paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
43c83c7e-cad8-4dc4-8e5e-7323e6bebed9 | salient-mask-guided-vision-transformer-for | 2305.07102 | null | https://arxiv.org/abs/2305.07102v1 | https://arxiv.org/pdf/2305.07102v1.pdf | Salient Mask-Guided Vision Transformer for Fine-Grained Classification | Fine-grained visual classification (FGVC) is a challenging computer vision problem, where the task is to automatically recognise objects from subordinate categories. One of its main difficulties is capturing the most discriminative inter-class variances among visually similar classes. Recently, methods with Vision Tran... | ['Fahad Shahbaz Khan', 'Hisham Cholakkal', 'Aliakbar Abdurahimov', 'Muhammad Hamza Sharif', 'Dmitry Demidov'] | 2023-05-11 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 5.54764688e-01 -2.31005296e-01 1.46387801e-01 -3.18058133e-02
-7.57707238e-01 -5.14259696e-01 7.97461331e-01 4.56874333e-02
-2.49845281e-01 6.77442431e-01 1.97501690e-03 -1.62448138e-02
2.82575339e-02 -6.16841257e-01 -6.28933847e-01 -1.09163678e+00
2.29726210e-01 1.27697140e-01 7.97872603e-01 -8.49928632... | [9.668898582458496, 1.9131972789764404] |
70e773b0-6d76-44a4-817e-9f4886c6713f | ordered-neurons-integrating-tree-structures | 1810.09536 | null | https://arxiv.org/abs/1810.09536v6 | https://arxiv.org/pdf/1810.09536v6.pdf | Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks | Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information a... | ['Alessandro Sordoni', 'Yikang Shen', 'Shawn Tan', 'Aaron Courville'] | 2018-10-22 | ordered-neurons-integrating-tree-structures-1 | https://openreview.net/forum?id=B1l6qiR5F7 | https://openreview.net/pdf?id=B1l6qiR5F7 | iclr-2019-5 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 2.19344541e-01 5.10271251e-01 -2.93645948e-01 -5.96200049e-01
-2.64397800e-01 -6.21636748e-01 2.08025128e-01 5.78623533e-01
-3.76789033e-01 8.84712696e-01 4.64488506e-01 -6.33024096e-01
2.94607401e-01 -1.17475355e+00 -9.14214373e-01 -5.44948757e-01
-2.77794987e-01 5.68874180e-01 4.55346256e-01 -6.68430552... | [10.37945556640625, 9.291975021362305] |
2da586c2-2995-4b72-bd0f-d290684d94e9 | optimal-transport-minimization-crowd | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Optimal_Transport_Minimization_Crowd_Localization_on_Density_Maps_for_Semi-Supervised_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Optimal_Transport_Minimization_Crowd_Localization_on_Density_Maps_for_Semi-Supervised_CVPR_2023_paper.pdf | Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting | The accuracy of crowd counting in images has improved greatly in recent years due to the development of deep neural networks for predicting crowd density maps. However, most methods do not further explore the ability to localize people in the density map, with those few works adopting simple methods, like finding t... | ['Antoni B. Chan', 'Wei Lin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['crowd-counting'] | ['computer-vision'] | [-3.58016461e-01 -7.83237964e-02 -7.79785169e-03 -6.07622445e-01
-6.23939633e-01 -1.64298669e-01 5.78893363e-01 2.65204817e-01
-8.29181194e-01 9.90205407e-01 2.99383819e-01 8.08291435e-02
2.38008156e-01 -8.91202748e-01 -6.49639606e-01 -5.88856578e-01
-1.14205413e-01 8.73795450e-01 5.41993201e-01 5.97628169... | [8.365384101867676, -0.34966716170310974] |
cdac7f2f-4083-4006-b8d7-9be0dd8bdb0b | online-nonnegative-matrix-factorization-with | 1608.00075 | null | http://arxiv.org/abs/1608.00075v2 | http://arxiv.org/pdf/1608.00075v2.pdf | Online Nonnegative Matrix Factorization with General Divergences | We develop a unified and systematic framework for performing online
nonnegative matrix factorization under a wide variety of important divergences.
The online nature of our algorithm makes it particularly amenable to
large-scale data. We prove that the sequence of learned dictionaries converges
almost surely to the set... | ['Renbo Zhao', 'Vincent Y. F. Tan', 'Huan Xu'] | 2016-07-30 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [-3.70411351e-02 -2.20700428e-01 1.37961814e-02 -8.19464326e-02
-8.90230536e-01 -7.48437107e-01 2.03644454e-01 -1.03669509e-01
-2.89732486e-01 4.39660102e-01 1.67738870e-01 -2.57711887e-01
-3.83787602e-01 -4.77586329e-01 -7.01822639e-01 -1.10982549e+00
-2.01659247e-01 2.08932668e-01 -2.55411416e-01 -2.80709326... | [7.071174144744873, 4.51841926574707] |
eb6672cf-ef1f-480f-a239-2e028d77d2bc | learnable-distribution-calibration-for-few | 2210.00232 | null | https://arxiv.org/abs/2210.00232v1 | https://arxiv.org/pdf/2210.00232v1.pdf | Learnable Distribution Calibration for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) faces challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study, we propose a learnable distribution calibration (LDC) approach, with the aim to systematically solve these two challenges using a unified f... | ['Qixiang Ye', 'Qi Tian', 'Ren Wang', 'Lingxi Xie', 'Boyu Yang', 'Binghao Liu'] | 2022-10-01 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 7.92799592e-02 1.83040395e-01 -3.49113524e-01 -3.72662276e-01
-9.46877480e-01 -2.65763909e-01 6.13720298e-01 5.30925393e-02
