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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
684060a2-2aa6-4337-8135-864b36e32469 | benchmarking-self-supervised-video | 2306.06010 | null | https://arxiv.org/abs/2306.06010v1 | https://arxiv.org/pdf/2306.06010v1.pdf | Benchmarking self-supervised video representation learning | Self-supervised learning is an effective way for label-free model pre-training, especially in the video domain where labeling is expensive. Existing self-supervised works in the video domain use varying experimental setups to demonstrate their effectiveness and comparison across approaches becomes challenging with no s... | ['Yogesh Singh Rawat', 'Vibhav Vineet', 'Ashlesha Kumar', 'Akash Kumar'] | 2023-06-09 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 5.87349892e-01 -1.13662459e-01 -7.24859893e-01 -7.21291661e-01
-6.16676688e-01 -6.44861639e-01 6.66272819e-01 -1.76342409e-02
-5.12274444e-01 7.38723755e-01 1.87555090e-01 -1.71632811e-01
-8.04387927e-02 -4.26214457e-01 -9.23978448e-01 -5.53853869e-01
-4.36249167e-01 4.42892492e-01 2.15329751e-01 -1.13886446... | [9.227495193481445, 1.6222912073135376] |
ccef70e5-7132-4a95-9553-8ca660f5d526 | topological-interpretability-for-deep | 2305.08642 | null | https://arxiv.org/abs/2305.08642v1 | https://arxiv.org/pdf/2305.08642v1.pdf | Topological Interpretability for Deep-Learning | With the increasing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly accelerating. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as... | ['Georgia Tourassi', 'Lynne Penberthy', 'Heidi A. Hanson', 'Adam Spannaus'] | 2023-05-15 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 4.13253844e-01 7.61701703e-01 -1.39806300e-01 -5.77072859e-01
-4.79939669e-01 -4.04718548e-01 5.69543958e-01 9.15944755e-01
-3.70289870e-02 5.92785120e-01 2.21742928e-01 -6.33977115e-01
-6.82896972e-01 -8.60299587e-01 -3.15758020e-01 -6.09941602e-01
-3.03398669e-01 7.96754718e-01 -1.60170987e-01 -2.00846270... | [8.613893508911133, 5.636249542236328] |
4c233cdb-0708-4c3b-89bd-9814d9cc5d64 | a-communication-efficient-adaptive-algorithm | 2301.08869 | null | https://arxiv.org/abs/2301.08869v2 | https://arxiv.org/pdf/2301.08869v2.pdf | A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret | We consider the problem of online stochastic optimization in a distributed setting with $M$ clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the e... | ['Qing Zhao', 'Kobi Cohen', 'Tamir Gabay', 'Sudeep Salgia'] | 2023-01-21 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-3.30981284e-01 4.83532310e-01 -2.48589098e-01 -5.94093502e-01
-1.29042172e+00 -8.59225571e-01 3.60129513e-02 2.14304939e-01
-8.93168628e-01 1.08485866e+00 -6.61787316e-02 -3.24978471e-01
-8.28521669e-01 -8.33229482e-01 -9.58370745e-01 -7.49895334e-01
-3.56724083e-01 3.63199592e-01 -4.30655062e-01 2.81504035... | [4.656428813934326, 3.4431564807891846] |
dd794aba-528a-4561-9c7b-2d6c55f3143d | meta-album-multi-domain-meta-dataset-for-few-1 | 2302.08909 | null | https://arxiv.org/abs/2302.08909v1 | https://arxiv.org/pdf/2302.08909v1.pdf | Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification | We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per class, with verified licences. They stem from diverse domains, such as ecology (... | ['Phan Anh Vu', 'Joaquin Vanschoren', 'Haozhe Sun', 'Jan N van Rijn', 'Felix Mohr', 'Mike Huisman', 'Isabelle Guyon', 'Sergio Escalera', 'Dustin Carrión-Ojeda', 'Ihsan Ullah'] | 2023-02-16 | meta-album-multi-domain-meta-dataset-for-few | https://meta-album.github.io/paper/Meta-Album.pdf | https://meta-album.github.io/paper/Meta-Album.pdf | neurips-2022-9 | ['optical-character-recognition', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.58799756e-01 -1.57896698e-01 -3.82109523e-01 -5.65792397e-02
-1.03454828e+00 -5.41610241e-01 6.91017866e-01 -1.93540740e-03
-2.27960840e-01 8.07448685e-01 2.33617891e-02 -2.51207083e-01
-1.00110814e-01 -7.30660200e-01 -7.75106966e-01 -5.45399547e-01
-4.20335889e-01 3.64987046e-01 3.54339004e-01 -1.89326540... | [9.855874061584473, 2.6736977100372314] |
8154d8ad-5798-43e1-81c4-364d02084908 | discourse-aware-emotion-cause-extraction-in | 2210.14419 | null | https://arxiv.org/abs/2210.14419v1 | https://arxiv.org/pdf/2210.14419v1.pdf | Discourse-Aware Emotion Cause Extraction in Conversations | Emotion Cause Extraction in Conversations (ECEC) aims to extract the utterances which contain the emotional cause in conversations. Most prior research focuses on modelling conversational contexts with sequential encoding, ignoring the informative interactions between utterances and conversational-specific features for... | ['Chen Gong', 'Guohong Fu', 'Yun Yuan', 'Nan Yu', 'Dexin Kong'] | 2022-10-26 | null | null | null | null | ['causal-emotion-entailment', 'discourse-parsing', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.71142840e-01 7.19162941e-01 -1.77740436e-02 -6.61275506e-01
-5.61362088e-01 -4.60288882e-01 8.90430868e-01 8.01640451e-02
4.03284580e-02 7.36610711e-01 1.03217924e+00 -6.18050732e-02
2.47747581e-02 -6.04880571e-01 -3.22889656e-01 -5.05614042e-01
-2.47184277e-01 2.53847778e-01 -3.17457795e-01 -6.46616280... | [12.867389678955078, 6.378283977508545] |
61a84730-c3fa-4103-9b2a-0e7220dd3740 | proactive-robot-assistance-via-spatio | 2211.15501 | null | https://arxiv.org/abs/2211.15501v1 | https://arxiv.org/pdf/2211.15501v1.pdf | Proactive Robot Assistance via Spatio-Temporal Object Modeling | Proactive robot assistance enables a robot to anticipate and provide for a user's needs without being explicitly asked. We formulate proactive assistance as the problem of the robot anticipating temporal patterns of object movements associated with everyday user routines, and proactively assisting the user by placing o... | ['Sonia Chernova', 'Maithili Patel'] | 2022-11-28 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 2.31384503e-04 7.10589111e-01 -1.92685217e-01 -4.70655590e-01
-1.53373048e-01 -2.64195025e-01 3.59262049e-01 7.78455287e-03
-3.55736673e-01 7.15776980e-01 7.17612505e-01 1.35580469e-02
-1.82645157e-01 -6.65665030e-01 -9.27641690e-01 -4.52920288e-01
-6.40004575e-01 9.78252828e-01 -6.97743073e-02 -1.80649653... | [4.823270797729492, 0.5902055501937866] |
a078b8be-9829-4fbb-b323-4a03d40c4c6c | transfer4d-a-framework-for-frugal-motion | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Maheshwari_Transfer4D_A_Framework_for_Frugal_Motion_Capture_and_Deformation_Transfer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Maheshwari_Transfer4D_A_Framework_for_Frugal_Motion_Capture_and_Deformation_Transfer_CVPR_2023_paper.pdf | Transfer4D: A Framework for Frugal Motion Capture and Deformation Transfer | Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses. The goal of our work is to democratize this task by developing a fru... | ['Ramya Hebbalaguppe', 'Rahul Narain', 'Shubh Maheshwari'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-retargeting'] | ['computer-vision'] | [ 3.95838886e-01 -8.15844834e-02 2.45585740e-01 1.05808571e-01
-2.85950869e-01 -7.43362963e-01 5.88681042e-01 -4.52257782e-01
-3.73208314e-01 3.40334833e-01 1.11917406e-03 6.23427629e-02
4.67482179e-01 -6.35772109e-01 -5.38207769e-01 -4.25202310e-01
2.37152323e-01 5.27283907e-01 9.20885921e-01 -1.34860337... | [7.350462436676025, -0.9038503170013428] |
477e5c92-49c8-489c-b1e6-68633bcd6d49 | community-time-activity-trajectory-modelling | 2212.06366 | null | https://arxiv.org/abs/2212.06366v1 | https://arxiv.org/pdf/2212.06366v1.pdf | Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression | Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identic... | ['Jianli Chen', 'Yuqing Hu', 'Chen Xia'] | 2022-12-13 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-3.02471340e-01 -1.34224206e-01 -4.40191090e-01 3.34087983e-02
1.12763457e-01 -3.62568758e-02 4.59528834e-01 5.54937005e-01
-5.06667018e-01 7.56959260e-01 1.08615196e+00 -5.04117489e-01
-2.88442135e-01 -1.44882762e+00 -2.80214548e-01 -8.24813068e-01
-3.19977015e-01 1.62730515e-01 2.23738700e-01 -1.96049735... | [6.409538269042969, 1.8763974905014038] |
9bc6268b-cee8-4573-908c-3b4f402d8ac4 | inkorrect-online-handwriting-spelling | 2202.13794 | null | https://arxiv.org/abs/2202.13794v1 | https://arxiv.org/pdf/2202.13794v1.pdf | Inkorrect: Online Handwriting Spelling Correction | We introduce Inkorrect, a data- and label-efficient approach for online handwriting (Digital Ink) spelling correction - DISC. Unlike previous work, the proposed method does not require multiple samples from the same writer, or access to character level segmentation. We show that existing automatic evaluation metrics do... | ['Claudiu Musat', 'Jesse Berent', 'Henry Rowley', 'Andrii Maksai'] | 2022-02-28 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 2.03088894e-01 -4.01770502e-01 -3.65380079e-01 -2.72356808e-01
-4.53326285e-01 -1.12659800e+00 6.12406552e-01 5.37701666e-01
-4.43462074e-01 5.76139331e-01 -3.38748656e-02 -1.03519224e-01
-5.06968677e-01 -4.40109581e-01 -4.19807762e-01 -3.74850452e-01
4.53427404e-01 7.89856076e-01 3.32090467e-01 1.15388207... | [11.861337661743164, 2.4577767848968506] |
54e6064c-e0c3-4ad3-b713-8efc66591903 | sugar-spherical-ultrafast-graph-attention | 2307.00511 | null | https://arxiv.org/abs/2307.00511v1 | https://arxiv.org/pdf/2307.00511v1.pdf | SUGAR: Spherical Ultrafast Graph Attention Framework for Cortical Surface Registration | Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving pro... | ['Hesheng Liu', 'Danhong Wang', 'Dan Hu', 'Ping Zhang', 'Qingyu Hu', 'Wei zhang', 'Ying Zhou', 'Weiwei Wang', 'Weigang Cui', 'Cong Lin', 'Zhenyu Sun', 'Danyang Wang', 'Youjia Zhang', 'Ning An', 'Jianxun Ren'] | 2023-07-02 | null | null | null | null | ['graph-attention'] | ['graphs'] | [ 1.42487377e-01 1.90942839e-01 -4.50188387e-03 -5.98581135e-01
-1.09794962e+00 -4.11777735e-01 5.38874030e-01 2.47495502e-01
-5.19379377e-01 4.03366655e-01 5.38918853e-01 1.60114720e-01
-2.64346451e-01 -4.79781479e-01 -5.68207741e-01 -4.66011763e-01
-4.30044323e-01 4.67235565e-01 -2.83837188e-02 8.69496241... | [13.985238075256348, -2.5164852142333984] |
5714c76d-1436-4966-87a5-6102347185b6 | is-preprocessing-of-text-really-worth-your | 1806.02908 | null | http://arxiv.org/abs/1806.02908v2 | http://arxiv.org/pdf/1806.02908v2.pdf | Is preprocessing of text really worth your time for online comment classification? | A large proportion of online comments present on public domains are
constructive, however a significant proportion are toxic in nature. The
comments contain lot of typos which increases the number of features manifold,
making the ML model difficult to train. Considering the fact that the data
scientists spend approxima... | ['Fahim Mohammad'] | 2018-06-07 | null | null | null | null | ['toxic-comment-classification'] | ['natural-language-processing'] | [-1.45101503e-01 1.36659727e-01 1.75110735e-02 -3.44871432e-01
-4.33707207e-01 -7.60963321e-01 5.00090003e-01 7.31200516e-01
-4.42634672e-01 5.59965491e-01 4.07052398e-01 -6.51494026e-01
2.77577341e-01 -6.20762169e-01 -3.81533504e-01 -3.54458272e-01
3.65681857e-01 1.19353354e-01 1.01755597e-01 -3.26051354... | [8.881903648376465, 10.287038803100586] |
cfc4b977-b000-47ce-90e4-e507245b69b0 | unsupervised-semantic-aggregation-and | 2010.05517 | null | https://arxiv.org/abs/2010.05517v1 | https://arxiv.org/pdf/2010.05517v1.pdf | Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning | Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learning. In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples. In this paper, we combine bo... | ['Qi Wang', 'Yuan Yuan', 'Junyu Gao', 'Tao Han'] | 2020-10-12 | null | http://proceedings.neurips.cc/paper/2020/hash/71a58e8cb75904f24cde464161c3e766-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/71a58e8cb75904f24cde464161c3e766-Paper.pdf | neurips-2020-12 | ['template-matching'] | ['computer-vision'] | [ 2.13310376e-01 1.62954301e-01 -4.24279898e-01 -9.37885523e-01
-1.06665683e+00 -3.80972892e-01 3.14887911e-01 -3.62577289e-02
-2.94251263e-01 6.74711227e-01 -2.01167837e-02 9.49658453e-02
-3.72625962e-02 -4.73870307e-01 -3.78042966e-01 -9.37954068e-01
2.78265536e-01 3.67080271e-01 -1.43045932e-01 3.15465957... | [9.462248802185059, 3.7115354537963867] |
45df5d3e-38b6-4815-b186-ffb1f3dea1bb | controllable-data-augmentation-for-context | 2304.13902 | null | https://arxiv.org/abs/2304.13902v2 | https://arxiv.org/pdf/2304.13902v2.pdf | Controllable Data Augmentation for Context-Dependent Text-to-SQL | The limited scale of annotated data constraints existing context-dependent text-to-SQL models because of the complexity of labeling. The data augmentation method is a commonly used method to solve this problem. However, the data generated by current augmentation methods often lack diversity. In this paper, we introduce... | ['Wanxiang Che', 'Longxu Dou', 'Dingzirui Wang'] | 2023-04-27 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-3.18654180e-02 2.73328155e-01 -1.94766209e-01 -7.43423283e-01
