input | 2111.13065 | null | https://arxiv.org/abs/2111.13065v1 | https://arxiv.org/pdf/2111.13065v1.pdf | Robust Object Detection with Multi-input Multi-output Faster R-CNN | Recent years have seen impressive progress in visual recognition on many benchmarks, however, generalization to the real-world in out-of-distribution setting remains a significant challenge. A state-of-the-art method for robust visual recognition is model ensembling. however, recently it was shown that similarly compet... | ['Andrzej Czyzewski', 'Sebastian Cygert'] | 2021-11-25 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 2.58036494e-01 1.69180200e-01 -3.43825333e-02 -5.33787012e-01
-1.20455372e+00 -4.07580316e-01 6.82723403e-01 1.75453827e-01
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3.23775448e-02 -6.16518140e-01 -1.17470086e+00 -8.05383801e-01
1.61524087e-01 6.83477402e-01 4.48085964e-01 -8.08010518... | [8.755318641662598, 1.937585473060608] |
09977628-0cbf-45c5-8441-46e457b2fdf1 | emergency-response-person-localization-and | 2305.15795 | null | https://arxiv.org/abs/2305.15795v1 | https://arxiv.org/pdf/2305.15795v1.pdf | Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar | The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infras... | ['Michael Muma', 'Abdelhak M. Zoubir', 'Oskar von Stryk', 'Stefan Fabian', 'Ibrahim Kakouche', 'Christian Eckrich', 'Christian A. Schroth'] | 2023-05-25 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 3.66776794e-01 -4.54744637e-01 5.80886483e-01 -1.75622016e-01
-7.00135648e-01 -4.43842590e-01 1.57736182e-01 9.94375944e-02
-6.76425874e-01 9.34619784e-01 3.44702631e-01 9.20545831e-02
-6.66832507e-01 -5.85384488e-01 5.42686842e-02 -9.05560911e-01
-5.47799110e-01 6.45598531e-01 -1.28293306e-01 -4.90988493... | [6.817476272583008, 0.5689353346824646] |
9d430bca-99de-4b19-b592-758735dbc554 | neighborhood-matching-network-for-entity | 2005.05607 | null | https://arxiv.org/abs/2005.05607v1 | https://arxiv.org/pdf/2005.05607v1.pdf | Neighborhood Matching Network for Entity Alignment | Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topolog... | ['Zheng Wang', 'Xiao Liu', 'Dongyan Zhao', 'Yuting Wu', 'Yansong Feng'] | 2020-05-12 | neighborhood-matching-network-for-entity-1 | https://aclanthology.org/2020.acl-main.578 | https://aclanthology.org/2020.acl-main.578.pdf | acl-2020-6 | ['graph-sampling'] | ['graphs'] | [-1.20362826e-01 2.73807943e-01 -6.72747016e-01 -2.57763982e-01
-5.48896253e-01 -5.61225772e-01 5.28482676e-01 6.88189447e-01
2.44956203e-02 4.29018587e-01 5.24776340e-01 5.35740778e-02
-2.95391142e-01 -1.21264875e+00 -6.42609000e-01 -3.59279543e-01
-4.69407350e-01 6.62719667e-01 1.17505908e-01 -3.63410980... | [8.705121994018555, 7.913496971130371] |
5b3155be-39d4-4861-9ab7-85987e31aa55 | vector-quantized-input-contextualized-soft | 2205.11024 | null | https://arxiv.org/abs/2205.11024v2 | https://arxiv.org/pdf/2205.11024v2.pdf | Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding | Prompt Tuning has been largely successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks. Thus far, soft prompt tuning learns a fixed set of task-specific continuous vectors, i.e., soft tokens that remain static across the task samples. A fixed prompt... | ['Soujanya Poria', 'Steven C. H. Hoi', 'Amrita Saha', 'Rishabh Bhardwaj'] | 2022-05-23 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 3.88154149e-01 3.28985065e-01 -3.46755683e-01 -8.61684680e-01
-1.20057523e+00 -8.77371311e-01 8.39935899e-01 3.68074656e-01
-5.61019838e-01 7.52430022e-01 5.64881146e-01 -3.95231396e-01
1.50606290e-01 -5.03171563e-01 -8.41458678e-01 -4.07517672e-01
2.88479358e-01 6.01409674e-01 1.36939555e-01 -5.06745636... | [10.88086223602295, 8.355691909790039] |
a2e7eaba-477c-4327-a1b3-314412180704 | computational-model-discovery-with | 2001.00008 | null | https://arxiv.org/abs/2001.00008v1 | https://arxiv.org/pdf/2001.00008v1.pdf | Computational model discovery with reinforcement learning | The motivation of this study is to leverage recent breakthroughs in artificial intelligence research to unlock novel solutions to important scientific problems encountered in computational science. To address the human intelligence limitations in discovering reduced-order models, we propose to supplement human thinking... | ['Adrián Lozano-Durán', 'Maxime Bassenne'] | 2019-12-29 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 2.45307580e-01 2.25387961e-01 4.33204919e-02 5.88489622e-02
-5.16715109e-01 -5.56620836e-01 6.62759721e-01 1.87864453e-01
-3.55260164e-01 1.17224157e+00 -4.06301409e-01 -7.38521457e-01
-6.50646031e-01 -8.91274095e-01 -8.71740758e-01 -6.41260386e-01
-2.01013267e-01 6.59886599e-01 -3.99413794e-01 -3.93632591... | [6.4554643630981445, 3.468336820602417] |
b4a75a0a-f400-49dd-8110-7eb88db84217 | uadb-unsupervised-anomaly-detection-booster | 2306.01997 | null | https://arxiv.org/abs/2306.01997v1 | https://arxiv.org/pdf/2306.01997v1.pdf | UADB: Unsupervised Anomaly Detection Booster | Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on implicit assumptions about anomalous patterns (e.g., scattered/sparsely/densely clustered) to detect anomalies. However, real-world data ar... | ['Jiang Bian', 'Yi Chang', 'Huishuai Zhang', 'Xiaofan Gui', 'Shun Zheng', 'Wei Cao', 'Xinyi Shen', 'Zhining Liu', 'Hangting Ye'] | 2023-06-03 | null | null | null | null | ['unsupervised-anomaly-detection'] | ['methodology'] | [ 2.20981110e-02 -1.58136442e-01 -1.27470121e-01 -1.38302505e-01
-3.22636455e-01 -3.35124195e-01 5.74933827e-01 2.48966515e-01
-5.99850267e-02 5.08702874e-01 -1.94069251e-01 -6.14699244e-01
-3.19482893e-01 -8.18970323e-01 -7.89378166e-01 -6.86357319e-01
-3.60476494e-01 4.46721107e-01 4.43758786e-01 -3.84101987... | [7.592746257781982, 2.4586031436920166] |
16872667-1f53-4253-8010-fe681d23b706 | a-neural-graph-based-local-coherence-model | null | null | https://aclanthology.org/2021.findings-emnlp.199 | https://aclanthology.org/2021.findings-emnlp.199.pdf | A Neural Graph-based Local Coherence Model | Entity grids and entity graphs are two frameworks for modeling local coherence. These frameworks represent entity relations between sentences and then extract features from such representations to encode coherence. The benefits of convolutional neural models for extracting informative features from entity grids have be... | ['Iryna Gurevych', 'Leonardo F. R. Ribeiro', 'Mohsen Mesgar'] | null | null | null | null | findings-emnlp-2021-11 | ['sentence-ordering', 'coherence-evaluation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.25838715e-01 6.11128092e-01 -4.00443852e-01 -5.27596235e-01
-8.21302295e-01 -2.32000008e-01 6.97036564e-01 5.56684613e-01
-1.54550731e-01 5.48150897e-01 1.39275789e+00 4.94723916e-02
-7.82401487e-02 -1.07890379e+00 -5.09023130e-01 -1.41498566e-01
-4.87947196e-01 2.67396659e-01 3.32217030e-02 -3.45694780... | [11.791855812072754, 9.163168907165527] |
419dc9ae-92e9-41b9-9d20-97bdd1e7352e | on-a-built-in-conflict-between-deep-learning | 2208.11633 | null | https://arxiv.org/abs/2208.11633v1 | https://arxiv.org/pdf/2208.11633v1.pdf | On a Built-in Conflict between Deep Learning and Systematic Generalization | In this paper, we hypothesize that internal function sharing is one of the reasons to weaken o.o.d. or systematic generalization in deep learning for classification tasks. Under equivalent prediction, a model partitions an input space into multiple parts separated by boundaries. The function sharing prefers to reuse bo... | ['Yuanpeng Li'] | 2022-08-24 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 1.95652574e-01 4.84799266e-01 -3.70455086e-01 -4.41150278e-01
3.64424139e-01 -5.19014835e-01 2.03082204e-01 -3.79465938e-01
-1.59948200e-01 7.82635868e-01 7.28273168e-02 -5.29481351e-01
-2.96052188e-01 -1.01309180e+00 -8.91740799e-01 -7.30175257e-01
-1.34487990e-02 -7.51986355e-02 2.02197760e-01 -6.82058781... | [8.827778816223145, 3.2615926265716553] |
d230aaa3-a13f-4188-9d9a-cb6c9bc1d1de | deep-variation-prior-joint-image-denoising | 2209.09214 | null | https://arxiv.org/abs/2209.09214v1 | https://arxiv.org/pdf/2209.09214v1.pdf | Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data | With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup that requires a large set of paired noisy images and ground truth for training. The strong data requirement can be mitigated by unsupervised... | ['Rihuan Ke'] | 2022-09-19 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 4.25033003e-01 -8.57319310e-02 4.32265729e-01 -3.72737199e-01
-1.25627446e+00 -2.39176661e-01 5.91730475e-01 -2.11302742e-01
-4.58362311e-01 7.25633562e-01 5.69660440e-02 1.97012946e-01