-4.94541496e-01 9.67248499e-01 -1.70108080e-01 1.79664776e-01
2.74948403e-02 -9.20410812e-01 -9.65222657e-01 -1.01969779e+00
6.81784302e-02 6.75685465e-01 2.60947734e-01 1.25032604... | [9.856146812438965, 3.242680311203003] |
3aab6d5c-0b40-4490-a599-d2ad2fd38770 | cebed-a-benchmark-for-deep-data-driven-ofdm | 2306.13761 | null | https://arxiv.org/abs/2306.13761v1 | https://arxiv.org/pdf/2306.13761v1.pdf | CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation | Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic comparison between them is difficult due to inconsistencies in the experimental conditions and the lack of a standardized experimental design. In ... | ['Greg Dudek', 'Steve Liu', 'Di wu', 'Amal Feriani'] | 2023-06-23 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.12570867e-01 -5.62926054e-01 -4.48860899e-02 -4.37479138e-01
-9.05096412e-01 -1.85782224e-01 4.05782908e-01 9.49137881e-02
-4.18953657e-01 1.10117996e+00 3.63700241e-02 -7.76055515e-01
-1.85614452e-01 -7.23329723e-01 -4.95216370e-01 -1.09346676e+00
-9.17823017e-01 2.03982703e-02 -3.19870561e-01 -2.91809887... | [6.31269645690918, 1.4407579898834229] |
2ff18ab5-82ed-42f4-918d-36cbad403013 | the-muse-2022-multimodal-sentiment-analysis | 2207.05691 | null | https://arxiv.org/abs/2207.05691v2 | https://arxiv.org/pdf/2207.05691v2.pdf | The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress | The Multimodal Sentiment Analysis Challenge (MuSe) 2022 is dedicated to multimodal sentiment and emotion recognition. For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset that contains audio-visual recordings of German football coaches, labelled for... | ['Björn W. Schuller', 'Erik Cambria', 'Alan Cowen', 'Andreas König', 'Eva-Maria Meßner', 'Lukas Stappen', 'Niklas Müller', 'Alexander Kathan', 'Panagiotis Tzirakis', 'Alice Baird', 'Shahin Amiriparian', 'Lukas Christ'] | 2022-06-23 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-9.22822654e-02 -1.62277073e-01 4.02430534e-01 -3.05348635e-01
-7.51334786e-01 -3.82865369e-01 2.43723348e-01 4.38759565e-01
-4.92409825e-01 4.02157664e-01 3.57959956e-01 5.35539329e-01
3.24800313e-02 -3.21122944e-01 -9.91424024e-02 -6.96628630e-01
-3.78610671e-01 -8.93735047e-03 -4.34207231e-01 -6.08059108... | [13.418718338012695, 5.04036283493042] |
5448be65-df8f-4329-8c18-8c125bbc73bc | an-approach-modality-reduction-and-face | 1312.1681 | null | https://arxiv.org/abs/1312.1681v1 | https://arxiv.org/pdf/1312.1681v1.pdf | An Approach: Modality Reduction and Face-Sketch Recognition | To recognize face sketch through face photo database is a challenging task for todays researchers. Because face photo images in training set and face sketch images in testing set have different modality. Difference between two face photos of difference person is smaller than the difference between same person in a face... | ['Sourav Pramanik', 'Dr. Debotosh Bhattacharjee'] | 2013-12-05 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 2.06148162e-01 -2.29164064e-01 3.22193131e-02 -4.24889982e-01
-9.37549323e-02 -7.27491081e-01 6.21576071e-01 -9.41037953e-01
-4.36089821e-02 5.41545093e-01 -9.64619592e-02 2.51775265e-01
6.73357174e-02 -8.21350098e-01 -5.06201804e-01 -6.31745577e-01
2.22816244e-01 2.72923797e-01 -2.40850642e-01 1.27676561... | [13.142444610595703, 0.6790149807929993] |
f89dc484-9950-4df1-9687-cd7faecbfcbb | cafeboost-causal-feature-boost-to-eliminate | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_CafeBoost_Causal_Feature_Boost_To_Eliminate_Task-Induced_Bias_for_Class_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_CafeBoost_Causal_Feature_Boost_To_Eliminate_Task-Induced_Bias_for_Class_CVPR_2023_paper.pdf | CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning | Continual learning requires a model to incrementally learn a sequence of tasks and aims to predict well on all the learned tasks so far, which notoriously suffers from the catastrophic forgetting problem. In this paper, we find a new type of bias appearing in continual learning, coined as task-induced bias. We plac... | ['Lili Pan', 'Qingbo Wu', 'Fanman Meng', 'Lanxiao Wang', 'Heqian Qiu', 'Haitao Wen', 'Hongliang Li', 'Benliu Qiu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 2.37132281e-01 1.59017250e-01 -2.69568533e-01 -5.99718213e-01
-2.88337588e-01 -2.51904666e-01 7.93306768e-01 -1.13393150e-01
-5.35256565e-01 1.12709165e+00 1.80239469e-01 -3.58911663e-01
-1.84274182e-01 -5.73934257e-01 -1.05445623e+00 -6.51766658e-01
1.24197990e-01 3.55740428e-01 5.63215852e-01 -2.04318285... | [9.819576263427734, 3.43099308013916] |
456bb86b-1648-48b2-a507-070a8c27ee9c | exemplar-based-contrastive-self-supervised | 2202.02601 | null | https://arxiv.org/abs/2202.02601v1 | https://arxiv.org/pdf/2202.02601v1.pdf | Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning | Humans are capable of learning new concepts from only a few (labeled) exemplars, incrementally and continually. This happens within the context that we can differentiate among the exemplars, and between the exemplars and large amounts of other data (unlabeled and labeled). This suggests, in human learning, supervised l... | ['Daniel T. Chang'] | 2022-02-05 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.84377396e-01 2.69865394e-01 -4.18630123e-01 -6.20784104e-01