-1.13022995e+00 -7.65369654e-01 3.83436471e-01 3.85486394e-01
-1.93807185e-01 5.27194083e-01 4.73393649e-01 -4.24634695e-01
-3.03768795e-02 -1.07093418e+00 -8.33245516e-01 1.94486365e-01
6.49690151e-01 4.77098018e-01 5.60651898e-01 -5.61113000... | [9.904613494873047, 7.8536505699157715] |
0b7daaf2-2fb0-40ff-9576-14ce54cfb1b5 | cloud-detection-machine-learning-algorithms | 2012.10396 | null | https://arxiv.org/abs/2012.10396v1 | https://arxiv.org/pdf/2012.10396v1.pdf | Cloud detection machine learning algorithms for PROBA-V | This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant sources of e... | ['Gustau Camps-Valls', 'Jordi Muñoz-Marí', 'Gonzalo Mateo-García', 'Luis Gómez-Chova'] | 2020-12-09 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 1.78938553e-01 -8.91280651e-01 1.73288897e-01 -2.18539283e-01
-6.39017701e-01 -9.46700752e-01 5.56572735e-01 2.14809254e-01
-1.31298378e-01 8.94872606e-01 -7.07248211e-01 -5.89837790e-01
-6.03548512e-02 -1.02823484e+00 -1.61938086e-01 -1.08617222e+00
-3.44071835e-01 1.31353572e-01 2.83443153e-01 -9.46713835... | [9.68233871459961, -1.7948395013809204] |
cca88d25-3553-4829-a11c-ba2439929e55 | vis2mus-exploring-multimodal-representation | 2211.05543 | null | https://arxiv.org/abs/2211.05543v1 | https://arxiv.org/pdf/2211.05543v1.pdf | Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation | In this study, we explore the representation mapping from the domain of visual arts to the domain of music, with which we can use visual arts as an effective handle to control music generation. Unlike most studies in multimodal representation learning that are purely data-driven, we adopt an analysis-by-synthesis appro... | ['Gus Xia', 'Ying Shan', 'Kai Shao', 'Yixiao Zhang', 'Runbang Zhang'] | 2022-11-10 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.84226060e-01 -2.24578172e-01 -1.79846466e-01 9.10136290e-03
-4.99047041e-01 -1.09341407e+00 8.14767659e-01 -3.78598809e-01
9.89553034e-02 3.46313596e-01 3.96323383e-01 -3.99930924e-02
-1.14156157e-01 -7.47123361e-01 -6.68804526e-01 -5.29831111e-01
6.04060471e-01 3.09763312e-01 -3.43176067e-01 -6.66136026... | [15.865254402160645, 5.385765552520752] |
20a2af42-bbff-4ef3-88c6-4ce91a8243ce | slaps-self-supervision-improves-structure-1 | 2102.05034 | null | https://arxiv.org/abs/2102.05034v2 | https://arxiv.org/pdf/2102.05034v2.pdf | SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks | Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer a task-specific latent structure and then apply a GNN to the inferred graph. Unfortunately, the space of possible graph st... | ['Seyed Mehran Kazemi', 'Layla El Asri', 'Bahare Fatemi'] | 2021-02-09 | slaps-self-supervision-improves-structure | http://proceedings.neurips.cc/paper/2021/hash/bf499a12e998d178afd964adf64a60cb-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/bf499a12e998d178afd964adf64a60cb-Paper.pdf | neurips-2021-12 | ['graph-structure-learning'] | ['graphs'] | [ 3.15622836e-01 5.58804333e-01 -3.59754801e-01 -3.86230171e-01
-5.41603751e-02 -5.00637829e-01 3.98548603e-01 2.34934911e-01
-3.99364531e-03 7.72768736e-01 6.24786830e-04 -4.81880724e-01
-2.83583492e-01 -9.17269588e-01 -9.10887361e-01 -4.96903241e-01
-3.20883304e-01 8.49286199e-01 2.94731945e-01 -8.17947164... | [7.090664386749268, 6.20127534866333] |
2f1517b0-ae16-47a9-bf7a-8f6a19c5145f | clearumor-at-semeval-2019-task-7-convolving | 1904.03084 | null | http://arxiv.org/abs/1904.03084v1 | http://arxiv.org/pdf/1904.03084v1.pdf | CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors | This paper describes our submission to SemEval-2019 Task 7: RumourEval:
Determining Rumor Veracity and Support for Rumors. We participated in both
subtasks. The goal of subtask A is to classify the type of interaction between
a rumorous social media post and a reply post as support, query, deny, or
comment. The goal of... | ['Lukas Schmelzeisen', 'Steffen Staab', 'Ipek Baris'] | 2019-04-05 | clearumor-at-semeval-2019-task-7-convolving-1 | https://aclanthology.org/S19-2193 | https://aclanthology.org/S19-2193.pdf | semeval-2019-6 | ['rumour-detection'] | ['natural-language-processing'] | [-3.85425836e-01 5.07472873e-01 -2.14994624e-01 -4.08591002e-01
-3.72321218e-01 -1.22972809e-01 9.86147344e-01 3.72778177e-01
-5.46868205e-01 6.52027965e-01 7.96158016e-01 -3.65015090e-01
5.92766881e-01 -3.87398601e-01 -5.85014403e-01 -1.65811703e-02
1.58342347e-02 4.96121109e-01 5.32254949e-02 -4.78150755... | [8.216269493103027, 10.152532577514648] |
780ff761-615c-40cc-9113-ec1f32b4e0da | melody-transcription-via-generative-pre | 2212.01884 | null | https://arxiv.org/abs/2212.01884v1 | https://arxiv.org/pdf/2212.01884v1.pdf | Melody transcription via generative pre-training | Despite the central role that melody plays in music perception, it remains an open challenge in music information retrieval to reliably detect the notes of the melody present in an arbitrary music recording. A key challenge in melody transcription is building methods which can handle broad audio containing any number o... | ['Percy Liang', 'John Thickstun', 'Chris Donahue'] | 2022-12-04 | null | null | null | null | ['chord-recognition', 'music-information-retrieval'] | ['audio', 'music'] | [ 2.36427501e-01 -2.97133297e-01 -9.53321997e-03 1.66209936e-01
-1.60161531e+00 -1.24850523e+00 2.32089475e-01 -6.91817626e-02
-1.46649957e-01 4.98638153e-01 6.89200521e-01 8.78905356e-02
-2.89746404e-01 -5.14043450e-01 -4.30073887e-01 -2.70174176e-01
4.37433943e-02 4.42985445e-01 -5.24862930e-02 -5.76504827... | [15.904976844787598, 5.386116027832031] |
0ccdc824-5880-4176-a7b3-faf2e5b73688 | barlow-graph-auto-encoder-for-unsupervised | 2110.15742 | null | https://arxiv.org/abs/2110.15742v3 | https://arxiv.org/pdf/2110.15742v3.pdf | Barlow Graph Auto-Encoder for Unsupervised Network Embedding | Network embedding has emerged as a promising research field for network analysis. Recently, an approach, named Barlow Twins, has been proposed for self-supervised learning in computer vision by applying the redundancy-reduction principle to the embedding vectors corresponding to two distorted versions of the image samp... | ['Martin Kleinsteuber', 'Rayyan Ahmad Khan'] | 2021-10-29 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [ 1.52760014e-01 4.98062491e-01 -3.66096079e-01 -5.65978885e-02
-4.08011377e-02 -2.31341913e-01 8.74937892e-01 4.00646031e-01
-4.40087989e-02 3.90574872e-01 2.97567934e-01 -1.60675555e-01
-5.51667094e-01 -8.11589181e-01 -5.64655364e-01 -7.21965253e-01
-2.14998603e-01 5.06121278e-01 -4.98279184e-02 -7.10572004... | [7.1565375328063965, 6.128909587860107] |
6ae0e98a-1dac-4e00-9eec-928eefcda99d | a-bilingual-openworld-video-text-dataset-and | 2112.04888 | null | https://arxiv.org/abs/2112.04888v1 | https://arxiv.org/pdf/2112.04888v1.pdf | A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer | Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data. In this work, we introduce a large-scale, Bilingual, Open World Video text benchmark dataset(BOVText). There are four features for BOVText. Firstly, we provide 2,000+ videos with more than 1,750,000+ frame... | ['Hong Zhou', 'Yejun Tang', 'Jiahong Li', 'Zhuang Li', 'Sibo Wang', 'Debing Zhang', 'Yuanqiang Cai', 'Weijia Wu'] | 2021-12-09 | null | null | null | null | ['text-spotting', 'text-annotation'] | ['computer-vision', 'natural-language-processing'] | [ 6.20701090e-02 -5.70590258e-01 -4.12322193e-01 -1.27327055e-01
-7.21069396e-01 -2.98314989e-01 5.21991849e-01 -2.52003938e-01
-5.31149209e-01 4.82572585e-01 5.75217545e-01 -1.28231004e-01
1.91922873e-01 -3.89823318e-01 -9.34675157e-01 -4.87134546e-01
4.46458876e-01 3.62582058e-01 3.74426007e-01 -2.91544676... | [11.89548110961914, 2.120465040206909] |
b24502b3-02eb-43f3-88d2-15d1337a026f | improved-batching-strategy-for-irregular-time | 2207.05708 | null | https://arxiv.org/abs/2207.05708v1 | https://arxiv.org/pdf/2207.05708v1.pdf | Improved Batching Strategy For Irregular Time-Series ODE | Irregular time series data are prevalent in the real world and are challenging to model with a simple recurrent neural network (RNN). Hence, a model that combines the use of ordinary differential equations (ODE) and RNN was proposed (ODE-RNN) to model irregular time series with higher accuracy, but it suffers from high... | ['Nathan Perlmutter', 'Ahmed Khorshid', 'Yony Bresler', 'Ting Fung Lam'] | 2022-07-12 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-2.64397949e-01 -2.44849831e-01 3.26319695e-01 1.43906251e-01
-4.14285570e-01 -1.93215996e-01 3.81878823e-01 -7.74229243e-02
-2.33519062e-01 7.06041932e-01 4.45136651e-02 -6.46147430e-01
-6.69354871e-02 -7.31831849e-01 -5.82612216e-01 -5.23232341e-01
-3.04065496e-01 1.86741844e-01 9.38793942e-02 -5.19912064... | [7.082246780395508, 3.2000296115875244] |
5593fd21-1088-4f42-907b-6e911e5499b9 | palm-predicting-actions-through-language | 2306.16545 | null | https://arxiv.org/abs/2306.16545v1 | https://arxiv.org/pdf/2306.16545v1.pdf | Palm: Predicting Actions through Language Models @ Ego4D Long-Term Action Anticipation Challenge 2023 | We present Palm, a solution to the Long-Term Action Anticipation (LTA) task utilizing vision-language and large language models. Given an input video with annotated action periods, the LTA task aims to predict possible future actions. We hypothesize that an optimal solution should capture the interdependency between pa... | ['Xi Wang', 'Luc van Gool', 'Otmar Hilliges', 'Daoji Huang'] | 2023-06-28 | null | null | null | null | ['action-anticipation', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 7.58775026e-02 3.98115605e-01 -3.76920640e-01 -6.36610210e-01
-4.05625671e-01 -2.75831550e-01 8.95638227e-01 -2.67884701e-01
-2.45458841e-01 4.59919184e-01 1.15407252e+00 5.69353849e-02
2.28048131e-01 -4.24018592e-01 -8.56084406e-01 -5.74542545e-02
-2.63726383e-01 3.72565180e-01 -3.84506285e-02 1.58375964... | [8.119029998779297, 0.5043160915374756] |
221be931-42d5-4a1e-b707-434d02a33223 | discovering-new-intents-via-constrained-deep | 1911.08891 | null | https://arxiv.org/abs/1911.08891v1 | https://arxiv.org/pdf/1911.08891v1.pdf | Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement | Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but... | ['Hanlei Zhang', 'Ting-En Lin', 'Hua Xu'] | 2019-11-20 | null | null | null | null | ['text-clustering', 'open-intent-discovery', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.42393032e-01 5.71920127e-02 8.75244513e-02 -6.78868532e-01
-7.54394352e-01 -8.84419620e-01 6.88163459e-01 1.56690761e-01
-4.53435302e-01 3.97558302e-01 5.56385040e-01 -4.19315584e-02
5.92301637e-02 -2.74032027e-01 -2.62080729e-01 -5.04064858e-01
1.60907924e-01 8.42439950e-01 1.63172185e-01 -2.72733837... | [12.271068572998047, 7.408139705657959] |
4246c238-d065-4c21-9843-f454d9c4bf18 | solar-active-region-magnetogram-image-dataset | 2305.09492 | null | https://arxiv.org/abs/2305.09492v2 | https://arxiv.org/pdf/2305.09492v2.pdf | Solar Active Region Magnetogram Image Dataset for Studies of Space Weather | In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Im... | ['Ellery Wuest', 'Jeremy A. Grajeda', 'Ty Vincent', 'Laura E. Boucheron'] | 2023-05-16 | null | null | null | null | ['solar-flare-prediction'] | ['time-series'] | [ 3.37645859e-01 -4.31143820e-01 -3.70087802e-01 -4.45100069e-01
-1.76505372e-01 -6.14878058e-01 8.99617970e-01 4.37446125e-02
3.16919506e-01 6.99247539e-01 2.12366104e-01 -3.53502095e-01
-3.10784072e-01 -8.30200791e-01 -3.22744757e-01 -9.56620038e-01
1.95842180e-02 6.01860523e-01 -8.44943374e-02 -4.99737382... | [6.704005718231201, 2.859804630279541] |
155554cd-826a-418b-9671-9b2c1543f4bc | iris-r-cnn-accurate-iris-segmentation-in-non | 1903.10140 | null | http://arxiv.org/abs/1903.10140v1 | http://arxiv.org/pdf/1903.10140v1.pdf | Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment | Despite the significant advances in iris segmentation, accomplishing accurate
iris segmentation in non-cooperative environment remains a grand challenge. In
this paper, we present a deep learning framework, referred to as Iris R-CNN, to
offer superior accuracy for iris segmentation. The proposed framework is
derived fr... | ['Chunyang Feng', 'Yufeng Sun', 'Xin Li'] | 2019-03-25 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 1.75019220e-01 6.51522831e-04 -3.21492940e-01 -4.25975323e-01
-3.21488202e-01 -1.58229902e-01 3.23538959e-01 -3.65564942e-01
-2.64475673e-01 5.50376654e-01 2.08584890e-01 -4.00519311e-01
-1.99277401e-01 -3.74270558e-01 -3.34690571e-01 -9.41147387e-01
1.79152414e-01 4.94305161e-04 -2.31712073e-01 3.92896160... | [3.763547897338867, -3.6194405555725098] |