-5.15064418e-01 -6.50329590e-01 -7.65892565e-01 -1.41164446e+00
1.12211294e-01 1.45316571e-01 -1.44954711e-01 -9.72827822... | [11.5934476852417, -2.3936054706573486] |
b1529c6b-a4b5-47dc-b821-676b7d62f7b1 | dual-path-learning-for-domain-adaptation-of | 2108.06337 | null | https://arxiv.org/abs/2108.06337v1 | https://arxiv.org/pdf/2108.06337v1.pdf | Dual Path Learning for Domain Adaptation of Semantic Segmentation | Domain adaptation for semantic segmentation enables to alleviate the need for large-scale pixel-wise annotations. Recently, self-supervised learning (SSL) with a combination of image-to-image translation shows great effectiveness in adaptive segmentation. The most common practice is to perform SSL along with image tran... | ['Wenqiang Zhang', 'Fang Wen', 'Dong Chen', 'Jianmin Bao', 'Fangyun Wei', 'Yiting Cheng'] | 2021-08-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cheng_Dual_Path_Learning_for_Domain_Adaptation_of_Semantic_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cheng_Dual_Path_Learning_for_Domain_Adaptation_of_Semantic_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.01825488e-01 2.66356796e-01 -2.27804974e-01 -3.90186250e-01
-9.35312927e-01 -5.93940794e-01 3.88403594e-01 -1.54646561e-01
-3.23999554e-01 6.19628668e-01 -2.34226152e-01 -3.14757615e-01
2.27549180e-01 -7.45868206e-01 -9.16116059e-01 -5.08237660e-01
5.17317533e-01 4.72130775e-01 8.00073504e-01 -2.74459362... | [9.653471946716309, 1.3098806142807007] |
d5192b83-7c4f-4e82-898f-c1337da4b22f | fastlr-non-autoregressive-lipreading-model | 2008.02516 | null | https://arxiv.org/abs/2008.02516v4 | https://arxiv.org/pdf/2008.02516v4.pdf | FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire | Lipreading is an impressive technique and there has been a definite improvement of accuracy in recent years. However, existing methods for lipreading mainly build on autoregressive (AR) model, which generate target tokens one by one and suffer from high inference latency. To breakthrough this constraint, we propose Fas... | ['Nicholas Jing Yuan', 'Yi Ren', 'Jinglin Liu', 'Chen Zhang', 'Zhou Zhao', 'Baoxing Huai'] | 2020-08-06 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 4.41989273e-01 -9.52187553e-02 -2.86031276e-01 -6.87796846e-02
-1.17516255e+00 -3.00326288e-01 4.02205080e-01 -3.77666146e-01
-2.27866188e-01 6.32066607e-01 5.84335744e-01 -3.46875429e-01
3.39733958e-01 -2.17875287e-01 -7.31248498e-01 -6.17709696e-01
4.69145387e-01 -1.24225751e-01 2.79122859e-01 -6.65276796... | [14.330039978027344, 4.992620468139648] |
cb0e4ca4-83cf-4ebd-98f9-79a86b2d987d | long-term-feature-banks-for-detailed-video | 1812.05038 | null | http://arxiv.org/abs/1812.05038v2 | http://arxiv.org/pdf/1812.05038v2.pdf | Long-Term Feature Banks for Detailed Video Understanding | To understand the world, we humans constantly need to relate the present to
the past, and put events in context. In this paper, we enable existing video
models to do the same. We propose a long-term feature bank---supportive
information extracted over the entire span of a video---to augment
state-of-the-art video model... | ['Philipp Krähenbühl', 'Chao-yuan Wu', 'Haoqi Fan', 'Ross Girshick', 'Kaiming He', 'Christoph Feichtenhofer'] | 2018-12-12 | long-term-feature-banks-for-detailed-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_Long-Term_Feature_Banks_for_Detailed_Video_Understanding_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Long-Term_Feature_Banks_for_Detailed_Video_Understanding_CVPR_2019_paper.pdf | cvpr-2019-6 | ['egocentric-activity-recognition'] | ['computer-vision'] | [-4.58700895e-01 -3.32430869e-01 -2.29956776e-01 -5.54184318e-01
-8.38493556e-02 -6.59558296e-01 8.54365706e-01 -1.73397779e-01
-3.98458660e-01 3.25737089e-01 6.10163808e-01 -1.06468111e-01
3.06201488e-01 -7.08041012e-01 -8.28899086e-01 2.78896131e-02
-6.63094699e-01 -1.70669809e-01 4.23846960e-01 -4.52963352... | [8.505716323852539, 0.569106936454773] |
d0ed48b7-35f0-4562-856f-681e08080504 | rethinking-self-supervision-objectives-for-1 | 2110.07198 | null | https://arxiv.org/abs/2110.07198v2 | https://arxiv.org/pdf/2110.07198v2.pdf | Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling | Given the claims of improved text generation quality across various pre-trained neural models, we consider the coherence evaluation of machine generated text to be one of the principal applications of coherence models that needs to be investigated. Prior work in neural coherence modeling has primarily focused on devisi... | ['Xiang Lin', 'Shafiq Joty', 'Prathyusha Jwalapuram'] | 2021-10-14 | rethinking-self-supervision-objectives-for | https://aclanthology.org/2022.acl-long.418 | https://aclanthology.org/2022.acl-long.418.pdf | acl-2022-5 | ['coherence-evaluation'] | ['natural-language-processing'] | [ 3.26858550e-01 4.24743146e-01 -2.04715937e-01 -3.18454891e-01
-8.81531477e-01 -4.84715849e-01 1.14316308e+00 7.52028450e-02
-5.90231895e-01 9.50093448e-01 7.27029324e-01 -3.39590311e-01
-4.65829708e-02 -5.24434507e-01 -5.77145219e-01 -4.89032656e-01
-1.43572643e-01 8.13598454e-01 2.13226125e-01 -3.13196629... | [11.617504119873047, 8.962964057922363] |
eac7594f-6b43-47a9-8798-2a0335007460 | jointly-harnessing-prior-structures-and | 2207.03714 | null | https://arxiv.org/abs/2207.03714v1 | https://arxiv.org/pdf/2207.03714v1.pdf | Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation | Sign language is the window for people differently-abled to express their feelings as well as emotions. However, it remains challenging for people to learn sign language in a short time. To address this real-world challenge, in this work, we study the motion transfer system, which can transfer the user photo to the sig... | ['Yi Yang', 'Bang Zhang', 'Xiaohan Wang', 'Zhedong Zheng', 'Yucheng Suo'] | 2022-07-08 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 8.72156303e-03 -1.91721305e-01 -3.19103122e-01 -2.19291553e-01
-3.81478667e-01 -3.70964825e-01 5.31519473e-01 -9.83912706e-01
-4.04848814e-01 6.64208353e-01 3.30963463e-01 1.37035698e-01
1.56903550e-01 -4.52445120e-01 -7.36459374e-01 -6.41708076e-01
2.83091784e-01 -1.62994668e-01 4.37567264e-01 -2.12141559... | [10.936177253723145, -0.8680644035339355] |
8cdad5f2-b3a0-414e-8525-f7a94900b1d3 | gt-gan-general-purpose-time-series-synthesis | 2210.02040 | null | https://arxiv.org/abs/2210.02040v3 | https://arxiv.org/pdf/2210.02040v3.pdf | GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks | Time series synthesis is an important research topic in the field of deep learning, which can be used for data augmentation. Time series data types can be broadly classified into regular or irregular. However, there are no existing generative models that show good performance for both types without any model changes. T... | ['Noseong Park', 'Seunghyeon Cho', 'Haryong Song', 'Jeonghak Kim', 'Jinsung Jeon'] | 2022-10-05 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.38634536e-02 -2.96863705e-01 6.83634058e-02 1.42735660e-01
-3.54099810e-01 -5.75774074e-01 7.44826555e-01 -4.77897942e-01
8.83352086e-02 6.89708829e-01 -1.32791653e-01 -5.64758182e-01
9.99901071e-02 -1.06682444e+00 -6.74251437e-01 -8.38523448e-01
-8.21009874e-02 4.21083719e-02 -4.93533611e-02 -6.49180293... | [7.021917819976807, 3.081449270248413] |
003a1f6e-db86-4a35-8362-efaf1cfdd30c | pepe-personalized-post-editing-model | 2209.10139 | null | https://arxiv.org/abs/2209.10139v2 | https://arxiv.org/pdf/2209.10139v2.pdf | PePe: Personalized Post-editing Model utilizing User-generated Post-edits | Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly reflect personal style. In this paper, we introduce a personalized automatic post-editing framework to address this challenge, which effectiv... | ['Jaegul Choo', 'Cheonbok Park', 'Yunwon Tae', 'Taehee Kim', 'Jihyeon Lee'] | 2022-09-21 | null | null | null | null | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 3.78574610e-01 -4.01772171e-01 -3.70497078e-01 -6.71924591e-01
-1.14392030e+00 -7.87492216e-01 7.97916830e-01 -2.77320266e-01
-4.24768478e-01 8.72892678e-01 2.87632734e-01 -4.53989923e-01
3.06859016e-01 -2.51026362e-01 -3.45152259e-01 -2.78909862e-01
8.18514287e-01 7.96679914e-01 -3.28198403e-01 -4.91528809... | [11.690176010131836, 10.173251152038574] |
