paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c3a5b1ab-499f-4ebf-8a47-89fbb08d35a9 | fine-grained-action-analysis-a-multi-modality | 2307.02730 | null | https://arxiv.org/abs/2307.02730v1 | https://arxiv.org/pdf/2307.02730v1.pdf | Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating | The fine-grained action analysis of the existing action datasets is challenged by insufficient action categories, low fine granularities, limited modalities, and tasks. In this paper, we propose a Multi-modality and Multi-task dataset of Figure Skating (MMFS) which was collected from the World Figure Skating Championsh... | ['Gui-Hong Lao', 'Hao liu', 'Ning Zhou', 'Wen-Yue Chen', 'Si-Fan Zhang', 'Yu-Ning Ding', 'Sheng-Lan Liu'] | 2023-07-06 | null | null | null | null | ['action-quality-assessment', 'action-analysis', 'action-recognition-in-videos', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.58222961e-01 -4.05199081e-01 -5.30981898e-01 -1.77923083e-01
-9.75451171e-01 -3.78289312e-01 5.12768865e-01 -3.23180735e-01
-3.76249433e-01 5.04737914e-01 1.01077521e+00 4.74801093e-01
-3.13586533e-01 -6.42701149e-01 -4.54653561e-01 -5.95708072e-01
-2.73142140e-02 -2.43664607e-01 7.01741219e-01 -2.60623962... | [7.906041145324707, 0.4084053039550781] |
59df01f0-601a-456c-9086-0debf34c3baf | local-implicit-ray-function-for-generalizable | 2304.12746 | null | https://arxiv.org/abs/2304.12746v1 | https://arxiv.org/pdf/2304.12746v1.pdf | Local Implicit Ray Function for Generalizable Radiance Field Representation | We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering approach for novel view rendering. Current generalizable neural radiance fields (NeRF) methods sample a scene with a single ray per pixel and may therefore render blurred or aliased views when the input views and rendered views capture scen... | ['Qing Wang', 'Xuan Wang', 'Xiaoyu Li', 'Ying Feng', 'Qi Zhang', 'Xin Huang'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Local_Implicit_Ray_Function_for_Generalizable_Radiance_Field_Representation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Local_Implicit_Ray_Function_for_Generalizable_Radiance_Field_Representation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 3.08036000e-01 -2.22455248e-01 4.14969593e-01 -4.76354808e-01
-6.12945855e-01 -6.14919245e-01 5.80740452e-01 -4.71958816e-01
1.63580582e-01 4.54100460e-01 1.44673914e-01 -2.78003305e-01
-6.83154613e-02 -1.44330239e+00 -9.61726427e-01 -5.60681045e-01
2.72373319e-01 2.27892801e-01 1.93514094e-01 -2.84937263... | [9.518056869506836, -3.073570966720581] |
5c53dd4d-02d1-4817-b8e8-5e53965b3ead | gipso-geometrically-informed-propagation-for | 2207.09763 | null | https://arxiv.org/abs/2207.09763v1 | https://arxiv.org/pdf/2207.09763v1.pdf | GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation | 3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This can significantly hinder the navigation capabilities of self-driving vehicles. This paper advances the state o... | ['Fabio Poiesi', 'Elisa Ricci', 'Giuseppe Fiameni', 'Fabio Galasso', 'Nicu Sebe', 'Stéphane Lathuilière', 'Evgeny Krivosheev', 'Cristiano Saltori'] | 2022-07-20 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 5.81439026e-02 -1.17176659e-02 1.50225507e-02 -4.60712194e-01
-4.46897060e-01 -7.99400151e-01 6.90052450e-01 -1.05525598e-01
-5.73150337e-01 6.69887543e-01 -7.45710790e-01 -3.96885365e-01
1.54273376e-01 -8.88056159e-01 -1.22160494e+00 -4.72892851e-01
6.09069690e-02 1.07427394e+00 8.80107164e-01 -6.10047400... | [8.146190643310547, -2.6270854473114014] |
997cc91c-0404-47e6-9027-37d3dd498862 | towards-understanding-distributional-1 | null | null | https://openreview.net/forum?id=nK7eZEURiJ4 | https://openreview.net/pdf?id=nK7eZEURiJ4 | Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm | Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive... | ['Linglong Kong', 'Bei Jiang', 'Xiaodong Yan', 'Aref Sadeghi', 'Yafei Wang', 'Enze Shi', 'Yi Liu', 'Yingnan Zhao', 'Ke Sun'] | 2021-09-29 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.45523989e-01 3.70799690e-01 -5.03195703e-01 -3.83269310e-01
-1.19989729e+00 -4.85634834e-01 3.56886804e-01 9.37523842e-02
-8.15416694e-01 8.89251709e-01 1.50322556e-01 -5.31245291e-01
-5.68193436e-01 -6.75764084e-01 -7.93722451e-01 -1.21707988e+00
-1.75419778e-01 6.53598011e-01 -2.01263800e-01 -1.01659976... | [4.091377258300781, 2.572293758392334] |
a475caa0-da23-44d5-832d-e8951f2941c8 | practical-and-consistent-estimation-of-f | 1905.11112 | null | https://arxiv.org/abs/1905.11112v2 | https://arxiv.org/pdf/1905.11112v2.pdf | Practical and Consistent Estimation of f-Divergences | The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning. Most works study this problem under very weak assumptions, in which case it is provably hard. We consider the case of stronger structural assumptions that are commonly sa... | ['Paul K. Rubenstein', 'Josip Djolonga', 'Olivier Bousquet', 'Ilya Tolstikhin', 'Carlos Riquelme'] | 2019-05-27 | practical-and-consistent-estimation-of-f-1 | http://papers.nips.cc/paper/8661-practical-and-consistent-estimation-of-f-divergences | http://papers.nips.cc/paper/8661-practical-and-consistent-estimation-of-f-divergences.pdf | neurips-2019-12 | ['mutual-information-estimation'] | ['methodology'] | [ 3.93769518e-02 1.84491158e-01 -1.20985679e-01 -4.99425620e-01
-5.25610864e-01 -4.04802501e-01 5.61660767e-01 3.64722103e-01
-5.55042267e-01 9.51127887e-01 1.99659187e-02 -4.15084809e-01
-4.07409370e-01 -8.77255619e-01 -5.54466367e-01 -6.61871493e-01
-2.74996430e-01 6.30843580e-01 -8.79527107e-02 1.04113281... | [7.430787086486816, 4.031768798828125] |
fbfed48d-4aab-4215-818d-68416d97df48 | gentle-a-genre-diverse-multilayer-challenge | 2306.01966 | null | https://arxiv.org/abs/2306.01966v1 | https://arxiv.org/pdf/2306.01966v1.pdf | GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation | We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-of domain evaluation: dictionary entries, esports commentaries, legal documents, medical notes, poetry, mathematical proofs, syllabuses, and threat letters. GENTLE is manually annotated for a... | ['Amir Zeldes', 'YIlun Zhu', 'Siyao Peng', 'Yang Janet Liu', 'Jessica Lin', 'Lauren Levine', 'Luke Gessler', 'Shabnam Behzad', 'Tatsuya Aoyama'] | 2023-06-03 | null | null | null | null | ['mathematical-proofs', 'discourse-parsing', 'dependency-parsing', 'coreference-resolution'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-7.78588876e-02 4.41544801e-01 -6.77082360e-01 -2.11974174e-01
-1.12353158e+00 -1.07707238e+00 7.29149282e-01 5.69191337e-01
-7.87540793e-01 1.23192501e+00 8.57489765e-01 -5.21729469e-01
-6.56920299e-02 -1.61972448e-01 -3.76683295e-01 -3.20828021e-01
-2.37648010e-01 9.72757220e-01 1.17319122e-01 -5.41937113... | [9.432639122009277, 9.410720825195312] |
ab7c4818-467f-49c1-88cc-473e0364b745 | joint-semantic-mining-for-weakly-supervised | null | null | http://proceedings.neurips.cc/paper/2021/hash/642e92efb79421734881b53e1e1b18b6-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/642e92efb79421734881b53e1e1b18b6-Paper.pdf | Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection | Training saliency detection models with weak supervisions, e.g., image-level tags or captions, is appealing as it removes the costly demand of per-pixel annotations. Despite the rapid progress of RGB-D saliency detection in fully-supervised setting, it however remains an unexplored territory when only weak supervision ... | ['Li Cheng', 'Huchuan Lu', 'Yongri Piao', 'Miao Zhang', 'Cheng Yan', 'Qi Bi', 'Wei Ji', 'Jingjing Li'] | 2021-12-01 | null | https://openreview.net/forum?id=mv-1sL8FMN5 | https://openreview.net/pdf?id=mv-1sL8FMN5 | neurips-2021-12 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 7.41076589e-01 6.01424456e-01 -2.12224215e-01 -5.63967466e-01
-8.70739818e-01 -3.11721265e-01 6.09956026e-01 2.13063046e-01
-5.08743942e-01 5.56580842e-01 2.06465293e-02 -1.23946562e-01
9.06571895e-02 -4.56911236e-01 -1.01027489e+00 -6.53198779e-01
3.22773159e-01 2.48554304e-01 7.72924781e-01 -1.13818318... | [9.711653709411621, -0.6276580691337585] |
ca3a493f-9ac5-47d4-a1e1-bd4d33b34c01 | priorcvae-scalable-mcmc-parameter-inference | 2304.04307 | null | https://arxiv.org/abs/2304.04307v2 | https://arxiv.org/pdf/2304.04307v2.pdf | PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling | In applied fields where the speed of inference and model flexibility are crucial, the use of Bayesian inference for models with a stochastic process as their prior, e.g. Gaussian processes (GPs) is ubiquitous. Recent literature has demonstrated that the computational bottleneck caused by GP priors or their finite reali... | ['Seth Flaxman', 'Max Cairney-Leeming', 'Elizaveta Semenova'] | 2023-04-09 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-8.50734580e-03 8.64002779e-02 4.71398048e-02 -1.71824917e-02
-5.30201912e-01 -6.74803495e-01 1.07217658e+00 -1.52478412e-01
-2.73341596e-01 1.01356077e+00 5.53402863e-02 -5.38955927e-01
-3.21202189e-01 -1.07914734e+00 -7.99702346e-01 -1.03211486e+00
1.55907581e-02 8.16819727e-01 1.82916164e-01 1.74180403... | [6.864471912384033, 3.869149923324585] |
aaaf8124-9f38-4bc9-b429-4ce7c88ed0c5 | count-and-similarity-aware-r-cnn-for | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2678_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620086.pdf | Count- and Similarity-aware R-CNN for Pedestrian Detection | Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and proposal similarity information within a two-stage pedestrian detection framework. Both pedestrian count and prop... | ['Rao Muhammad Anwer', 'Hisham Cholakkal', 'Fahad Shahbaz Khan', 'Yanwei Pang', 'Mubarak Shah', 'Ling Shao', 'Jin Xie'] | null | null | null | null | eccv-2020-8 | ['human-instance-segmentation'] | ['computer-vision'] | [-7.66316056e-02 -1.45457089e-01 -5.50978407e-02 -5.21145225e-01
-9.07489836e-01 -3.11311483e-01 4.97999460e-01 2.87156135e-01
-8.32939506e-01 5.26066005e-01 -2.29750529e-01 -7.24064931e-02
8.16031933e-01 -6.82187676e-01 -7.08182871e-01 -5.90506136e-01
-3.88206504e-02 3.68614107e-01 1.01905608e+00 -5.02373204... | [7.917999267578125, -0.5806717872619629] |
9863d609-be65-42d3-b64f-59c4c36a67ed | occluded-video-instance-segmentation-dataset | 2111.07950 | null | https://arxiv.org/abs/2111.07950v1 | https://arxiv.org/pdf/2111.07950v1.pdf | Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge | Although deep learning methods have achieved advanced video object recognition performance in recent years, perceiving heavily occluded objects in a video is still a very challenging task. To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation... | ['Song Bai', 'Philip H. S. Torr', 'Alan Yuille', 'Serge Belongie', 'Xiang Bai', 'Xiaoyu Liu', 'Xinggang Wang', 'Yao Hu', 'Yan Gao', 'Jiyang Qi'] | 2021-11-15 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.09864527e-01 -1.08348101e-01 -4.47608232e-01 -4.27959830e-01
-5.77561140e-01 -4.75869894e-01 3.48377883e-01 -4.51454639e-01
-2.27142468e-01 4.89996761e-01 1.96293175e-01 -1.27096027e-01
2.85952777e-01 -3.19131255e-01 -1.12313199e+00 -4.78251070e-01
-1.35904491e-01 3.63033354e-01 4.88145798e-01 1.79373756... | [9.27838134765625, 0.11483034491539001] |
a9ac4b59-752a-47f2-8160-48e63301ba33 | flame-few-shot-learning-from-natural-language | 2306.08042 | null | https://arxiv.org/abs/2306.08042v1 | https://arxiv.org/pdf/2306.08042v1.pdf | FLamE: Few-shot Learning from Natural Language Explanations | Natural language explanations have the potential to provide rich information that in principle guides model reasoning. Yet, recent work by Lampinen et al. (2022) has shown limited utility of natural language explanations in improving classification. To effectively learn from explanations, we present FLamE, a two-stage ... | ['Chenhao Tan', 'Yiming Zhang', 'Yangqiaoyu Zhou'] | 2023-06-13 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 2.74574369e-01 1.06607497e+00 -7.19857693e-01 -6.25381649e-01
-9.58895862e-01 -3.36670011e-01 1.12433577e+00 1.61638036e-01
3.32739390e-02 1.02887571e+00 8.02062809e-01 -8.50340486e-01
-1.58124808e-02 -5.99504590e-01 -5.53647518e-01 -3.05317581e-01
3.75965983e-01 7.17343986e-01 9.87833273e-03 -2.48529553... | [9.581384658813477, 6.870041370391846] |
772afc58-d0e7-46bc-99a0-74792ba4fe20 | aligning-bird-eye-view-representation-of | 2305.02909 | null | https://arxiv.org/abs/2305.02909v1 | https://arxiv.org/pdf/2305.02909v1.pdf | Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow | Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a popular solution to this challenge. Such concatenation is possible thanks to the removal of ego vehicle motion using its odometry. This method... | ['Elwan Héry', 'Vincent Frémont', 'Minh-Quan Dao'] | 2023-05-04 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [-1.24769464e-01 -4.01563734e-01 2.05833763e-01 -6.41227737e-02
-4.60603297e-01 -9.08631742e-01 8.57876658e-01 2.66927741e-02
-4.00840491e-01 1.19226061e-01 -4.65187989e-02 -1.27795786e-01
3.68868262e-01 -8.64286423e-01 -8.19771707e-01 -5.67230463e-01
2.31922753e-02 5.08780956e-01 8.49386811e-01 -2.06613749... | [7.709722995758057, -2.4682867527008057] |
a8054300-14f1-459a-aa2b-e5e7dc5866ea | instance-segmentation-based-semantic-matting | 1904.05457 | null | http://arxiv.org/abs/1904.05457v1 | http://arxiv.org/pdf/1904.05457v1.pdf | Instance Segmentation based Semantic Matting for Compositing Applications | Image compositing is a key step in film making and image editing that aims to