1f1dbe07-3eca-427d-9a84-bbc4ce972859 | spring-a-fast-stochastic-proximal-alternating | 2002.12266 | null | https://arxiv.org/abs/2002.12266v3 | https://arxiv.org/pdf/2002.12266v3.pdf | SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization | We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems. Large-scale imaging problems are becoming increasingly prevalent due to advances in data acquisition and computational capabilities. Motivated by the suc... | ['Carola-Bibiane Schönlieb', 'Mike Davies', 'Jingwei Liang', 'Junqi Tang', 'Derek Driggs'] | 2020-02-27 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 1.31752700e-01 -2.80463517e-01 2.12524876e-01 -3.15320194e-01
-1.66989112e+00 -6.56732202e-01 6.80352747e-02 -4.31210518e-01
-3.30238402e-01 7.09269881e-01 4.65221375e-01 -3.85955185e-01
-6.07803822e-01 2.50408556e-02 -1.02827954e+00 -8.37894738e-01
-4.91029948e-01 3.52016926e-01 -5.23342848e-01 1.40128523... | [7.05544376373291, 4.500284194946289] |
b1ad88dd-d3b2-42a2-9405-4ab7b700bd0e | inforex-a-collaborative-systemfor-text | null | null | https://aclanthology.org/R19-1083 | https://aclanthology.org/R19-1083.pdf | Inforex --- a Collaborative Systemfor Text Corpora Annotation and Analysis Goes Open | In the paper we present the latest changes introduce to Inforex {---} a web-based system for qualitative and collaborative text corpora annotation and analysis. One of the most important news is the release of source codes. Now the system is available on the GitHub repository (https://github.com/CLARIN-PL/Inforex) as a... | ["Micha{\\l} Marci{\\'n}czuk", 'Marcin Oleksy'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['morphological-disambiguation', 'text-annotation', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.85879314e-01 2.39062726e-01 1.75301090e-01 -5.23145199e-01
-8.99329305e-01 -1.12688839e+00 4.94626820e-01 8.49125385e-01
-5.85108578e-01 8.42422962e-01 5.76799989e-01 -3.06042284e-01
-2.21132651e-01 -2.95151234e-01 1.02784149e-01 -1.81051031e-01
3.12410116e-01 7.62856364e-01 1.47540197e-01 -6.32651031... | [9.66866397857666, 9.313729286193848] |
336e9550-2656-4a17-bf63-035185863458 | avface-towards-detailed-audio-visual-4d-face | 2304.13115 | null | https://arxiv.org/abs/2304.13115v2 | https://arxiv.org/pdf/2304.13115v2.pdf | AVFace: Towards Detailed Audio-Visual 4D Face Reconstruction | In this work, we present a multimodal solution to the problem of 4D face reconstruction from monocular videos. 3D face reconstruction from 2D images is an under-constrained problem due to the ambiguity of depth. State-of-the-art methods try to solve this problem by leveraging visual information from a single image or v... | ['Dimitris Samaras', 'Aggelina Chatziagapi'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chatziagapi_AVFace_Towards_Detailed_Audio-Visual_4D_Face_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chatziagapi_AVFace_Towards_Detailed_Audio-Visual_4D_Face_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.95716962e-02 1.07739188e-01 -2.21710876e-02 -1.14244044e-01
-6.09067976e-01 -4.78502721e-01 5.27795136e-01 -3.71188223e-01
-4.41094898e-02 4.05616671e-01 -8.38654116e-03 2.30264999e-02
3.39856893e-01 -4.53619629e-01 -6.26153827e-01 -6.36878788e-01
2.65476733e-01 5.02219081e-01 1.36789560e-01 -4.32576127... | [13.141488075256348, -0.09941704571247101] |
09f97267-f3d8-47f6-99d4-ec539e9232f5 | spectrogram-channels-u-net-a-source | 1810.11520 | null | http://arxiv.org/abs/1810.11520v2 | http://arxiv.org/pdf/1810.11520v2.pdf | Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source | Sound source separation has attracted attention from Music Information
Retrieval(MIR) researchers, since it is related to many MIR tasks such as
automatic lyric transcription, singer identification, and voice conversion. In
this paper, we propose an intuitive spectrogram-based model for source
separation by adapting U-... | ['Se-Young Yun', 'Jaehoon Oh', 'Duyeon Kim'] | 2018-10-26 | null | null | null | null | ['singer-identification'] | ['music'] | [ 2.08330527e-01 -6.35272801e-01 -1.51324317e-01 1.93758860e-01
-1.02546275e+00 -8.11536789e-01 1.97853148e-01 -1.54466294e-02
-1.27986044e-01 4.19522554e-01 4.16141927e-01 -1.78291678e-01
-1.45644709e-01 -2.15351790e-01 -2.87150413e-01 -7.25928664e-01
3.89214516e-01 -1.72669724e-01 3.53280939e-02 -1.69271886... | [15.642047882080078, 5.577869415283203] |
75757269-13c9-4321-932b-c795a584a74a | curriculum-meta-learning-for-order-robust | 2101.01926 | null | https://arxiv.org/abs/2101.01926v3 | https://arxiv.org/pdf/2101.01926v3.pdf | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction | Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, this task is prone to two serious challenges, namely catastrophic forgetting and order-sensitivity. We propose a novel curriculum-meta learni... | ['Guoqiang Xu', 'Yujin Zhu', 'Guilin Qi', 'Reza Haffari', 'Yuan-Fang Li', 'Xuekai Li', 'Tongtong Wu'] | 2021-01-06 | null | null | null | null | ['continual-relation-extraction'] | ['natural-language-processing'] | [ 2.61464659e-02 1.79767415e-01 -3.74513090e-01 -1.38779134e-01
-7.48827577e-01 -4.28919435e-01 3.78557175e-01 6.22318685e-01
-3.40451151e-01 9.89126503e-01 1.31740332e-01 -5.14806569e-01
-5.52038789e-01 -8.35468948e-01 -7.76790440e-01 -4.02196646e-01
-5.83862537e-04 5.47308445e-01 4.00821567e-01 -5.31336784... | [9.182561874389648, 8.528240203857422] |
107e25b1-0a26-45f6-a87e-ff3372b81535 | ts-moco-time-series-momentum-contrast-for | 2306.06522 | null | https://arxiv.org/abs/2306.06522v1 | https://arxiv.org/pdf/2306.06522v1.pdf | TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning | Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach this problem and propose a novel encoding framework that relies on self-supervised learning with momentum contrast to learn representations fr... | ['Enkelejda Kasneci', 'Tobias Grosse-Puppendahl', 'Ozan Özdenizci', 'David Bethge', 'Philipp Hallgarten'] | 2023-06-10 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 5.70478439e-01 1.49477839e-01 -5.12816608e-02 -8.04923058e-01
-5.88867664e-01 -4.77394044e-01 4.23386931e-01 1.64546072e-01
-5.52083611e-01 1.04173255e+00 8.47916082e-02 -1.25681773e-01
-3.02501917e-01 -4.28540200e-01 -6.46423221e-01 -8.24993014e-01
-2.60460347e-01 2.68209130e-01 -3.55072260e-01 6.74791783... | [13.1885347366333, 3.4859421253204346] |
e7c53082-6d93-4090-a5dd-fb53a692cb8e | siamixformer-a-siamese-transformer-network | 2208.00657 | null | https://arxiv.org/abs/2208.00657v1 | https://arxiv.org/pdf/2208.00657v1.pdf | SiamixFormer: A Siamese Transformer Network For Building Detection And Change Detection From Bi-Temporal Remote Sensing Images | Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is ba... | ['Foad Ghaderi', 'Amir mohammadian'] | 2022-08-01 | null | null | null | null | ['2d-semantic-segmentation', 'change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing', 'extracting-buildings-in-remote-sensing-images'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [ 2.18949094e-01 -3.73239458e-01 4.14522260e-01 -2.64012039e-01
-6.90891206e-01 -5.08801006e-02 6.75413847e-01 4.81004894e-01
-6.21490180e-01 4.03925627e-01 3.72136414e-01 -2.19514985e-02
-4.89157476e-02 -1.68363953e+00 -5.95916629e-01 -8.87916446e-01
-2.26487398e-01 7.69420043e-02 5.61715364e-01 -5.46604633... | [9.652117729187012, -1.2960597276687622] |
68f86d80-4eca-46c7-95c4-730241a8504c | analysis-of-augmentations-for-contrastive-ecg | 2206.07656 | null | https://arxiv.org/abs/2206.07656v1 | https://arxiv.org/pdf/2206.07656v1.pdf | Analysis of Augmentations for Contrastive ECG Representation Learning | This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-supervised framework consists of two main parts: the contrastive learning and the downstream... | ['Javad Hashemi', 'Ali Etemad', 'Sahar Soltanieh'] | 2022-05-30 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 5.82664609e-01 1.29761100e-01 -1.64657742e-01 -4.64815974e-01
-7.38974392e-01 -4.51369107e-01 2.19295964e-01 3.36737126e-01
-5.36146760e-01 7.44232774e-01 1.37517005e-01 -5.03056169e-01
-2.89079726e-01 -5.76824903e-01 -4.98286784e-01 -6.67366505e-01
-4.69202816e-01 3.33509207e-01 1.29629791e-01 -2.99041629... | [14.25288200378418, 3.32623028755188] |
538f9bcd-25a2-4a9b-926b-a25dc615f4f6 | pose-guided-fashion-image-synthesis-using | 1906.07251 | null | https://arxiv.org/abs/1906.07251v2 | https://arxiv.org/pdf/1906.07251v2.pdf | Pose Guided Fashion Image Synthesis Using Deep Generative Model | Generating a photorealistic image with intended human pose is a promising yet challenging research topic for many applications such as smart photo editing, movie making, virtual try-on, and fashion display. In this paper, we present a novel deep generative model to transfer an image of a person from a given pose to a n... | ['Shanglin Yang', 'Wei Sun', 'Jawadul H. Bappy', 'Hui Zhou', 'Yi Xu', 'Tianfu Wu'] | 2019-06-17 | null | null | null | null | ['pose-guided-image-generation'] | ['computer-vision'] | [ 6.90656185e-01 3.06574792e-01 4.08280432e-01 -4.26622719e-01
-2.37304583e-01 -5.67513406e-01 8.60168338e-01 -3.70139718e-01
-2.86504682e-02 5.13180196e-01 6.42398149e-02 1.26696482e-01
4.03812855e-01 -9.69891548e-01 -1.05802238e+00 -5.45358717e-01
5.74197352e-01 2.94562638e-01 1.51068075e-02 -1.43280119... | [11.88856315612793, -0.7727894186973572] |
ec1914ed-c48f-41f6-9e0e-c98a26d15bb3 | white-box-multi-objective-adversarial-attack | 2305.03655 | null | https://arxiv.org/abs/2305.03655v2 | https://arxiv.org/pdf/2305.03655v2.pdf | White-Box Multi-Objective Adversarial Attack on Dialogue Generation | Pre-trained transformers are popular in state-of-the-art dialogue generation (DG) systems. Such language models are, however, vulnerable to various adversarial samples as studied in traditional tasks such as text classification, which inspires our curiosity about their robustness in DG systems. One main challenge of at... | ['Cong Liu', 'Yingfan Gao', 'Zexin Li', 'Yufei Li'] | 2023-05-05 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 1.98685989e-01 3.98110420e-01 2.38083135e-02 -1.75771296e-01
-9.98387992e-01 -9.53750074e-01 7.44960070e-01 -1.63202256e-01
-2.99381018e-01 1.03562665e+00 2.12197274e-01 -4.89928544e-01
4.24763888e-01 -9.57739234e-01 -5.43569446e-01 -5.79324126e-01
2.90828586e-01 4.45675999e-01 8.78400877e-02 -9.11460161... | [6.168035507202148, 8.166693687438965] |
a719ac44-8cf0-4aff-99dc-d885066e58a9 | graspness-discovery-in-clutters-for-fast-and | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Graspness_Discovery_in_Clutters_for_Fast_and_Accurate_Grasp_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Graspness_Discovery_in_Clutters_for_Fast_and_Accurate_Grasp_Detection_ICCV_2021_paper.pdf | Graspness Discovery in Clutters for Fast and Accurate Grasp Detection | Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt uniform sampling to select grasp candidates. However, we discover that ignoring where to grasp greatly harms the speed and accuracy of c... | ['Cewu Lu', 'Jin Gao', 'Hongjie Fang', 'Minghao Gou', 'Hao-Shu Fang', 'Chenxi Wang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['robotic-grasping'] | ['robots'] | [-1.39410764e-01 -2.88626403e-01 -2.74313658e-01 -3.15450788e-01
-6.48312092e-01 -5.74378908e-01 -5.24863154e-02 -1.09351547e-02
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-3.93521518e-01 -8.11182082e-01 -1.06732297e+00 -7.25689173e-01
-3.99976730e-01 4.24919814e-01 5.99793077e-01 -1.50784865... | [5.790431022644043, -0.8974326848983765] |
d5dd4a54-c3b5-43ed-97a5-6c0db6ec1437 | faq-feature-aggregated-queries-for | 2303.08319 | null | https://arxiv.org/abs/2303.08319v2 | https://arxiv.org/pdf/2303.08319v2.pdf | FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors | Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features from the neighboring frames. With Transformerbased object detectors getting a better performance on the image domain tasks, recent works bega... | ['Linjie Yang', 'Yiming Cui'] | 2023-03-15 | null | null | null | null | ['video-object-detection'] | ['computer-vision'] | [ 3.76398899e-02 -3.96673054e-01 -2.39023846e-02 -2.84423321e-01
-6.56104028e-01 -3.52590024e-01 5.10044515e-01 3.60083058e-02
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2.36956887e-02 1.60736650e-01 1.22368860e+00 -2.54241019... | [8.77437973022461, -0.18057717382907867] |
852346cf-7ce1-4aaf-8e0b-ca050d160150 | kracl-contrastive-learning-with-graph-context | 2208.07622 | null | https://arxiv.org/abs/2208.07622v2 | https://arxiv.org/pdf/2208.07622v2.pdf | KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion | Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion. Most existing KGE methods suffer from the sparsity challenge, where it is harder to predict entities that appear less frequently in knowledge graphs... | ['Minnan Luo', 'Jundong Li', 'Qinghua Zheng', 'Qingyue Zhang', 'Shangbin Feng', 'Zilong Chen', 'Zhaoxuan Tan'] | 2022-08-16 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.05922419e-01 2.86367625e-01 -6.06864631e-01 -2.24915326e-01