3a09b35b-3587-452d-be6d-b622880f2cc9 | generating-dataset-for-large-scale-3d-facial | 2109.08043 | null | https://arxiv.org/abs/2109.08043v1 | https://arxiv.org/pdf/2109.08043v1.pdf | Generating Dataset For Large-scale 3D Facial Emotion Recognition | The tremendous development in deep learning has led facial expression recognition (FER) to receive much attention in the past few years. Although 3D FER has an inherent edge over its 2D counterpart, work on 2D images has dominated the field. The main reason for the slow development of 3D FER is the unavailability of la... | ['Syed Zulqarnain Gilani', 'Faizan Farooq Khan'] | 2021-09-16 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-1.56413913e-01 1.15177426e-02 1.31143764e-01 -8.81245911e-01
-4.27290052e-01 -1.51310429e-01 3.30421656e-01 -6.13332570e-01
-1.41487703e-01 3.98104459e-01 -4.33519706e-02 -8.94805416e-02
4.46273804e-01 -6.73503816e-01 -4.32012886e-01 -4.24055934e-01
-1.06918104e-01 1.71293959e-01 -3.61682385e-01 -2.45653108... | [13.544835090637207, 1.5708718299865723] |
b9ecadd8-2c7d-47f7-916c-9e72c706c203 | identifying-adversarial-sentences-by | 1912.08981 | null | https://arxiv.org/abs/1912.08981v1 | https://arxiv.org/pdf/1912.08981v1.pdf | Identifying Adversarial Sentences by Analyzing Text Complexity | Attackers create adversarial text to deceive both human perception and the current AI systems to perform malicious purposes such as spam product reviews and fake political posts. We investigate the difference between the adversarial and the original text to prevent the risk. We prove that the text written by a human is... | ['Hoang-Quoc Nguyen-Son', 'Tran Phuong Thao', 'Shinsaku Kiyomoto', 'Seira Hidano'] | 2019-12-19 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 8.6202279e-02 4.0093738e-01 5.0214939e-02 -1.0088666e-01
-2.9380581e-01 -1.0484366e+00 1.0576190e+00 -2.0062317e-01
-2.6924777e-01 6.6025990e-01 1.1266548e-01 -3.0535805e-01
6.5060925e-01 -9.5087874e-01 -5.2247226e-01 -4.5609483e-01
5.0884646e-01 8.7011628e-02 -1.4039434e-01 -6.4584398e-01
6.8483621e-01... | [6.071565628051758, 8.12752914428711] |
48379ef5-a71b-49a9-ab40-8b0cf8979dc0 | virtual-multi-modality-self-supervised-1 | 2110.03278 | null | https://arxiv.org/abs/2110.03278v2 | https://arxiv.org/pdf/2110.03278v2.pdf | Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction | Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image. The VMFM method r... | ['Yandong Guo', 'Ziwen Li', 'Cheng Lu', 'Han Huang', 'Bo Xu'] | 2021-10-07 | virtual-multi-modality-self-supervised | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Virtual_Multi-Modality_Self-Supervised_Foreground_Matting_for_Human-Object_Interaction_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Virtual_Multi-Modality_Self-Supervised_Foreground_Matting_for_Human-Object_Interaction_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-matting'] | ['computer-vision'] | [ 7.72235751e-01 2.11894318e-01 -2.26699817e-03 -4.30105120e-01
-9.32583570e-01 -3.73519033e-01 5.45891881e-01 -4.46718842e-01
-2.91592270e-01 5.87318480e-01 -1.14431627e-01 -2.20262393e-01
4.93685842e-01 -5.35884142e-01 -1.27838957e+00 -8.73709798e-01
4.81782854e-01 5.84596515e-01 6.55106723e-01 3.69625598... | [10.6184720993042, -0.8966217637062073] |
f04d0123-2bb6-4547-bd16-190736aed9be | accounting-for-variations-in-speech-emotion | 2109.04316 | null | https://arxiv.org/abs/2109.04316v1 | https://arxiv.org/pdf/2109.04316v1.pdf | Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network | In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models. Previously, neural network designs, such as Multitask Learning, have accounted for variations in emotional expressions due to demographic and contextual factors. However, existing models face a fe... | ['Emily Mower Provost', 'Amrit Romana', 'Lance Ying'] | 2021-09-09 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-2.39530846e-01 -1.17440417e-01 -3.23302478e-01 -9.40522611e-01
-7.22645879e-01 -2.17897117e-01 4.94438976e-01 1.86886430e-01
-5.65494895e-01 6.83789134e-01 2.37227678e-01 1.55459568e-01
-3.74641269e-01 -2.27038756e-01 -2.07937390e-01 -6.21843874e-01
-7.27417544e-02 6.41177893e-01 -1.87440708e-01 4.08348292... | [13.39714527130127, 5.701663970947266] |
fb46f250-b293-42c1-94c3-251f36875bd3 | scene-recognition-based-on-dnn-and-game | 1912.01293 | null | https://arxiv.org/abs/1912.01293v4 | https://arxiv.org/pdf/1912.01293v4.pdf | Scene recognition based on DNN and game theory with its applications in human-robot interaction | Scene recognition model based on the DNN and game theory with its applications in human-robot interaction is proposed in this paper. The use of deep learning methods in the field of scene recognition is still in its infancy, but has become an important trend in the future. As the innovative idea of the paper, we propos... | ['G. H. Chen', 'D. Z. Zhao', 'W. Z. Wang', 'R. Q. Wang', 'D. S. Luo'] | 2019-12-03 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.50977725e-01 -1.60848737e-01 1.66384764e-02 -3.19965392e-01
1.52466968e-01 6.52840361e-02 3.91304910e-01 -3.09404403e-01
-7.82341361e-01 4.98414576e-01 -1.32123098e-01 1.21356942e-01
-4.47253138e-01 -8.96044970e-01 -3.03564250e-01 -9.51746166e-01
2.65691191e-01 2.52323091e-01 3.19947302e-01 -3.16365272... | [9.53616714477539, -0.45816493034362793] |
f81bc3fe-a9bc-4a2b-96bd-1e42de600eda | road-aware-monocular-structure-from-motion | 2112.08635 | null | https://arxiv.org/abs/2112.08635v1 | https://arxiv.org/pdf/2112.08635v1.pdf | Road-aware Monocular Structure from Motion and Homography Estimation | Structure from motion (SFM) and ground plane homography estimation are critical to autonomous driving and other robotics applications. Recently, much progress has been made in using deep neural networks for SFM and homography estimation respectively. However, directly applying existing methods for ground plane homograp... | ['Qian Zhang', 'Jiao Lu', 'Jiaxin Zhang', 'Teng Chen', 'Wei Sui'] | 2021-12-16 | null | null | null | null | ['road-segementation', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [ 6.21680766e-02 3.72443795e-01 -9.72111002e-02 -5.29573679e-01
-6.75251484e-01 -2.81074464e-01 4.97215152e-01 -3.98504972e-01
-5.69388330e-01 4.39187646e-01 -4.27036136e-01 -1.93311628e-02
2.72264063e-01 -9.93260682e-01 -1.25325906e+00 -5.29064298e-01
3.70187193e-01 8.67503941e-01 5.88928401e-01 -1.90212637... | [8.24503231048584, -2.1238863468170166] |
e161c837-e104-4089-bec0-2114231ca1e4 | collocation-polarity-disambiguation-using-web | null | null | https://aclanthology.org/D12-1015 | https://aclanthology.org/D12-1015.pdf | Collocation Polarity Disambiguation Using Web-based Pseudo Contexts | null | ['Ting Liu', 'Bing Qin', 'Yanyan Zhao'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['subjectivity-analysis'] | ['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... | [-7.3288679122924805, 3.753092050552368] |
aefb5c4a-429f-4b9d-8191-90e31f5bd100 | visual-spatial-reasoning | 2205.00363 | null | https://arxiv.org/abs/2205.00363v3 | https://arxiv.org/pdf/2205.00363v3.pdf | Visual Spatial Reasoning | Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset cont... | ['Nigel Collier', 'Guy Emerson', 'Fangyu Liu'] | 2022-04-30 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [-1.00489408e-01 1.29761904e-01 -2.44769212e-02 -4.01216418e-01
-3.45711529e-01 -7.07167804e-01 1.19360662e+00 1.15980700e-01
-5.26086450e-01 4.58227992e-01 1.97285250e-01 -6.08394086e-01
-2.23400474e-01 -4.53530610e-01 -6.42675519e-01 -3.47308755e-01
5.98860085e-02 5.37753046e-01 6.05762422e-01 -4.93935406... | [10.462747573852539, 1.7850518226623535] |
3c6d525b-f229-4349-a0ae-cd537eaa76af | knowledge-augmented-reasoning-distillation | 2305.18395 | null | https://arxiv.org/abs/2305.18395v1 | https://arxiv.org/pdf/2305.18395v1.pdf | Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks | Large Language Models (LLMs) have shown promising performance in knowledge-intensive reasoning tasks that require a compound understanding of knowledge. However, deployment of the LLMs in real-world applications can be challenging due to their high computational requirements and concerns on data privacy. Previous studi... | ['Sung Ju Hwang', 'Kenji Kawaguchi', 'Jinheon Baek', 'Seanie Lee', 'Minki Kang'] | 2023-05-28 | null | null | null | null | ['memorization', 'strategyqa'] | ['natural-language-processing', 'reasoning'] | [ 2.04950646e-01 5.82922697e-01 -4.20856446e-01 -2.78864175e-01
-1.08221030e+00 -4.90806848e-01 5.60992062e-01 2.23205924e-01
-6.28588498e-01 1.04462361e+00 4.93399709e-01 -5.19230783e-01
-5.88569880e-01 -8.80008101e-01 -7.13673353e-01 -2.78153241e-01
2.28449807e-01 8.90814424e-01 1.05898477e-01 -3.67455065... | [10.739303588867188, 7.984165191650391] |