segment a foreground object and combine it with a new background. Automatic
image compositing can be done easily in a studio using chroma-keying when the
background is pure blue or green. However, image compositing in natural scenes
with comp... | ['Guanqing Hu', 'James J. Clark'] | 2019-04-10 | null | null | null | null | ['semantic-image-matting'] | ['computer-vision'] | [ 1.18880594e+00 8.55870843e-02 4.06253397e-01 -5.09040475e-01
-5.46502113e-01 -6.44673705e-01 6.76239073e-01 1.50252357e-01
-2.82453924e-01 4.66803372e-01 -3.97216678e-01 -4.32787061e-01
2.07794651e-01 -1.05170488e+00 -8.66373658e-01 -4.08517241e-01
6.79274082e-01 6.12998486e-01 6.79815173e-01 -3.53944212... | [10.757508277893066, -0.7810491919517517] |
af6b2958-7c88-42fc-ae21-48067f6b6db7 | data-audit-identifying-attribute-utility-and | 2304.03218 | null | https://arxiv.org/abs/2304.03218v1 | https://arxiv.org/pdf/2304.03218v1.pdf | Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models | To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorithmic auditing has received substantial attention. To guide their audit procedures, existing methods rely on heuristic approaches or high-lev... | ['Mathias Unberath', 'Mohammad Mehdi Farhangi', 'Nicholas Petrick', 'Nathan Drenkow', 'Mitchell Pavlak'] | 2023-04-06 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 7.86190450e-01 3.58706176e-01 -4.96194333e-01 -5.94477236e-01
-9.48351741e-01 -4.85664457e-01 6.44260287e-01 4.07054961e-01
-4.79184747e-01 7.69688904e-01 2.70885646e-01 -7.93265820e-01
-3.08858305e-01 -6.64613366e-01 -9.04267848e-01 -5.91113985e-01
-8.04931298e-02 1.12498216e-01 -3.98225248e-01 3.78391445... | [8.752418518066406, 5.0290727615356445] |
9a857eb5-88ba-4f85-856e-207708801152 | multiple-tasks-integration-tagging-syntactic | null | null | https://aclanthology.org/2021.eacl-main.66 | https://aclanthology.org/2021.eacl-main.66.pdf | Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task | Departing from both sequential pipelines and monotask systems, we propose Multiple Tasks Integration (MTI), a multitask paradigm orthogonal to weight sharing. The essence of MTI is to process the input iteratively but concurrently at multiple levels of analysis, where each decision is based on all of the structures tha... | ["Timoth{\\'e}e Bernard"] | 2021-04-01 | null | null | null | eacl-2021-2 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.62946606e-01 6.07586384e-01 1.34947971e-01 -5.59210122e-01
-7.68708467e-01 -7.97445118e-01 5.50084412e-01 3.96294624e-01
-7.06612885e-01 7.47803748e-01 4.87386018e-01 -6.04480147e-01
-6.99415430e-02 -5.14919221e-01 -5.28147459e-01 -3.50864679e-01
-1.41784161e-01 7.91302323e-01 5.20873845e-01 -2.91359127... | [10.354477882385254, 9.538748741149902] |
7b2e06ab-45f9-4dda-8e0e-a35304ffaada | compressive-change-retrieval-for-moving | 1608.02051 | null | http://arxiv.org/abs/1608.02051v1 | http://arxiv.org/pdf/1608.02051v1.pdf | Compressive Change Retrieval for Moving Object Detection | Change detection, or anomaly detection, from street-view images acquired by
an autonomous robot at multiple different times, is a major problem in robotic
mapping and autonomous driving. Formulation as an image comparison task, which
operates on a given pair of query and reference images is common to many
existing appr... | ['Tomoya Murase', 'Kanji Tanaka'] | 2016-08-06 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 5.82168937e-01 -8.32727849e-01 2.61974260e-02 -3.91582698e-01
-8.76413226e-01 -4.73581135e-01 7.72471368e-01 2.57516146e-01
-4.61020917e-01 4.73130375e-01 -2.63454348e-01 2.57925875e-02
-2.37256095e-01 -7.14602113e-01 -8.58736455e-01 -6.73837364e-01
1.76226512e-01 2.30951086e-02 6.32271767e-01 -4.19557452... | [7.690834999084473, -1.8791528940200806] |
d2a609b1-13ce-4772-bb33-d7f4940fab91 | combining-multi-level-contexts-of-superpixel | 1803.05200 | null | http://arxiv.org/abs/1803.05200v1 | http://arxiv.org/pdf/1803.05200v1.pdf | Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling | Modern deep learning algorithms have triggered various image segmentation
approaches. However most of them deal with pixel based segmentation. However,
superpixels provide a certain degree of contextual information while reducing
computation cost. In our approach, we have performed superpixel level semantic
segmentatio... | ['Swarnendu Ghosh', 'Sandipan Choudhuri', 'Nibaran Das', 'Mita Nasipuri', 'Ritesh Sarkhel', 'Aritra Das'] | 2018-03-14 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 2.42114693e-01 2.10100368e-01 -9.09593776e-02 -6.49738491e-01
-8.97810161e-01 -3.91902477e-01 6.40953064e-01 3.07189256e-01
-7.08189666e-01 1.08811033e+00 -2.18358964e-01 2.13307049e-02
-2.75253862e-01 -9.39607084e-01 -5.22220790e-01 -8.76566052e-01
1.90098822e-01 5.47608912e-01 6.95400536e-01 1.87708214... | [9.508308410644531, 0.23753029108047485] |
6d7fc980-2744-4cb1-ba57-92012f774047 | opd-single-view-3d-openable-part-detection | 2203.16421 | null | https://arxiv.org/abs/2203.16421v1 | https://arxiv.org/pdf/2203.16421v1.pdf | OPD: Single-view 3D Openable Part Detection | We address the task of predicting what parts of an object can open and how they move when they do so. The input is a single image of an object, and as output we detect what parts of the object can open, and the motion parameters describing the articulation of each openable part. To tackle this task, we create two datas... | ['Angel X. Chang', 'Manolis Savva', 'Yongsen Mao', 'Hanxiao Jiang'] | 2022-03-30 | null | null | null | null | ['opd-single-view-3d-openable-part-detection'] | ['computer-vision'] | [ 9.71038714e-02 3.45857978e-01 -1.82159021e-01 -3.30160285e-04
-2.70728827e-01 -9.57602620e-01 5.11599541e-01 -5.42812526e-01
-2.02756315e-01 2.95051664e-01 2.48811558e-01 -1.92644462e-01
1.84091344e-01 -6.59032464e-01 -1.14587319e+00 -4.95736510e-01
1.02346979e-01 7.16706157e-01 4.81083155e-01 -4.52622958... | [7.07502555847168, -1.9152122735977173] |
3b558ead-3ba7-4732-a4e4-41729a0f78a9 | simple-questions-generate-named-entity | 2112.08808 | null | https://arxiv.org/abs/2112.08808v4 | https://arxiv.org/pdf/2112.08808v4.pdf | Simple Questions Generate Named Entity Recognition Datasets | Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities. This research introduces an ask-to-generate approach that automatically generates NER datasets by asking questions in simple natural lang... | ['Jaewoo Kang', 'Jinhyuk Lee', 'Seunghyun Yoon', 'Jaehyo Yoo', 'Hyunjae Kim'] | 2021-12-16 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [-6.62103668e-02 4.33867365e-01 -3.33858058e-02 -3.55734855e-01
-1.53091967e+00 -7.93514431e-01 7.04985082e-01 2.02977151e-01
-8.14973116e-01 1.10141945e+00 5.83855212e-01 4.79458421e-02
2.92179346e-01 -9.44100678e-01 -4.83345777e-01 1.42972782e-01
4.93451148e-01 6.63005650e-01 1.53219476e-01 -5.75365782... | [9.692569732666016, 9.411559104919434] |
71c56c8e-4b15-4015-8c82-f4e780b6a723 | unsupervised-induction-of-tree-substitution | null | null | https://aclanthology.org/D10-1117 | https://aclanthology.org/D10-1117.pdf | Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing | Inducing a grammar directly from text is one of the oldest and most challenging tasks in Computational Linguistics. Significant progress has been made for inducing dependency grammars, however the models employed are overly simplistic, particularly in comparison to supervised parsing models. In this paper we present an... | ['Trevor Cohn', 'Phil Blunsom'] | 2010-10-01 | null | null | null | null | ['dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.32587090e-01 7.74202228e-01 -1.92136437e-01 -8.02594364e-01
-9.42462146e-01 -5.46085715e-01 5.73157012e-01 3.41440797e-01
-4.67817664e-01 8.99046719e-01 3.72677356e-01 -6.26893818e-01
2.26671621e-01 -5.70708632e-01 -4.17692155e-01 -5.84861219e-01
-8.57846290e-02 1.12893760e+00 1.20781839e-01 -2.67973751... | [10.332280158996582, 9.751289367675781] |
c81b5ac0-c443-4300-b06a-a73e8fc5c62f | refinegan-universally-generating-waveform | 2111.00962 | null | https://arxiv.org/abs/2111.00962v3 | https://arxiv.org/pdf/2111.00962v3.pdf | RefineGAN: Universally Generating Waveform Better than Ground Truth with Highly Accurate Pitch and Intensity Responses | Most GAN(Generative Adversarial Network)-based approaches towards high-fidelity waveform generation heavily rely on discriminators to improve their performance. However, GAN methods introduce much uncertainty into the generation process and often result in mismatches of pitch and intensity, which is fatal when it comes... | ['Jing Guo', 'Wenxiao Zhao', 'Shengyuan Xu'] | 2021-11-01 | null | null | null | null | ['audio-generation', 'singing-voice-synthesis'] | ['audio', 'speech'] | [-4.71940860e-02 2.34151870e-01 2.95288116e-01 6.79454654e-02
-1.33058894e+00 -7.06158817e-01 3.13449442e-01 -2.86718518e-01
1.00029796e-01 7.95681119e-01 2.65316993e-01 3.06951609e-02
2.72350907e-01 -7.60351121e-01 -7.87964523e-01 -8.35732758e-01
2.40238652e-01 1.49763033e-01 -9.46638584e-02 -3.01088065... | [15.45550537109375, 6.07197904586792] |
fefc8a2d-2796-4f0c-b011-a4b90b1f349d | meeqa-natural-questions-in-meeting | 2305.08502 | null | https://arxiv.org/abs/2305.08502v1 | https://arxiv.org/pdf/2305.08502v1.pdf | MeeQA: Natural Questions in Meeting Transcripts | We present MeeQA, a dataset for natural-language question answering over meeting transcripts. It includes real questions asked during meetings by its participants. The dataset contains 48K question-answer pairs, extracted from 422 meeting transcripts, spanning multiple domains. Questions in transcripts pose a special c... | ['Eyal Kolman', 'Amir Kantor', 'Tom Braude', 'Reut Apel'] | 2023-05-15 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 2.67932475e-01 5.09399652e-01 4.10616279e-01 -7.26293087e-01
-1.90694356e+00 -9.23364103e-01 3.13996822e-01 1.56008422e-01
-2.84666270e-01 9.23104048e-01 7.77829707e-01 -3.04710239e-01
-2.10383475e-01 -3.34843099e-01 -4.16773558e-01 -2.04680279e-01
3.53966296e-01 7.26919532e-01 3.48929048e-01 -5.64620554... | [11.642705917358398, 8.02055835723877] |
8001af62-95d7-4765-bf0a-250b87446769 | deception-detection-in-videos-using-the | 2105.13659 | null | https://arxiv.org/abs/2105.13659v1 | https://arxiv.org/pdf/2105.13659v1.pdf | Deception Detection in Videos using the Facial Action Coding System | Facts are important in decision making in every situation, which is why it is important to catch deceptive information before they are accepted as facts. Deception detection in videos has gained traction in recent times for its various real-life application. In our approach, we extract facial action units using the fac... | ['Muhammad Waqas Anwar', 'Fan Zhang', 'Usama Ijaz Bajwa', 'Hammad Ud Din Ahmed'] | 2021-05-28 | null | null | null | null | ['deception-detection-in-videos', 'deception-detection-in-videos', 'deception-detection'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 1.11254618e-01 1.11645773e-01 2.36239493e-01 -5.58387995e-01
-3.79795045e-01 -3.49130988e-01 1.01923263e+00 -4.80471492e-01
-6.16750002e-01 9.11285520e-01 1.90965787e-01 -1.79130554e-01
-9.07584503e-02 -4.41179335e-01 -5.28900683e-01 -8.63085330e-01
9.39178318e-02 6.19370081e-02 -1.09604530e-01 -3.12845826... | [13.093595504760742, 1.904586911201477] |
c6c50372-fd41-42f6-85f4-f170ca172ddc | conversational-information-seeking | 2201.08808 | null | https://arxiv.org/abs/2201.08808v2 | https://arxiv.org/pdf/2201.08808v2.pdf | Conversational Information Seeking | Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph pro... | ['Filip Radlinski', 'Jeff Dalton', 'Johanne R. Trippas', 'Hamed Zamani'] | 2022-01-21 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 9.91870165e-02 1.54611722e-01 -3.97031307e-01 -4.00646001e-01
-4.03733611e-01 -1.00159752e+00 1.06669939e+00 2.24717140e-01
-4.12013173e-01 3.30127776e-01 4.94988769e-01 -6.97713494e-01
-3.12862456e-01 -1.20588213e-01 2.59161055e-01 -1.58927411e-01
-6.36591613e-02 3.33973676e-01 1.01459093e-01 -5.67014039... | [12.249733924865723, 7.774843692779541] |
6819881e-9b7f-4475-aa47-38dedf6843b1 | learning-to-cluster-faces-via-transformer | 2104.11502 | null | https://arxiv.org/abs/2104.11502v1 | https://arxiv.org/pdf/2104.11502v1.pdf | Learning to Cluster Faces via Transformer | Face clustering is a useful tool for applications like automatic face annotation and retrieval. The main challenge is that it is difficult to cluster images from the same identity with different face poses, occlusions, and image quality. Traditional clustering methods usually ignore the relationship between individual ... | ['Hanqing Wu', 'Hao Li', 'Xiuyu Sun', 'Kai Wang', 'Baigui Sun', 'Xioajiang Peng', 'Jinxing Ye'] | 2021-04-23 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-9.93765667e-02 8.70367661e-02 -1.68361828e-01 -8.67081881e-01
-5.17752588e-01 -3.46282214e-01 3.27551395e-01 -2.32830435e-01
2.10292712e-01 2.62176305e-01 7.70756081e-02 2.45393187e-01
-2.81660527e-01 -5.76010942e-01 -5.56092978e-01 -8.60642731e-01
-5.35483211e-02 7.10830450e-01 -1.81955218e-01 1.92672044... | [13.485862731933594, 1.01964271068573] |