-2.70918816e-01 -3.53844523e-01 3.37222487e-01 3.34492058e-01
-2.72546470e-01 9.01741564e-01 6.11881673e-01 5.04425131e-02
-5.20027339e-01 -1.22027099e+00 -8.49788725e-01 -4.86075789e-01
-2.27264181e-01 3.71948898e-01 -1.19616218e-01 -1.75880849... | [8.800956726074219, 7.889023303985596] |
a4104120-15f0-4b57-b8ae-93ad1479b2a6 | extraction-of-heart-rate-from-ppg-signal-a | null | null | https://ieeexplore.ieee.org/abstract/document/9068845 | https://www.researchgate.net/publication/340687904_Extraction_of_Heart_Rate_from_PPG_Signal_A_Machine_Learning_Approach_using_Decision_Tree_Regression_Algorithm | Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm | This research article shows a novel technique which used to measure the heart rate (HR) from wearable devices such as fingertip device, wrist type device. For HR monitoring Photoplethysmography (PPG) signal is severely used. HR measurement precision is affected by noise and motion artifacts (MA) at the moment of physic... | ['Md. Abdullah Al Mahmud', 'A.H.M. Zadidul Karim', 'Md. Sazal Miah', 'Shikder Shafiul Bashar'] | 2019-12-22 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 7.66374692e-02 -4.94839884e-02 7.96187967e-02 -9.83458832e-02
-1.38269916e-01 -1.89655855e-01 -6.22034743e-02 -4.67714891e-02
-2.71799058e-01 8.97207201e-01 -9.10369027e-03 6.46560937e-02
-2.46153221e-01 -5.69403827e-01 6.47261888e-02 -7.51119256e-01
-1.86579525e-02 -1.22846663e-01 -2.64421970e-01 -2.76995183... | [13.967494010925293, 3.0176289081573486] |
de2075a6-785c-4799-bbc0-de02d63b7296 | dl-droid-deep-learning-based-android-malware | 1911.10113 | null | https://arxiv.org/abs/1911.10113v1 | https://arxiv.org/pdf/1911.10113v1.pdf | DL-Droid: Deep learning based android malware detection using real devices | The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detec... | ['Suleiman Y. Yerima', 'Sakir Sezer', 'Mohammed K. Alzaylaee'] | 2019-11-22 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 9.41664279e-02 -4.27359223e-01 -7.22123563e-01 2.47587025e-01
-4.28765833e-01 -7.65471756e-01 6.94629490e-01 -2.71613508e-01
-1.13469772e-01 5.01981020e-01 -3.70595574e-01 -1.04863811e+00
2.58821011e-01 -5.71141303e-01 -5.89249313e-01 -5.35456538e-01
-3.50897163e-01 -1.69808462e-01 2.84916371e-01 -2.36697480... | [14.420245170593262, 9.678712844848633] |
a833d59b-8b36-49cc-8193-655c3ec74492 | whittle-index-policy-for-crawling-ephemeral | 1503.08558 | null | http://arxiv.org/abs/1503.08558v1 | http://arxiv.org/pdf/1503.08558v1.pdf | Whittle Index Policy for Crawling Ephemeral Content | We consider a task of scheduling a crawler to retrieve content from several
sites with ephemeral content. A user typically loses interest in ephemeral
content, like news or posts at social network groups, after several days or
hours. Thus, development of timely crawling policy for such ephemeral
information sources is ... | ['Borkar Vivek EE-IIT', 'Avrachenkov Konstantin INRIA Sophia Antipolis'] | 2015-03-30 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-2.26821527e-01 -1.22526288e-01 -4.05756027e-01 2.79700339e-01
-8.33855271e-01 -8.26903701e-01 5.62235296e-01 1.77607134e-01
-4.82574075e-01 9.66851354e-01 -1.27209201e-01 -3.30504179e-01
-5.64580023e-01 -8.18915725e-01 -5.91039121e-01 -7.11205125e-01
-3.34407032e-01 4.88296807e-01 4.87009317e-01 -1.52444869... | [4.642611026763916, 3.4131360054016113] |
bb48a2a8-c831-473d-9deb-add9eede3d56 | earthnets-empowering-ai-in-earth-observation | 2210.04936 | null | https://arxiv.org/abs/2210.04936v2 | https://arxiv.org/pdf/2210.04936v2.pdf | EarthNets: Empowering AI in Earth Observation | Earth observation, aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With a growing number of satellites in orbit, an increasing number of datasets with diverse sensors and research domains are being published to facilitate the re... | ['Xiao Xiang Zhu', 'Yilei Shi', 'Yi Wang', 'Fahong Zhang', 'Zhitong Xiong'] | 2022-10-10 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-2.42200315e-01 -6.34766281e-01 -6.12753808e-01 -4.23842251e-01
-2.29883984e-01 -4.49459344e-01 6.49719238e-01 1.19052701e-01
-1.30581513e-01 6.67901337e-01 3.15806448e-01 -5.13412714e-01
-2.64241725e-01 -1.45050621e+00 -3.81468743e-01 -7.43066728e-01
-3.06179166e-01 1.08830355e-01 -1.93826109e-01 -3.36571127... | [9.673970222473145, -1.4104920625686646] |
99808287-2eb1-40e9-843d-76a6bb26d885 | acl-spc-adaptive-closed-loop-system-for-self | 2303.01979 | null | https://arxiv.org/abs/2303.01979v3 | https://arxiv.org/pdf/2303.01979v3.pdf | ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion | Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been steep progress in the supervised methods on the synthetic point cloud completion task, it is hardly applicable in real-world scenarios due to ... | ['Kyoung Mu Lee', 'Reyhaneh Neshatavar', 'Mohsen Yavartanoo', 'Sangmin Hong'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hong_ACL-SPC_Adaptive_Closed-Loop_System_for_Self-Supervised_Point_Cloud_Completion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hong_ACL-SPC_Adaptive_Closed-Loop_System_for_Self-Supervised_Point_Cloud_Completion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 1.86214164e-01 5.46442270e-02 7.35184327e-02 -2.78905362e-01
-1.03827834e+00 -5.68678260e-01 4.34513658e-01 7.08009079e-02
-2.56405205e-01 6.55265987e-01 -5.35929978e-01 -2.00471625e-01
-4.10322957e-02 -7.09596813e-01 -1.13387036e+00 -5.65555573e-01
6.57754317e-02 9.66750205e-01 3.96529227e-01 1.62268523... | [8.184493064880371, -3.201247215270996] |
a735781f-e201-4983-8df3-4b7bb8a2a289 | codetalker-speech-driven-3d-facial-animation | 2301.02379 | null | https://arxiv.org/abs/2301.02379v2 | https://arxiv.org/pdf/2301.02379v2.pdf | CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior | Speech-driven 3D facial animation has been widely studied, yet there is still a gap to achieving realism and vividness due to the highly ill-posed nature and scarcity of audio-visual data. Existing works typically formulate the cross-modal mapping into a regression task, which suffers from the regression-to-mean proble... | ['Tien-Tsin Wong', 'Jue Wang', 'Xiaodong Cun', 'Yuechen Zhang', 'Menghan Xia', 'Jinbo Xing'] | 2023-01-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xing_CodeTalker_Speech-Driven_3D_Facial_Animation_With_Discrete_Motion_Prior_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xing_CodeTalker_Speech-Driven_3D_Facial_Animation_With_Discrete_Motion_Prior_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-face-animation'] | ['computer-vision'] | [ 1.19553581e-01 2.62791276e-01 -8.23957995e-02 -2.90849805e-01
-1.14181077e+00 -1.95603281e-01 7.47560859e-01 -7.51780689e-01
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-4.58837301e-02 5.32767661e-02 -2.70902157e-01 -2.72488147... | [13.233363151550293, -0.4067801535129547] |
fc00b3ea-2b0f-4db4-bda0-9f0d6dbc3b8e | inter-frame-accelerate-attack-against-video | 2305.06540 | null | https://arxiv.org/abs/2305.06540v1 | https://arxiv.org/pdf/2305.06540v1.pdf | Inter-frame Accelerate Attack against Video Interpolation Models | Deep learning based video frame interpolation (VIF) method, aiming to synthesis the intermediate frames to enhance video quality, have been highly developed in the past few years. This paper investigates the adversarial robustness of VIF models. We apply adversarial attacks to VIF models and find that the VIF models ar... | ['Xiaochun Cao', 'Baoyuan Wu', 'Wenyuan Yang', 'Liang Yi', 'Zhikai Chen', 'Junpei Liao'] | 2023-05-11 | null | null | null | null | ['video-recognition', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 2.43704587e-01 -1.46201193e-01 1.12726642e-02 1.46586010e-02
-5.47340989e-01 -6.39851451e-01 5.26163816e-01 -4.56099212e-01
-2.83701539e-01 5.11866987e-01 1.61231339e-01 -4.91630822e-01
2.77913243e-01 -7.82796919e-01 -1.03750682e+00 -7.23154604e-01
-2.25074098e-01 -4.70930040e-01 4.56061810e-01 -3.14593315... | [5.382009983062744, 7.935990810394287] |
6fc3aa8c-990a-4e18-afde-fb71f5836050 | point-cloud-quality-assessment-using-3d | 2209.15475 | null | https://arxiv.org/abs/2209.15475v1 | https://arxiv.org/pdf/2209.15475v1.pdf | Point Cloud Quality Assessment using 3D Saliency Maps | Point cloud quality assessment (PCQA) has become an appealing research field in recent days. Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, call... | ['Shan Liu', 'Jun Sun', 'Yiling Xu', 'Qi Yang', 'Yujie Zhang', 'Zhengyu Wang'] | 2022-09-30 | null | null | null | null | ['saliency-detection'] | ['computer-vision'] | [-2.55654231e-02 -5.59108973e-01 1.14016846e-01 -2.06646994e-01
-9.91995156e-01 -1.60112590e-01 4.62442547e-01 3.38718414e-01
9.49768648e-02 2.99755871e-01 2.06269890e-01 1.48590580e-01
-1.89377040e-01 -7.94230759e-01 -4.56459045e-01 -5.22552550e-01
1.86726555e-01 7.66100883e-02 7.10478187e-01 -3.06040555... | [9.754021644592285, -0.6240609288215637] |
9a3cb7c1-76a1-4ac4-8ff6-0b31bcd6edf6 | vrconvmf-visual-recurrent-convolutional | 2202.10241 | null | https://arxiv.org/abs/2202.10241v1 | https://arxiv.org/pdf/2202.10241v1.pdf | VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation | Sparsity of user-to-item rating data becomes one of challenging issues in the recommender systems, which severely deteriorates the recommendation performance. Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both th... | ['Feng Xia', 'Nan Jiang', 'Kai Lin', 'Zhe Li', 'Honglong Chen', 'Zhu Wang'] | 2022-02-16 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.69687861e-01 -8.19503427e-01 -2.24689603e-01 -2.30278596e-01
-2.06482023e-01 -6.46260798e-01 3.21224779e-01 -2.42914129e-02
-1.01063676e-01 3.21799010e-01 8.77529204e-01 -1.88960716e-01
-4.01429385e-01 -5.32177091e-01 -1.94754362e-01 -7.27408469e-01
3.51813048e-01 -2.21337691e-01 -6.83968561e-03 -3.45705003... | [10.193473815917969, 5.572422981262207] |
db2812b7-8954-403b-a022-f6ec323d6b03 | unsupervised-statistical-feature-guided | 2306.05285 | null | https://arxiv.org/abs/2306.05285v1 | https://arxiv.org/pdf/2306.05285v1.pdf | Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition | Recognizing human activities from sensor data is a vital task in various domains, but obtaining diverse and labeled sensor data remains challenging and costly. In this paper, we propose an unsupervised statistical feature-guided diffusion model for sensor-based human activity recognition. The proposed method aims to ge... | ['Paul Lukowicz', 'Stephan Sigg', 'Sungho Suh', 'Vitor Fortes Rey', 'Si Zuo'] | 2023-05-30 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 8.46662402e-01 -1.03726119e-01 -8.00113305e-02 -2.93682456e-01
-8.34070265e-01 -4.45369065e-01 7.44733751e-01 -1.01616040e-01
-3.19862276e-01 1.09075320e+00 4.65205342e-01 2.03716993e-01
-9.19018239e-02 -8.75916421e-01 -6.05908394e-01 -9.58045483e-01
-8.44189227e-02 1.74927220e-01 -1.11268096e-01 2.31230706... | [7.818946361541748, 1.0461063385009766] |
6acbebc5-95df-43d2-bf74-ebba0c34a38f | dense-rgb-slam-with-neural-implicit-maps | 2301.08930 | null | https://arxiv.org/abs/2301.08930v2 | https://arxiv.org/pdf/2301.08930v2.pdf | Dense RGB SLAM with Neural Implicit Maps | There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging g... | ['Ping Tan', 'Zilong Dong', 'Luwei Yang', 'Weihao Yuan', 'Xiaodong Gu', 'Heng Li'] | 2023-01-21 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 4.22025360e-02 -2.41376668e-01 -4.85754684e-02 -5.69558322e-01
-7.24143445e-01 -7.28112519e-01 5.85160792e-01 -2.81379938e-01
-4.12042201e-01 5.10319054e-01 2.59364545e-01 -1.85904875e-01
1.09984711e-01 -7.35787868e-01 -9.35173392e-01 -4.03864771e-01
2.32431680e-01 6.16863489e-01 2.19990999e-01 -3.20687979... | [7.571420669555664, -2.269552230834961] |
43a2ad52-4db0-4da5-b802-4ad2ea1fcbf2 | symmetric-dilated-convolution-for-surgical | 2007.06373 | null | https://arxiv.org/abs/2007.06373v2 | https://arxiv.org/pdf/2007.06373v2.pdf | Symmetric Dilated Convolution for Surgical Gesture Recognition | Automatic surgical gesture recognition is a prerequisite of intra-operative computer assistance and objective surgical skill assessment. Prior works either require additional sensors to collect kinematics data or have limitations on capturing temporal information from long and untrimmed surgical videos. To tackle these... | ['Jian Chang', 'Jian Jun Zhang', 'Xiaosong Yang', 'Yao Lyu', 'Yinyu Nie', 'Jinglu Zhang', 'Hailin Li'] | 2020-07-13 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 2.78525084e-01 3.74764167e-02 -4.09316897e-01 -2.93906212e-01
-8.12725902e-01 -3.90855193e-01 3.04044306e-01 -3.56781185e-01