b183a6c7-d5d1-4023-8a51-6ef1c030c2e7 | monolingual-versus-multilingual-bertology-for | 2108.13741 | null | https://arxiv.org/abs/2108.13741v3 | https://arxiv.org/pdf/2108.13741v3.pdf | Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization | Recent researches have demonstrated that BERT shows potential in a wide range of natural language processing tasks. It is adopted as an encoder for many state-of-the-art automatic summarizing systems, which achieve excellent performance. However, so far, there is not much work done for Vietnamese. In this paper, we sho... | ['Huy Quoc To', 'Anh Gia-Tuan Nguyen', 'Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen'] | 2021-08-31 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [-2.34348968e-01 1.97961062e-01 -3.32519233e-01 -2.01924041e-01
-1.08358908e+00 -3.93140942e-01 8.43909442e-01 5.37995458e-01
-6.67319477e-01 1.21722353e+00 1.07394660e+00 -2.39077225e-01
3.36151451e-01 -3.41567516e-01 -4.42519665e-01 -1.88076839e-01
1.51981503e-01 5.41645706e-01 2.16313288e-01 -7.32891262... | [12.386953353881836, 9.534976959228516] |
4efe39fe-626c-4c8e-a3a2-672a9426052c | hdrunet-single-image-hdr-reconstruction-with | 2105.13084 | null | https://arxiv.org/abs/2105.13084v2 | https://arxiv.org/pdf/2105.13084v2.pdf | HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization | Most consumer-grade digital cameras can only capture a limited range of luminance in real-world scenes due to sensor constraints. Besides, noise and quantization errors are often introduced in the imaging process. In order to obtain high dynamic range (HDR) images with excellent visual quality, the most common solution... | ['Chao Dong', 'Yu Qiao', 'Zhengwen Zhang', 'Yihao Liu', 'Xiangyu Chen'] | 2021-05-27 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 5.70865333e-01 -4.82855529e-01 1.06758587e-01 -4.93512034e-01
-8.18153381e-01 -1.44814819e-01 1.65653244e-01 -2.94800609e-01
-6.07219636e-01 6.66225374e-01 -1.02910702e-03 -3.36410291e-02
-8.00282732e-02 -7.90654957e-01 -8.04659843e-01 -8.46865594e-01
2.99674898e-01 -2.20987350e-01 4.06211525e-01 -2.26394013... | [10.844559669494629, -2.2987563610076904] |
f69ee596-28f7-4d3e-ad1a-64912c29f99a | 3d-shape-retrieval-basing-on-representatives | 1810.09008 | null | http://arxiv.org/abs/1810.09008v3 | http://arxiv.org/pdf/1810.09008v3.pdf | 3D shape retrieval basing on representatives of classes | In this paper, we present an improvement of our proposed technique for 3D
shape retrieval in classified databases [2] which is based on representatives
of classes. Instead of systematically matching the object-query with all 3D
models of the database, our idea presented in [2] consist, for a classified
database, to rep... | ['M. Benjelloun', 'E. W. Dadi', 'E. M. Daoudi'] | 2018-10-21 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 2.62454629e-01 1.50330648e-01 -4.58661765e-02 -2.22436726e-01
-4.87713099e-01 -4.10257697e-01 5.50828159e-01 5.78701675e-01
-3.19679916e-01 6.24726951e-01 -1.43933177e-01 -6.44086674e-02
-3.31724405e-01 -1.15270174e+00 -2.67813474e-01 -5.97663403e-01
3.35235149e-02 1.10768116e+00 8.33808005e-01 -2.89298266... | [8.974051475524902, 2.1923165321350098] |
fb9f46db-f944-4d12-9de9-5cdaec200d6a | mpool-motif-based-graph-pooling | 2303.03654 | null | https://arxiv.org/abs/2303.03654v1 | https://arxiv.org/pdf/2303.03654v1.pdf | MPool: Motif-Based Graph Pooling | Graph Neural networks (GNNs) have recently become a powerful technique for many graph-related tasks including graph classification. Current GNN models apply different graph pooling methods that reduce the number of nodes and edges to learn the higher-order structure of the graph in a hierarchical way. All these methods... | ['Esra Akbas', 'Max Khanov', 'Muhammad Ifte Khairul Islam'] | 2023-03-07 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-1.02157585e-01 -5.34436032e-02 -2.99585521e-01 -2.73153603e-01
-9.52865109e-02 -5.20069003e-01 4.87971246e-01 5.39726436e-01
-2.51392215e-01 2.50278443e-01 8.42284113e-02 -3.01003695e-01
-8.74943361e-02 -1.37820327e+00 -7.84296334e-01 -7.54164994e-01
-4.00833756e-01 -2.87890639e-02 5.45765340e-01 -4.96315360... | [7.106286525726318, 6.309908866882324] |
3e394370-427b-452b-8425-fc083657ab93 | contrastive-inverse-regression-for-dimension | 2305.12287 | null | https://arxiv.org/abs/2305.12287v1 | https://arxiv.org/pdf/2305.12287v1.pdf | Contrastive inverse regression for dimension reduction | Supervised dimension reduction (SDR) has been a topic of growing interest in data science, as it enables the reduction of high-dimensional covariates while preserving the functional relation with certain response variables of interest. However, existing SDR methods are not suitable for analyzing datasets collected from... | ['Didong Li', 'Hengrui Luo', 'Sam Hawke'] | 2023-05-20 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.18933815e-01 -7.58701861e-02 -4.11352217e-01 -3.00717324e-01
-7.18094885e-01 -2.87867814e-01 5.02047896e-01 1.78517565e-01
-3.06672692e-01 6.71783149e-01 4.51771408e-01 3.67015898e-02
-7.52562404e-01 -6.02095664e-01 -3.20915490e-01 -1.21801281e+00
-2.29386747e-01 5.44706464e-01 -5.44723272e-01 1.21511715... | [7.597624778747559, 4.387448310852051] |
461d5489-8a00-4d48-81d4-d7776d41758f | sim-semantic-aware-instance-mask-generation | 2303.08578 | null | https://arxiv.org/abs/2303.08578v1 | https://arxiv.org/pdf/2303.08578v1.pdf | SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation | Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without explicitly exploiting the high-level semantic information of the objects, which will become ineffective w... | ['Lei Zhang', 'Liyi Chen', 'Shuai Li', 'Yabin Zhang', 'Chenhang He', 'Ruihuang Li'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_SIM_Semantic-Aware_Instance_Mask_Generation_for_Box-Supervised_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_SIM_Semantic-Aware_Instance_Mask_Generation_for_Box-Supervised_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.02809131e-01 3.85777146e-01 -3.11866373e-01 -7.16376603e-01
-6.76509321e-01 -4.04193252e-01 4.22706991e-01 3.90632153e-01
-4.14366901e-01 5.28827965e-01 -1.56708613e-01 2.92024408e-02
1.42517492e-01 -6.92512751e-01 -9.31869149e-01 -9.29816544e-01
3.38817209e-01 4.42964047e-01 8.70442152e-01 1.65022403... | [9.538690567016602, 0.6253294944763184] |
3137ae03-7336-4e0c-82e4-8e7429d37e90 | morphological-sampling-theorem-and-its | 2305.13279 | null | https://arxiv.org/abs/2305.13279v1 | https://arxiv.org/pdf/2305.13279v1.pdf | Morphological Sampling Theorem and its Extension to Grey-value Images | Sampling is a basic operation in image processing. In classic literature, a morphological sampling theorem has been established, which shows how sampling interacts by morphological operations with image reconstruction. Many aspects of morphological sampling have been investigated for binary images, but only some of the... | ['Michael Breuß', 'Vivek Sridhar'] | 2023-05-22 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 9.15000379e-01 2.62513727e-01 3.91017824e-01 -2.02252775e-01
3.55053246e-02 -5.34348905e-01 7.51364231e-01 2.45332301e-01
-6.01936460e-01 5.91794491e-01 -2.54601657e-01 -3.82938534e-01
-3.50148767e-01 -1.09611380e+00 -3.41199815e-01 -9.52268541e-01
-3.48136246e-01 8.54044929e-02 3.65006626e-01 -4.00182694... | [11.114587783813477, -2.4598209857940674] |
4e9c90b5-b53c-4d4c-9bd7-b96e5bf44c0f | unsupervised-context-aware-sentence | 2206.03281 | null | https://arxiv.org/abs/2206.03281v1 | https://arxiv.org/pdf/2206.03281v1.pdf | Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval | Recent research demonstrates the effectiveness of using pretrained language models (PLM) to improve dense retrieval and multilingual dense retrieval. In this work, we present a simple but effective monolingual pretraining task called contrastive context prediction~(CCP) to learn sentence representation by modeling sent... | ['Daxin Jiang', 'Ming Gong', 'Nan Duan', 'Linjun Shou', 'Houxing Ren', 'Yaobo Liang', 'Ning Wu'] | 2022-06-07 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-7.80194402e-02 -3.31527919e-01 -2.59756207e-01 -4.57556427e-01
-1.40651572e+00 -4.98323411e-01 7.01414585e-01 2.56855816e-01
-9.78819609e-01 7.92619109e-01 6.22261047e-01 -3.49787325e-01
2.22884163e-01 -5.67429960e-01 -6.79680765e-01 -3.63383979e-01
9.62044150e-02 5.40234685e-01 1.09016612e-01 -7.24426389... | [11.350184440612793, 9.793774604797363] |
2d772dca-c2db-42d9-91c4-750d3c263cbd | counting-the-uncountable-deep-semantic | 1809.07091 | null | http://arxiv.org/abs/1809.07091v2 | http://arxiv.org/pdf/1809.07091v2.pdf | Counting the uncountable: deep semantic density estimation from Space | We propose a new method to count objects of specific categories that are
significantly smaller than the ground sampling distance of a satellite image.