cf1fb002-c3dd-4110-abf3-970c3e102254 | id-pose-sparse-view-camera-pose-estimation-by | 2306.17140 | null | https://arxiv.org/abs/2306.17140v1 | https://arxiv.org/pdf/2306.17140v1.pdf | ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models | Given sparse views of an object, estimating their camera poses is a long-standing and intractable problem. We harness the pre-trained diffusion model of novel views conditioned on viewpoints (Zero-1-to-3). We present ID-Pose which inverses the denoising diffusion process to estimate the relative pose given two input im... | ['Ying Shan', 'Yan-Pei Cao', 'Weihao Cheng'] | 2023-06-29 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [ 1.36429891e-01 1.35360926e-01 1.09578736e-01 -3.81496251e-01
-1.02280974e+00 -5.50574481e-01 3.80429357e-01 -7.45025277e-01
-8.03412423e-02 1.03398904e-01 1.59886897e-01 3.11089516e-01
-3.41859683e-02 -5.75345457e-01 -7.40555882e-01 -5.95254600e-01
7.17940331e-02 1.07825077e+00 2.73365378e-01 1.84292912... | [8.4166841506958, -2.822279930114746] |
39e840af-b98a-4c13-9414-9101bf7d19e8 | cubeslam-monocular-3d-object-detection-and | 1806.00557 | null | http://arxiv.org/abs/1806.00557v2 | http://arxiv.org/pdf/1806.00557v2.pdf | CubeSLAM: Monocular 3D Object SLAM | We present a method for single image 3D cuboid object detection and
multi-view object SLAM in both static and dynamic environments, and demonstrate
that the two parts can improve each other. Firstly for single image object
detection, we generate high-quality cuboid proposals from 2D bounding boxes and
vanishing points ... | ['Shichao Yang', 'Sebastian Scherer'] | 2018-06-01 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [-3.60538214e-01 -3.10378999e-01 -2.64042675e-01 -3.02761912e-01
-5.93421221e-01 -7.97514260e-01 2.65033364e-01 -2.82919824e-01
-4.34648752e-01 1.95472464e-01 -2.08070755e-01 2.47149467e-01
3.74175668e-01 -3.50493193e-01 -8.89008999e-01 -5.03293633e-01
3.86129111e-01 9.65966105e-01 7.11564660e-01 6.50875717... | [7.3649678230285645, -2.3587968349456787] |
1d028ff0-e2a5-479a-bf0e-67b813e959d9 | hybrid-curriculum-learning-for-emotion | 2112.11718 | null | https://arxiv.org/abs/2112.11718v2 | https://arxiv.org/pdf/2112.11718v2.pdf | Hybrid Curriculum Learning for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance. Motivated by recent studies which have proven that feeding training examples in a meaningful order rather than considering them randomly can boost the performance of models, we propose an ERC-oriented hybrid curriculum learni... | ['Longjun Cai', 'Yue Mao', 'Yi Shen', 'Lin Yang'] | 2021-12-22 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.66145378e-01 1.46058947e-01 4.95500714e-02 -7.61457503e-01
-8.26194704e-01 -4.02174413e-01 4.54491407e-01 3.09964806e-01
-3.92560571e-01 2.06845716e-01 4.35292572e-01 -2.11167857e-01
4.55971062e-02 -3.79441798e-01 -3.85899067e-01 -6.27364874e-01
1.13913171e-01 4.15790975e-01 -2.22983152e-01 -4.80056107... | [13.081714630126953, 6.0574140548706055] |
28ce24b0-da5c-4a4d-8367-406f7bd6c5bb | dore-document-ordered-relation-extraction | 2210.16064 | null | https://arxiv.org/abs/2210.16064v2 | https://arxiv.org/pdf/2210.16064v2.pdf | DORE: Document Ordered Relation Extraction based on Generative Framework | In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dependencies. However, previous generative methods using lexical representation do not naturally fit document-level relation extraction (DocRE)... | ['Zheng Zhang', 'Xipeng Qiu', 'Hang Yan', 'Yuqing Yang', 'Qipeng Guo'] | 2022-10-28 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 7.59808123e-02 3.00542593e-01 -3.01971436e-01 -1.65558770e-01
-4.06869560e-01 -4.55141693e-01 7.01895356e-01 -8.20580423e-02
1.07087158e-01 8.93378079e-01 3.11162651e-01 -4.91454005e-01
-5.37438281e-02 -1.04158342e+00 -6.64640605e-01 -4.65706766e-01
3.57523002e-02 5.44919193e-01 1.77944452e-01 -3.49376947... | [9.431200981140137, 8.711225509643555] |
31f65c50-fa1f-4e1e-9ddd-5f2ae1bfb65f | bridging-the-gap-between-local-semantic | 2210.08875 | null | https://arxiv.org/abs/2210.08875v1 | https://arxiv.org/pdf/2210.08875v1.pdf | Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval | This paper addresses the problem of semantic-based image retrieval of natural scenes. A typical content-based image retrieval system deals with the query image and images in the dataset as a collection of low-level features and retrieves a ranked list of images based on the similarities between features of the query im... | ['Yousef Alqasrawi'] | 2022-10-17 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.18319976e-01 -3.10364932e-01 -1.53691038e-01 -5.11311710e-01
-6.09252095e-01 -4.21801686e-01 6.77555025e-01 6.18985593e-01
-5.83457470e-01 1.48698762e-01 3.11842233e-01 2.09542334e-01
-5.06445527e-01 -9.86222148e-01 -3.27538162e-01 -4.53177452e-01
2.15764731e-01 4.56375360e-01 6.33292079e-01 -3.62428814... | [10.814258575439453, 0.3817130923271179] |
4056fb9c-b51d-4ad5-9f2a-57bc2b67453d | learning-so3-equivariant-representations-with | 1711.06721 | null | http://arxiv.org/abs/1711.06721v3 | http://arxiv.org/pdf/1711.06721v3.pdf | Learning SO(3) Equivariant Representations with Spherical CNNs | We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel sphe... | ['Ameesh Makadia', 'Christine Allen-Blanchette', 'Kostas Daniilidis', 'Carlos Esteves'] | 2017-11-17 | learning-so3-equivariant-representations-with-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Carlos_Esteves_Learning_SO3_Equivariant_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Carlos_Esteves_Learning_SO3_Equivariant_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-classification'] | ['computer-vision'] | [ 5.68882637e-02 1.53031096e-01 2.80543983e-01 -2.33059496e-01
-4.14848119e-01 -9.50941443e-01 9.56188560e-01 -5.47357380e-01
-6.18654847e-01 1.82660744e-01 4.71896261e-01 -2.83492744e-01
-1.06111594e-01 -6.79221392e-01 -1.21621668e+00 -6.90058112e-01
-1.94533363e-01 2.18136296e-01 1.47602558e-01 -3.78815383... | [8.885322570800781, 2.3575592041015625] |
152ba743-14cd-4772-80d2-3f6111862e03 | a-restarted-large-scale-spectral-clustering | 2306.15138 | null | https://arxiv.org/abs/2306.15138v2 | https://arxiv.org/pdf/2306.15138v2.pdf | A Restarted Large-Scale Spectral Clustering with Self-Guiding and Block Diagonal Representation | Spectral clustering is one of the most popular unsupervised machine learning methods. Constructing similarity matrix is crucial to this type of method. In most existing works, the similarity matrix is computed once for all or is updated alternatively. However, the former is difficult to reflect comprehensive relationsh... | ['Gang Wu', 'Yongyan Guo'] | 2023-06-27 | null | null | null | null | ['clustering'] | ['methodology'] | [ 1.16813146e-01 -4.89952087e-01 -2.11564332e-01 -2.55812500e-02
-4.85155374e-01 -4.55362648e-01 2.09333479e-01 9.03085619e-02
-3.60637963e-01 4.54600543e-01 -1.77355036e-01 -1.44622177e-01
-4.65929300e-01 -6.55063450e-01 -2.73173124e-01 -1.25942659e+00
-9.23759714e-02 4.49977547e-01 4.16094303e-01 -3.73436846... | [7.576948165893555, 4.727859020233154] |
de9f7847-8660-43fc-8f97-1e46ff293843 | domain-specific-chatbots-for-science-using | 2306.10067 | null | https://arxiv.org/abs/2306.10067v1 | https://arxiv.org/pdf/2306.10067v1.pdf | Domain-specific ChatBots for Science using Embeddings | Large language models (LLMs) have emerged as powerful machine-learning systems capable of handling a myriad of tasks. Tuned versions of these systems have been turned into chatbots that can respond to user queries on a vast diversity of topics, providing informative and creative replies. However, their application to p... | ['Kevin G. Yager'] | 2023-06-15 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-2.17533223e-02 -1.73537377e-02 -2.28107721e-01 -2.68579304e-01
-1.10815823e+00 -8.20995271e-01 8.44884932e-01 3.36383224e-01
-4.43212777e-01 6.24257505e-01 3.62018287e-01 -6.33000076e-01
-1.24344088e-01 -5.68929076e-01 -4.60556597e-01 -3.82742882e-01
2.19867975e-01 5.85055053e-01 1.77072749e-01 -2.53806114... | [10.473663330078125, 8.328375816345215] |
adf1c6a5-499a-4865-b160-6b7c54299b95 | time-perception-machine-temporal-point | 1808.04063 | null | http://arxiv.org/abs/1808.04063v2 | http://arxiv.org/pdf/1808.04063v2.pdf | Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction | Numerous powerful point process models have been developed to understand
temporal patterns in sequential data from fields such as health-care,
electronic commerce, social networks, and natural disaster forecasting. In this
paper, we develop novel models for learning the temporal distribution of human
activities in stre... | ['Guang-Tong Zhou', 'Yatao Zhong', 'Luke Bornn', 'Greg Mori', 'Bicheng Xu'] | 2018-08-13 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 5.85095249e-02 -3.61743867e-01 -4.00091231e-01 -3.03325176e-01
-1.68137431e-01 -2.74472564e-01 7.40716517e-01 4.79351819e-01
-3.78309220e-01 4.75461304e-01 7.86724627e-01 -6.82151467e-02
-3.57391238e-01 -8.93969119e-01 -7.09033430e-01 -4.54523116e-01
-5.44422984e-01 3.06506455e-01 2.92703658e-01 1.85619727... | [7.083510398864746, 3.165496587753296] |
72ea918a-0ac2-4965-8019-cbdf700d1225 | brits-bidirectional-recurrent-imputation-for | 1805.10572 | null | http://arxiv.org/abs/1805.10572v1 | http://arxiv.org/pdf/1805.10572v1.pdf | BRITS: Bidirectional Recurrent Imputation for Time Series | Time series are widely used as signals in many classification/regression
tasks. It is ubiquitous that time series contains many missing values. Given
multiple correlated time series data, how to fill in missing values and to
predict their class labels? Existing imputation methods often impose strong
assumptions of the ... | ['Lei LI', 'Hao Zhou', 'Jian Li', 'Dong Wang', 'Yitan Li', 'Wei Cao'] | 2018-05-27 | brits-bidirectional-recurrent-imputation-for-1 | http://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series | http://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series.pdf | neurips-2018-12 | ['multivariate-time-series-imputation', 'traffic-data-imputation'] | ['time-series', 'time-series'] | [ 3.95398259e-01 -3.64812255e-01 -2.78497219e-01 -5.54195344e-01
-6.24212861e-01 -2.73456037e-01 1.81659952e-01 -1.71971709e-01
-1.14000835e-01 1.04225600e+00 4.41527247e-01 -3.85018289e-01
-5.26492178e-01 -7.84084141e-01 -1.07779491e+00 -8.18040073e-01
-1.22705527e-01 4.85012412e-01 -4.93458986e-01 -3.56266767... | [7.041081428527832, 3.1626834869384766] |
ed274fc4-9a7a-4fce-9365-52390abe92c7 | a-tip-attribute-aware-text-infilling-via-pre | null | null | https://aclanthology.org/2022.coling-1.511 | https://aclanthology.org/2022.coling-1.511.pdf | A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model | Text infilling aims to restore incomplete texts by filling in blanks, which has attracted more attention recently because of its wide application in ancient text restoration and text rewriting. However, attribute- aware text infilling is yet to be explored, and existing methods seldom focus on the infilling length of e... | ['Manabu Okumura', 'Kotaro Funakoshi', 'Jingyi You', 'Dongyuan Li'] | null | null | null | null | coling-2022-10 | ['ancient-tex-restoration', 'text-infilling'] | ['miscellaneous', 'natural-language-processing'] | [ 5.28209269e-01 -4.51182164e-02 -5.96885122e-02 -2.05854446e-01
-5.90165615e-01 -4.74169791e-01 7.52889931e-01 4.12824631e-01
-2.71843553e-01 5.94635487e-01 7.99268425e-01 -5.78082979e-01
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7.38640785e-01 3.84993315e-01 1.59641847e-01 -4.61830586... | [11.8569974899292, 9.047163963317871] |
086097f6-fdee-4a62-9f84-ff6809fd4bc1 | graph-collaborative-signals-denoising-and | 2304.03344 | null | https://arxiv.org/abs/2304.03344v2 | https://arxiv.org/pdf/2304.03344v2.pdf | Graph Collaborative Signals Denoising and Augmentation for Recommendation | Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insuff... | ['Zhang Dong', 'Philip S. Yu', 'Jiawei Zhang', 'Hao Peng', 'Ke Xu', 'Ziwei Fan'] | 2023-04-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-4.58014160e-01 -2.08058104e-01 -4.73071158e-01 -1.51731342e-01
-1.37775511e-01 -6.14312291e-01 2.54412413e-01 1.82104230e-01
-5.87133616e-02 2.64070749e-01 7.65901625e-01 -3.75246614e-01
-7.82792449e-01 -1.11765516e+00 -2.78543741e-01 -4.52362180e-01
-4.95159924e-01 1.33417070e-01 9.43940952e-02 -5.31591654... | [10.146980285644531, 5.593122482299805] |
0bdfe3aa-455f-4086-a933-9f41c6b6750a | learning-sensorimotor-primitives-of | 2203.03797 | null | https://arxiv.org/abs/2203.03797v1 | https://arxiv.org/pdf/2203.03797v1.pdf | Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations | This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person. The sub-tasks consist of moving the robot's end-effector until it reaches a sub-goal region in ... | ['Abdeslam Boularias', 'Kostas Bekris', 'Bowen Wen', 'Junchi Liang'] | 2022-03-08 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.18263465e-01 1.09139256e-01 -2.46471077e-01 -2.34352246e-01
-1.26459956e-01 -5.07778943e-01 6.90831780e-01 7.79530481e-02
-8.11277211e-01 8.62624168e-01 -3.37724090e-01 -8.39201435e-02
-2.19507620e-01 -2.07042217e-01 -9.48991537e-01 -8.17406416e-01
-3.91529560e-01 7.84559488e-01 4.59670961e-01 4.70835231... | [4.629035949707031, 0.8072786331176758] |