-8.19948077e-01 2.04853103e-01 3.45903963e-01 -3.24150503e-01
-3.25769842e-01 -1.00373197e-02 -7.15048432e-01 -6.65576577e-01
-2.50289172e-01 -5.81367053e-02 3.09683651e-01 -1.49684235... | [14.068485260009766, -3.3385677337646484] |
0bc2bf60-9e8e-44d9-b7fa-c8129536a80e | click-is-not-equal-to-like-counterfactual | 2009.09945 | null | https://arxiv.org/abs/2009.09945v4 | https://arxiv.org/pdf/2009.09945v4.pdf | Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue | Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on the click behavior data. This is known as the Click-Through Rate (CTR) prediction, which has become t... | ['Tat-Seng Chua', 'Xiangnan He', 'Wenjie Wang', 'Hanwang Zhang', 'Fuli Feng'] | 2020-09-21 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-5.16663156e-02 1.58398133e-02 -7.17846155e-01 -4.33739185e-01
-2.78072834e-01 -4.25164342e-01 3.45726669e-01 -8.08987394e-02
-2.96410233e-01 5.19625902e-01 4.18654114e-01 -7.99600005e-01
-2.27513880e-01 -1.02282143e+00 -8.10525417e-01 -3.00187379e-01
7.21752197e-02 6.25987574e-02 9.45233107e-02 -2.65159011... | [9.83612060546875, 5.578089237213135] |
db7c9ae4-c168-4435-98d4-911c22f4ee7e | suggestion-mining-from-opinionated-text | null | null | https://aclanthology.org/P16-3018 | https://aclanthology.org/P16-3018.pdf | Suggestion Mining from Opinionated Text | null | ['Sapna Negi'] | 2016-08-01 | null | null | null | acl-2016-8 | ['suggestion-mining'] | ['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... | [-7.208926677703857, 3.8354179859161377] |
ab072d0d-f89c-4320-9029-0e794b1a5be9 | cuet-nlp-dravidianlangtech-acl2022 | null | null | https://aclanthology.org/2022.dravidianlangtech-1.27 | https://aclanthology.org/2022.dravidianlangtech-1.27.pdf | CUET-NLP@DravidianLangTech-ACL2022: Investigating Deep Learning Techniques to Detect Multimodal Troll Memes | With the substantial rise of internet usage, social media has become a powerful communication medium to convey information, opinions, and feelings on various issues. Recently, memes have become a popular way of sharing information on social media. Usually, memes are visuals with text incorporated into them and quickly ... | ['Mohammed Moshiul Hoque', 'Omar Sharif', 'Eftekhar Hossain', 'Nusratul Jannat', 'Md Hasan'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['meme-classification'] | ['natural-language-processing'] | [-2.94065356e-01 -1.45389974e-01 5.60130998e-02 4.03789520e-01
-5.07082701e-01 -5.07775187e-01 1.02683949e+00 5.17825007e-01
-6.44970596e-01 6.21518910e-01 2.50917792e-01 -3.31617221e-02
2.83788264e-01 -6.31506562e-01 -3.16949248e-01 -4.68616396e-01
5.88057674e-02 -1.29930809e-01 5.81314228e-03 -5.01690805... | [8.515472412109375, 10.733499526977539] |
d7b3e00c-bf06-4186-a0e0-858dea3ed781 | debiased-contrastive-learning-for-sequential | 2303.11780 | null | https://arxiv.org/abs/2303.11780v1 | https://arxiv.org/pdf/2303.11780v1.pdf | Debiased Contrastive Learning for Sequential Recommendation | Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GNNs). However, inference from the highly sparse user behavior data may hinder the representation ability of sequential pattern encoding. To a... | ['Kangyi Lin', 'Da Luo', 'Chunzhen Huang', 'Lianghao Xia', 'Chao Huang', 'Yuhao Yang'] | 2023-03-21 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 3.77004802e-01 -5.35466611e-01 -6.95351303e-01 -3.60633790e-01
-2.09514990e-01 -6.37774229e-01 5.06520092e-01 2.33856648e-01
-3.01900446e-01 3.67693305e-01 6.29866302e-01 -5.25716305e-01
-2.95174956e-01 -6.53840423e-01 -7.00687170e-01 -5.89304030e-01
-1.62017360e-01 3.77443522e-01 -2.57369429e-02 -3.41484219... | [10.142488479614258, 5.595671653747559] |
45abafad-cbf3-4caa-b7a2-4c06e5d64264 | single-image-3d-face-reconstruction-under | 2205.04126 | null | https://arxiv.org/abs/2205.04126v2 | https://arxiv.org/pdf/2205.04126v2.pdf | Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image | In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process. This approximation performs well when the distance between camera and face is far enough. However, in some scenarios that the face is very close to camera or moving along the c... | ['Zhen Lei', 'Xiaobo Li', 'Yuanzhang Chang', 'Xiangyu Zhu', 'Jiangjing Lyu', 'Miao Xu', 'Bowen Pan', 'Yueying Kao'] | 2022-05-09 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-2.50490934e-01 -8.97345096e-02 2.21036952e-02 -5.42487323e-01
-3.69972169e-01 -1.90554962e-01 2.97260940e-01 -9.98220682e-01
1.11069111e-02 1.32641211e-01 6.48645237e-02 1.64500520e-01
2.00743690e-01 -4.97536361e-01 -8.23499382e-01 -6.78296030e-01
3.14274609e-01 7.86837220e-01 -4.42617774e-01 6.28916547... | [13.204896926879883, 0.08960448205471039] |
9877a4ff-96d0-4c3e-9920-92710790b751 | reliable-image-dehazing-by-nerf | 2303.09153 | null | https://arxiv.org/abs/2303.09153v1 | https://arxiv.org/pdf/2303.09153v1.pdf | Reliable Image Dehazing by NeRF | We present an image dehazing algorithm with high quality, wide application, and no data training or prior needed. We analyze the defects of the original dehazing model, and propose a new and reliable dehazing reconstruction and dehazing model based on the combination of optical scattering model and computer graphics li... | ['Yueting Chen', 'Qi Li', 'Zhihai Xu', 'Huajun Feng', 'Shiqi Chen', 'Zheyan Jin'] | 2023-03-16 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [-2.20092535e-02 -6.62215352e-01 1.04270768e+00 -7.44071007e-02
1.32701710e-01 -1.80973798e-01 2.27887258e-01 -5.24624109e-01
-4.26441669e-01 5.82665861e-01 8.08568075e-02 -4.11985852e-02
1.96640231e-02 -1.09273458e+00 -3.52078587e-01 -1.08844888e+00
-5.40804677e-02 -4.93955091e-02 7.97437847e-01 -5.25804400... | [10.836413383483887, -3.161076307296753] |
c8064d9f-c190-4c6e-a13a-af6a67cae562 | joint-falsification-and-fidelity-settings | 2305.06111 | null | https://arxiv.org/abs/2305.06111v1 | https://arxiv.org/pdf/2305.06111v1.pdf | Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis | Safety validation is a crucial component in the development and deployment of autonomous systems, such as self-driving vehicles and robotic systems. Ensuring safe operation necessitates extensive testing and verification of control policies, typically conducted in simulation environments. High-fidelity simulators accur... | ['Mykel J. Kochenderfer', 'Ali Baheri'] | 2023-05-10 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 2.29986638e-01 2.38508359e-01 -3.43313456e-01 -2.28277221e-02
-5.95253527e-01 -7.18847871e-01 4.50436711e-01 1.28788829e-01
-3.07379603e-01 9.94779885e-01 -6.67252779e-01 -9.71983194e-01
-4.27336544e-01 -7.20923960e-01 -9.68774319e-01 -5.03634453e-01
-5.20297348e-01 2.05923036e-01 1.99630186e-01 -4.50190865... | [4.922143936157227, 2.255983591079712] |
626ccc71-7fec-4b8e-8d81-2ac380229d04 | multi-modal-gait-recognition-via-effective | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cui_Multi-Modal_Gait_Recognition_via_Effective_Spatial-Temporal_Feature_Fusion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cui_Multi-Modal_Gait_Recognition_via_Effective_Spatial-Temporal_Feature_Fusion_CVPR_2023_paper.pdf | Multi-Modal Gait Recognition via Effective Spatial-Temporal Feature Fusion | Gait recognition is a biometric technology that identifies people by their walking patterns. The silhouettes-based method and the skeletons-based method are the two most popular approaches. However, the silhouette data are easily affected by clothing occlusion, and the skeleton data lack body shape information. To ... | ['Yimei Kang', 'Yufeng Cui'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['gait-recognition'] | ['computer-vision'] | [-1.60196781e-01 -7.11403906e-01 -3.06660861e-01 -1.20222807e-01
-5.35735548e-01 2.56287046e-02 3.97980452e-01 -2.46491238e-01
-2.88188934e-01 4.35641438e-01 4.51716214e-01 5.09284198e-01
1.42047346e-01 -7.30422795e-01 -2.77515352e-01 -1.09833109e+00
-3.43502052e-02 1.71182647e-01 2.43683353e-01 -1.88609123... | [14.285103797912598, 1.4211947917938232] |
1e28e6cb-d4e4-4a1d-94dc-93dc32a4b0dd | chexpert-a-large-chest-radiograph-dataset | 1901.07031 | null | http://arxiv.org/abs/1901.07031v1 | http://arxiv.org/pdf/1901.07031v1.pdf | CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison | Large, labeled datasets have driven deep learning methods to achieve
expert-level performance on a variety of medical imaging tasks. We present
CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240
patients. We design a labeler to automatically detect the presence of 14
observations in radiology r... | ['Jesse K. Sandberg', 'Safwan S. Halabi', 'Katie Shpanskaya', 'Behzad Haghgoo', 'Silviana Ciurea-Ilcus', 'Pranav Rajpurkar', 'Henrik Marklund', 'Andrew Y. Ng', 'Robyn Ball', 'Ricky Jones', 'David B. Larson', 'David A. Mong', 'Yifan Yu', 'Jeremy Irvin', 'Curtis P. Langlotz', 'Chris Chute', 'Michael Ko', 'Matthew P. Lung... | 2019-01-21 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [-2.04269644e-02 3.82437557e-01 -2.17524797e-01 -8.69126558e-01
-1.73792922e+00 -5.61547101e-01 1.67286266e-02 4.34167206e-01
-5.52196860e-01 6.74258947e-01 2.44740456e-01 -7.77685463e-01
-2.65188903e-01 -3.19705755e-01 -9.46086943e-01 -4.45928037e-01
-1.00176908e-01 9.56310689e-01 3.00941437e-01 6.65887535... | [15.190557479858398, -1.9663363695144653] |
ea98db92-a034-4e7a-9b48-99f33931e8c0 | tag-enhanced-tree-structured-neural-networks | 1803.01165 | null | http://arxiv.org/abs/1803.01165v1 | http://arxiv.org/pdf/1803.01165v1.pdf | Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification | Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporatin... | ['Houfeng Wang', 'Xu sun', 'Jingfeng Yang', 'Yizhong Wang', 'Sujian Li'] | 2018-03-03 | tag-enhanced-tree-structured-neural-networks-2 | https://aclanthology.org/I17-1050 | https://aclanthology.org/I17-1050.pdf | ijcnlp-2017-11 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 2.48369783e-01 6.16661906e-01 -3.83502752e-01 -5.10719299e-01
-2.56674826e-01 -2.73600906e-01 5.84181905e-01 1.00927033e-01
-3.46628189e-01 6.75876796e-01 8.02972496e-01 -5.40899396e-01
1.03715777e-01 -9.38656569e-01 -5.45927525e-01 -4.84103352e-01
4.52590846e-02 1.09130479e-01 2.61292279e-01 -2.88151711... | [10.56445026397705, 9.240799903869629] |
9d25f77e-a6d4-4def-81bb-ba736d53eda2 | de-confounded-variational-encoder-decoder-for | null | null | https://aclanthology.org/2021.acl-long.430 | https://aclanthology.org/2021.acl-long.430.pdf | De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation | Logical table-to-text generation aims to automatically generate fluent and logically faithful text from tables. The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text. The underlying reason may be that deep learning models often capture surface-leve... | ['Yaohui Jin', 'Hao He', 'Yitian Li', 'Jidong Tian', 'Wenqing Chen'] | 2021-08-01 | null | null | null | acl-2021-5 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 1.86372548e-01 6.40067935e-01 -3.69193256e-01 -5.33584952e-01
-1.06021845e+00 -3.70505631e-01 7.55444109e-01 6.01829477e-02
-3.88287492e-02 1.27240181e+00 5.68192303e-01 -4.81830776e-01
-2.91467495e-02 -1.15745342e+00 -1.30150437e+00 -2.35445783e-01
2.16483116e-01 7.75002897e-01 -2.62123525e-01 -2.27731854... | [11.457183837890625, 8.743942260742188] |
d525e643-1a79-44e6-b73f-799cf11c77c2 | manet-multimodal-attention-network-based | 2002.12573 | null | https://arxiv.org/abs/2002.12573v1 | https://arxiv.org/pdf/2002.12573v1.pdf | MANet: Multimodal Attention Network based Point- View fusion for 3D Shape Recognition | 3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrati... | ['Yaxin Zhao', 'Tangkun Zhang', 'Jichao Jiao'] | 2020-02-28 | null | null | null | null | ['3d-shape-recognition'] | ['computer-vision'] | [-3.99959296e-01 -6.30506575e-01 6.62842169e-02 -4.95042831e-01
-5.72919607e-01 -3.67760837e-01 5.52925229e-01 -1.28678858e-01
-7.49805868e-02 -1.82800516e-01 2.98458904e-01 1.68756947e-01
-3.42597991e-01 -7.85347044e-01 -4.24366474e-01 -9.19028342e-01
4.81764466e-01 3.98653060e-01 1.38233125e-01 -8.20935890... | [8.131691932678223, -3.855595588684082] |
56207355-7b54-4acd-88c4-1d18e7513309 | better-fine-tuning-by-reducing | 2008.03156 | null | https://arxiv.org/abs/2008.03156v1 | https://arxiv.org/pdf/2008.03156v1.pdf | Better Fine-Tuning by Reducing Representational Collapse | Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used... | ['Sonal Gupta', 'Luke Zettlemoyer', 'Armen Aghajanyan', 'Akshat Shrivastava', 'Naman Goyal', 'Anchit Gupta'] | 2020-08-06 | null | https://openreview.net/forum?id=OQ08SN70M1V | https://openreview.net/pdf?id=OQ08SN70M1V | iclr-2021-1 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-5.72046973e-02 3.42394978e-01 -3.69744718e-01 -4.24239069e-01
-9.30153310e-01 -1.02500832e+00 9.35064077e-01 -2.79035531e-02