This task is hard due to the cluttered nature of scenes where different object
categories occur. Target objects can be partially occluded, vary in appearance
within the ... | ['Jan D. Wegner', 'Andres C. Rodriguez'] | 2018-09-19 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.13943480e-01 -3.10729116e-01 3.11052442e-01 -4.70534295e-01
-4.98266786e-01 -7.29502857e-01 3.40197921e-01 9.09911876e-04
-7.57894278e-01 8.52407277e-01 -3.36826921e-01 -2.18916371e-01
-1.55450776e-02 -1.19210255e+00 -7.84585118e-01 -7.42024481e-01
-1.36563763e-01 1.01459634e+00 5.50656915e-01 6.44623876... | [8.836224555969238, -0.2695405185222626] |
e8d9eb93-a16a-4441-a752-09c03850500e | data-driven-bilateral-generalized-two | 2306.07045 | null | https://arxiv.org/abs/2306.07045v1 | https://arxiv.org/pdf/2306.07045v1.pdf | Data-Driven Bilateral Generalized Two-Dimensional Quaternion Principal Component Analysis with Application to Color Face Recognition | A new data-driven bilateral generalized two-dimensional quaternion principal component analysis (BiG2DQPCA) is presented to extract the features of matrix samples from both row and column directions. This general framework directly works on the 2D color images without vectorizing and well preserves the spatial and colo... | ['Yong Zhang', 'Dun-Wei Gong', 'Zhi-Gang Jia', 'Mei-Xiang Zhao'] | 2023-06-12 | null | null | null | null | ['face-recognition', 'image-reconstruction'] | ['computer-vision', 'computer-vision'] | [-2.71980733e-01 -5.10235429e-01 -9.12932083e-02 -2.50323892e-01
-2.98022002e-01 3.43516842e-02 4.18535233e-01 -5.79413414e-01
-5.96657634e-01 3.70820194e-01 -2.76035309e-01 -6.86641112e-02
-1.09613098e-01 -5.77418387e-01 -2.17366070e-01 -1.03841913e+00
4.96203713e-02 1.59624472e-01 -3.74037355e-01 -2.28945494... | [10.7764310836792, -1.6159309148788452] |
28d7d6e1-a869-4234-b5a8-9f83543d5bb8 | improving-few-shot-performance-of-language | 2212.02216 | null | https://arxiv.org/abs/2212.02216v1 | https://arxiv.org/pdf/2212.02216v1.pdf | Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration | Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations of test instances, i.e., in-context learning. However, the performance of in-context learning is susceptible to the choice of prompt format, training ex... | ['Xu Cheng', 'Zhirui Zhang', 'Meixi Chen', 'Feng Nie'] | 2022-12-05 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.40064681e-01 -2.56333232e-01 -6.05386913e-01 -9.33181167e-01
-1.04703653e+00 -1.93831265e-01 5.66411972e-01 4.18493718e-01
-5.07869363e-01 5.37746549e-01 1.29634887e-01 -2.18897566e-01
-1.62394002e-01 -9.26533222e-01 -5.98539114e-01 -4.99978453e-01
4.18537766e-01 3.44417959e-01 4.65570807e-01 -3.90656203... | [10.169939994812012, 3.6057944297790527] |
fd4a3bf5-e839-4219-b846-c580598ebf44 | u2-former-a-nested-u-shaped-transformer-for | 2112.02279 | null | https://arxiv.org/abs/2112.02279v2 | https://arxiv.org/pdf/2112.02279v2.pdf | U2-Former: A Nested U-shaped Transformer for Image Restoration | While Transformer has achieved remarkable performance in various high-level vision tasks, it is still challenging to exploit the full potential of Transformer in image restoration. The crux lies in the limited depth of applying Transformer in the typical encoder-decoder framework for image restoration, resulting from h... | ['Guangming Lu', 'Jinxing Li', 'Wenjie Pei', 'Xin Feng', 'Haobo Ji'] | 2021-12-04 | null | null | null | null | ['image-dehazing', 'reflection-removal'] | ['computer-vision', 'computer-vision'] | [ 4.26253825e-01 -1.62893727e-01 4.00664389e-01 -1.74926728e-01
-7.25635111e-01 2.15817653e-02 4.45964634e-01 -1.63710237e-01
-1.34948462e-01 3.84720653e-01 4.79579419e-01 -4.15727854e-01
-1.53478533e-01 -8.34982991e-01 -7.77722776e-01 -1.02709520e+00
9.01779681e-02 -4.69578177e-01 2.94486344e-01 -4.23335373... | [11.124495506286621, -2.3519446849823] |
ffe5c744-4fd8-4e7c-b96b-35d222edf50e | two-stream-3d-semantic-scene-completion | 1804.03550 | null | https://arxiv.org/abs/1804.03550v4 | https://arxiv.org/pdf/1804.03550v4.pdf | Two Stream 3D Semantic Scene Completion | Inferring the 3D geometry and the semantic meaning of surfaces, which are occluded, is a very challenging task. Recently, a first end-to-end learning approach has been proposed that completes a scene from a single depth image. The approach voxelizes the scene and predicts for each voxel if it is occupied and, if it is ... | ['Yueh-Tung Chen', 'Johann Sawatzky', 'Martin Garbade', 'Juergen Gall'] | 2018-04-10 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 4.84700024e-01 3.18141639e-01 2.51334999e-02 -6.41060174e-01
-7.46199250e-01 -4.69174147e-01 4.02051896e-01 2.75070399e-01
-1.55242831e-01 1.62041470e-01 1.58988968e-01 -1.01586908e-01
1.35626927e-01 -9.90418017e-01 -9.90243196e-01 -3.29573184e-01
1.50197700e-01 6.63122356e-01 4.07069474e-01 2.65507042... | [8.432531356811523, -2.895667552947998] |
15948948-a938-4bf5-87c7-395b2d3c3bc3 | mde-multi-distance-embeddings-for-link | 1905.10702 | null | https://arxiv.org/abs/1905.10702v8 | https://arxiv.org/pdf/1905.10702v8.pdf | MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs | Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. ... | ['Jens Lehmann', 'Hamed Shariat Yazdi', 'Damien Graux', 'Afshin Sadeghi'] | 2019-05-25 | null | null | null | null | ['relational-pattern-learning'] | ['knowledge-base'] | [-4.26908135e-02 5.02343655e-01 -7.48951316e-01 -2.26523712e-01
-1.54371023e-01 -6.20504200e-01 7.53651977e-01 3.99218023e-01
-1.20512426e-01 3.45393986e-01 3.66597086e-01 -4.42259669e-01
-8.13533843e-01 -1.17305851e+00 -6.30669236e-01 -3.56683105e-01
-4.69620019e-01 6.82146072e-01 1.12063684e-01 -2.92156696... | [8.722485542297363, 7.80568790435791] |
8b4ebd5b-6157-4bf5-baa8-860ca055354f | commonly-uncommon-semantic-sparsity-in | 1612.00901 | null | http://arxiv.org/abs/1612.00901v1 | http://arxiv.org/pdf/1612.00901v1.pdf | Commonly Uncommon: Semantic Sparsity in Situation Recognition | Semantic sparsity is a common challenge in structured visual classification
problems; when the output space is complex, the vast majority of the possible
predictions are rarely, if ever, seen in the training set. This paper studies
semantic sparsity in situation recognition, the task of producing structured
summaries o... | ['Vicente Ordonez', 'Luke Zettlemoyer', 'Ali Farhadi', 'Mark Yatskar'] | 2016-12-03 | commonly-uncommon-semantic-sparsity-in-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Yatskar_Commonly_Uncommon_Semantic_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yatskar_Commonly_Uncommon_Semantic_CVPR_2017_paper.pdf | cvpr-2017-7 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.60076201e-01 1.75843403e-01 -4.36620414e-01 -5.33503473e-01
-7.77597308e-01 -5.93063414e-01 8.47949922e-01 2.40843371e-01
-3.63170713e-01 6.93659365e-01 7.84928799e-01 2.83777881e-02
2.40146428e-01 -3.86219144e-01 -1.06799400e+00 -4.90680963e-01
-7.37137999e-03 5.96675634e-01 2.55104929e-01 2.13319659... | [10.342270851135254, 1.4233859777450562] |
a1d54ab5-a424-4fff-853e-0de8e52a0594 | evaluation-discrepancy-discovery-a-sentence | 2101.09079 | null | https://arxiv.org/abs/2101.09079v1 | https://arxiv.org/pdf/2101.09079v1.pdf | Evaluation Discrepancy Discovery: A Sentence Compression Case-study | Reliable evaluation protocols are of utmost importance for reproducible NLP research. In this work, we show that sometimes neither metric nor conventional human evaluation is sufficient to draw conclusions about system performance. Using sentence compression as an example task, we demonstrate how a system can game a we... | ['Yevgeniy Puzikov'] | 2021-01-22 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 1.72642738e-01 2.79862314e-01 1.65587753e-01 -6.19541109e-01
-1.07265389e+00 -8.62845063e-01 8.01404119e-01 8.20922911e-01
-8.88446927e-01 9.37336326e-01 3.26782048e-01 -4.17692184e-01
-1.76836491e-01 -4.69269037e-01 -4.20206577e-01 -1.34500951e-01
3.00860792e-01 7.37865329e-01 3.26837271e-01 -3.06771338... | [11.695161819458008, 9.034539222717285] |
0c8ee844-7f2b-4f13-bacd-5b36ef5ed094 | distributional-reinforcement-learning-with-3 | 2007.12354 | null | https://arxiv.org/abs/2007.12354v3 | https://arxiv.org/pdf/2007.12354v3.pdf | Distributional Reinforcement Learning via Moment Matching | We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return. We formulate a method that learns a finit... | ['Sunil Gupta', 'Svetha Venkatesh', 'Thanh Tang Nguyen'] | 2020-07-24 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-4.35865074e-01 1.65418044e-01 -2.89155841e-01 -1.13276146e-01
-1.20420933e+00 -9.85398054e-01 6.25946939e-01 -4.45898622e-02