0214456f-d20d-4f72-953b-658462e5ffa4 | covid-19-literature-mining-and-retrieval | 2205.14781 | null | https://arxiv.org/abs/2205.14781v1 | https://arxiv.org/pdf/2205.14781v1.pdf | COVID-19 Literature Mining and Retrieval using Text Mining Approaches | The novel coronavirus disease (COVID-19) began in Wuhan, China, in late 2019 and to date has infected over 148M people worldwide, resulting in 3.12M deaths. On March 10, 2020, the World Health Organisation (WHO) declared it as a global pandemic. Many academicians and researchers started to publish papers describing the... | ['Rohit Chivukula', 'T. Jaya Lakshmi', 'Satti Thanuja Pavani', 'Sanku Satya Uday'] | 2022-05-29 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [-1.96763143e-01 -4.31715906e-01 -3.85618895e-01 -1.10943712e-01
-6.39163315e-01 -6.25980794e-01 7.58208632e-01 6.31125808e-01
-5.65505922e-01 8.20305943e-01 6.92532241e-01 -3.38380575e-01
-2.01005742e-01 -7.24038064e-01 -2.00505167e-01 -4.30843055e-01
-1.29452124e-01 7.09561169e-01 1.23791121e-01 -1.83194116... | [8.770913124084473, 8.604622840881348] |
dd033814-dc0c-4561-a050-4bac814116de | sllen-semantic-aware-low-light-image | 2211.11571 | null | https://arxiv.org/abs/2211.11571v2 | https://arxiv.org/pdf/2211.11571v2.pdf | SLLEN: Semantic-aware Low-light Image Enhancement Network | How to effectively explore semantic feature is vital for low-light image enhancement (LLE). Existing methods usually utilize the semantic feature that is only drawn from the output produced by high-level semantic segmentation (SS) network. However, if the output is not accurately estimated, it would affect the high-lev... | ['Chuheng Chen', 'DaCheng Tao', 'Jinhui Tang', 'Jinshan Pan', 'Charles A. Guo', 'Mingye Ju'] | 2022-11-21 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 3.07201684e-01 6.15759343e-02 -1.01882100e-01 -4.01018977e-01
-4.63114470e-01 -8.15598667e-02 2.73283631e-01 -2.08335057e-01
-4.35312212e-01 6.97822332e-01 1.66415334e-01 9.24662650e-02
1.15068695e-02 -1.02457213e+00 -6.77790940e-01 -8.57029617e-01
4.89950091e-01 -3.70480299e-01 3.13489288e-01 -2.04919592... | [9.660361289978027, -0.9963712692260742] |
b8e79b6a-86b4-4c47-aa60-4329dc5ca4c5 | a-computation-and-communication-efficient | 2205.15580 | null | https://arxiv.org/abs/2205.15580v2 | https://arxiv.org/pdf/2205.15580v2.pdf | A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, compressed communication, and partial participation. We prove that the new method has optimal oracle complexity and state-of-the-art communication complexity in the ... | ['Peter Richtárik', 'Alexander Tyurin'] | 2022-05-31 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.61561936e-01 1.57203898e-01 -3.35457832e-01 -1.61788315e-01
-1.27423751e+00 -9.60423231e-01 3.32225233e-01 2.83184111e-01
-8.19869459e-01 8.01073432e-01 4.85420048e-01 -6.44702733e-01
-5.25654733e-01 -6.84404492e-01 -1.06308341e+00 -8.81386280e-01
-4.08674657e-01 4.18467075e-01 -1.72282368e-01 1.77482888... | [6.224489212036133, 4.996263027191162] |
ad035f51-78f6-4700-b7ef-d65306c22ad6 | can-deep-network-balance-copy-move-forgery | 2305.10247 | null | https://arxiv.org/abs/2305.10247v1 | https://arxiv.org/pdf/2305.10247v1.pdf | Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment? | Copy-move forgery detection is a crucial research area within digital image forensics, as it focuses on identifying instances where objects in an image are duplicated and placed in different locations. The detection of such forgeries is particularly important in contexts where they can be exploited for malicious purpos... | ['Shizhen Chang'] | 2023-05-17 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.79333556e-01 -5.27786016e-01 3.34049314e-02 -4.26654220e-02
-1.21163428e+00 -6.53603971e-01 7.48971522e-01 6.05574157e-03
-1.63071245e-01 1.52797222e-01 -8.48917216e-02 -3.40023756e-01
2.99475323e-02 -4.59847897e-01 -6.25401378e-01 -8.64932179e-01
-3.12668495e-02 7.62034357e-02 4.35374349e-01 2.52218898... | [12.351665496826172, 0.9808393716812134] |
1b6352f1-d74e-4866-a735-7189b065a6ab | use-of-transfer-learning-and-wavelet | 2103.03602 | null | https://arxiv.org/abs/2103.03602v1 | https://arxiv.org/pdf/2103.03602v1.pdf | Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection | Breast cancer is one of the most common cause of deaths among women. Mammography is a widely used imaging modality that can be used for cancer detection in its early stages. Deep learning is widely used for the detection of cancerous masses in the images obtained via mammography. The need to improve accuracy remains co... | ['Muhammad Bilal', 'Junaid Qadir', 'Muhammad Shahzad Younis', 'Ahmed Rasheed'] | 2021-03-05 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.28284049e-01 4.20886278e-02 -3.43715698e-01 -4.70094144e-01
-6.22908890e-01 3.01310439e-02 1.19403034e-01 3.69048089e-01
-8.67748260e-01 2.59559840e-01 -9.28686038e-02 -6.47557139e-01
5.76719232e-02 -1.17187965e+00 -4.92496371e-01 -6.37482762e-01
-1.25031129e-01 3.99958104e-01 5.47271430e-01 -2.14798734... | [15.193923950195312, -2.5699000358581543] |
fc49298a-e63e-4401-a2b9-bef744ef3f13 | pcc-paraphrasing-with-bottom-k-sampling-and | 2208.08110 | null | https://arxiv.org/abs/2208.08110v3 | https://arxiv.org/pdf/2208.08110v3.pdf | PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation | Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficulty measure and goes through the curriculums only once. This paper presents \textbf{PCC}: \textbf{P}a... | ['Wai Lam', 'Hongyuan Lu'] | 2022-08-17 | null | null | null | null | ['paraphrase-generation', 'few-shot-text-classification', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 5.25871992e-01 1.80441812e-01 -1.31898418e-01 -3.78869802e-01
-1.01332462e+00 -6.45978987e-01 8.90467227e-01 2.21409708e-01
-4.36850399e-01 8.69860113e-01 4.99980420e-01 -3.67536277e-01
1.22480445e-01 -8.18357766e-01 -5.92846930e-01 -6.28779054e-01
5.93030035e-01 7.69243181e-01 2.09094405e-01 -7.83079803... | [11.832868576049805, 9.1710205078125] |
44c04c98-9a72-4837-8206-46427f58599d | differentially-private-cross-camera-person-re | 2306.02765 | null | https://arxiv.org/abs/2306.02765v1 | https://arxiv.org/pdf/2306.02765v1.pdf | Differentially Private Cross-camera Person Re-identification | Camera-based person re-identification is a heavily privacy-invading task by design, benefiting from rich visual data to match together person representations across different cameras. This high-dimensional data can then easily be used for other, perhaps less desirable, applications. We here investigate the possibility ... | ['Keiichi Yasumoto', 'Yuki Matsuda', 'Lucas Maris'] | 2023-06-05 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 6.38629138e-01 6.17303886e-03 2.22981483e-01 -5.64256489e-01
-4.21673596e-01 -9.82278943e-01 8.69947433e-01 2.22911350e-02
-8.77984822e-01 5.81348181e-01 3.70047778e-01 -8.80232230e-02
9.47599784e-02 -5.13630569e-01 -5.17981768e-01 -7.62187541e-01
1.38303228e-02 -2.36050449e-02 -1.41297638e-01 2.33165234... | [12.796225547790527, 0.8035993576049805] |
999adc9f-92ae-4af3-8d05-64688b8d9296 | do-you-follow-me-a-survey-of-recent | 2207.14627 | null | https://arxiv.org/abs/2207.14627v1 | https://arxiv.org/pdf/2207.14627v1.pdf | "Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking | While communicating with a user, a task-oriented dialogue system has to track the user's needs at each turn according to the conversation history. This process called dialogue state tracking (DST) is crucial because it directly informs the downstream dialogue policy. DST has received a lot of interest in recent years w... | ['Benoit Favre', 'Lina M. Rojas-Barahona', 'Léo Jacqmin'] | 2022-07-29 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.72402614e-01 5.60671866e-01 -3.61871839e-01 -6.88061297e-01
-3.17641318e-01 -7.16648936e-01 9.47601974e-01 5.79272173e-02
-4.55468595e-01 8.84524047e-01 7.91511059e-01 -3.88596743e-01
4.62194271e-02 -3.28309685e-01 2.53810018e-01 -2.05188438e-01
1.31594792e-01 5.01011550e-01 1.76025387e-02 -7.93514073... | [12.888623237609863, 7.985328674316406] |
75e60d44-2667-4d23-94e7-00f5ef8acdf1 | language-agnostic-semantic-consistent-text-to | null | null | https://aclanthology.org/2022.mml-1.1 | https://aclanthology.org/2022.mml-1.1.pdf | Language-agnostic Semantic Consistent Text-to-Image Generation | Recent GAN-based text-to-image generation models have advanced that they can generate photo-realistic images matching semantically with descriptions. However, research on multi-lingual text-to-image generation has not been carried out yet much. There are two problems when constructing a multilingual text-to-image gener... | ['Byoung-Tak Zhang', 'SeongHo Choi', 'Woo Suk Choi', 'SeongJun Jung'] | null | null | null | null | mml-acl-2022-5 | ['multilingual-text-to-image-generation', 'multi-lingual-text-to-image-generation'] | ['computer-vision', 'natural-language-processing'] | [ 4.40251082e-01 4.26246598e-02 9.91045311e-02 -4.15148348e-01
-1.21522307e+00 -7.36014187e-01 9.59948301e-01 -7.04330623e-01
-3.01263742e-02 9.69393075e-01 2.88367331e-01 -4.64263149e-02
6.50545537e-01 -9.40624595e-01 -9.56802905e-01 -5.67130804e-01
7.87421703e-01 8.77031505e-01 -1.87668353e-01 -2.24624172... | [12.068793296813965, -0.1793341040611267] |
417bbcfd-836f-4476-a77e-d13b71249745 | encore-ensemble-learning-using-convolution | 1906.08691 | null | https://arxiv.org/abs/1906.08691v1 | https://arxiv.org/pdf/1906.08691v1.pdf | ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair | Automated generate-and-validate (G&V) program repair techniques typically rely on hard-coded rules, only fix bugs following specific patterns, and are hard to adapt to different programming languages. We propose ENCORE, a new G&V technique, which uses ensemble learning on convolutional neural machine translation (NMT) ... | ['Moshi Wei', 'Thibaud Lutellier', 'Lawrence Pang', 'Viet Hung Pham', 'Lin Tan'] | 2019-06-20 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-3.35617512e-01 -3.67081732e-01 -3.32292736e-01 -1.64777145e-01
-1.04724610e+00 -8.12717974e-01 3.16190459e-02 2.70758539e-01
-1.54974476e-01 5.80391645e-01 -1.96844831e-01 -9.34301019e-01
2.01033533e-01 -8.72678459e-01 -1.45558560e+00 6.40037730e-02
-1.97704002e-01 -2.08866656e-01 2.99226135e-01 -2.40837306... | [7.609827995300293, 7.734960079193115] |
90c06851-7bc7-416e-a98e-4ee1f6374c33 | video-pose-track-with-graph-guided-sparse | 2303.00138 | null | https://arxiv.org/abs/2303.00138v3 | https://arxiv.org/pdf/2303.00138v3.pdf | Texture-Based Input Feature Selection for Action Recognition | The performance of video action recognition has been significantly boosted by using motion representations within a two-stream Convolutional Neural Network (CNN) architecture. However, there are a few challenging problems in action recognition in real scenarios, e.g., the variations in viewpoints and poses, and the cha... | ['Yalong Jiang'] | 2023-02-28 | null | null | null | null | ['human-parsing', 'multi-person-pose-estimation', 'multi-person-pose-estimation-and-tracking', 'graph-matching'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [ 5.48392892e-01 6.41962364e-02 -1.30952463e-01 -4.76173431e-01
-2.99701840e-01 -2.05880091e-01 3.23290169e-01 -7.18417704e-01
-3.18765759e-01 4.79915917e-01 2.22033471e-01 2.51289219e-01
4.43324745e-01 -5.84138334e-01 -8.99676919e-01 -7.53876746e-01
4.18150783e-01 2.76538551e-01 9.28986967e-01 -1.05737112... | [8.201454162597656, 0.644717276096344] |
64c2ff14-05a2-446f-a303-bcc2e8b06ced | openood-benchmarking-generalized-out-of | 2210.07242 | null | https://arxiv.org/abs/2210.07242v1 | https://arxiv.org/pdf/2210.07242v1.pdf | OpenOOD: Benchmarking Generalized Out-of-Distribution Detection | Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature. However, the field currently lacks a unified, strictly formulated, and comprehensive benchmark, which often results in unfair compa... | ['Ziwei Liu', 'Yixuan Li', 'Dan Hendrycks', 'Wayne Zhang', 'Kaiyang Zhou', 'Xuefeng Du', 'Yiyou Sun', 'Bo Li', 'Guangyao Chen', 'Haoqi Wang', 'Wenxuan Peng', 'Kunyuan Ding', 'Zitang Zhou', 'Dejian Zou', 'Pengyun Wang', 'Jingkang Yang'] | 2022-10-13 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 8.14220160e-02 -5.60872257e-03 -2.09857583e-01 -2.08898932e-01
-7.85869539e-01 -4.37517792e-01 7.60293663e-01 7.66070306e-01
-3.34647410e-02 5.59457958e-01 -7.03819767e-02 -3.09524089e-01
-3.14360589e-01 -6.70935035e-01 -3.31015378e-01 -5.30339539e-01
-3.34107190e-01 2.78517544e-01 3.97595525e-01 1.17919125... | [7.613095760345459, 2.6619298458099365] |
e5e180f4-fa71-45be-8eb7-5e8d661dfcc1 | incomplete-multi-view-clustering-via-1 | 2305.11489 | null | https://arxiv.org/abs/2305.11489v1 | https://arxiv.org/pdf/2305.11489v1.pdf | Incomplete Multi-view Clustering via Diffusion Completion | Incomplete multi-view clustering is a challenging and non-trivial task to provide effective data analysis for large amounts of unlabeled data in the real world. All incomplete multi-view clustering methods need to address the problem of how to reduce the impact of missing views. To address this issue, we propose diffus... | ['Sifan Fang'] | 2023-05-19 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-2.49792978e-01 -1.61074132e-01 -3.74471098e-01 -2.09909841e-01
-8.01583290e-01 -6.32767618e-01 3.90499145e-01 -2.08589256e-01
5.86438663e-02 2.16355383e-01 3.28209430e-01 2.36264735e-01
-1.42021522e-01 -5.28148532e-01 -3.18363786e-01 -9.15185630e-01
2.49026820e-01 7.09125042e-01 1.39754564e-01 2.50127077... | [8.302175521850586, 4.598137855529785] |