-3.67334396e-01 9.39019740e-01 3.67476434e-01 -3.29275727e-01
1.44364256e-02 -6.18183553e-01 -9.59485352e-01 -3.90574872e-01
2.09389955e-01 6.54753685e-01 1.10285319e-01 -5.45998037... | [10.572759628295898, 8.180859565734863] |
c4f335fd-2fc0-4196-82eb-b487c9c6fd2f | baa-ngp-bundle-adjusting-accelerated-neural | 2306.04166 | null | https://arxiv.org/abs/2306.04166v2 | https://arxiv.org/pdf/2306.04166v2.pdf | BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives | Implicit neural representation has emerged as a powerful method for reconstructing 3D scenes from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. In order to expand the use cases for implicit neural representations, we need to incorporate camera... | ['Michael Yip', 'Alexey Supikov', 'Shreya Saha', 'Jingpei Lu', 'Shan Lin', 'Sainan Liu'] | 2023-06-07 | null | null | null | null | ['pose-estimation', '3d-scene-reconstruction', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.42565978e-01 -1.48171768e-01 2.18410417e-01 -3.80253792e-01
-8.38450789e-01 -6.10328913e-01 5.07496297e-01 -1.69104502e-01
-2.62168169e-01 3.16786647e-01 1.69330686e-01 -1.41152680e-01
2.57133633e-01 -7.36572206e-01 -1.09068930e+00 -3.39948893e-01
2.57948011e-01 4.89092976e-01 1.02839202e-01 3.24079059... | [8.744787216186523, -2.8266215324401855] |
44ac45e0-d291-42e0-80ec-4888bc5ce17e | change-point-models-for-real-time-cyber | 2003.04185 | null | https://arxiv.org/abs/2003.04185v1 | https://arxiv.org/pdf/2003.04185v1.pdf | Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment | Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate or effective c... | ['Gurcan Comert', 'Mhafuzul Islam', 'Mizanur Rahman', 'Mashrur Chowdhury'] | 2020-03-05 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-2.27689669e-01 -4.45624650e-01 1.59462795e-01 2.64172405e-01
5.54201454e-02 -6.72316730e-01 5.35254717e-01 5.28451502e-01
-1.54338211e-01 6.97888136e-01 -6.80599809e-01 -1.05490530e+00
-2.15447992e-01 -8.59335959e-01 -4.64400023e-01 -4.73416537e-01
-5.43666422e-01 -8.49763975e-02 7.38470674e-01 -1.34332761... | [5.306484699249268, 7.2557148933410645] |
7f07692f-11dc-410c-a579-5ad26a8942d2 | a-hybrid-supervisedunsupervised-machine | 1706.07103 | null | http://arxiv.org/abs/1706.07103v1 | http://arxiv.org/pdf/1706.07103v1.pdf | A hybrid supervised/unsupervised machine learning approach to solar flare prediction | We introduce a hybrid approach to solar flare prediction, whereby a
supervised regularization method is used to realize feature importance and an
unsupervised clustering method is used to realize the binary flare/no-flare
decision. The approach is validated against NOAA SWPC data. | ['Federico Benvenuto', 'Cristina Campi', 'Michele Piana', 'Anna Maria Massone'] | 2017-06-21 | null | null | null | null | ['solar-flare-prediction'] | ['time-series'] | [ 3.25198293e-01 -4.18940246e-01 -1.53733164e-01 -7.08411634e-01
-4.34648067e-01 -5.21727264e-01 3.84574234e-01 -8.12726468e-02
1.90091133e-01 9.88905549e-01 4.95628417e-02 -2.67674532e-02
-6.73401773e-01 -6.44916236e-01 5.61242960e-02 -1.01388514e+00
3.51192743e-01 2.62240261e-01 1.01166293e-01 -3.26677233... | [6.614551544189453, 2.784780979156494] |
8e77d7ca-dc64-4fbb-84cc-156651cb1c5e | uncovering-the-hidden-dynamics-of-video-self | 2306.02014 | null | https://arxiv.org/abs/2306.02014v1 | https://arxiv.org/pdf/2306.02014v1.pdf | Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts | Video self-supervised learning (VSSL) has made significant progress in recent years. However, the exact behavior and dynamics of these models under different forms of distribution shift are not yet known. In this paper, we comprehensively study the behavior of six popular self-supervised methods (v-SimCLR, v-MOCO, v-BY... | ['Ali Etemad', 'Ahmad Beirami', 'Pritam Sarkar'] | 2023-06-03 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.54809856e-01 -4.57364410e-01 -5.00024259e-01 -2.84737140e-01
-5.93859136e-01 -8.50798607e-01 6.22104168e-01 -6.21245280e-02
-3.07367891e-01 6.72334909e-01 4.34188172e-02 -3.14882487e-01
-5.29963821e-02 -4.92131680e-01 -1.03448951e+00 -8.25950682e-01
-2.95040369e-01 3.29830498e-01 4.95958537e-01 -3.34629208... | [9.188011169433594, 1.2706488370895386] |
97be0c09-9d9e-4602-a0ea-f6bc419caf21 | active-learning-with-effective-scoring | 2208.14856 | null | https://arxiv.org/abs/2208.14856v2 | https://arxiv.org/pdf/2208.14856v2.pdf | Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization | Temporal Action Localization (TAL) aims to predict both action category and temporal boundary of action instances in untrimmed videos, i.e., start and end time. Fully-supervised solutions are usually adopted in most existing works, and proven to be effective. One of the practical bottlenecks in these solutions is the l... | ['Wensheng Zhang', 'Chenyang Zhang', 'Yongqiang Tang', 'Xuebing Yang', 'Ding Li'] | 2022-08-31 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 3.57907951e-01 -1.77899301e-02 -7.42803097e-01 -2.93644249e-01
-1.10338950e+00 -3.39386106e-01 3.50948811e-01 6.56354707e-03
-5.87044477e-01 7.45328903e-01 3.44421744e-01 2.50157028e-01
-1.72372863e-01 -1.79418877e-01 -5.07235527e-01 -9.16737020e-01
-2.12385744e-01 1.42370239e-01 6.78199351e-01 6.43151462... | [8.486466407775879, 0.6189032793045044] |
18c2fc1f-8003-4209-b6a6-ebad06c2519d | combining-learned-skills-and-reinforcement | 1908.00722 | null | https://arxiv.org/abs/1908.00722v3 | https://arxiv.org/pdf/1908.00722v3.pdf | Learning to combine primitive skills: A step towards versatile robotic manipulation | Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision. Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapted to dynamic scene changes. Recent learning methods can operate direct... | ['Ivan Laptev', 'Cordelia Schmid', 'Josef Sivic', 'Igor Kalevatykh', 'Alexander Pashevich', 'Robin Strudel'] | 2019-08-02 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.25618356e-01 2.37113953e-01 -1.15975142e-02 -3.81372757e-02
-3.84881407e-01 -6.94109023e-01 7.69433677e-01 -1.71592399e-01
-5.56329668e-01 9.63849187e-01 -1.04751930e-01 -3.77060473e-01
-1.53250232e-01 -3.23329419e-01 -1.12022197e+00 -2.87515491e-01
-1.45778164e-01 5.22676468e-01 3.19316298e-01 -4.75039303... | [4.591673851013184, 0.7569230794906616] |
7d7779b6-fc7c-4d96-9036-576e321f2e3a | exploring-faithful-rationale-for-multi-hop | 2212.01060 | null | https://arxiv.org/abs/2212.01060v1 | https://arxiv.org/pdf/2212.01060v1.pdf | Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning | The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the performance of prediction drops dramatically when being removed. Though being explainable, most rationale extraction methods for multi-hop ... | ['Deyu Zhou', 'Yingjie Zhu', 'Jiasheng Si'] | 2022-12-02 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 2.52532870e-01 8.51002753e-01 -6.07584596e-01 -1.36634022e-01
-7.37179816e-01 -5.78833103e-01 4.91555154e-01 6.27772808e-01
4.70706433e-01 6.37062728e-01 6.74123466e-01 -5.97751677e-01
-4.37757939e-01 -9.06201720e-01 -9.42120671e-01 -4.24638838e-01
-1.77881315e-01 1.48543790e-01 2.87798613e-01 -2.45551407... | [9.041824340820312, 7.718967437744141] |
82f48131-d8e9-4081-a69d-e77181a11439 | denoising-diffusion-post-processing-for-low | 2303.09627 | null | https://arxiv.org/abs/2303.09627v2 | https://arxiv.org/pdf/2303.09627v2.pdf | Denoising Diffusion Post-Processing for Low-Light Image Enhancement | Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. However, as a result of enhancement, a variety of image degradations such as noise and color bias are revealed. Furthermore, each particular LLIE approach may introduce a different form of flaw wi... | ['Anna S. Bosman', 'Savvas Panagiotou'] | 2023-03-16 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.72945654e-01 -3.90371650e-01 4.96266484e-01 -2.53492892e-01
-5.12718022e-01 -3.39374900e-01 6.99652076e-01 -8.83392990e-02
-2.41437823e-01 5.94161630e-01 1.78971797e-01 -4.74786796e-02
1.35244653e-01 -9.47625756e-01 -6.10753715e-01 -1.06349492e+00
4.02660608e-01 -4.97226626e-01 3.34661126e-01 -9.76217911... | [10.834832191467285, -2.5727505683898926] |
d2ca233e-8850-4ae8-86a4-187e7e175685 | opinion-based-relational-pivoting-for-cross | null | null | https://openreview.net/forum?id=38nXXqT4hC6 | https://openreview.net/pdf?id=38nXXqT4hC6 | Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction | Domain adaptation methods often exploit domain-transferable input features, a.k.a. pivots.
The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from \textit{resta... | ['Anonymous'] | 2021-05-16 | null | null | null | acl-arr-may-2021-5 | ['term-extraction'] | ['natural-language-processing'] | [ 2.49940649e-01 3.28200817e-01 -5.75336754e-01 -7.03522921e-01
-1.07721317e+00 -1.04205215e+00 1.09150326e+00 4.46853489e-01
-3.32785070e-01 9.36245382e-01 4.50639725e-01 -5.17985940e-01
-3.25664729e-01 -6.78602934e-01 -7.21743047e-01 -3.62332702e-01
-5.30424193e-02 8.87508273e-01 1.17785484e-02 -7.17840612... | [11.337977409362793, 6.864887237548828] |
8e027908-720a-4a18-bc20-769794e96914 | realistic-saliency-guided-image-enhancement-1 | 2306.06092 | null | https://arxiv.org/abs/2306.06092v1 | https://arxiv.org/pdf/2306.06092v1.pdf | Realistic Saliency Guided Image Enhancement | Common editing operations performed by professional photographers include the cleanup operations: de-emphasizing distracting elements and enhancing subjects. These edits are challenging, requiring a delicate balance between manipulating the viewer's attention while maintaining photo realism. While recent approaches can... | ['Yağız Aksoy', 'Eli Shechtman', 'Eric Kee', 'Zoya Bylinskii', 'S. Mahdi H. Miangoleh'] | 2023-06-09 | realistic-saliency-guided-image-enhancement | http://openaccess.thecvf.com//content/CVPR2023/html/Miangoleh_Realistic_Saliency_Guided_Image_Enhancement_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Miangoleh_Realistic_Saliency_Guided_Image_Enhancement_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-enhancement'] | ['computer-vision'] | [ 5.35818517e-01 -2.37102471e-02 2.96457767e-01 -3.25877070e-01
-4.74906385e-01 -4.04441029e-01 4.78404492e-01 1.81726158e-01
-4.84222114e-01 4.48186129e-01 3.12740028e-01 -3.38075534e-02
1.51146874e-01 -4.56097782e-01 -6.28630757e-01 -4.06390220e-01
2.03686550e-01 -2.63621688e-01 3.40653330e-01 -3.07578862... | [11.161893844604492, -1.146140217781067] |
6827aa7a-ec26-4b97-9516-ad69a8c6a2cb | learning-to-adapt-for-stereo | 1904.02957 | null | http://arxiv.org/abs/1904.02957v1 | http://arxiv.org/pdf/1904.02957v1.pdf | Learning to Adapt for Stereo | Real world applications of stereo depth estimation require models that are
robust to dynamic variations in the environment. Even though deep learning
based stereo methods are successful, they often fail to generalize to unseen
variations in the environment, making them less suitable for practical
applications such as a... | ['Philip H. S. Torr', 'Thomas Joy', 'Thalaiyasingam Ajanthan', 'Oscar Rahnama', 'Luigi Di Stefano', 'Alessio Tonioni'] | 2019-04-05 | learning-to-adapt-for-stereo-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Tonioni_Learning_to_Adapt_for_Stereo_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tonioni_Learning_to_Adapt_for_Stereo_CVPR_2019_paper.pdf | cvpr-2019-6 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.25666523e-01 -1.17474273e-01 5.94525300e-02 -7.23646939e-01
-5.00970781e-01 -5.73654115e-01 5.88126659e-01 -1.57599188e-02
-5.73387325e-01 8.87145042e-01 1.36620291e-02 1.42879248e-01
1.02684595e-01 -7.28359938e-01 -1.06209528e+00 -6.48296118e-01
2.67314970e-01 6.33440971e-01 7.36150801e-01 -2.49506906... | [8.691291809082031, -2.3132879734039307] |
3d889169-213a-4b9b-bb4f-6acca7e8b9f8 | when-worlds-collide-integrating-different | null | null | http://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness | http://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness.pdf | When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness | Machine learning is now being used to make crucial decisions about people's lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques fro... | ['Matt J. Kusner', 'Chris Russell', 'Ricardo Silva', 'Joshua Loftus'] | 2017-12-01 | null | null | null | neurips-2017-12 | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.21642309e-01 4.03840303e-01 -8.50112438e-01 -8.58727872e-01
-4.77175385e-01 -2.51204103e-01 6.09796584e-01 3.53112936e-01
-6.82290435e-01 1.35065448e+00 4.39495325e-01 -8.24192822e-01
-2.43393004e-01 -9.49132442e-01 -6.14364147e-01 -4.42777663e-01
1.33774638e-01 4.74549949e-01 -3.83747369e-01 -2.77325436... | [8.766182899475098, 5.404683589935303] |