-7.41575301e-01 1.06319630e+00 5.04144952e-02 -4.63976383e-01
-5.47097802e-01 -7.59798586e-01 -1.01852930e+00 -1.12730813e+00
-5.94645798e-01 9.88664091e-01 -3.88122797e-01 -3.13069373... | [4.099051475524902, 2.588942766189575] |
54e102fb-5bce-4226-8618-97b21f4a46f9 | nonlinear-residual-echo-suppression-based-on | 2005.07631 | null | https://arxiv.org/abs/2005.07631v1 | https://arxiv.org/pdf/2005.07631v1.pdf | Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet | Acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and far-end signal. Usually a post processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of fully convolutio... | ['Kai Chen', 'Jing Lu', 'Teng Xiang', 'Hongsheng Chen'] | 2020-05-15 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.09767802e-01 -5.10663271e-01 7.96993792e-01 -2.18698457e-01
-4.21864957e-01 -3.59272450e-01 9.66763124e-02 -2.14320138e-01
-6.66722476e-01 1.79245248e-01 3.52388740e-01 -9.51417089e-02
-1.05674885e-01 -2.52430946e-01 -2.15392381e-01 -7.41509259e-01
-7.94921517e-02 -6.55096233e-01 3.93244714e-01 -2.74980307... | [15.037907600402832, 5.9467315673828125] |
ca4e56b7-9607-4d92-9517-af79ba7aa0e5 | automatic-discovery-of-interpretable-planning | 2005.11730 | null | https://arxiv.org/abs/2005.11730v3 | https://arxiv.org/pdf/2005.11730v3.pdf | Automatic Discovery of Interpretable Planning Strategies | When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective dec... | ['Julian Skirzyński', 'Falk Lieder', 'Frederic Becker'] | 2020-05-24 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 1.64264828e-01 2.58482546e-01 -5.70567250e-01 -3.08391511e-01
-5.86787939e-01 -7.50919461e-01 4.10149813e-01 1.05809592e-01
-5.42268932e-01 9.12691534e-01 1.96287811e-01 -7.27379858e-01
-1.76861510e-01 -3.43751162e-01 -4.49642539e-01 -2.72635937e-01
1.44786969e-01 8.28649342e-01 -9.67254192e-02 -1.79620340... | [4.214734077453613, 1.7548210620880127] |
0fc9add8-b05a-4bcb-b267-489765963b6b | self-supervised-3d-face-reconstruction-via-1 | 2110.04800 | null | https://arxiv.org/abs/2110.04800v1 | https://arxiv.org/pdf/2110.04800v1.pdf | Self-Supervised 3D Face Reconstruction via Conditional Estimation | We present a conditional estimation (CEST) framework to learn 3D facial parameters from 2D single-view images by self-supervised training from videos. CEST is based on the process of analysis by synthesis, where the 3D facial parameters (shape, reflectance, viewpoint, and illumination) are estimated from the face image... | ['Rita Singh', 'Bhiksha Raj', 'Weiyang Liu', 'Yandong Wen'] | 2021-10-10 | self-supervised-3d-face-reconstruction-via | http://openaccess.thecvf.com//content/ICCV2021/html/Wen_Self-Supervised_3D_Face_Reconstruction_via_Conditional_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wen_Self-Supervised_3D_Face_Reconstruction_via_Conditional_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 9.51384846e-03 7.44119063e-02 -6.44075945e-02 -6.92261159e-01
-6.18621707e-01 -4.45815921e-01 4.63713437e-01 -5.27627170e-01
-2.35006034e-01 2.20098808e-01 2.52505720e-01 4.18378174e-01
8.20821710e-03 -3.20806026e-01 -6.25007689e-01 -9.94518638e-01
1.81161940e-01 3.06127220e-01 -3.40840518e-01 1.34463802... | [13.06727409362793, 0.032123927026987076] |
6734c61d-feee-4cfb-96f9-489c2f206fee | template-nerf-towards-modeling-dense-shape | 2111.04237 | null | https://arxiv.org/abs/2111.04237v1 | https://arxiv.org/pdf/2111.04237v1.pdf | Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images | We present neural radiance fields (NeRF) with templates, dubbed Template-NeRF, for modeling appearance and geometry and generating dense shape correspondences simultaneously among objects of the same category from only multi-view posed images, without the need of either 3D supervision or ground-truth correspondence kno... | ['Qingfu Zhang', 'Xi Lin', 'Zhiyuan Yang', 'Jianfei Guo'] | 2021-11-08 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 5.82682610e-01 1.71903983e-01 1.78423002e-01 -6.59482598e-01
-9.02093470e-01 -7.44476974e-01 7.21155822e-01 -2.71895349e-01
9.70017985e-02 2.52026320e-01 -1.16098881e-01 1.51698872e-01
-7.94569626e-02 -7.98865080e-01 -1.24487174e+00 -6.34993434e-01
4.72575754e-01 5.28484821e-01 1.79641306e-01 -1.55522704... | [8.610577583312988, -3.146728754043579] |
8cfca4b8-8260-4531-9c0a-b46eeb23f95c | musical-instrument-recognition-using-their | 1705.04971 | null | http://arxiv.org/abs/1705.04971v1 | http://arxiv.org/pdf/1705.04971v1.pdf | Musical Instrument Recognition Using Their Distinctive Characteristics in Artificial Neural Networks | In this study an Artificial Neural Network was trained to classify musical
instruments, using audio samples transformed to the frequency domain. Different
features of the sound, in both time and frequency domain, were analyzed and
compared in relation to how much information that could be derived from that
limited data... | ['Babak Toghiani-Rizi', 'Marcus Windmark'] | 2017-05-14 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [ 2.03396022e-01 9.10448208e-02 2.86413699e-01 2.11258322e-01
-1.47629753e-01 -7.07093835e-01 5.62796295e-02 9.49746892e-02
-4.83725727e-01 8.17659140e-01 -4.41927388e-02 -5.80329895e-02
-4.93215382e-01 -9.37818408e-01 -7.31480122e-02 -5.37577868e-01
-3.47984731e-01 2.73328245e-04 -3.98643985e-02 -2.78060704... | [15.7870512008667, 5.28786039352417] |
3c2e931b-61b2-4be1-91d8-71367ec777cc | real-time-speech-enhancement-with-dynamic | 2302.10377 | null | https://arxiv.org/abs/2302.10377v1 | https://arxiv.org/pdf/2302.10377v1.pdf | Real-time speech enhancement with dynamic attention span | For real-time speech enhancement (SE) including noise suppression, dereverberation and acoustic echo cancellation, the time-variance of the audio signals becomes a severe challenge. The causality and memory usage limit that only the historical information can be used for the system to capture the time-variant character... | ['Yan Lu', 'Yuan Zhang', 'Xiulian Peng', 'Yuan Zhou', 'Chengyu Zheng'] | 2023-02-21 | null | null | null | null | ['acoustic-echo-cancellation', 'speech-enhancement', 'acoustic-echo-cancellation'] | ['medical', 'speech', 'speech'] | [ 1.00908950e-01 -4.08505172e-01 2.23582566e-01 -1.46549001e-01
-3.91893893e-01 -1.03224352e-01 1.51086867e-01 -1.85362265e-01
-5.23390114e-01 3.57919633e-01 4.41759706e-01 -1.08282574e-01
-2.84429103e-01 -3.85282755e-01 -3.29873711e-01 -7.51488864e-01
-4.71265391e-02 -6.78223193e-01 2.95852631e-01 -2.53969669... | [15.006919860839844, 5.924919605255127] |
93921dd8-19cc-40b4-b6a2-3e43fa50ad91 | fingpt-open-source-financial-large-language | 2306.06031 | null | https://arxiv.org/abs/2306.06031v1 | https://arxiv.org/pdf/2306.06031v1.pdf | FinGPT: Open-Source Financial Large Language Models | Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-quality financial data is the first challenge for financial LLMs (FinLLMs). While proprietary models like BloombergGPT have taken advantage of... | ['Christina Dan Wang', 'Xiao-Yang Liu', 'Hongyang Yang'] | 2023-06-09 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-8.49996865e-01 -2.79493760e-02 -2.59268403e-01 -4.06511784e-01
-1.04410350e+00 -7.96447337e-01 7.29839087e-01 2.13516861e-01
-5.69465160e-01 3.76194715e-01 5.50012410e-01 -6.72755659e-01
-7.74474740e-02 -8.39833677e-01 -5.28161764e-01 8.03378895e-02
-2.18516856e-01 7.69068539e-01 -1.19851179e-01 -8.56392980... | [10.637198448181152, 8.35982608795166] |
2672a54a-cae0-4386-901d-2cc2adffd766 | a-template-independent-approach-for | null | null | https://www.ital-ia2023.it/workshop/ai-per-la-finanza-ed-il-commercio | https://www.ital-ia2023.it/submission/17/paper | A template-independent approach for information extraction in real estate documents | Business corporations manage tons of unstructured data daily, such as PDFs and websites. Recent advances in the deep
learning field help find insight from this unstructured information. New models leverage the power of the Transformer
architecture to accomplish natural language understanding tasks on these data, join... | ['Ignazio Gallo', 'Stefano Taverni', 'Gabriele Destro', 'Nicola Landro'] | 2023-05-30 | null | null | null | ital-ia-2023-5 | ['optical-character-recognition', 'retrieval', 'question-answering', 'information-retrieval', 'natural-language-understanding'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.43259078e-01 2.33408332e-01 -1.87469289e-01 -1.47106752e-01
-1.20809746e+00 -1.15663946e+00 6.57391846e-01 3.92483205e-01
-2.83295631e-01 2.29556799e-01 4.42183346e-01 -5.38307548e-01
-1.17557265e-01 -1.02672029e+00 -6.36052847e-01 -8.98300037e-02
8.82138386e-02 7.52960920e-01 1.44984797e-01 -5.91369905... | [9.990751266479492, 7.943849086761475] |