96dc2989-65d3-46c3-a1c3-d5cde855fff3 | reinforcement-learning-for-adaptive-video | 2105.08205 | null | https://arxiv.org/abs/2105.08205v1 | https://arxiv.org/pdf/2105.08205v1.pdf | Reinforcement Learning for Adaptive Video Compressive Sensing | We apply reinforcement learning to video compressive sensing to adapt the compression ratio. Specifically, video snapshot compressive imaging (SCI), which captures high-speed video using a low-speed camera is considered in this work, in which multiple (B) video frames can be reconstructed from a snapshot measurement. O... | ['Weisong Shi', 'Aggelos K Katsaggelos', 'Xin Yuan', 'Sidi Lu'] | 2021-05-18 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 5.26182294e-01 -4.26529437e-01 -2.65647680e-01 -1.02405362e-01
-6.84432626e-01 -2.53547430e-01 1.84627444e-01 -6.45562172e-01
-3.85076791e-01 5.72095633e-01 1.37528524e-01 -2.49131724e-01
-4.12564687e-02 -5.21131158e-01 -1.12606847e+00 -5.81678629e-01
-1.50160074e-01 -2.16431513e-01 2.31063724e-01 4.92045954... | [11.069012641906738, -2.0969159603118896] |
d076e7c0-7129-4f7b-a411-e8bfdaae744e | geometric-approach-for-non-pharmaceutical | 2301.08698 | null | https://arxiv.org/abs/2301.08698v1 | https://arxiv.org/pdf/2301.08698v1.pdf | Geometric approach for non pharmaceutical interventions in epidemiology | Various non pharmaceutical interventions have been settled to minimise the burden of the COVID-19 outbreak. We build a framework to analyse the dynamics of non pharmaceutical interventions, to distinguish between mitigations measures leading to objective scientific improvements and mitigations based on both political a... | ['Jean-Jacques Loeb', 'Laurent Evain'] | 2023-01-20 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 4.97607619e-01 4.21949029e-01 1.23903766e-01 2.20494062e-01
-1.32995769e-02 -6.12412572e-01 8.74877334e-01 5.76936483e-01
-6.01359963e-01 1.04234338e+00 1.66469425e-01 -5.15386581e-01
-8.85700285e-01 -8.02627683e-01 -3.42765033e-01 -9.68388259e-01
-2.48124152e-01 4.87839907e-01 -1.76411751e-03 -4.12723213... | [5.922079086303711, 4.32187032699585] |
ed552948-8068-42f3-a3e9-7556c15187aa | attention-based-joint-detection-of-object-and | 2007.02419 | null | https://arxiv.org/abs/2007.02419v1 | https://arxiv.org/pdf/2007.02419v1.pdf | Attention-based Joint Detection of Object and Semantic Part | In this paper, we address the problem of joint detection of objects like dog and its semantic parts like face, leg, etc. Our model is created on top of two Faster-RCNN models that share their features to perform a novel Attention-based feature fusion of related Object and Part features to get enhanced representations o... | ['Tara Vijaykumar', 'Keval Morabia', 'Jatin Arora'] | 2020-07-05 | null | null | null | null | ['semantic-part-detection'] | ['computer-vision'] | [ 5.56406863e-02 1.70855924e-01 -8.06412324e-02 -5.66854596e-01
-7.98532248e-01 -1.29508734e-01 5.11889756e-01 1.30766779e-01
-5.45325756e-01 3.91846746e-01 -1.05165750e-01 1.60141110e-01
3.12718391e-01 -5.47462225e-01 -1.00764525e+00 -3.66563827e-01
-2.00155839e-01 2.81927139e-01 9.43076789e-01 -8.76279697... | [9.251012802124023, 0.8955840468406677] |
0d1be7fe-65e7-4fcf-a902-2e01b7184e95 | keyword-localisation-in-untranscribed-speech | 2202.01107 | null | https://arxiv.org/abs/2202.01107v1 | https://arxiv.org/pdf/2202.01107v1.pdf | Keyword localisation in untranscribed speech using visually grounded speech models | Keyword localisation is the task of finding where in a speech utterance a given query keyword occurs. We investigate to what extent keyword localisation is possible using a visually grounded speech (VGS) model. VGS models are trained on unlabelled images paired with spoken captions. These models are therefore self-supe... | ['Herman Kamper', 'Dan Oneata', 'Kayode Olaleye'] | 2022-02-02 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 5.55539310e-01 6.31982207e-01 3.79014872e-02 -4.91206914e-01
-1.41333842e+00 -7.53119528e-01 8.45518112e-01 2.54946947e-01
-6.53979421e-01 3.08305830e-01 2.30847865e-01 -3.52299631e-01
2.84677058e-01 -1.81479916e-01 -1.02775729e+00 -8.70922327e-01
-3.23387906e-02 3.89802814e-01 5.60464501e-01 1.20846145... | [10.490619659423828, 1.384629249572754] |
46f2a5d3-1fdb-4046-9905-658481ed2e72 | relation-regularized-scene-graph-generation | 2202.10826 | null | https://arxiv.org/abs/2202.10826v1 | https://arxiv.org/pdf/2202.10826v1.pdf | Relation Regularized Scene Graph Generation | Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this obser... | ['Xuelong Li', 'Heng Tao Shen', 'Nicu Sebe', 'Peng Wang', 'Jingkuan Song', 'Lianli Gao', 'Yuyu Guo'] | 2022-02-22 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.90565085e-01 4.34642106e-01 -1.24409646e-01 -6.58674896e-01
4.18093391e-02 -3.05019230e-01 5.56397319e-01 3.90622497e-01
4.43653651e-02 1.74630731e-01 2.33711123e-01 -1.49446532e-01
-1.83058500e-01 -1.18946767e+00 -1.13025987e+00 -4.12877440e-01
-9.01548639e-02 2.12063909e-01 4.16879296e-01 1.79030150... | [10.284773826599121, 1.6479332447052002] |
629bb3c0-af70-4657-a1a0-30fe6ea3932b | federated-chinese-word-segmentation-with | null | null | https://aclanthology.org/2021.findings-acl.376 | https://aclanthology.org/2021.findings-acl.376.pdf | Federated Chinese Word Segmentation with Global Character Associations | null | ['Yan Song', 'Han Qin', 'Guimin Chen', 'Yuanhe Tian'] | null | null | null | null | findings-acl-2021-8 | ['chinese-word-segmentation'] | ['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... | [-7.474621772766113, 3.589365243911743] |
f6b87e0c-fdb9-4bea-a066-9088c8a02b88 | linear-bandit-algorithms-with-sublinear-time | 2103.02729 | null | https://arxiv.org/abs/2103.02729v2 | https://arxiv.org/pdf/2103.02729v2.pdf | Linear Bandit Algorithms with Sublinear Time Complexity | We propose two linear bandits algorithms with per-step complexity sublinear in the number of arms $K$. The algorithms are designed for applications where the arm set is extremely large and slowly changing. Our key realization is that choosing an arm reduces to a maximum inner product search (MIPS) problem, which can be... | ['Sujay Sanghavi', 'Inderjit S. Dhillon', 'Eric Price', 'Sanjay Shakkottai', 'Tongzheng Ren', 'Shuo Yang'] | 2021-03-03 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.44626272e-02 2.02452078e-01 -8.06927979e-01 -3.84055793e-01
-1.33035541e+00 -1.09998477e+00 -1.45551041e-01 1.63789943e-01
-5.98277032e-01 7.68390238e-01 9.82701313e-03 -8.33204329e-01
-4.94923770e-01 -7.08163679e-01 -1.24582422e+00 -6.73584282e-01
-3.42300534e-01 7.40565956e-01 7.76643753e-02 -9.29493383... | [4.7486467361450195, 3.5106918811798096] |
a39d3ddc-6a38-4f5c-9f3d-965c6d1f87d3 | deep-reinforcement-learning-for-multi-class | 2205.12070 | null | https://arxiv.org/abs/2205.12070v1 | https://arxiv.org/pdf/2205.12070v1.pdf | Deep Reinforcement Learning for Multi-class Imbalanced Training | With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement l... | ['David A. Clifton', 'Andrew A. S. Soltan', 'Alexander S. Lachapelle', "Odhran O'Donoghue", 'Rasheed el-Bouri', 'Jenny Yang'] | 2022-05-24 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 8.46969783e-02 2.52711158e-02 -5.24158001e-01 -4.73458081e-01
-8.41375351e-01 -9.44815949e-02 -1.17962919e-01 7.97508717e-01
-4.76822555e-01 1.07079232e+00 -9.49860588e-02 -5.14501333e-01
-3.19938034e-01 -8.71098816e-01 -3.28167021e-01 -4.77046341e-01
-3.19691360e-01 1.00958848e+00 -2.18733966e-01 -5.00932150... | [15.068684577941895, -2.414060354232788] |
3ccf2841-36fc-4387-8a66-9740a77471ea | expression-conditional-gan-for-facial | 1905.05416 | null | https://arxiv.org/abs/1905.05416v1 | https://arxiv.org/pdf/1905.05416v1.pdf | Expression Conditional GAN for Facial Expression-to-Expression Translation | In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression gener... | ['Wei Wang', 'Xinya Chen', 'Songsong Wu', 'Yan Yan', 'Hao Tang', 'Nicu Sebe', 'Dan Xu'] | 2019-05-14 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 4.87570435e-01 -2.18471542e-01 -3.03838570e-02 -8.37355733e-01
-3.99514377e-01 -4.76219922e-01 7.26607800e-01 -7.67789781e-01
-1.10795952e-01 8.48546088e-01 -1.75092742e-01 4.44627017e-01
3.87024641e-01 -7.23999023e-01 -5.73861301e-01 -1.01314104e+00
3.61677885e-01 -3.78953107e-02 -3.72973859e-01 -2.32755288... | [13.102945327758789, 0.4330434799194336] |
a13bc368-193f-4110-aa4a-36b9d1524d2d | bigissue-a-realistic-bug-localization | 2207.10739 | null | https://arxiv.org/abs/2207.10739v2 | https://arxiv.org/pdf/2207.10739v2.pdf | BigIssue: A Realistic Bug Localization Benchmark | As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored.... | ['Caiming Xiong', 'Yingbo Zhou', 'Bo Pang', 'Erik Nijkamp', 'Paul Kassianik'] | 2022-07-21 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-2.06421509e-01 1.48610696e-01 -4.07524824e-01 -1.60827905e-01
-9.42082703e-01 -2.77545184e-01 1.67882279e-01 7.20837712e-01
9.66967419e-02 4.44408089e-01 4.42175120e-02 -5.60561299e-01
6.15015216e-02 -6.81487858e-01 -6.60481334e-01 2.44980082e-01
-5.17576039e-01 6.55079773e-03 4.52163547e-01 -4.38817590... | [7.589453220367432, 7.704395771026611] |
f175fd59-7e95-4343-ba48-41767cddba17 | a-large-dataset-of-historical-japanese | 2004.08686 | null | https://arxiv.org/abs/2004.08686v1 | https://arxiv.org/pdf/2004.08686v1.pdf | A Large Dataset of Historical Japanese Documents with Complex Layouts | Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale. One major hurdle is the lack of large datasets for training robust models. In particular, little training data exist for Asian lang... | ['Melissa Dell', 'Kaixuan Zhang', 'Zejiang Shen'] | 2020-04-18 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 1.29585853e-03 -2.45461076e-01 -4.96414565e-02 -2.09610865e-01
-1.10504186e+00 -1.02103996e+00 5.77550292e-01 3.09635282e-01
-2.23277658e-01 3.06419641e-01 6.00111902e-01 -5.43023825e-01
-7.60202259e-02 -6.51943684e-01 -6.60899580e-01 -5.38980722e-01
-3.74672301e-02 4.89258617e-01 -1.23732917e-01 -1.63590014... | [11.68596363067627, 2.6655690670013428] |
bb5aaccf-a37c-4848-b89b-9b7258549968 | dynamic-curriculum-learning-for-low-resource | 2011.14608 | null | https://arxiv.org/abs/2011.14608v1 | https://arxiv.org/pdf/2011.14608v1.pdf | Dynamic Curriculum Learning for Low-Resource Neural Machine Translation | Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In p... | ['Jingbo Zhu', 'Tong Xiao', 'Qi Ju', 'Shen Huang', 'Zeyang Wang', 'Kai Feng', 'Yufan Jiang', 'Bojie Hu', 'Chen Xu'] | 2020-11-30 | null | https://aclanthology.org/2020.coling-main.352 | https://aclanthology.org/2020.coling-main.352.pdf | coling-2020-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.80788803e-01 -2.83623803e-02 -5.34372866e-01 -3.67338508e-01
-1.11740398e+00 -7.50178635e-01 7.58889496e-01 -1.18102349e-01
-8.37336779e-01 9.82651889e-01 1.45588741e-01 -9.18815136e-01
3.70231956e-01 -4.91492659e-01 -1.01048982e+00 -3.53400856e-01
4.34592336e-01 1.18552673e+00 2.73472816e-01 -5.46503484... | [11.599347114562988, 10.18521499633789] |
ea9b2db7-7a7a-45ea-af36-debd5575c6b7 | 190910305 | 1909.10305 | null | https://arxiv.org/abs/1909.10305v2 | https://arxiv.org/pdf/1909.10305v2.pdf | Deep Multi-Facial patches Aggregation Network for Expression Classification from Face Images | Emotional Intelligence in Human-Computer Interaction has attracted increasing attention from researchers in multidisciplinary research fields including psychology, computer vision, neuroscience, artificial intelligence, and related disciplines. Human prone to naturally interact with computers face-to-face. Human Expres... | ['Alice Othmani', 'Amine Djerghri', 'Ahmed Rachid Hazourli'] | 2019-09-23 | null | null | null | null | ['facial-expression-generation', 'emotional-intelligence'] | ['computer-vision', 'natural-language-processing'] | [ 2.40297645e-01 4.92069274e-02 1.06844634e-01 -7.04278409e-01
-1.22178495e-01 -4.81164828e-02 3.25282037e-01 -4.32592511e-01
-3.32474530e-01 6.54728949e-01 -1.27512828e-01 3.02767128e-01
1.21679775e-01 -4.14961159e-01 -2.13453278e-01 -7.64069974e-01
-1.13081440e-01 -1.14766225e-01 -5.06859481e-01 -3.27643275... | [13.562982559204102, 1.8475781679153442] |
795b47dc-4643-4152-bf45-0d8feccd21e1 | read-listen-and-see-leveraging-multimodal | 2105.12306 | null | https://arxiv.org/abs/2105.12306v1 | https://arxiv.org/pdf/2105.12306v1.pdf | Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking | Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in the Chinese language. Most of the Chinese spelling errors are misused semantically, phonetically or graphically similar characters. Previous attempts noticed this phenomenon and try to use the similarity for this tas... | ['Xian-Ling Mao', 'Heyan Huang', 'Yunbo Cao', 'Zizhen Wang', 'Chao Li', 'Qingyu Zhou', 'Zhongli Li', 'Heng-Da Xu'] | 2021-05-26 | null | https://aclanthology.org/2021.findings-acl.64 | https://aclanthology.org/2021.findings-acl.64.pdf | findings-acl-2021-8 | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 6.20556235e-01 -4.55984950e-01 1.98837504e-01 -1.02791011e-01
-1.12532449e+00 -8.84150445e-01 8.23642671e-01 1.70062721e-01
-4.09968823e-01 4.78265464e-01 1.97846755e-01 -4.77729797e-01
5.01108766e-01 -2.69669980e-01 -4.15831178e-01 -7.65600383e-01
5.41289032e-01 3.26724201e-01 3.53984177e-01 -4.09361795... | [10.929628372192383, 10.82861614227295] |