-4.93361324e-01 -5.78433454e-01 7.65220821e-01 7.47362375e-01
-5.57260513e-01 9.32002783e-01 6.17750213e-02 8.82994309e-02
-4.64685053e-01 -9.15627658e-01 -4.55295205e-01 -5.33486426e-01
-3.27892274e-01 6.53993547e-01 4.48474109e-01 -4.51436371... | [10.016103744506836, 3.010403633117676] |
08c7bf55-a613-45fa-a0d3-7bd96f71876c | edgeface-efficient-face-recognition-model-for | 2307.01838 | null | https://arxiv.org/abs/2307.01838v1 | https://arxiv.org/pdf/2307.01838v1.pdf | EdgeFace: Efficient Face Recognition Model for Edge Devices | In this paper, we present EdgeFace, a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achieves excellent face recognition performance optimized for edge de... | ['Sebastien Marcel', 'Ketan Kotwal', 'Hatef Otroshi Shahreza', 'Christophe Ecabert', 'Anjith George'] | 2023-07-04 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [-3.14756095e-01 -2.44815022e-01 -2.96858549e-01 -5.29187918e-01
-1.74975529e-01 5.46082258e-02 3.91292810e-01 -7.32879460e-01
6.32357597e-02 3.20395082e-01 5.67570329e-02 -3.39442849e-01
-2.70917505e-01 -6.75655782e-01 -5.95685959e-01 -4.19925719e-01
-3.00541371e-01 3.62687498e-01 -3.85579735e-01 1.39048472... | [13.31177806854248, 0.7361772656440735] |
5f44fc15-baca-4eaf-add0-abd56802dc50 | bencoref-a-multi-domain-dataset-of-nominal | 2304.03682 | null | https://arxiv.org/abs/2304.03682v3 | https://arxiv.org/pdf/2304.03682v3.pdf | BenCoref: A Multi-Domain Dataset of Nominal Phrases and Pronominal Reference Annotations | Coreference Resolution is a well studied problem in NLP. While widely studied for English and other resource-rich languages, research on coreference resolution in Bengali largely remains unexplored due to the absence of relevant datasets. Bengali, being a low-resource language, exhibits greater morphological richness c... | ['Nabeel Mohammed', 'Mohammad Mamun Or Rashid', 'Mojammel Hossain', 'Shadman Rohan'] | 2023-04-07 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-8.53983015e-02 7.37060383e-02 -3.12316149e-01 -3.81151229e-01
-1.39135551e+00 -9.98700380e-01 7.98894942e-01 1.90536648e-01
-5.69056988e-01 9.61558104e-01 9.09068525e-01 -9.87491235e-02
-2.22970068e-01 -5.28202653e-01 -3.89213085e-01 -6.71164036e-01
1.24320671e-01 1.15108573e+00 2.01075196e-01 -5.85797489... | [9.327056884765625, 9.485779762268066] |
642b9df0-2aed-4b8f-ae93-bba4129775e6 | kerm-knowledge-enhanced-reasoning-for-vision | 2303.15796 | null | https://arxiv.org/abs/2303.15796v1 | https://arxiv.org/pdf/2303.15796v1.pdf | KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation | Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representation... | ['Shuqiang Jiang', 'YaoWei Wang', 'Jiahao Yang', 'Zihan Wang', 'Xiangyang Li'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_KERM_Knowledge_Enhanced_Reasoning_for_Vision-and-Language_Navigation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_KERM_Knowledge_Enhanced_Reasoning_for_Vision-and-Language_Navigation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['vision-and-language-navigation'] | ['robots'] | [-2.02339053e-01 -2.87211627e-01 -1.16869472e-01 -3.57705146e-01
-3.03066075e-01 -4.67081517e-01 7.52274215e-01 2.56695688e-01
-4.71053481e-01 6.90539360e-01 4.84297097e-01 -1.25659645e-01
-4.14726794e-01 -8.87887299e-01 -6.01141870e-01 -5.71093321e-01
3.37646855e-03 9.12368521e-02 6.82967126e-01 -4.92835552... | [4.496055603027344, 0.45615705847740173] |
1ede3539-6fdc-4572-aa53-04216483f3dc | confidence-driven-bounding-box-localization | 2303.01803 | null | https://arxiv.org/abs/2303.01803v1 | https://arxiv.org/pdf/2303.01803v1.pdf | Confidence-driven Bounding Box Localization for Small Object Detection | Despite advancements in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We for the first time observe that existing bounding box regression methods tend to produce distorted gradients for small objects and result in less accurate localization. To ad... | ['Xianbin Cao', 'Yanjing Li', 'Baochang Zhang', 'Huixin Sun'] | 2023-03-03 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.34782314e-01 7.77818039e-02 -2.23425120e-01 -6.17111981e-01
-1.46001172e+00 -5.90829611e-01 4.73496884e-01 4.21638042e-01
-3.24956954e-01 4.08056498e-01 -1.13530912e-01 -4.96911779e-02
3.43583375e-01 -3.82791102e-01 -8.46939683e-01 -5.24557471e-01
-5.32333665e-02 4.79728997e-01 9.12058473e-01 2.85435230... | [9.18389892578125, 1.090420126914978] |
c9d4635c-a3ac-4cc3-bada-8b6d3d5ef2f5 | digging-deeper-into-egocentric-gaze | 1904.06090 | null | http://arxiv.org/abs/1904.06090v1 | http://arxiv.org/pdf/1904.06090v1.pdf | Digging Deeper into Egocentric Gaze Prediction | This paper digs deeper into factors that influence egocentric gaze. Instead
of training deep models for this purpose in a blind manner, we propose to
inspect factors that contribute to gaze guidance during daily tasks. Bottom-up
saliency and optical flow are assessed versus strong spatial prior baselines.