1603d66d-f2f6-4bcc-b088-5d92ff01ad93 | quantification-of-disaggregation-difficulty | 2101.07191 | null | https://arxiv.org/abs/2101.07191v1 | https://arxiv.org/pdf/2101.07191v1.pdf | Quantification of Disaggregation Difficulty with Respect to the Number of Meters | A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption profiles of appliances within a residence by analyzing the aggregated consumption signal. Among efficient NILM methods are event-based algorithms in which events of the aggregated signal a... | ['Sadegh Bolouki', 'Mohammad T H Beheshti', 'Elnaz Azizi'] | 2021-01-18 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.65009201e-01 1.06658451e-01 -2.35770661e-02 -2.09415689e-01
-5.77329457e-01 -6.10635459e-01 5.18804610e-01 6.47032678e-01
-1.67402670e-01 7.10688353e-01 2.91734990e-02 -8.83247256e-02
-3.67609829e-01 -1.10080075e+00 -2.88084477e-01 -9.71876323e-01
-5.24750724e-02 9.34388936e-02 -5.55796064e-02 1.15640350... | [5.97746467590332, 2.581223249435425] |
be4ca019-a2c7-4d91-b8e2-419445a6201f | modified-splice-and-its-extension-to-non | 1307.4048 | null | http://arxiv.org/abs/1307.4048v1 | http://arxiv.org/pdf/1307.4048v1.pdf | Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition | In this paper, a modification to the training process of the popular SPLICE
algorithm has been proposed for noise robust speech recognition. The
modification is based on feature correlations, and enables this stereo-based
algorithm to improve the performance in all noise conditions, especially in
unseen cases. Further,... | ['D. S. Pavan Kumar', 'S. Umesh', 'N. Vishnu Prasad', 'Vikas Joshi'] | 2013-07-15 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 7.65056685e-02 -6.66854950e-03 6.00708485e-01 -6.60175622e-01
-1.82000303e+00 -7.18270361e-01 6.85804784e-01 -5.72544694e-01
-4.87577647e-01 6.20541811e-01 5.26114881e-01 -3.04993868e-01
2.00127527e-01 -3.45445514e-01 -5.28672099e-01 -1.05705631e+00
2.02799425e-01 1.47020057e-01 2.17468798e-01 -1.83923423... | [14.83006763458252, 5.941051959991455] |
d2b36af0-5f2e-45f9-9876-ec9d7bc6bc26 | single-image-reflection-removal-with-1 | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.pdf | Single Image Reflection Removal With Absorption Effect | In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitl... | ['Alex C. Kot', 'Ling-Yu Duan', 'Xudong Jiang', 'Jinnan Chen', 'Boxin Shi', 'Qian Zheng'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['reflection-removal'] | ['computer-vision'] | [ 8.48084688e-01 -1.32214561e-01 4.09844905e-01 -1.63480252e-01
-8.87629747e-01 -3.36974710e-01 1.72977760e-01 -4.28561121e-01
-3.22946250e-01 5.47200143e-01 1.63169857e-02 -1.95634156e-01
1.62358195e-01 -8.27994645e-01 -8.74857783e-01 -1.24163508e+00
2.92606115e-01 -2.40010377e-02 5.66214584e-02 2.90585905... | [10.577493667602539, -2.6907458305358887] |
9defa53d-84d3-4a29-8cf4-6fe25079639b | fair-and-optimal-classification-via | 2211.01528 | null | https://arxiv.org/abs/2211.01528v3 | https://arxiv.org/pdf/2211.01528v3.pdf | Fair and Optimal Classification via Post-Processing | To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of fair algorithms. To this end... | ['Han Zhao', 'Lang Yin', 'Ruicheng Xian'] | 2022-11-03 | null | null | null | null | ['classification'] | ['methodology'] | [ 2.98076123e-01 2.47131452e-01 -6.66588604e-01 -8.50961566e-01
-8.96854043e-01 -4.93767112e-01 3.00051033e-01 3.84513319e-01
-5.83363116e-01 9.17957008e-01 1.27593502e-01 -5.53081632e-01
-4.36785907e-01 -6.51649058e-01 -4.74004298e-01 -7.38829970e-01
1.22578003e-01 3.08623314e-01 -6.95983827e-01 2.95693398... | [8.857048988342285, 5.271857738494873] |
e0a726bb-37bc-4b62-837c-66049b5091aa | learning-clip-guided-visual-text-fusion | 2304.10091 | null | https://arxiv.org/abs/2304.10091v1 | https://arxiv.org/pdf/2304.10091v1.pdf | Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition | Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image. However, the performance is not reliable for images with challenging factors, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can make full ... | ['Xiao Wang', 'Xiaohao Wu', 'Zihan Yang', 'Jiandong Jin', 'Jun Zhu'] | 2023-04-20 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 1.02909297e-01 -5.78465939e-01 -1.92967087e-01 -7.88815558e-01
-7.58293569e-01 -1.64714083e-01 5.13806701e-01 1.30138814e-01
-4.72296655e-01 6.62701845e-01 4.15456414e-01 1.16207696e-01
4.24893469e-01 -6.98299408e-01 -7.19865322e-01 -7.85773277e-01
4.99908537e-01 -5.27216829e-02 2.11150974e-01 1.90056220... | [14.325281143188477, 0.9915963411331177] |
bfd5fba3-9b35-4500-b42c-6f96a60139b7 | statistical-learning-machines-from-atr-to-dna | 1906.10019 | null | https://arxiv.org/abs/1906.10019v4 | https://arxiv.org/pdf/1906.10019v4.pdf | Machine Learning Construction: implications to cybersecurity | Statistical learning is the process of estimating an unknown probabilistic input-output relationship of a system using a limited number of observations. A statistical learning machine (SLM) is the algorithm, function, model, or rule, that learns such a process; and machine learning (ML) is the conventional name of this... | ['Waleed A. Yousef'] | 2019-06-24 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [ 4.80800509e-01 -1.58615783e-01 -1.17152810e-01 -7.06087947e-02
-5.13285756e-01 -7.43890464e-01 7.57253230e-01 4.27518040e-01
-2.05370620e-01 5.50975144e-01 -4.47920054e-01 -7.89772272e-01
-7.31640995e-01 -7.74188221e-01 -5.67268312e-01 -7.83794224e-01
-3.85052651e-01 3.61937046e-01 1.69120833e-01 -9.84353870... | [7.547479629516602, 2.6940598487854004] |
e3c4cdde-b241-4691-b692-5e10a0c5ca0f | estimating-uncertainty-of-earthquake-rupture | 1911.09660 | null | https://arxiv.org/abs/1911.09660v2 | https://arxiv.org/pdf/1911.09660v2.pdf | Estimating uncertainty of earthquake rupture using Bayesian neural network | Bayesian neural networks (BNN) are the probabilistic model that combines the strengths of both neural network (NN) and stochastic processes. As a result, BNN can combat overfitting and perform well in applications where data is limited. Earthquake rupture study is such a problem where data is insufficient, and scientis... | ['Md Mesbah Uddin', 'Sabber Ahamed'] | 2019-11-21 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-4.36959594e-01 -3.56053561e-01 2.07661673e-01 2.41998062e-02
-7.16701329e-01 -2.64229387e-01 2.63747960e-01 1.05633098e-03
-3.43301594e-01 1.07432926e+00 1.95330605e-01 -3.44295114e-01
-5.87243915e-01 -9.89436328e-01 -7.65199423e-01 -1.22294068e+00
-2.59614617e-01 3.66745323e-01 6.17942393e-01 -2.91175038... | [6.386481761932373, 3.0308454036712646] |
6e77fa82-ddd4-45ea-b9e2-a13385aaffb7 | ynu-hpcc-at-semeval-2019-task-9-using-a-bert | null | null | https://aclanthology.org/S19-2224 | https://aclanthology.org/S19-2224.pdf | YNU-HPCC at SemEval-2019 Task 9: Using a BERT and CNN-BiLSTM-GRU Model for Suggestion Mining | Consumer opinions towards commercial entities are generally expressed through online reviews, blogs, and discussion forums. These opinions largely express positive and negative sentiments towards a given entity,but also tend to contain suggestions for improving the entity. In this task, we extract suggestions from give... | ['Xue-jie Zhang', 'Jin Wang', 'Ping Yue'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-6.46372318e-01 5.34763932e-01 -6.84793591e-01 -6.67266667e-01
-3.41345556e-03 -7.35690176e-01 5.51790118e-01 7.47574747e-01
-4.92547065e-01 1.01646113e+00 5.10525167e-01 -3.05410981e-01
3.42932135e-01 -9.22102511e-01 -1.04616508e-01 -3.08162093e-01
1.29597023e-01 -1.52753014e-02 3.03074300e-01 -6.34513974... | [11.183854103088379, 6.775284290313721] |
6d910563-2a03-48c2-90f1-eabd0dfb20b8 | a-multi-task-network-for-joint-specular | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fu_A_Multi-Task_Network_for_Joint_Specular_Highlight_Detection_and_Removal_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fu_A_Multi-Task_Network_for_Joint_Specular_Highlight_Detection_and_Removal_CVPR_2021_paper.pdf | A Multi-Task Network for Joint Specular Highlight Detection and Removal | Specular highlight detection and removal are fundamental and challenging tasks. Although recent methods achieve promising results on the two tasks by supervised training on synthetic training data, they are typically solely designed for highlight detection or removal, and their performance usually deteriorates sign... | ['Chunxia Xiao', 'Ping Li', 'Lei Zhu', 'Qing Zhang', 'Gang Fu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 6.69789732e-01 -4.51613218e-01 3.00569564e-01 4.87524271e-03