6b5a0642-45ad-4be5-a4ed-532fdfcf145b | temporal-collaborative-filtering-with | null | null | https://www.cs.cmu.edu/~jgc/publication/PublicationPDF/Temporal_Collaborative_Filtering_With_Bayesian_Probabilidtic_Tensor_Factorization.pdf | https://www.cs.cmu.edu/~jgc/publication/PublicationPDF/Temporal_Collaborative_Filtering_With_Bayesian_Probabilidtic_Tensor_Factorization.pdf | Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization | Real-world relational data are seldom stationary, yet
traditional collaborative filtering algorithms generally
rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that
is able to take time into account. By introducing additional factors for time, we formalize this ... | ['Tzu-Kuo Huang', 'Liang Xiong', 'Jaime G. Carbonell', 'Jeff Schneider', 'Xi Chen'] | 2019-05-04 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.24392071e-01 -3.98001969e-01 -5.83974540e-01 -4.57742065e-01
-2.95095265e-01 -3.30683410e-01 5.47421217e-01 -8.79174396e-02
-4.05290484e-01 2.34885603e-01 1.80309683e-01 -3.91450435e-01
-7.68948734e-01 -9.21878695e-01 -4.77995902e-01 -5.06496847e-01
-2.47589931e-01 6.55623853e-01 3.53267133e-01 -1.25562504... | [9.6392822265625, 5.500072002410889] |
08089f43-90ee-4de5-849b-3ae9ee1f5f91 | learning-free-form-deformation-for-3d-face | 2105.14857 | null | https://arxiv.org/abs/2105.14857v2 | https://arxiv.org/pdf/2105.14857v2.pdf | Learning Free-Form Deformation for 3D Face Reconstruction from In-The-Wild Images | The 3D Morphable Model (3DMM), which is a Principal Component Analysis (PCA) based statistical model that represents a 3D face using linear basis functions, has shown promising results for reconstructing 3D faces from single-view in-the-wild images. However, 3DMM has restricted representation power due to the limited n... | ['Seong-Whan Lee', 'Myeong-Seok Oh', 'Harim Jung'] | 2021-05-31 | null | null | null | null | ['3d-face-reconstruction', '3d-face-modeling', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.15591609e-01 2.08997279e-01 9.29341651e-03 -3.03153366e-01
-4.93090034e-01 -4.06574547e-01 4.43816543e-01 -5.66761553e-01
3.21627975e-01 1.92542091e-01 5.42392731e-02 1.06080644e-01
5.79087250e-02 -8.08827460e-01 -6.75271988e-01 -7.14384496e-01
-1.14904260e-02 7.70028949e-01 -1.08677901e-01 -3.10587753... | [13.101070404052734, 0.006868502125144005] |
09d9fa24-527c-4347-a04f-00b418bb54e2 | quaternion-neural-networks-for-multi-channel | 2005.08566 | null | https://arxiv.org/abs/2005.08566v2 | https://arxiv.org/pdf/2005.08566v2.pdf | Quaternion Neural Networks for Multi-channel Distant Speech Recognition | Despite the significant progress in automatic speech recognition (ASR), distant ASR remains challenging due to noise and reverberation. A common approach to mitigate this issue consists of equipping the recording devices with multiple microphones that capture the acoustic scene from different perspectives. These multi-... | ['Titouan Parcollet', 'Mirco Ravanelli', 'Nicholas Lane', 'Mohamed Morchid', 'Xinchi Qiu'] | 2020-05-18 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [-4.60805409e-02 -2.40175929e-02 3.75001281e-01 -1.34582043e-01
-9.10331190e-01 -2.94077903e-01 4.78312343e-01 -1.53768688e-01
-7.35641956e-01 4.02405888e-01 3.69558096e-01 -3.11828732e-01
3.48784715e-01 -5.39160252e-01 -9.68371928e-01 -6.47888541e-01
-1.44409612e-01 2.61519309e-02 -1.86827764e-01 -3.69533122... | [14.865700721740723, 6.187085151672363] |
813180e6-2582-4ff7-b01f-f596259a2b6d | rsvqa-visual-question-answering-for-remote | 2003.07333 | null | https://arxiv.org/abs/2003.07333v2 | https://arxiv.org/pdf/2003.07333v2.pdf | RSVQA: Visual Question Answering for Remote Sensing Data | This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specif... | ['Sylvain Lobry', 'Devis Tuia', 'Jesse Murray', 'Diego Marcos'] | 2020-03-16 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 3.36215436e-01 -6.64133579e-02 1.75739303e-01 -4.73060846e-01
-7.10407972e-01 -7.01894224e-01 5.91152310e-01 5.05307615e-01
-4.39792067e-01 4.72558409e-01 -1.22211151e-01 -8.47482264e-01
-2.34584108e-01 -1.49569070e+00 -5.29008806e-01 -4.52768683e-01
-1.43725321e-01 2.94993311e-01 1.89545289e-01 -5.93415976... | [9.723637580871582, -1.2558733224868774] |
45599fd9-85d1-45f9-85a8-f148198b6fa1 | multi-view-optimization-of-local-feature | 2003.08348 | null | https://arxiv.org/abs/2003.08348v2 | https://arxiv.org/pdf/2003.08348v2.pdf | Multi-View Optimization of Local Feature Geometry | In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry. Current approaches to local feature detection are inherently limited in their keypoint localization accuracy because they only operate on a single view. This limitation has a... | ['Johannes L. Schönberger', 'Marc Pollefeys', 'Mihai Dusmanu'] | 2020-03-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2556_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460647.pdf | eccv-2020-8 | ['camera-localization'] | ['computer-vision'] | [ 8.95986101e-04 -4.96977806e-01 -1.43794760e-01 -3.77801478e-01
-1.24174654e+00 -1.03193355e+00 5.85381925e-01 1.25087738e-01
-3.93577605e-01 3.68391782e-01 -4.02899571e-02 5.40441871e-02
-1.95585966e-01 -3.59535903e-01 -8.01121771e-01 -4.18296814e-01
2.68508017e-01 2.33657569e-01 3.46316427e-01 1.42071560... | [7.770287990570068, -2.305175304412842] |
53cb034d-a78a-4047-9755-519acce60731 | super-efficient-super-resolution-for-fast | 2112.14340 | null | https://arxiv.org/abs/2112.14340v1 | https://arxiv.org/pdf/2112.14340v1.pdf | Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge | Autonomous systems are highly vulnerable to a variety of adversarial attacks on Deep Neural Networks (DNNs). Training-free model-agnostic defenses have recently gained popularity due to their speed, ease of deployment, and ability to work across many DNNs. To this end, a new technique has emerged for mitigating attacks... | ['Danny Loh', 'Paul Whatmough', 'James Ward', 'Dibakar Gope', 'Kartikeya Bhardwaj'] | 2021-12-29 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.37506360e-01 1.00059360e-02 8.53851363e-02 -4.54693705e-01
-9.39256012e-01 -7.24850953e-01 4.86588538e-01 -6.66197240e-01
-7.95115650e-01 7.87303388e-01 -1.40193209e-01 -4.32362527e-01
2.67860204e-01 -7.46046960e-01 -1.09010375e+00 -5.71382880e-01
-1.11725681e-01 -1.99435875e-01 5.11724770e-01 -5.43495774... | [5.5665459632873535, 7.915107250213623] |
767b77ef-8106-4631-9258-fc3834967246 | deep-forecast-deep-learning-based-spatio | 1707.08110 | null | http://arxiv.org/abs/1707.08110v1 | http://arxiv.org/pdf/1707.08110v1.pdf | Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting | The paper presents a spatio-temporal wind speed forecasting algorithm using
Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated
by recent advances in renewable energy integration and smart grids, we apply
our proposed algorithm for wind speed forecasting. Renewable energy resources
(wind and... | ['Borhan M. Sanandaji', 'Amir Ghaderi', 'Faezeh Ghaderi'] | 2017-07-24 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-4.47209746e-01 -3.98683816e-01 -1.46024689e-01 -1.23234853e-01
2.61779636e-01 -5.68928063e-01 9.34532821e-01 1.57942753e-02
1.38808459e-01 9.52206671e-01 3.80103797e-01 -6.08544886e-01
-2.42939949e-01 -1.31629074e+00 -4.93144393e-01 -8.97269487e-01
-6.37706876e-01 5.97355552e-02 -2.99961179e-01 -5.87650061... | [6.329470157623291, 2.821597099304199] |
59ab8eae-13ba-468e-b120-2332cd27ca78 | a-mask-based-model-for-mandarin-chinese | null | null | http://www.interspeech2020.org/uploadfile/2020/1021/20201021034849937.pdf | http://www.interspeech2020.org/uploadfile/2020/1021/20201021034849937.pdf | A Mask-based Model for Mandarin Chinese Polyphone Disambiguation | Polyphone disambiguation serves as an essential part of Mandarin text-to-speech (TTS) system. However, conventional system modeling the entire Pinyin set causes the case that prediction belongs to the unrelated polyphonic character instead of the current input one, which has negative impacts on TTS performance. To addr... | ['Haiteng Zhang'] | 2020-10-21 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 1.4964750e-01 -2.1888910e-02 -1.4091270e-01 -2.8943697e-01
-7.0542395e-01 -2.1897736e-01 1.7653441e-01 -1.7972720e-01
-4.9266687e-01 5.9230471e-01 3.7586066e-01 -3.4950125e-01
1.9154653e-01 -4.2769691e-01 -4.9836373e-01 -6.2153554e-01
4.4974405e-01 -2.4637088e-01 5.9336710e-01 -1.3184555e-01
1.9715129e-01... | [14.703180313110352, 6.6198625564575195] |
5682e5fd-b794-462e-89dd-96f7248bfa3e | scideberta-learning-deberta-for-science | null | null | https://ieeexplore.ieee.org/abstract/document/9791256 | https://ieeexplore.ieee.org/abstract/document/9791256 | SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks | Deep learning-based language models (LMs) have transcended the gold standard (human baseline) of SQuAD 1.1 and GLUE benchmarks in April and July 2019, respectively. As of 2022, the top five LMs on the SuperGLUE benchmark leaderboard have exceeded the gold standard. Even people with good general knowledge will struggle ... | ['Eunhui Kim', 'Yuna Jeong'] | 2022-06-08 | null | null | null | ieee-access-2022-6 | ['general-knowledge', 'joint-entity-and-relation-extraction'] | ['miscellaneous', 'natural-language-processing'] | [-4.35214847e-01 -1.25207230e-01 -8.63627344e-02 -3.24632347e-01
-6.72310472e-01 -8.76361549e-01 6.15076602e-01 -1.51117012e-01
-7.31764376e-01 9.49687958e-01 -2.69510180e-01 -6.46170139e-01
-2.32848987e-01 -8.09648097e-01 -1.26093233e+00 -1.02169193e-01
1.66859061e-01 6.81443751e-01 -1.84263363e-01 -4.58951622... | [10.134785652160645, 8.773910522460938] |
4836162e-df9a-4356-9ac3-374f1bc44330 | mlrmbo-a-modular-framework-for-model-based | 1703.03373 | null | http://arxiv.org/abs/1703.03373v3 | http://arxiv.org/pdf/1703.03373v3.pdf | mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions | We present mlrMBO, a flexible and comprehensive R toolbox for model-based
optimization (MBO), also known as Bayesian optimization, which addresses the
problem of expensive black-box optimization by approximating the given
objective function through a surrogate regression model. It is designed for
both single- and multi... | ['Michel Lang', 'Jakob Richter', 'Daniel Horn', 'Janek Thomas', 'Jakob Bossek', 'Bernd Bischl'] | 2017-03-09 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.68802679e-01 -4.32630479e-01 -2.77967632e-01 -4.69581425e-01
-1.04243743e+00 -5.09341180e-01 2.39923209e-01 9.98672917e-02
-4.00698125e-01 8.17154706e-01 -1.14553213e-01 -3.55976909e-01
-5.06300926e-01 -2.11724266e-01 -5.04724085e-01 -8.59981179e-01
-7.68348798e-02 9.17567611e-01 -2.61544466e-01 -1.70487657... | [6.518116474151611, 3.9694504737854004] |
eee1da0e-06f5-48b8-a75d-2dad5bf45479 | end-to-end-simultaneous-learning-of-single | 2107.02958 | null | https://arxiv.org/abs/2107.02958v1 | https://arxiv.org/pdf/2107.02958v1.pdf | End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data | Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations. Here, we present an end-to-end unsupervised approach that learns individual particle orientations from cryo-EM data while reconstructing the average 3D map of the biomolecule, starting from a... | ['Daniel Ratner', 'Chuck Yoon', 'Michael Kagan', 'Geoffrey Woollard', 'Harshit Gupta', 'Frederic Poitevin', 'Youssef S. G. Nashed'] | 2021-07-07 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 3.46297145e-01 1.74459472e-01 6.60915136e-01 -4.77407515e-01
-7.60190666e-01 -5.20060897e-01 8.03702474e-01 -3.35725754e-01
-7.01841950e-01 8.08639109e-01 2.55063355e-01 -1.47941053e-01
2.39016786e-01 -5.86569309e-01 -1.16583359e+00 -1.25496256e+00
1.93916082e-01 1.36488712e+00 -1.15826271e-01 3.23078126... | [13.276959419250488, -3.073982000350952] |
6d0aa7ce-9bd7-4ebe-a58f-894d441da6cf | towards-fleet-wide-sharing-of-wind-turbine | 2212.03529 | null | https://arxiv.org/abs/2212.03529v2 | https://arxiv.org/pdf/2212.03529v2.pdf | Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning | Terabytes of data are collected every day by wind turbine manufacturers from their fleets. The data contain valuable real-time information for turbine health diagnostics and performance monitoring, for predicting rare failures and the remaining service life of critical parts. And yet, this wealth of data from wind turb... | ['Angela Meyer', 'Stefan Jonas', 'Lorin Jenkel'] | 2022-12-07 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-4.12245959e-01 2.15449244e-01 -5.40798753e-02 -1.89760268e-01
-2.04659984e-01 -6.59490108e-01 -6.27862960e-02 1.40040204e-01
-1.12459518e-01 7.86327958e-01 -4.70540076e-01 -2.64075994e-01
-7.48546839e-01 -9.80976701e-01 -1.71397507e-01 -9.36518669e-01
-6.71972871e-01 7.24296987e-01 -2.64362454e-01 1.71970017... | [5.8369140625, 6.43674898147583] |
c954bd39-af9b-41ef-acea-8a12b9bf7f41 | domain-adaptation-in-robot-fault-diagnostic | 1809.08626 | null | https://arxiv.org/abs/1809.08626v3 | https://arxiv.org/pdf/1809.08626v3.pdf | Domain Adaptation for Robot Predictive Maintenance Systems | Industrial robots play an increasingly important role in a growing number of fields. For example, robotics is used to increase productivity while reducing costs in various aspects of manufacturing. Since robots are often set up in production lines, the breakdown of a single robot has a negative impact on the entire pro... | ['Arash Golibagh Mahyari', 'Thomas Locker'] | 2018-09-23 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 4.57695663e-01 1.47767857e-01 1.28484771e-01 -2.26336882e-01