Task-specific... | ['Hamed R. -Tavakoli', 'Esa Rahtu', 'Ali Borji', 'Juho Kannala'] | 2019-04-12 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 1.15677357e-01 -2.29353651e-01 -3.06185901e-01 -2.15769187e-01
-2.71267798e-02 -2.00944200e-01 4.28243667e-01 -2.58219630e-01
-3.26606095e-01 4.54855084e-01 7.42912054e-01 -6.06547184e-02
-1.84868306e-01 -2.54179835e-01 -7.00960338e-01 -8.21906686e-01
6.86611831e-02 -5.06765425e-01 3.73795539e-01 -2.80407965... | [13.983227729797363, 0.04935143142938614] |
d8b480f7-f86a-4e23-ab26-77e3727e7d23 | neural-distribution-learning-for-generalized | null | null | https://openreview.net/forum?id=SyG4RiR5Ym | https://openreview.net/pdf?id=SyG4RiR5Ym | Neural Distribution Learning for generalized time-to-event prediction | Predicting the time to the next event is an important task in various domains.
However, due to censoring and irregularly sampled sequences, time-to-event prediction has resulted in limited success only for particular tasks, architectures and data. Using recent advances in probabilistic programming and density networks... | ['Jung-Woo Ha', 'Jaegul Choo', 'Jaesung Huh', 'Adrian Kim', 'Egil Martinsson'] | 2018-09-27 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [ 1.73386574e-01 1.69591215e-02 -4.59311754e-01 -7.47982681e-01
-9.59849238e-01 -2.79901356e-01 6.45146370e-01 4.57228988e-01
-5.56147695e-01 1.10090292e+00 1.19580003e-02 -5.51081240e-01
-6.00088775e-01 -7.67108798e-01 -5.86132765e-01 -6.30171239e-01
-6.33069336e-01 1.05255115e+00 1.01431958e-01 4.46327209... | [7.753687381744385, 5.583449363708496] |
3cae8110-4ca7-47d6-ac67-e01ed4b44f2a | leveraging-a-semantically-annotated-corpus-to | null | null | https://aclanthology.org/W15-0101 | https://aclanthology.org/W15-0101.pdf | Leveraging a Semantically Annotated Corpus to Disambiguate Prepositional Phrase Attachment | null | ['Guy Emerson', 'Ann Copestake'] | 2015-04-01 | null | null | null | ws-2015-4 | ['prepositional-phrase-attachment'] | ['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... | [-7.437986373901367, 3.823320150375366] |
a5245720-42e3-445e-b7a0-6f6a48d1a473 | feature-representation-for-icu-mortality | 1512.05294 | null | http://arxiv.org/abs/1512.05294v2 | http://arxiv.org/pdf/1512.05294v2.pdf | Feature Representation for ICU Mortality | Good predictors of ICU Mortality have the potential to identify high-risk
patients earlier, improve ICU resource allocation, or create more accurate
population-level risk models. Machine learning practitioners typically make
choices about how to represent features in a particular model, but these
choices are seldom eva... | ['Harini Suresh'] | 2015-12-16 | null | null | null | null | ['icu-mortality', 'l2-regularization'] | ['medical', 'methodology'] | [ 8.11955854e-02 -3.28158528e-01 -4.52038437e-01 -3.07856500e-01
-7.03067362e-01 -7.09494576e-02 1.67672843e-01 1.07570088e+00
-5.91745913e-01 9.12873089e-01 6.10880494e-01 -7.00550735e-01
-5.93156815e-01 -6.87085271e-01 -2.13655625e-02 -5.46835005e-01
-1.13325916e-01 6.48194075e-01 -2.94634372e-01 1.30174354... | [8.070352554321289, 6.0597310066223145] |
5f64f003-14be-4840-848f-7af4f383f0df | combining-noise-to-image-and-image-to-image | 1905.13456 | null | https://arxiv.org/abs/1905.13456v3 | https://arxiv.org/pdf/1905.13456v3.pdf | Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection | Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the dat... | ['Yujiro Furukawa', 'Leonardo Rundo', 'Changhee Han', 'Yudai Nagano', 'Ryosuke Araki', 'Hideki Nakayama', 'Hideaki Hayashi', 'Giancarlo Mauri'] | 2019-05-31 | null | null | null | null | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 6.04375482e-01 2.64958590e-01 1.44660622e-01 -7.80097991e-02
-1.10513270e+00 -2.48351574e-01 4.96438444e-01 -4.55227494e-01
-4.13405657e-01 1.01533651e+00 1.08797848e-01 -2.46876642e-01
1.93368882e-01 -1.00459445e+00 -7.17026353e-01 -1.27860904e+00
3.61334175e-01 6.83568895e-01 -3.48423235e-02 -2.18850806... | [14.055320739746094, -2.0179402828216553] |
012c9b68-6a87-4647-a249-fb62b59557ad | simplymime-a-control-at-our-fingertips | 2304.11377 | null | https://arxiv.org/abs/2304.11377v1 | https://arxiv.org/pdf/2304.11377v1.pdf | SimplyMime: A Control at Our Fingertips | The utilization of consumer electronics, such as televisions, set-top boxes, home theaters, and air conditioners, has become increasingly prevalent in modern society as technology continues to evolve. As new devices enter our homes each year, the accumulation of multiple infrared remote controls to operate them not onl... | ['Anitha Subramanian', 'Saraju P. Mohanty', 'Athresh Kiran', 'Gaurav Reddy Tadkapally', 'Sibi Chakkaravarthy Sethuraman'] | 2023-04-22 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.74308711e-01 -3.89485151e-01 -5.51067293e-02 -1.06443353e-01
3.23743224e-02 -7.78863430e-01 3.11823189e-01 -3.01906884e-01
-5.99935472e-01 3.51389617e-01 -2.27825776e-01 -2.37540349e-01
-2.48825103e-01 -8.31204772e-01 -2.97017768e-02 -7.15103447e-01
1.45084113e-01 -4.90889465e-03 3.13874424e-01 -5.81485890... | [6.532510280609131, -0.19912363588809967] |
26257370-1d9e-4855-a7d1-9c31e3184ecc | bridging-the-gap-in-multilingual-semantic | null | null | https://aclanthology.org/2020.coling-main.120 | https://aclanthology.org/2020.coling-main.120.pdf | Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach | Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-re... | ['Roberto Navigli', 'Simone Conia'] | 2020-12-01 | null | null | null | coling-2020-8 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-1.78949523e-03 -8.11440498e-02 -5.98958910e-01 -4.78490353e-01
-1.00587130e+00 -1.08016789e+00 8.92544448e-01 3.34141463e-01
-8.42726588e-01 9.19062197e-01 8.26183438e-01 -4.50004905e-01
2.57873051e-02 -2.58942574e-01 -4.35031325e-01 -2.33836249e-01
4.07073587e-01 4.89265591e-01 2.13523239e-01 -6.67532742... | [10.473494529724121, 9.606160163879395] |
bfb1b1df-01a7-4ac5-b090-6ecee2609c1e | multi-task-convolutional-neural-network-for | 1702.04710 | null | http://arxiv.org/abs/1702.04710v2 | http://arxiv.org/pdf/1702.04710v2.pdf | Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition | This paper explores multi-task learning (MTL) for face recognition. We answer
the questions of how and why MTL can improve the face recognition performance.