e0c03d7c-d4b5-4aec-a094-b56a1475d549 | leafmask-towards-greater-accuracy-on-leaf | 2108.03568 | null | https://arxiv.org/abs/2108.03568v1 | https://arxiv.org/pdf/2108.03568v1.pdf | LeafMask: Towards Greater Accuracy on Leaf Segmentation | Leaf segmentation is the most direct and effective way for high-throughput plant phenotype data analysis and quantitative researches of complex traits. Currently, the primary goal of plant phenotyping is to raise the accuracy of the autonomous phenotypic measurement. In this work, we present the LeafMask neural network... | ['Jun Yue', 'Zhenbo Li', 'Dantong Niu', 'Liao Qu', 'Ruohao Guo'] | 2021-08-08 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 6.36958063e-01 -8.90594814e-03 -2.14266554e-01 -3.66691709e-01
-5.87011814e-01 -9.38201487e-01 -1.17302522e-01 1.42562717e-01
1.32759482e-01 4.13706899e-01 -3.04408699e-01 -4.36283797e-01
1.87893547e-02 -8.26262116e-01 -5.31226993e-01 -9.06966090e-01
2.55456716e-01 3.35983753e-01 4.74224031e-01 -1.17456272... | [9.05756664276123, -1.6268718242645264] |
399204a1-02e2-445d-9545-0e07213bef41 | afrinames-most-asr-models-butcher-african | 2306.00253 | null | https://arxiv.org/abs/2306.00253v2 | https://arxiv.org/pdf/2306.00253v2.pdf | AfriNames: Most ASR models "butcher" African Names | Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a laboratory result for a patient. However, where named entities such as `... | ['Sahib Singh', 'Amina Mardiyyah Rufai', 'Chris Chinenye Emezue', 'Atnafu Lambebo Tonja', 'Bonaventure F. P. Dossou', 'Tejumade Afonja', 'Tobi Olatunji'] | 2023-06-01 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 2.90937304e-01 4.04986233e-01 5.32882437e-02 -5.89426994e-01
-1.18650746e+00 -7.01122463e-01 5.42370617e-01 5.29679433e-02
-7.68766403e-01 1.14640307e+00 7.25809157e-01 -6.65783048e-01
1.57779574e-01 -5.23188233e-01 -4.08682644e-01 -4.67409730e-01
4.07884978e-02 8.24588120e-01 -1.25950933e-01 -4.25110191... | [14.059050559997559, 6.968752861022949] |
3256c2a0-6e6a-41b1-8b21-6ef9df75ef73 | meeting-summarization-with-pre-training-and | 2111.08210 | null | https://arxiv.org/abs/2111.08210v1 | https://arxiv.org/pdf/2111.08210v1.pdf | Meeting Summarization with Pre-training and Clustering Methods | Automatic meeting summarization is becoming increasingly popular these days. The ability to automatically summarize meetings and to extract key information could greatly increase the efficiency of our work and life. In this paper, we experiment with different approaches to improve the performance of query-based meeting... | ['Xiang Xiao', 'Wei Ji', 'Andras Huebner'] | 2021-11-16 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 2.21776024e-01 6.61378920e-01 1.99858636e-01 -3.29846114e-01
-1.36035550e+00 -3.66961420e-01 7.87527204e-01 5.82043111e-01
-6.32474303e-01 6.04520023e-01 1.29967213e+00 -8.35520551e-02
2.16102764e-01 -5.34239292e-01 -5.64870834e-01 -1.89951330e-01
2.22918794e-01 5.69352567e-01 2.84084350e-01 -4.09963906... | [12.523730278015137, 9.476539611816406] |
a672cfe2-2ca7-48c4-a4dd-6fed6889b716 | enhancing-modality-agnostic-representations | 2302.04308 | null | https://arxiv.org/abs/2302.04308v1 | https://arxiv.org/pdf/2302.04308v1.pdf | Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation | In the medical vision domain, different imaging modalities provide complementary information. However, in practice, not all modalities may be available during inference. Previous approaches, e.g., knowledge distillation or image synthesis, often assume the availability of full modalities for all patients during trainin... | ['Prateek Prasanna', 'Chao Chen', 'Joseph Bae', 'Xuan Xu', 'Xiaoling Hu', 'Aishik Konwer'] | 2023-02-08 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 8.61536562e-01 4.46320653e-01 -5.53765118e-01 -3.12919021e-01
-1.24937904e+00 -5.88656604e-01 7.78385758e-01 -6.67629614e-02
-5.76642931e-01 1.02668655e+00 2.44027808e-01 -4.34694827e-01
-3.20526212e-02 -6.67467654e-01 -1.02324247e+00 -8.08055758e-01
2.66747713e-01 5.49522161e-01 -2.64250517e-01 7.61676803... | [14.547357559204102, -2.139209508895874] |
4a488659-75dc-4bfb-b5b9-d3d409d00826 | an-ensemble-teacher-student-learning-approach | 2210.06382 | null | https://arxiv.org/abs/2210.06382v1 | https://arxiv.org/pdf/2210.06382v1.pdf | An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition | We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for training data. Through boosting under DP, a student model derived from the training data suffers little model degradation from the models trained ... | ['Chin-Hui Lee', 'Sabato Marco Siniscalchi', 'Jun Qi', 'Chao-Han Huck Yang'] | 2022-10-12 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 5.12550831e-01 5.14043510e-01 -1.17070787e-01 -9.03683424e-01
-1.27135837e+00 -7.85743892e-01 5.00243425e-01 3.25549021e-02
-3.93643081e-01 7.20990717e-01 -2.64212079e-02 -4.77533191e-01
1.98845580e-01 -6.23336434e-01 -9.84853804e-01 -1.24607360e+00
1.20389961e-01 7.48958960e-02 -3.02014239e-02 3.98010373... | [5.885386943817139, 6.760809898376465] |
376701f0-cf59-4172-be01-3c23383eed37 | generating-templated-caption-for-video | 2301.05997 | null | https://arxiv.org/abs/2301.05997v2 | https://arxiv.org/pdf/2301.05997v2.pdf | Exploiting Prompt Caption for Video Grounding | Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the \emph{sparsity dilemma} in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. In this paper, we contend tha... | ['Yuexian Zou', 'Yaowei Li', 'Zhihong Zhu', 'Xuxin Cheng', 'Meng Cao', 'Hongxiang Li'] | 2023-01-15 | null | null | null | null | ['video-grounding', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 4.46633190e-01 4.56955209e-02 -4.52384591e-01 -3.99335355e-01
-1.10246849e+00 -5.66304088e-01 6.42483532e-01 -1.26981974e-01
-3.17773938e-01 7.03611791e-01 8.00396979e-01 1.20212115e-01
2.54436642e-01 -2.44602188e-01 -1.08089066e+00 -4.08507019e-01
-9.65509638e-02 1.98395993e-03 1.23836756e-01 -1.19456284... | [10.304241180419922, 0.7369306683540344] |
e5ce2c6a-deb5-4a39-b487-d6cb819c71ff | benchmarking-automatic-machine-learning | 1808.06492 | null | http://arxiv.org/abs/1808.06492v1 | http://arxiv.org/pdf/1808.06492v1.pdf | Benchmarking Automatic Machine Learning Frameworks | AutoML serves as the bridge between varying levels of expertise when
designing machine learning systems and expedites the data science process. A
wide range of techniques is taken to address this, however there does not exist
an objective comparison of these techniques. We present a benchmark of current
open source Aut... | ['Alexander Allen', 'Adithya Balaji'] | 2018-08-17 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-6.21809304e-01 1.69128031e-01 -2.21052811e-01 -6.16774440e-01
-6.96488738e-01 -8.73189509e-01 5.56661546e-01 5.10138981e-02
-1.62683725e-01 9.58650708e-01 9.97895300e-02 -5.04134417e-01
-3.67338508e-01 -3.93229395e-01 -5.82713544e-01 -1.47695586e-01
3.10591310e-01 7.82697976e-01 5.04630841e-02 -9.80959460... | [8.950610160827637, 7.333078861236572] |
568963e6-d295-4e41-a4b2-897b62124295 | armoured-adversarially-robust-models-using | null | null | https://openreview.net/forum?id=JoCR4h9O3Ew | https://openreview.net/pdf?id=JoCR4h9O3Ew | ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity | Adversarial attacks pose a major challenge for modern deep neural networks. Recent advancements show that adversarially robust generalization requires a huge amount of labeled data for training. If annotation becomes a burden, can unlabeled data help bridge the gap? In this paper, we propose ARMOURED, an adversarially ... | ['Chuan-Sheng Foo', 'Yu Jing Goh', 'Kiran Chari', 'Xun Xu', 'Cuong Manh Nguyen', 'Kangkang Lu'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['multi-view-learning'] | ['computer-vision'] | [ 1.90272212e-01 1.44911617e-01 -7.19961524e-02 -5.22681713e-01
-1.14642286e+00 -1.05171263e+00 3.00607920e-01 -3.85293007e-01
-4.25327033e-01 9.59671080e-01 -1.33024141e-01 -3.97135735e-01
7.19487444e-02 -5.41795194e-01 -9.70427573e-01 -8.14541340e-01
6.51510507e-02 2.95865029e-01 -1.83550760e-01 -4.03150469... | [5.614206314086914, 7.904454708099365] |
55de8a8a-6b22-492f-9e8c-4993648a025c | exploiting-the-complementarity-of-2d-and-3d | 2304.02991 | null | https://arxiv.org/abs/2304.02991v1 | https://arxiv.org/pdf/2304.02991v1.pdf | Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation | 3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality. However, the task is extremely challenging due to ambiguities coming from the unstructured, sparse, and uncolored nature of the 3D point clouds. A possible solution is to combine the 3D ... | ['Luigi Di Stefano', 'Samuele Salti', 'Pierluigi Zama Ramirez', 'Adriano Cardace'] | 2023-04-06 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 2.96718508e-01 6.90914169e-02 -2.10361853e-01 -3.34780514e-01
-6.41715348e-01 -8.47235322e-01 5.84451497e-01 1.60870567e-01
-4.39236909e-01 3.92590493e-01 -7.17758834e-02 -1.18641600e-01
-3.28393020e-02 -7.37792790e-01 -6.18948400e-01 -7.79963315e-01
3.09981287e-01 8.76327574e-01 6.95093453e-01 -3.38500470... | [8.262799263000488, -2.49794864654541] |
0c90cb85-09fd-439d-87eb-ac88fe6ac18a | using-user-s-local-context-to-support-local | 2205.12408 | null | https://arxiv.org/abs/2205.12408v2 | https://arxiv.org/pdf/2205.12408v2.pdf | Using user's local context to support local news | American local newspapers have been experiencing a large loss of reader retention and business within the past 15 years due to the proliferation of online news sources. Local media companies are starting to shift from an advertising-supported business model to one based on subscriptions to mitigate this problem. With t... | ['Bamshad Mobasher', 'Payam Pourashraf'] | 2022-05-24 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-2.67892808e-01 -1.71932057e-02 -8.05989444e-01 -6.43620610e-01
-5.41769981e-01 -2.96066642e-01 7.43221760e-01 5.81512809e-01
-5.48108876e-01 5.36138058e-01 1.02738917e+00 -1.39164358e-01
-4.28297907e-01 -9.66058552e-01 -4.27868396e-01 -2.82181859e-01
4.09569651e-01 4.58517820e-01 4.25536215e-01 -6.60810590... | [9.993576049804688, 5.866909503936768] |
e5a13451-d79f-45b3-ac6a-b7e44f48e64d | adaptive-elastic-training-for-sparse-deep | 2110.07029 | null | https://arxiv.org/abs/2110.07029v1 | https://arxiv.org/pdf/2110.07029v1.pdf | Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers | Motivated by extreme multi-label classification applications, we consider training deep learning models over sparse data in multi-GPU servers. The variance in the number of non-zero features across training batches and the intrinsic GPU heterogeneity combine to limit accuracy and increase the time to convergence. We ad... | ['Alexander Sim', 'Kesheng Wu', 'Florin Rusu', 'Yujing Ma'] | 2021-10-13 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [-3.02179605e-01 -5.41141927e-01 8.53400975e-02 -6.09840810e-01
-7.99049556e-01 -2.94136077e-01 1.92795694e-01 6.80548370e-01
-6.91606700e-01 2.65307128e-01 -2.24258855e-01 -1.14867665e-01
1.32262617e-01 -6.07141256e-01 -3.70536268e-01 -7.06405222e-01
5.51629923e-02 9.55335140e-01 4.25245285e-01 2.46175662... | [8.503183364868164, 3.427165985107422] |
3c65668a-c012-42a9-87ef-5311cdb75c37 | evaluating-translation-quality-and-clir | null | null | https://aclanthology.org/L16-1064 | https://aclanthology.org/L16-1064.pdf | Evaluating Translation Quality and CLIR Performance of Query Sessions | This paper presents the evaluation of the translation quality and Cross-Lingual Information Retrieval (CLIR) performance when using session information as the context of queries. The hypothesis is that previous queries provide context that helps to solve ambiguous translations in the current query. We tested several st... | ['I{\\~n}aki Alegria', 'Xabier Saralegi', 'Eneko Agirre'] | 2016-05-01 | evaluating-translation-quality-and-clir-1 | https://aclanthology.org/L16-1064 | https://aclanthology.org/L16-1064.pdf | lrec-2016-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.32738769e-01 -4.71907467e-01 -4.62881207e-01 -2.16753483e-01
-1.67092204e+00 -1.32934570e+00 9.43543971e-01 4.42411721e-01
-9.37552512e-01 8.18291068e-01 5.80471039e-01 -5.64841807e-01
-5.76612830e-01 -3.91947389e-01 -5.30324578e-01 -4.89562511e-01
2.31247678e-01 7.57910430e-01 5.94623566e-01 -8.45796704... | [11.516316413879395, 9.840658187866211] |
e12fc9e9-57b0-4b87-866a-ff1755f63306 | neural-complexity-statistical-mechanical | 2303.03128 | null | https://arxiv.org/abs/2303.03128v1 | https://arxiv.org/pdf/2303.03128v1.pdf | Neural complexity -- Statistical-mechanical approach of human electroencephalograms | The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within $q$-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of ty... | ['Henrique Santos Lima', 'Constantino Tsallis', 'Dimitri Marques Abramov'] | 2023-03-06 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 8.55584219e-02 -2.66654268e-02 3.95414084e-01 -2.41182759e-01