2.98010409e-02 -1.98596597e-01 1.71287671e-01 5.92600584e-01
-1.84469998e-01 7.57001162e-01 -7.83452630e-01 -7.14859888e-02
-5.66530645e-01 -8.06050003e-01 -7.43968308e-01 -8.54430020e-01
8.06767941e-02 6.23518765e-01 2.63934165e-01 -1.23997293... | [6.841690540313721, 2.352938413619995] |
e1c4f836-4dd5-4a23-9f95-a1266b862741 | predicting-lymph-node-metastasis-using | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.pdf | Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution | Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commonly assumed standard... | [' Jianhua Yao', ' Xinjuan Fan', ' Bjoern Menze', ' Sen Yang', ' Jiarui Sun', ' Jun Zhang', ' Niyun Zhou', ' Hailing Liu', ' Yuqi Fang', ' Fan Yang', 'Yu Zhao'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['histopathological-image-classification'] | ['medical'] | [ 4.94865716e-01 1.93110537e-02 -2.93299019e-01 -2.83929497e-01
-1.21117818e+00 -7.99939856e-02 4.68387872e-01 2.87470520e-01
-3.24893802e-01 6.45807981e-01 -1.19566955e-01 -2.93577820e-01
-4.10468012e-01 -1.08232892e+00 -6.60697162e-01 -1.20032668e+00
1.17643224e-02 3.16488802e-01 -1.50712952e-01 5.21950461... | [15.109024047851562, -2.7713935375213623] |
51de5024-67f4-4f3b-bf12-eeab59460de4 | iitp-ai-nlp-ml-cl-scisumm-2020-cl-laysumm | null | null | https://aclanthology.org/2020.sdp-1.30 | https://aclanthology.org/2020.sdp-1.30.pdf | IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 | The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art. Scientific document summarization solves this problem by summarizing the essential fact and findings of the document. In the current paper, we present the partic... | ['Pushpak Bhattacharyya', 'Sriparna Saha', 'Naveen Saini', 'Harshavardhan Kundarapu', 'Santosh Kumar Mishra'] | null | null | null | null | emnlp-sdp-2020-11 | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 3.48479636e-02 1.11375395e-02 -2.35589538e-02 -7.85962641e-02
-1.64950991e+00 -5.72708368e-01 7.94352412e-01 4.39381182e-01
-2.63829261e-01 1.22870302e+00 7.83535957e-01 -6.40087575e-02
-2.36060247e-01 -3.74122351e-01 -7.52820790e-01 -3.78260106e-01
1.60203204e-01 6.00200057e-01 -2.57352740e-01 6.83675855... | [12.50955867767334, 9.556293487548828] |
7f76c06d-a3f8-41d1-8e40-7108e073cbd4 | reducing-the-gap-between-streaming-and-non | 2306.15171 | null | https://arxiv.org/abs/2306.15171v1 | https://arxiv.org/pdf/2306.15171v1.pdf | Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation | Transducer is one of the mainstream frameworks for streaming speech recognition. There is a performance gap between the streaming and non-streaming transducer models due to limited context. To reduce this gap, an effective way is to ensure that their hidden and output distributions are consistent, which can be achieved... | ["Ming'en Zhao", 'Genshun Wan', 'Jia Pan', 'Minghui Wu', 'Zhiqiang Ma', 'Yongchao Li', 'Dan Liu', 'Jiabin Xue', 'Lei Sun', 'Yu Fu', 'Haitao Tang'] | 2023-06-27 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 3.69040579e-01 9.74113867e-02 -6.49198368e-02 -4.36698914e-01
-1.09280288e+00 -2.53700495e-01 2.05964148e-01 2.06304207e-01
-4.96928543e-01 5.31811893e-01 4.64556247e-01 -2.53250539e-01
1.56370047e-02 -5.80505311e-01 -5.42361319e-01 -9.77497637e-01
1.28660128e-01 1.94313824e-01 6.32239103e-01 -9.65558439... | [14.54891586303711, 6.7562994956970215] |
789b39e0-597f-4c4a-9210-456087c4edfb | modeling-multi-scale-data-via-a-network-of | 2105.12226 | null | https://arxiv.org/abs/2105.12226v1 | https://arxiv.org/pdf/2105.12226v1.pdf | Modeling multi-scale data via a network of networks | Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher-scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated w... | ['Tijana Milenkovic', 'Pietro Hiram Guzzi', 'Meng Jiang', 'Shawn Gu'] | 2021-05-25 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 3.95248532e-01 1.02640808e+00 -2.26747274e-01 -3.59829605e-01
1.13806538e-01 -6.30346537e-01 5.56431472e-01 6.77937984e-01
1.14014685e-01 1.11303747e+00 -2.82397866e-01 -3.34709138e-01
-3.69905084e-01 -1.26768804e+00 -8.66444707e-01 -4.38461423e-01
-2.29105964e-01 8.77079785e-01 7.45881677e-01 -2.83255130... | [6.814568519592285, 5.625408172607422] |
a12e5696-84e6-4189-b072-8f3f8258dd6d | sequence-to-sequence-models-for-extracting | 2201.05658 | null | https://arxiv.org/abs/2201.05658v1 | https://arxiv.org/pdf/2201.05658v1.pdf | Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents | A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possib... | ['Rodrigo Nogueira', 'Roberto A. Lotufo', 'Guilherme Rosa', 'Fábio C. de Souza', 'Ramon Pires'] | 2022-01-14 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 5.79337001e-01 2.02747747e-01 -1.57864783e-02 -4.93016481e-01
-6.95417106e-01 -1.03620696e+00 6.04606628e-01 1.01431406e+00
-5.14098942e-01 5.96380234e-01 -2.44058549e-01 -6.67364061e-01
1.13942148e-02 -8.94178510e-01 -6.24676049e-01 -8.67373496e-02
2.25058511e-01 1.88854471e-01 5.44314802e-01 6.92284405... | [7.952281951904297, 7.827744960784912] |
054060d2-de39-4dee-99f9-6a34f95ef503 | pizza-a-new-benchmark-for-complex-end-to-end | 2212.00265 | null | https://arxiv.org/abs/2212.00265v1 | https://arxiv.org/pdf/2212.00265v1.pdf | PIZZA: A new benchmark for complex end-to-end task-oriented parsing | Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful representations such as the lambda calculus, which are expressive but costly to annotate. This paper continues the exploration of task-oriented parsi... | ['Khan Haidar', 'Weiqi Sun', 'Saarthak Khanna', 'Sandesh Swamy', 'Melanie Rubino', 'Nicolas Guenon des Mesnards', 'Konstantine Arkoudas'] | 2022-12-01 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [ 4.16422486e-01 7.52881289e-01 -6.40662089e-02 -6.99069619e-01
-1.06275022e+00 -8.45128238e-01 4.03243870e-01 1.14393560e-02
-1.47695810e-01 8.58506083e-01 6.20759130e-01 -8.52224886e-01
7.72232236e-03 -8.31398427e-01 -6.92809880e-01 -8.98247212e-02
-2.25957483e-01 7.39721894e-01 9.54093933e-02 -5.12970984... | [10.4133939743042, 9.321539878845215] |
a948681a-b9fd-4f38-a260-a5bf1e928a50 | knowledge-enhanced-iterative-instruction | 2209.03005 | null | https://arxiv.org/abs/2209.03005v1 | https://arxiv.org/pdf/2209.03005v1.pdf | Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering | Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing Retrieval-based approaches first generate instructions from the question and then use them to guide the multi-hop reasoning on the knowledg... | ['Dongyan Zhao', 'Chen Zhang', 'Quzhe Huang', 'Haowei Du'] | 2022-09-07 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.09303556e-01 7.64311850e-01 -9.06296447e-02 -9.47616100e-02
-1.01851809e+00 -5.80003798e-01 1.66256353e-01 7.07186460e-01
-2.62278140e-01 8.84959221e-01 1.34002911e-02 -6.22150898e-01
-3.67541194e-01 -1.25175273e+00 -1.07015920e+00 -1.10742420e-01
2.86697954e-01 9.49622929e-01 1.36589468e+00 -6.53604329... | [10.625969886779785, 7.956364154815674] |
bdecca1c-89bd-4467-acf0-2a79619971ae | warp-consistency-for-unsupervised-learning-of | 2104.03308 | null | https://arxiv.org/abs/2104.03308v3 | https://arxiv.org/pdf/2104.03308v3.pdf | Warp Consistency for Unsupervised Learning of Dense Correspondences | The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearance changes, which are ubiquitous in geometric and semantic matching tasks. Moreover, methods relying... | ['Luc van Gool', 'Fisher Yu', 'Martin Danelljan', 'Prune Truong'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Truong_Warp_Consistency_for_Unsupervised_Learning_of_Dense_Correspondences_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Truong_Warp_Consistency_for_Unsupervised_Learning_of_Dense_Correspondences_ICCV_2021_paper.pdf | iccv-2021-1 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [ 1.66367710e-01 -6.85740588e-03 -1.88832924e-01 -4.84533638e-01
-8.25767934e-01 -5.15965939e-01 7.06340313e-01 -6.29123002e-02
-3.06082249e-01 5.27412295e-01 -6.60753623e-02 1.19095191e-01
-6.57952130e-02 -6.44341648e-01 -1.08397150e+00 -5.15338123e-01
1.18833408e-01 8.91004801e-01 2.77499825e-01 -2.84330130... | [8.49324893951416, -2.207772731781006] |
cb312384-7c51-4594-ba88-b8692998bb28 | s-net-from-answer-extraction-to-answer | 1706.04815 | null | http://arxiv.org/abs/1706.04815v6 | http://arxiv.org/pdf/1706.04815v6.pdf | S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension | In this paper, we present a novel approach to machine reading comprehension
for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a
question with exact text spans in a passage, the MS-MARCO dataset defines the
task as answering a question from multiple passages and the words in the answer
are not neces... | ['Nan Yang', 'Furu Wei', 'Chuanqi Tan', 'Weifeng Lv', 'Ming Zhou', 'Bowen Du'] | 2017-06-15 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 3.92325252e-01 1.86258391e-01 -5.37109748e-02 -3.57172996e-01
-1.41734600e+00 -6.72543108e-01 4.53193635e-01 6.55301988e-01
-6.35779142e-01 8.05578649e-01 6.74187422e-01 -5.77040434e-01
-2.61882901e-01 -9.48466837e-01 -7.86224365e-01 8.93239528e-02
3.10310721e-01 3.88976067e-01 7.28705764e-01 -5.86087465... | [11.377016067504883, 8.057703018188477] |
a0e6b073-3e4a-4215-a085-d12da64e8089 | improving-encoder-by-auxiliary-supervision | null | null | https://aclanthology.org/2021.acl-long.466 | https://aclanthology.org/2021.acl-long.466.pdf | Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation | Table-to-text generation aims at automatically generating natural text to help people conveniently obtain salient information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems are still overlooked. Previous methods cannot deduce the factual results from the entity{'}s ... | ['Dayong Hu', 'Yinliang Yue', 'Can Ma', 'Liang Li'] | 2021-08-01 | null | null | null | acl-2021-5 | ['table-to-text-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 0.07101449 0.45584884 -0.2719588 -0.60746944 -0.6495738 -0.45486692
0.53584856 0.37886974 -0.19090001 1.1771047 0.6429435 -0.31371623
-0.13112876 -1.335747 -0.8113982 -0.31700778 0.20348895 0.75790846
0.03694075 -0.52060294 0.286199 -0.0599088 -1.5151111 0.57330483
1.1238253 0.99231637 0.... | [11.666080474853516, 8.821560859680176] |
7198677c-97e9-4390-825d-4e8a3e94c0da | detr-taming-your-multi-scale-detection | 2206.02977 | null | https://arxiv.org/abs/2206.02977v1 | https://arxiv.org/pdf/2206.02977v1.pdf | DETR++: Taming Your Multi-Scale Detection Transformer | Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification [12]. With the sweeping reform of Transformers [27] in natural language processing, Carion et al. [2] introduce the Transformer-based detection method, i.e., DETR. However, due to the q... | ['Jindong Chen', 'Xinying Song', 'Hao Zhang', 'Frederick Liu', 'Xiaoxue Zang', 'Lijuan Liu', 'Chi Zhang'] | 2022-06-07 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 9.72532555e-02 -1.40012205e-01 -2.36244783e-01 -4.53089811e-02
-5.43844163e-01 -6.48920894e-01 6.53807044e-01 -1.27014723e-02
-9.11000609e-01 1.84081256e-01 -3.51019427e-02 -5.22185981e-01
4.36600000e-01 -7.28989422e-01 -6.68442607e-01 -2.73591131e-02
7.60264173e-02 -1.36480927e-01 6.16476476e-01 -2.91513443... | [8.843342781066895, 0.06927932053804398] |
986f13e8-f170-4bdd-a9bf-a3cba4ee7be2 | handling-heavy-occlusion-in-dense-crowd | 2304.07705 | null | https://arxiv.org/abs/2304.07705v2 | https://arxiv.org/pdf/2304.07705v2.pdf | Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads | With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge.... | ['Zao Zhang', 'Dong Yuan', 'Zhongzheng Lai', 'Wei Bao', 'Huaming Chen', 'Yu Zhang'] | 2023-04-16 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-4.49333459e-01 -4.00584757e-01 1.57578602e-01 -7.42819011e-02
-2.06130430e-01 -1.83750764e-01 4.84346062e-01 4.87497309e-03
-7.73464501e-01 6.28167093e-01 -1.51674360e-01 1.30644739e-01
5.78710020e-01 -6.31432116e-01 -6.47152066e-01 -7.82091320e-01
-5.22722490e-02 3.42104465e-01 1.07717812e+00 1.37490416... | [7.910381317138672, -0.6702067255973816] |
9454efcf-7a85-4979-887b-1ebd01f0f88d | anomalous-sound-detection-based-on-machine | 2204.07353 | null | https://arxiv.org/abs/2204.07353v1 | https://arxiv.org/pdf/2204.07353v1.pdf | Anomalous Sound Detection Based on Machine Activity Detection | We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active. First, we train a model that detects machine activity by using normal data with machine activity labels and then use the activity-detection ... | ['Yohei Kawaguchi', 'Masaaki Yamamoto', 'Takashi Endo', 'Kota Dohi', 'Tomoya Nishida'] | 2022-04-15 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 5.28795004e-01 1.13858528e-01 3.08665156e-01 1.14004305e-02
-5.99978328e-01 -1.81582451e-01 3.36405337e-01 3.92252207e-01
-2.36972213e-01 3.70154560e-01 -1.79527758e-03 -1.96200117e-01
-5.06930649e-02 -8.45954239e-01 -4.29084927e-01 -9.93265092e-01
-2.56521642e-01 1.74846575e-01 4.52015847e-01 3.24090332... | [7.593717575073242, 2.4294686317443848] |
ca7504ba-34d3-4030-a39c-78b7afccf987 | saliency-based-sequential-image-attention | 1711.05165 | null | http://arxiv.org/abs/1711.05165v1 | http://arxiv.org/pdf/1711.05165v1.pdf | Saliency-based Sequential Image Attention with Multiset Prediction | Humans process visual scenes selectively and sequentially using attention.