First, we propose a multi-task Convolutional Neural Network (CNN) for face
recognition where identity classification is the main task and pose,
illumination, and e... | ['Xiaoming Liu', 'Xi Yin'] | 2017-02-15 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 1.09314092e-01 -5.23906112e-01 -1.84173912e-01 -6.89652622e-01
-9.06596541e-01 -4.73527461e-01 4.32954699e-01 -6.65561974e-01
-3.30260187e-01 5.63275397e-01 -2.57445201e-02 3.02992105e-01
-2.50653118e-01 -2.33259469e-01 -8.19565713e-01 -9.88663614e-01
2.02303737e-01 3.81215751e-01 -3.37025642e-01 -1.34131804... | [13.296011924743652, 0.6888681650161743] |
de124c9e-ac7d-4dad-bea8-5a24ec85163a | enhancements-to-the-boun-treebank-reflecting | 2207.11782 | null | https://arxiv.org/abs/2207.11782v1 | https://arxiv.org/pdf/2207.11782v1.pdf | Enhancements to the BOUN Treebank Reflecting the Agglutinative Nature of Turkish | In this study, we aim to offer linguistically motivated solutions to resolve the issues of the lack of representation of null morphemes, highly productive derivational processes, and syncretic morphemes of Turkish in the BOUN Treebank without diverging from the Universal Dependencies framework. In order to tackle these... | ['Balkız Öztürk', 'Tunga Güngör', 'Arzucan Özgür', 'Suzan Üsküdarlı', 'Şaziye Betül Özateş', 'Onur Güngör', 'Merve Gürbüz', 'Muhammet Şen', 'Salih Furkan Akkurt', 'Büşra Marşan'] | 2022-07-24 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-1.53756425e-01 4.33820218e-01 1.26053259e-01 -4.13936257e-01
-3.65295529e-01 -5.32044113e-01 3.00333261e-01 2.53114671e-01
-6.98244333e-01 1.03693593e+00 3.33654076e-01 -8.33082139e-01
-1.65289730e-01 -5.27090073e-01 -1.34437025e-01 -3.75715584e-01
9.29721892e-02 1.83478311e-01 2.45414585e-01 -3.18701327... | [10.398140907287598, 10.037576675415039] |
3f802eb3-5b13-42ff-ad04-b95419764984 | semantic-guided-single-image-reflection | 1907.11912 | null | https://arxiv.org/abs/1907.11912v3 | https://arxiv.org/pdf/1907.11912v3.pdf | Semantic Guided Single Image Reflection Removal | Reflection is common in images capturing scenes behind a glass window, which is not only a disturbance visually but also influence the performance of other computer vision algorithms. Single image reflection removal is an ill-posed problem because the color at each pixel needs to be separated into two values, i.e., the... | ['ShaoDi You', 'Yunfei Liu', 'Feng Lu', 'Yu Li'] | 2019-07-27 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 6.63683355e-01 -5.36008701e-02 3.11023057e-01 -3.24671537e-01
-2.00537339e-01 -3.03410292e-01 2.78076708e-01 -2.66764045e-01
-2.48243734e-01 5.67531765e-01 -1.54899359e-01 -2.83558015e-02
2.31029302e-01 -6.81349933e-01 -6.37723267e-01 -1.11314750e+00
6.38194025e-01 1.54874679e-02 7.96428621e-01 1.15084171... | [10.311537742614746, -2.682293653488159] |
1ec5b430-b157-4ce9-80fd-4fb0bae861ea | causality-compensated-attention-for | null | null | https://openreview.net/forum?id=8XqDnrmZQNF | https://openreview.net/pdf?id=8XqDnrmZQNF | Causality Compensated Attention for Contextual Biased Visual Recognition | Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the cont... | ['Thomas H. Li', 'Ge Li', 'Jingjia Huang', 'Ruyang Liu'] | 2023-02-25 | null | null | null | iclr-2023-2 | ['multi-label-image-classification'] | ['computer-vision'] | [ 1.90853477e-01 -1.10325634e-01 -2.65389681e-01 -4.37346488e-01
-3.93313974e-01 -2.05329716e-01 6.31311953e-01 2.55772084e-01
-1.59765616e-01 4.97162104e-01 1.77400887e-01 -1.69740275e-01
1.79532785e-02 -4.38877642e-01 -8.07657361e-01 -7.02261567e-01
3.59484255e-01 1.37762249e-01 1.06581263e-01 6.85516074... | [9.89003849029541, 2.0389726161956787] |
b5c77183-c19c-4324-bce2-c967be3596f0 | visually-grounded-word-embeddings-and-richer | 1707.01009 | null | http://arxiv.org/abs/1707.01009v5 | http://arxiv.org/pdf/1707.01009v5.pdf | Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation | In Multimodal Neural Machine Translation (MNMT), a neural model generates a
translated sentence that describes an image, given the image itself and one
source descriptions in English. This is considered as the multimodal image
caption translation task. The images are processed with Convolutional Neural
Network (CNN) to... | ['Stéphane Dupont', 'Jean-Benoit Delbrouck', 'Omar Seddati'] | 2017-07-04 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 5.39238989e-01 5.41193485e-01 -2.68300116e-01 -4.33006972e-01
-8.28435600e-01 -6.14876688e-01 1.07136583e+00 -2.75097936e-01
-4.87860799e-01 7.10433662e-01 3.49323839e-01 -2.34295964e-01
6.96932554e-01 -7.16988802e-01 -1.27200270e+00 -3.05716366e-01
4.58692789e-01 5.12461543e-01 -1.94095820e-01 -2.83152819... | [11.418649673461914, 1.4732046127319336] |