-2.93824971e-01 -3.25532228e-01 7.19567537e-01 4.81418908e-01
-7.94602573e-01 8.94370914e-01 -2.88399607e-01 1.23008139e-01
-4.78403449e-01 -5.53515255e-01 -4.62607950e-01 -1.09836197e+00
-7.96840310e-01 2.29882374e-01 9.73139107e-02 -8.81902650... | [12.84582805633545, 3.4634621143341064] |
a99b2ca8-7175-41a5-aa45-7017f9bdf704 | action2vec-a-crossmodal-embedding-approach-to | 1901.00484 | null | http://arxiv.org/abs/1901.00484v1 | http://arxiv.org/pdf/1901.00484v1.pdf | Action2Vec: A Crossmodal Embedding Approach to Action Learning | We describe a novel cross-modal embedding space for actions, named
Action2Vec, which combines linguistic cues from class labels with
spatio-temporal features derived from video clips. Our approach uses a
hierarchical recurrent network to capture the temporal structure of video
features. We train our embedding using a j... | ['Meera Hahn', 'Andrew Silva', 'James M. Rehg'] | 2019-01-02 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 1.53954148e-01 -1.75469309e-01 -6.42870784e-01 -2.30114713e-01
-7.61640668e-01 -4.83281195e-01 1.07366037e+00 1.14909850e-01
-7.41382003e-01 5.11528730e-01 1.13494575e+00 2.36531675e-01
-1.63379133e-01 -5.53084373e-01 -2.49598116e-01 -5.41393280e-01
-3.32568198e-01 8.93457211e-04 2.14097589e-01 -1.07319467... | [8.667016983032227, 0.9513075351715088] |
82d8475b-57ed-4fac-98c5-5557f5198220 | clevr-ref-diagnosing-visual-reasoning-with | 1901.00850 | null | http://arxiv.org/abs/1901.00850v2 | http://arxiv.org/pdf/1901.00850v2.pdf | CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions | Referring object detection and referring image segmentation are important
tasks that require joint understanding of visual information and natural
language. Yet there has been evidence that current benchmark datasets suffer
from bias, and current state-of-the-art models cannot be easily evaluated on
their intermediate ... | ['Yutong Bai', 'Runtao Liu', 'Alan Yuille', 'Chenxi Liu'] | 2019-01-03 | clevr-ref-diagnosing-visual-reasoning-with-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_CLEVR-Ref_Diagnosing_Visual_Reasoning_With_Referring_Expressions_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_CLEVR-Ref_Diagnosing_Visual_Reasoning_With_Referring_Expressions_CVPR_2019_paper.pdf | cvpr-2019-6 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 3.90731692e-01 7.59999573e-01 -2.99032569e-01 -3.82870972e-01
-7.03461766e-01 -9.30654228e-01 5.98921061e-01 1.03687435e-01
-1.49588183e-01 5.12366414e-01 1.90023948e-02 -5.58921695e-01
7.51247704e-02 -8.04148197e-01 -1.21324408e+00 -1.41327277e-01
2.99088061e-01 7.29716241e-01 3.99096340e-01 -7.57616758... | [10.764100074768066, 1.8905428647994995] |
6f2c5960-14c7-493b-8aa2-2be231f7b1ca | soccernet-caption-dense-video-captioning-for | 2304.04565 | null | https://arxiv.org/abs/2304.04565v1 | https://arxiv.org/pdf/2304.04565v1.pdf | SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries | Soccer is more than just a game - it is a passion that transcends borders and unites people worldwide. From the roar of the crowds to the excitement of the commentators, every moment of a soccer match is a thrill. Yet, with so many games happening simultaneously, fans cannot watch them all live. Notifications for main ... | ['Marc Van Droogenbroeck', 'Bernard Ghanem', 'Silvio Giancola', 'Anthony Cioppa', 'Hassan Mkhallati'] | 2023-04-10 | null | null | null | null | ['video-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 1.50030136e-01 1.08011469e-01 -9.15799737e-02 -8.84615555e-02
-1.20697546e+00 -9.26471949e-01 5.64809501e-01 2.62352079e-01
-3.93988788e-01 7.62165964e-01 9.63218212e-01 -9.38671455e-03
4.56927389e-01 -4.37591732e-01 -5.45861483e-01 -3.18290323e-01
5.55225313e-02 1.46141142e-01 5.79702258e-01 -7.82231808... | [10.378576278686523, 0.7320815920829773] |
8cc96861-838f-46a2-9776-05389ccee8fd | twin-s-a-digital-twin-for-skull-base-surgery | 2211.11863 | null | https://arxiv.org/abs/2211.11863v2 | https://arxiv.org/pdf/2211.11863v2.pdf | Twin-S: A Digital Twin for Skull-base Surgery | Purpose: Digital twins are virtual interactive models of the real world, exhibiting identical behavior and properties. In surgical applications, computational analysis from digital twins can be used, for example, to enhance situational awareness. Methods: We present a digital twin framework for skull-base surgeries, na... | ['Mathias Unberath', 'Adnan Munawar', 'Russell H. Taylor', 'Francis X. Creighton', 'Manish Sahu', 'Nimesh Nagururu', 'Hao Ding', 'Xiangyu Zhang', 'Anna Goodridge', 'Zhaoshuo Li', 'Ruixing Liang', 'Hongchao Shu'] | 2022-11-21 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-4.49947640e-02 1.77961275e-01 2.04223245e-01 3.88885736e-01
-5.55132270e-01 -6.14585757e-01 2.50564933e-01 9.10115615e-02
-3.07894260e-01 2.03222960e-01 -5.60893863e-02 -5.47996283e-01
-4.89143580e-02 -5.08261800e-01 -4.32146400e-01 -3.79521608e-01
-1.02830417e-01 6.63964272e-01 5.27474284e-01 -5.13849735... | [13.743624687194824, -3.025155544281006] |
ec243af8-d6c9-4708-99a7-8f1cb4105445 | patch-based-medical-image-segmentation-using | 2109.07138 | null | https://arxiv.org/abs/2109.07138v2 | https://arxiv.org/pdf/2109.07138v2.pdf | Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks | Tensor networks are efficient factorisations of high-dimensional tensors into a network of lower-order tensors. They have been most commonly used to model entanglement in quantum many-body systems and more recently are witnessing increased applications in supervised machine learning. In this work, we formulate image se... | ['Jens Petersen', 'Søren Alexander Flensborg', 'Erik B Dam', 'Raghavendra Selvan'] | 2021-09-15 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.56286347e-01 2.37890020e-01 -6.40855506e-02 -2.07125887e-01
-4.66807514e-01 -4.37736690e-01 4.55194294e-01 -2.23110378e-01
-6.48718417e-01 2.97662377e-01 9.64656174e-02 -3.14205766e-01
-2.14404404e-01 -3.78933012e-01 -3.62330377e-01 -1.21653438e+00
-4.19268459e-01 5.89921117e-01 2.71588027e-01 2.03829736... | [5.835435390472412, 4.976510047912598] |
e7c9151f-80ca-409a-9987-cfa4c17db8ee | selective-text-augmentation-with-word-roles | 2209.01560 | null | https://arxiv.org/abs/2209.01560v1 | https://arxiv.org/pdf/2209.01560v1.pdf | Selective Text Augmentation with Word Roles for Low-Resource Text Classification | Data augmentation techniques are widely used in text classification tasks to improve the performance of classifiers, especially in low-resource scenarios. Most previous methods conduct text augmentation without considering the different functionalities of the words in the text, which may generate unsatisfactory samples... | ['Hailiang Huang', 'Songqiao Han', 'Biyang Guo'] | 2022-09-04 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 3.25280368e-01 -2.38609642e-01 -2.93465555e-01 -3.83323491e-01
-1.68659657e-01 -2.13986322e-01 7.33322024e-01 3.67868960e-01
-6.24907613e-01 7.81389713e-01 5.19432902e-01 -2.42392868e-01
1.90603331e-01 -8.46836567e-01 -2.51056608e-02 -8.07879806e-01
5.88718534e-01 4.74623710e-01 1.14954144e-01 -6.27074003... | [10.497540473937988, 7.582983493804932] |
7d19660b-423f-423e-9026-4519329d366e | spectrum-shaping-for-multiple-link-discovery | 2108.04932 | null | https://arxiv.org/abs/2108.04932v1 | https://arxiv.org/pdf/2108.04932v1.pdf | Spectrum Shaping For Multiple Link Discovery in 6G THz Systems | This paper presents a novel antenna configuration to measure directions of multiple signal sources at the receiver in a THz mobile network via a single channel measurement. Directional communication is an intrinsic attribute of THz wireless networks and the knowledge of direction should be harvested continuously to mai... | ['Nazanin Rahnavard', 'Katarina Vuckovic', 'Farzam Hejazi'] | 2021-08-10 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 5.10721862e-01 5.36122441e-01 2.93823212e-01 -1.92471936e-01
-2.15317219e-01 -8.12165201e-01 4.86023068e-01 3.47985158e-04
-3.69179279e-01 7.25233078e-01 -8.70739296e-02 -7.75944293e-01
-5.63463449e-01 -9.79781985e-01 -2.56730169e-01 -1.23291600e+00
-2.73942709e-01 -2.37998385e-02 2.11194277e-01 1.08677313... | [6.310864448547363, 1.1832473278045654] |
fa7532ba-3b69-4715-837b-7fde2cb3135c | neural-volumetric-reconstruction-for-coherent | 2306.09909 | null | https://arxiv.org/abs/2306.09909v1 | https://arxiv.org/pdf/2306.09909v1.pdf | Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar | Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number ... | ['Suren Jayasuriya', 'Daniel C. Brown', 'Adithya Pediredla', 'Thomas Blanford', 'Juhyeon Kim', 'Albert W. Reed'] | 2023-06-16 | null | null | null | null | ['image-reconstruction', 'neural-rendering'] | ['computer-vision', 'computer-vision'] | [ 7.39259779e-01 -2.47180164e-01 6.81224108e-01 -3.55894774e-01
-1.00707686e+00 -6.37149215e-01 3.52130145e-01 -3.78193408e-01
-2.07202181e-01 4.89340186e-01 2.98730344e-01 -9.59787741e-02
3.05017736e-02 -5.28876066e-01 -8.21701527e-01 -7.12704182e-01
-8.73987302e-02 -1.69895142e-01 -1.48341730e-02 5.01380228... | [11.263164520263672, -2.618006467819214] |
fbca6167-1e29-4184-8916-37cea808b8a6 | finding-the-most-transferable-tasks-for-brain | 2301.00934 | null | https://arxiv.org/abs/2301.00934v1 | https://arxiv.org/pdf/2301.00934v1.pdf | Finding the Most Transferable Tasks for Brain Image Segmentation | Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best sour... | ['Xiao-Ping Zhang', 'Yang Li', 'Jingyun Yang', 'Yang Tan', 'Yicong Li'] | 2023-01-03 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 6.00774288e-01 -5.54315280e-04 -2.42200956e-01 -4.68804926e-01
-1.03115869e+00 -3.85468185e-01 5.17211020e-01 2.43919566e-01
-6.90110207e-01 7.39096642e-01 8.69912580e-02 -9.42656025e-02
-5.08507252e-01 -6.81892633e-01 -6.57174706e-01 -9.21492219e-01
2.46453851e-01 4.90494519e-01 5.21489978e-01 1.45853944... | [14.561254501342773, -2.0802745819091797] |
cf60f5df-fb30-4d8f-bbe5-55149ce0e5e9 | task-arithmetic-in-the-tangent-space-improved | 2305.12827 | null | https://arxiv.org/abs/2305.12827v2 | https://arxiv.org/pdf/2305.12827v2.pdf | Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models | Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different tasks, the model's performance can be improved on these tasks, while negating them leads to task forgetting. Yet, our understanding of the eff... | ['Pascal Frossard', 'Alessandro Favero', 'Guillermo Ortiz-Jimenez'] | 2023-05-22 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 4.16653514e-01 -2.91297376e-01 -9.86665674e-03 -3.48854899e-01
-3.08039963e-01 -4.63025868e-01 7.05950797e-01 1.53869167e-01
-6.17204249e-01 2.96397537e-01 5.12247980e-01 -2.25840017e-01
-3.54938567e-01 -3.83533001e-01 -7.74266422e-01 -6.96871579e-01
1.33561134e-01 2.14985982e-01 7.33624175e-02 -4.48044389... | [10.009806632995605, 2.453312635421753] |
a2d320a8-4225-4bbb-be3d-299db5d574a7 | relation-mention-extraction-from-noisy-data | 1811.01237 | null | http://arxiv.org/abs/1811.01237v1 | http://arxiv.org/pdf/1811.01237v1.pdf | Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning | In this paper we address a task of relation mention extraction from noisy
data: extracting representative phrases for a particular relation from noisy
sentences that are collected via distant supervision. Despite its significance
and value in many downstream applications, this task is less studied on noisy
data. The ma... | ['Minlie Huang', 'Yijie Zhang', 'Jun Feng', 'Xiaoyan Zhu', 'Yang Yang'] | 2018-11-03 | null | null | null | null | ['relation-mention-extraction'] | ['natural-language-processing'] | [ 3.55222672e-01 7.70870745e-01 -1.48093939e-01 -5.44996500e-01
-1.52618563e+00 -2.58628488e-01 1.55899301e-01 4.13283527e-01
-5.33427656e-01 1.11963224e+00 6.05570316e-01 -1.43935949e-01
7.42794499e-02 -7.29327142e-01 -6.39770508e-01 -6.69537604e-01
5.88706098e-02 3.30527455e-01 5.36679514e-02 -1.83211312... | [9.355069160461426, 8.720480918884277] |
2735c24e-9f37-4ff0-a959-a3729a3954e7 | lightweight-modeling-of-user-context | 2306.16029 | null | https://arxiv.org/abs/2306.16029v1 | https://arxiv.org/pdf/2306.16029v1.pdf | Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data | The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness pe... | ['Pan Hui', 'Franca Delmastro', 'Dimitris Chatzopoulos', 'Mattia Giovanni Campana'] | 2023-06-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 4.22535270e-01 -7.96099007e-02 -2.48049662e-01 -5.80824018e-01
-5.37152946e-01 -3.85610431e-01 4.64151442e-01 4.86882657e-01
-3.00030142e-01 6.23499811e-01 2.52124101e-01 -1.39806792e-01
-2.93537527e-01 -9.15786147e-01 -2.45889828e-01 -4.77001876e-01
-5.30998297e-02 2.49938428e-01 -7.32011208e-03 -1.14383432... | [7.490678787231445, 1.390409231185913] |
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