Central to models of human visual attention is the saliency map. We propose a
hierarchical visual architecture that operates on a saliency map and uses a
novel attention mechanism to sequentially focus on salient regions and take
additional glim... | ['Sean Welleck', 'Kyunghyun Cho', 'Zheng Zhang', 'Jialin Mao'] | 2017-11-14 | saliency-based-sequential-image-attention-1 | http://papers.nips.cc/paper/7102-saliency-based-sequential-image-attention-with-multiset-prediction | http://papers.nips.cc/paper/7102-saliency-based-sequential-image-attention-with-multiset-prediction.pdf | neurips-2017-12 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.96269190e-01 1.44185781e-01 -5.53487718e-01 -4.18569833e-01
-5.40317774e-01 -4.09869790e-01 4.78396952e-01 6.25781059e-01
-4.20902491e-01 4.17597651e-01 2.23150671e-01 -1.31156698e-01
-7.68745393e-02 -3.06582510e-01 -4.66531008e-01 -3.61020535e-01
7.47407274e-03 5.25431156e-01 4.57917303e-01 -6.31227270... | [9.945141792297363, 1.6879757642745972] |
b41881c3-00b8-4a80-ba7f-58efcd32ada9 | deep-angiogram-trivializing-retinal-vessel | 2307.00245 | null | https://arxiv.org/abs/2307.00245v1 | https://arxiv.org/pdf/2307.00245v1.pdf | Deep Angiogram: Trivializing Retinal Vessel Segmentation | Among the research efforts to segment the retinal vasculature from fundus images, deep learning models consistently achieve superior performance. However, this data-driven approach is very sensitive to domain shifts. For fundus images, such data distribution changes can easily be caused by variations in illumination co... | ['Ipek Oguz', 'Yuankai K. Tao', 'Jiacheng Wang', 'Xing Yao', 'Dewei Hu'] | 2023-07-01 | null | null | null | null | ['retinal-vessel-segmentation'] | ['medical'] | [ 1.84624404e-01 4.81804162e-02 -3.39643992e-02 -3.83975476e-01
-5.48385739e-01 -7.59305775e-01 1.58210173e-01 -3.39267433e-01
-2.35269234e-01 8.89963984e-01 1.18848838e-01 -2.81716138e-01
1.17342561e-01 -7.03653812e-01 -6.23216987e-01 -9.16189253e-01
3.08778703e-01 6.49038628e-02 5.16385555e-01 2.14150056... | [15.733181953430176, -3.916961908340454] |
fc6da0dc-fd99-41f3-a2cb-659bcb8a229c | autoregressive-visual-tracking | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.pdf | Autoregressive Visual Tracking | We present ARTrack, an autoregressive framework for visual object tracking. ARTrack tackles tracking as a coordinate sequence interpretation task that estimates object trajectories progressively, where the current estimate is induced by previous states and in turn affects subsequences. This time-autoregressive appr... | ['Yihong Gong', 'Dahu Shi', 'Yongchao Zheng', 'Yifan Bai', 'Xing Wei'] | 2023-01-01 | autoregressive-visual-tracking-1 | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.pdf | cvpr-2023-2023-2 | ['template-matching', 'visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.04369712e-01 -5.17607450e-01 -2.85428375e-01 9.30602625e-02
-5.56340814e-01 -8.94800723e-01 7.69696653e-01 -2.05133572e-01
-3.13072503e-01 3.60781342e-01 1.31866097e-01 -1.15783542e-01
2.23854724e-02 -1.13331676e-01 -8.23346376e-01 -7.77345479e-01
-1.32292330e-01 5.74275315e-01 8.43252301e-01 3.70709985... | [6.26374626159668, -2.1049695014953613] |
7e9d9924-a445-4e31-93c4-0971837d1ced | unsupervised-open-domain-keyphrase-generation | 2306.10755 | null | https://arxiv.org/abs/2306.10755v1 | https://arxiv.org/pdf/2306.10755v1.pdf | Unsupervised Open-domain Keyphrase Generation | In this work, we study the problem of unsupervised open-domain keyphrase generation, where the objective is a keyphrase generation model that can be built without using human-labeled data and can perform consistently across domains. To solve this problem, we propose a seq2seq model that consists of two modules, namely ... | ['Kevin Chen-Chuan Chang', 'Pritom Saha Akash', 'Lam Thanh Do'] | 2023-06-19 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 4.10315096e-01 3.32474977e-01 -4.41735685e-01 -4.12466079e-02
-1.20978892e+00 -1.08203614e+00 1.12856162e+00 2.47884005e-01
-3.93798530e-01 1.05493343e+00 8.45382988e-01 -1.85480639e-01
1.02011032e-01 -7.10565388e-01 -6.85179651e-01 -3.86019796e-01
1.64270729e-01 9.27022099e-01 3.60847056e-01 -6.33394778... | [12.240622520446777, 8.852910041809082] |
6ee861a4-998e-4fce-a089-fc46c928dd7a | predicting-heart-disease-and-reducing-survey | 2306.00023 | null | https://arxiv.org/abs/2306.00023v1 | https://arxiv.org/pdf/2306.00023v1.pdf | Predicting Heart Disease and Reducing Survey Time Using Machine Learning Algorithms | Currently, many researchers and analysts are working toward medical diagnosis enhancement for various diseases. Heart disease is one of the common diseases that can be considered a significant cause of mortality worldwide. Early detection of heart disease significantly helps in reducing the risk of heart failure. Conse... | ['Ashraf Obaidat', 'Shuxia Lu', 'Asma Yamin', 'Salahaldeen Rababa'] | 2023-05-30 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-9.99854282e-02 -2.57939667e-01 -5.09759307e-01 -4.92833555e-01
-6.01556599e-01 -1.62685141e-01 -1.06777787e-01 6.99059129e-01
-2.21116528e-01 6.61615968e-01 2.07825676e-01 -7.43688107e-01
-2.18459517e-01 -9.13177788e-01 1.08894102e-01 -3.59277755e-01
1.01033837e-01 4.34076190e-01 3.74090038e-02 1.81238398... | [8.450149536132812, 4.901702880859375] |
e3b77a71-8f2d-4429-b1fd-d7f95a6a9bfa | not-all-claims-are-created-equal-choosing-the | 1911.03850 | null | https://arxiv.org/abs/1911.03850v3 | https://arxiv.org/pdf/1911.03850v3.pdf | Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses | Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely discussed or used wi... | ['Erfan Sadeqi Azer', 'Ashish Sabharwal', 'Dan Roth', 'Daniel Khashabi'] | 2019-11-10 | not-all-claims-are-created-equal-choosing-the-1 | https://aclanthology.org/2020.acl-main.506 | https://aclanthology.org/2020.acl-main.506.pdf | acl-2020-6 | ['misconceptions'] | ['miscellaneous'] | [ 3.01815987e-01 2.32144281e-01 -3.52180451e-01 -6.15011752e-01
-7.28111148e-01 -8.41131806e-01 6.01812184e-01 8.62631857e-01
-7.71314681e-01 7.77345896e-01 5.18369198e-01 -9.17988718e-01
-2.81970650e-01 -6.25040293e-01 -3.99185777e-01 -6.73492312e-01
5.09885848e-01 4.66623098e-01 2.31495872e-01 2.20916439... | [10.07168960571289, 8.383220672607422] |
56bef685-2c5b-4e72-a2b2-11da204617d8 | script-induction-as-association-rule-mining | null | null | https://aclanthology.org/2020.nuse-1.7 | https://aclanthology.org/2020.nuse-1.7.pdf | Script Induction as Association Rule Mining | We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patter... | ['Benjamin Van Durme', 'Anton Belyy'] | 2020-07-01 | null | null | null | ws-2020-7 | ['cloze-test'] | ['natural-language-processing'] | [ 2.13199869e-01 3.44692945e-01 -1.03675675e+00 -4.85268712e-01
-3.14999729e-01 -6.56751752e-01 9.06126559e-01 2.98347026e-01
-4.04240310e-01 1.28089213e+00 3.52147520e-01 -7.46511877e-01
-7.35064387e-01 -1.05990076e+00 -3.84952605e-01 -3.59838933e-01
-3.44221443e-01 5.00741780e-01 -7.26668611e-02 1.73003495... | [8.085378646850586, 5.4573163986206055] |
430cff9c-aa34-45bb-a04b-0e5e302c4171 | fine-grained-text-style-transfer-with | 2305.19512 | null | https://arxiv.org/abs/2305.19512v2 | https://arxiv.org/pdf/2305.19512v2.pdf | Fine-grained Text Style Transfer with Diffusion-Based Language Models | Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain. Previous works on diffusion-based language models have shown that they can be trained without external knowledge (such as pre-tr... | ['Honglak Lee', 'Todd C. Hollon', 'Jiacheng Shi', 'Tiange Luo', 'Yiwei Lyu'] | 2023-05-31 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 2.42700994e-01 4.43048328e-01 -1.43049553e-01 -1.22457929e-01
-6.23459935e-01 -6.85246527e-01 1.18301618e+00 -4.17854339e-01
-3.86118174e-01 6.91858172e-01 5.89495003e-01 -2.85508394e-01
3.74091208e-01 -1.10340714e+00 -7.46649444e-01 -4.17738795e-01
4.83917236e-01 8.72820675e-01 3.24731082e-01 -5.72279990... | [11.398355484008789, -0.030244501307606697] |
6bdf0aa0-cd8c-47a8-a409-f27ffd8f994d | mobile-networks-for-computer-go | 2008.10080 | null | https://arxiv.org/abs/2008.10080v1 | https://arxiv.org/pdf/2008.10080v1.pdf | Mobile Networks for Computer Go | The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines. For example the use of residual networks gave a 600 ELO increase in the strength of Alpha Go. This paper propos... | ['Tristan Cazenave'] | 2020-08-23 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-4.70354527e-01 4.51506495e-01 -2.84173995e-01 -2.49573775e-02
3.09611380e-01 -3.33699793e-01 6.63789928e-01 -3.64719182e-01
-1.05581629e+00 8.26377690e-01 -7.98408836e-02 -5.90437353e-01
-3.69934112e-01 -9.48637366e-01 -7.26013541e-01 -7.60312140e-01
-1.51711777e-01 3.48183095e-01 7.68780708e-01 -8.86362970... | [3.511537551879883, 1.476969838142395] |
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