feea9ea6-4aee-4dfc-a478-8088ca184948 | videollm-modeling-video-sequence-with-large | 2305.13292 | null | https://arxiv.org/abs/2305.13292v2 | https://arxiv.org/pdf/2305.13292v2.pdf | VideoLLM: Modeling Video Sequence with Large Language Models | With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models are often task-specific and lack a comprehensive capability of handling diverse tasks. The success of large language models (LLMs) like GPT ha... | ['LiMin Wang', 'Tong Lu', 'Yu Qiao', 'Yali Wang', 'Yi Wang', 'Junting Pan', 'Yifei HUANG', 'Jilan Xu', 'Jiahao Wang', 'Yin-Dong Zheng', 'Guo Chen'] | 2023-05-22 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 3.51166308e-01 -1.25341907e-01 -3.88417363e-01 -4.19514716e-01
-7.44459212e-01 -6.28364623e-01 8.02034497e-01 -2.78325319e-01
-1.10720672e-01 4.78171945e-01 4.88877028e-01 -4.73407120e-01
2.72895008e-01 -4.72366631e-01 -1.09378934e+00 -1.46127537e-01
3.48395944e-01 8.87465402e-02 1.32604674e-01 1.58869792... | [10.415485382080078, 1.0253098011016846] |
c22a932a-5a80-48ee-ad1a-0a96bd9ea813 | manifold-aware-self-training-for-unsupervised | 2305.10808 | null | https://arxiv.org/abs/2305.10808v1 | https://arxiv.org/pdf/2305.10808v1.pdf | Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose | Domain gap between synthetic and real data in visual regression (\eg 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant ... | ['Kui Jia', 'YaoWei Wang', 'Zelin Xu', 'Ke Chen', 'Jiehong Lin', 'Yichen Zhang'] | 2023-05-18 | null | null | null | null | ['6d-pose-estimation-1', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 1.18284464e-01 1.43079832e-01 -7.23970115e-01 -4.79926169e-01
-1.07104349e+00 -5.98875225e-01 7.59582460e-01 -5.05524836e-02
-4.90164645e-02 5.02376735e-01 3.40371966e-01 3.11525404e-01
-8.81771222e-02 -3.18115354e-01 -9.19261932e-01 -6.30653977e-01
-1.22178502e-01 6.16029501e-01 2.69253384e-02 -2.22208232... | [7.642087936401367, -2.8075766563415527] |
509d2320-3cd9-43cf-940d-668ec2853653 | fast-non-local-neural-networks-with-spectral | null | null | https://doi.org/10.1145/3343031.3351029 | https://doi.org/10.1145/3343031.3351029 | Fast Non-Local Neural Networks with Spectral Residual Learning | Effectively modeling long-range spatial correlation is crucial in context-sensitive visual computing tasks, such as human pose estimation and video classification. Enlarging receptive field is popularly adopted in building such non-local deep networks. However, current solutions, including dilation convolution or self-... | ['Qi Tian', 'Lingxi Xie', 'Yadong Mu', 'Guiyu Tian', 'Lu Chi'] | 2019-10-15 | null | null | null | mm-19-proceedings-of-the-27th-acm | ['video-classification'] | ['computer-vision'] | [ 3.99835825e-01 -5.08179367e-01 -7.53381625e-02 -4.14279193e-01
-4.01272058e-01 -3.14619422e-01 3.60225052e-01 -6.52790070e-01
-5.54853916e-01 5.39485157e-01 4.63004470e-01 -2.56744176e-02
-2.26515159e-01 -6.18023813e-01 -8.20654809e-01 -9.34112728e-01
-8.40037614e-02 -4.03328061e-01 3.21493775e-01 -3.22599888... | [10.698599815368652, -1.6603575944900513] |
6b3489b6-6653-418b-96be-6c3c4d43e577 | domain-specific-pretraining-improves | 2302.09833 | null | https://arxiv.org/abs/2302.09833v2 | https://arxiv.org/pdf/2302.09833v2.pdf | Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification | Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models hav... | ['Ashwin Srinivasan', 'Lovekesh Vig', 'Shlomo Berkovsky', 'Antonio Di Ieva', 'Tanmay Tulsidas Verlekar', 'Tirtharaj Dash', 'Sidong Liu', 'Soham Rohit Chitnis'] | 2023-02-20 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 2.82437414e-01 3.54020059e-01 -2.50731468e-01 -2.83787817e-01
-1.33849287e+00 1.18368194e-01 4.84476298e-01 2.56262451e-01
-5.98559380e-01 6.59849405e-01 1.58412576e-01 -4.86734986e-01
-1.39751926e-01 -8.11124921e-01 -6.16570711e-01 -1.11742914e+00
-6.40028566e-02 6.75343812e-01 5.48268914e-01 -1.58726007... | [15.083391189575195, -2.822807550430298] |
41d35dac-005b-4229-a751-03f588d74443 | orthogonal-transform-based-generative | 2206.01743 | null | https://arxiv.org/abs/2206.01743v1 | https://arxiv.org/pdf/2206.01743v1.pdf | Orthogonal Transform based Generative Adversarial Network for Image Dehazing | Image dehazing has become one of the crucial preprocessing steps for any computer vision task. Most of the dehazing methods try to estimate the transmission map along with the atmospheric light to get the dehazed image in the image domain. In this paper, we propose a novel end-to-end architecture that directly estimate... | ['Vijeta Khare', 'Manish Khare', 'Mantra Sanathra', 'Ahlad Kumar'] | 2022-06-03 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.96238512e-01 1.84094638e-01 7.65825868e-01 3.77524160e-02
-2.09916279e-01 -2.08265349e-01 5.00484943e-01 -5.36432862e-01