-7.04291284e-01 -5.33615887e-01 6.00894690e-01 -2.31018085e-02
-3.54349375e-01 7.92724788e-01 -1.99677840e-01 9.45517328e-03
1.76512435e-01 -3.98818463e-01 -7.68347681e-01 -9.13816869e-01
-3.82583104e-02 -1.17819972e-01 6.50023937e-01 -1.01351924... | [10.865083694458008, -2.7282960414886475] |
1c5df531-2fdd-4205-b305-117f39d7c398 | xjnlp-at-semeval-2017-task-12-clinical | null | null | https://aclanthology.org/s17-2178 | https://aclanthology.org/s17-2178.pdf | XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model | null | ['Chen Li', 'Yu Long', 'Xuan Wang', 'Zhijing Li'] | 2017-08-01 | xjnlp-at-semeval-2017-task-12-clinical-1 | https://aclanthology.org/S17-2178 | https://aclanthology.org/S17-2178.pdf | semeval-2017-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392025709152222, 15.86921501159668] |
a898b9e1-06ab-4ba2-ad09-b4eb0bd6667c | alzheimer-s-disease-detection-from | 2106.08689 | null | https://arxiv.org/abs/2106.08689v1 | https://arxiv.org/pdf/2106.08689v1.pdf | Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models | In this paper, we combined linguistic complexity and (dis)fluency features with pretrained language models for the task of Alzheimer's disease detection of the 2021 ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech) challenge. An accuracy of 83.1% was achieved on the test set, which amounts to an imp... | ['Elma Kerz', 'Daniel Wiechmann', 'Xuefeng Yin', 'Yu Qiao'] | 2021-06-16 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [-2.70996034e-01 -1.31103426e-01 9.18161198e-02 -6.38426781e-01
-1.33298028e+00 -1.94708318e-01 5.21900058e-01 -2.11265936e-01
-6.90149844e-01 9.53010559e-01 6.16288006e-01 -3.29184592e-01
1.72355827e-02 -3.26843441e-01 -9.84059796e-02 -1.90815166e-01
-5.69344521e-01 4.91222620e-01 6.79079220e-02 -2.18787804... | [13.910907745361328, 5.38214111328125] |
23e4180d-302e-4041-8e30-6ea5e452adad | beyond-part-models-person-retrieval-with | 1711.09349 | null | http://arxiv.org/abs/1711.09349v3 | http://arxiv.org/pdf/1711.09349v3.pdf | Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline) | Employing part-level features for pedestrian image description offers
fine-grained information and has been verified as beneficial for person
retrieval in very recent literature. A prerequisite of part discovery is that
each part should be well located. Instead of using external cues, e.g., pose
estimation, to directly... | ['Shengjin Wang', 'Qi Tian', 'Yi Yang', 'Yifan Sun', 'Liang Zheng'] | 2017-11-26 | beyond-part-models-person-retrieval-with-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yifan_Sun_Beyond_Part_Models_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yifan_Sun_Beyond_Part_Models_ECCV_2018_paper.pdf | eccv-2018-9 | ['person-retrieval'] | ['computer-vision'] | [-2.06576034e-01 -1.00277968e-01 2.19725184e-02 -3.00537467e-01
-9.91610229e-01 -4.32857960e-01 6.65856481e-01 2.88917422e-01
-4.04866338e-01 6.26345277e-01 4.61919725e-01 5.21394312e-01
2.71408353e-02 -7.51906931e-01 -9.98991251e-01 -5.76855898e-01
-6.42164573e-02 5.21462679e-01 2.88142204e-01 -1.75629407... | [14.684309005737305, 0.8924669027328491] |
7dbf4046-bc7b-40d9-b500-f04620100e26 | participatory-research-as-a-path-to-community | 2306.08906 | null | https://arxiv.org/abs/2306.08906v1 | https://arxiv.org/pdf/2306.08906v1.pdf | Participatory Research as a Path to Community-Informed, Gender-Fair Machine Translation | Recent years have seen a strongly increased visibility of non-binary people in public discourse. Accordingly, considerations of gender-fair language go beyond a binary conception of male/female. However, language technology, especially machine translation (MT), still suffers from binary gender bias. Proposing a solutio... | ['Katharina Bühn', 'Daniela Duh', 'Sigrid Schefer-Wenzl', 'Igor Miladinovic', 'Arthur Mettinger', 'Lukas Daniel Klausner', 'Sabrina Burtscher', 'Katta Spiel', 'Manuel Lardelli', 'Dagmar Gromann'] | 2023-06-15 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 3.83297890e-01 7.23648906e-01 -3.84312391e-01 -2.67107934e-01
-5.81676126e-01 -8.79765689e-01 8.03581834e-01 2.68114656e-01
-5.53797305e-01 8.38224649e-01 7.29771733e-01 -1.03529894e+00
1.74228251e-01 -3.19345295e-01 -1.75419480e-01 -3.02990586e-01
1.10361052e+00 3.45060378e-01 -5.20213485e-01 -3.05003136... | [9.154427528381348, 9.898896217346191] |
5fd40019-951f-409f-9936-6d8634e17990 | motion-comfort-optimization-for-autonomous | 2306.09462 | null | https://arxiv.org/abs/2306.09462v1 | https://arxiv.org/pdf/2306.09462v1.pdf | Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques | This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring Autonomous Vehicle (AV) user comfort and psychoanalysis are listed here. At the same time, this article introduces the technology related to the s... | ['Ala Al-Fuqaha', 'Mohsen Guizani', 'Basheer Qolomany', 'Junaid Qadir', 'Mohamed Rahouti', 'Mohammed Aledhari'] | 2023-06-15 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-7.95750558e-01 1.65549934e-01 -8.58460441e-02 -3.28974962e-01
-1.57852113e-01 -6.26139641e-01 6.06687106e-02 -2.49347478e-01
-2.73120552e-01 4.38572347e-01 -8.15992057e-02 -3.18129450e-01
2.07988396e-01 -3.50685477e-01 -2.68408179e-01 -9.35533524e-01
2.95945406e-01 -4.15743023e-01 -4.21601161e-02 -6.01306438... | [5.712462425231934, 1.0746855735778809] |
54853f1c-58c6-4718-9808-36273b945e6d | bootstrapping-multilingual-amr-with | 2102.02189 | null | https://arxiv.org/abs/2102.02189v1 | https://arxiv.org/pdf/2102.02189v1.pdf | Bootstrapping Multilingual AMR with Contextual Word Alignments | We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision. Weachieve this goal by bootstrapping transformer-based multilingual word embeddings, in partic-ular those from cross-lingual RoBERTa (XLM-R large). We dev... | ['Todd Ward', 'Salim Roukos', 'Radu Florian', 'Tahira Naseem', 'Ramon Fernandez Astudillo', 'Young-suk Lee', 'Janaki Sheth'] | 2021-02-03 | null | https://aclanthology.org/2021.eacl-main.30 | https://aclanthology.org/2021.eacl-main.30.pdf | eacl-2021-2 | ['multilingual-word-embeddings'] | ['methodology'] | [-6.95891753e-02 6.32967055e-02 -6.38116181e-01 -2.66855568e-01
-1.26233566e+00 -7.86046565e-01 8.96150351e-01 3.27730209e-01
-9.96402502e-01 8.04302037e-01 7.29410052e-01 -7.99690247e-01
4.84934151e-01 -6.90840304e-01 -4.86423016e-01 -3.68725538e-01
3.95559758e-01 6.56010985e-01 -3.24367851e-01 -5.13875484... | [11.009774208068848, 9.964348793029785] |
3ca0e8d7-49c0-45fd-a7b0-68e7103d8e56 | inverse-rendering-of-translucent-objects | 2305.08336 | null | https://arxiv.org/abs/2305.08336v1 | https://arxiv.org/pdf/2305.08336v1.pdf | Inverse Rendering of Translucent Objects using Physical and Neural Renderers | In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we ... | ['Hajime Nagahara', 'Trung Thanh Ngo', 'Chenhao Li'] | 2023-05-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Inverse_Rendering_of_Translucent_Objects_Using_Physical_and_Neural_Renderers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Inverse_Rendering_of_Translucent_Objects_Using_Physical_and_Neural_Renderers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['inverse-rendering'] | ['computer-vision'] | [ 6.80268288e-01 -2.62233824e-01 8.54418457e-01 -5.05249262e-01
-4.48704720e-01 -3.18494469e-01 4.60600436e-01 -6.04594648e-01
-2.15394840e-01 4.71342146e-01 -1.46000758e-01 -2.67706484e-01
-9.99811590e-02 -7.86026180e-01 -8.65507424e-01 -8.50550473e-01
4.81514663e-01 2.32529461e-01 -1.01962574e-02 7.95896165... | [9.80324649810791, -3.0611770153045654] |
6e796397-d8d9-4fac-81cf-4c9a1690e392 | deep-learning-for-detecting-multiple-space | 1608.01529 | null | http://arxiv.org/abs/1608.01529v1 | http://arxiv.org/pdf/1608.01529v1.pdf | Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos | In this work, we propose an approach to the spatiotemporal localisation
(detection) and classification of multiple concurrent actions within temporally
untrimmed videos. Our framework is composed of three stages. In stage 1,
appearance and motion detection networks are employed to localise and score
actions from colour... | ['Fabio Cuzzolin', 'Suman Saha', 'Philip H. S. Torr', 'Michael Sapienza', 'Gurkirt Singh'] | 2016-08-04 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.57623959e-01 -2.70643085e-01 -1.11553684e-01 -4.33044843e-02
-7.70752788e-01 -5.48218548e-01 6.15057588e-01 -6.42955974e-02
-7.60361731e-01 6.58845544e-01 6.84611648e-02 1.90357283e-01
-2.92841911e-01 -2.07041442e-01 -6.46203697e-01 -7.78196633e-01
-6.21659338e-01 2.01750517e-01 1.01054585e+00 1.40442818... | [8.320929527282715, 0.4316580891609192] |
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