-4.47531551e-01 5.77669322e-01 -2.59379875e-02 -2.48638630e-01
-6.93506822e-02 -1.27791786e+00 -7.40093887e-01 -1.14447379e+00
2.22062603e-01 -1.83599204e-01 5.23272872e-01 -5.05976379... | [10.909393310546875, -3.144644021987915] |
a6614559-ab78-4216-bf15-f469264b0cff | 4d-seismic-history-matching-incorporating | 1905.07469 | null | https://arxiv.org/abs/1905.07469v1 | https://arxiv.org/pdf/1905.07469v1.pdf | 4D Seismic History Matching Incorporating Unsupervised Learning | The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimi... | ['Clement Etienam'] | 2019-05-16 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 1.47071198e-01 -4.32381555e-02 3.72009873e-01 8.20862055e-02
-6.35266840e-01 7.97628239e-02 7.88069367e-01 2.83466488e-01
-4.91749287e-01 6.89481974e-01 4.17846709e-01 -7.60558061e-03
-4.98201787e-01 -8.88071895e-01 -5.43340802e-01 -1.26768768e+00
-6.05917931e-01 5.49206257e-01 -1.33264601e-01 -6.38454854... | [6.856794834136963, 2.7442433834075928] |
5149174a-a322-44f2-adf0-93709c04c79b | hierarchical-compositional-representations | 2208.09424 | null | https://arxiv.org/abs/2208.09424v2 | https://arxiv.org/pdf/2208.09424v2.pdf | Hierarchical Compositional Representations for Few-shot Action Recognition | Recently action recognition has received more and more attention for its comprehensive and practical applications in intelligent surveillance and human-computer interaction. However, few-shot action recognition has not been well explored and remains challenging because of data scarcity. In this paper, we propose a nove... | ['Shiguang Shan', 'Xin Jin', 'Shuzhe Wu', 'Jie Zhang', 'Changzhen Li'] | 2022-08-19 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.08801019e-01 -4.49818999e-01 -4.43303287e-01 -2.79128820e-01
-7.01017201e-01 -2.03294918e-01 5.35143673e-01 -1.17471755e-01
-2.20723644e-01 4.02784199e-01 7.98393726e-01 1.14788465e-01
-3.20618421e-01 -6.57978773e-01 -4.67251271e-01 -9.57931280e-01
-1.26249894e-01 7.76369078e-03 9.67908740e-01 1.42051643... | [8.47197151184082, 0.750688374042511] |
0159fa85-143e-462c-a925-2879efa81364 | gan-based-deep-distributional-reinforcement | 1905.03929 | null | https://arxiv.org/abs/1905.03929v3 | https://arxiv.org/pdf/1905.03929v3.pdf | GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing | Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicin... | ['Yuxiu Hua', 'Xianfu Chen', 'Honggang Zhang', 'Zhifeng Zhao', 'Rongpeng Li'] | 2019-05-10 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-5.41859716e-02 1.98235616e-01 -3.07451814e-01 -2.21219867e-01
-6.11128747e-01 -4.66317654e-01 -3.26937027e-02 -8.36838484e-01
1.98600337e-01 1.31610107e+00 1.63073152e-01 -6.35049224e-01
-3.94811630e-01 -1.06511569e+00 -3.64168108e-01 -1.11082113e+00
-2.45008186e-01 3.72180998e-01 -3.99963379e-01 -1.55502751... | [5.9226393699646, 1.6820505857467651] |
996b1633-02e9-4e90-a9b3-58e2130c4fb2 | abaw-learning-from-synthetic-data-multi-task | 2207.01138 | null | https://arxiv.org/abs/2207.01138v2 | https://arxiv.org/pdf/2207.01138v2.pdf | ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges | This paper describes the fourth Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with European Conference on Computer Vision (ECCV), 2022. The 4th ABAW Competition is a continuation of the Competitions held at IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Conferences, and aim... | ['Dimitrios Kollias'] | 2022-07-03 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.42019311e-01 -9.06620175e-04 2.78071404e-01 -5.65130591e-01
-1.00032723e+00 -4.17827189e-01 6.33880079e-01 7.12399557e-02
-5.77076733e-01 6.72656775e-01 1.92737788e-01 5.16688883e-01
1.88047558e-01 -6.36156276e-02 -3.61998737e-01 -8.41077328e-01
-2.70747900e-01 3.19194287e-01 -2.11124599e-01 -4.73670006... | [13.571793556213379, 2.1999659538269043] |
5b72a6b9-a400-4373-a3fa-a0f850988f53 | unsupervised-few-shot-learning-via-self-1 | 1912.12178 | null | https://arxiv.org/abs/1912.12178v1 | https://arxiv.org/pdf/1912.12178v1.pdf | Unsupervised Few-shot Learning via Self-supervised Training | Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure ... | ['Zilong Ji', 'Si Wu', 'Xiaolong Zou', 'Tiejun Huang'] | 2019-12-20 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.86549360e-01 -7.48227686e-02 -2.80092120e-01 -4.96634245e-01
-5.12575626e-01 2.16280669e-02 7.36357272e-01 1.61139876e-01
-6.45043373e-01 9.21868503e-01 2.52833813e-01 4.23989981e-01
-2.83503681e-01 -1.01867306e+00 -5.22506773e-01 -6.25975192e-01
1.32691320e-02 7.45600879e-01 4.46604252e-01 -1.26911685... | [10.042914390563965, 3.125276565551758] |
d532d9da-a648-4523-857a-cd3129900b8b | q-based-equilibria | 2304.12647 | null | https://arxiv.org/abs/2304.12647v1 | https://arxiv.org/pdf/2304.12647v1.pdf | Q-based Equilibria | In dynamic environments, Q-learning is an adaptative rule that provides an estimate (a Q-value) of the continuation value associated with each alternative. A naive policy consists in always choosing the alternative with highest Q-value. We consider a family of Q-based policy rules that may systematically favor some alt... | ['Olivier Compte'] | 2023-04-25 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.32957917e-01 3.57774407e-01 -6.97131336e-01 -1.09436050e-01
-2.76426256e-01 -8.25908244e-01 6.76413178e-01 2.21555665e-01
-1.03417814e+00 1.15527809e+00 2.73655057e-01 -6.02183342e-01
-4.67775077e-01 -9.24457729e-01 -4.84959453e-01 -6.10971868e-01
-4.01977450e-01 2.28528768e-01 4.58980910e-02 -4.22432780... | [4.211745262145996, 2.723013162612915] |
ee8a5ad2-3d07-4b1d-83f4-be83d8a0aada | human-instance-segmentation-and-tracking-via | 2203.16966 | null | https://arxiv.org/abs/2203.16966v1 | https://arxiv.org/pdf/2203.16966v1.pdf | Human Instance Segmentation and Tracking via Data Association and Single-stage Detector | Human video instance segmentation plays an important role in computer understanding of human activities and is widely used in video processing, video surveillance, and human modeling in virtual reality. Most current VIS methods are based on Mask-RCNN framework, where the target appearance and motion information for dat... | ['Mingbo Zhao', 'Lu Cheng'] | 2022-03-31 | null | null | null | null | ['human-instance-segmentation', 'video-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.58310407e-01 -2.16399938e-01 -1.25840411e-01 -2.09678203e-01
1.89071804e-01 -2.89007664e-01 2.12731138e-01 7.57873207e-02
-5.54810047e-01 4.62604493e-01 -4.56804410e-02 4.29053187e-01
-3.73536460e-02 -6.59618199e-01 -4.50463712e-01 -7.59508014e-01
4.10305001e-02 2.19843969e-01 5.78329146e-01 -3.54423225... | [9.011638641357422, -0.23204058408737183] |
717e9604-a86d-4ac0-88d9-f59cdb80f7e0 | language-features-matter-effective-language | 1908.06327 | null | https://arxiv.org/abs/1908.06327v1 | https://arxiv.org/pdf/1908.06327v1.pdf | Language Features Matter: Effective Language Representations for Vision-Language Tasks | Shouldn't language and vision features be treated equally in vision-language (VL) tasks? Many VL approaches treat the language component as an afterthought, using simple language models that are either built upon fixed word embeddings trained on text-only data or are learned from scratch. We believe that language featu... | ['Andrea Burns', 'Reuben Tan', 'Kate Saenko', 'Stan Sclaroff', 'Bryan A. Plummer'] | 2019-08-17 | language-features-matter-effective-language-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Burns_Language_Features_Matter_Effective_Language_Representations_for_Vision-Language_Tasks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Burns_Language_Features_Matter_Effective_Language_Representations_for_Vision-Language_Tasks_ICCV_2019_paper.pdf | iccv-2019-10 | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.24053657e-01 -6.01007976e-02 -2.37689644e-01 -3.48110080e-01
-7.65326738e-01 -3.63764673e-01 8.08565915e-01 9.46231335e-02
-7.25554883e-01 2.81910837e-01 5.35067856e-01 -5.45671046e-01
2.80832559e-01 -5.75143933e-01 -7.16334820e-01 -2.97703683e-01
1.84326544e-01 2.04088449e-01 1.95132852e-01 -2.64976054... | [10.819847106933594, 1.7985570430755615] |
70acb4f5-d072-40c7-8bf7-af965bf2fb4e | retrieve-program-repeat-complex-knowledge | 2010.15875 | null | https://arxiv.org/abs/2010.15875v1 | https://arxiv.org/pdf/2010.15875v1.pdf | Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning | A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to un... | ['Wei Wu', 'Guilin Qi', 'Gholamreza Haffari', 'Yuan-Fang Li', 'Yuncheng Hua'] | 2020-10-29 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.46234185e-01 1.25271976e-01 1.47380844e-01 -5.28450131e-01
-1.37327230e+00 -9.19707596e-01 3.64868462e-01 2.65153021e-01
-4.07256931e-01 5.46450794e-01 -1.61369368e-02 -6.58915222e-01
7.61514083e-02 -6.94826007e-01 -7.88050234e-01 -2.02740729e-01
4.08653438e-01 7.75876224e-01 5.27855575e-01 -3.50265175... | [11.12990951538086, 8.049180030822754] |
87999a1f-1f62-48a4-b6f2-53bfe363387a | image-harmonization-datasets-hcoco-hadobe5k | 1908.10526 | null | https://arxiv.org/abs/1908.10526v4 | https://arxiv.org/pdf/1908.10526v4.pdf | Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2night | Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, which aims to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of... | ['Wenyan Cong', 'Zhixin Ling', 'Weiyuan Li', 'Li Niu', 'Jianfu Zhang', 'Liu Liu', 'Liqing Zhang'] | 2019-08-28 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 3.43350261e-01 -4.62814569e-01 1.96221948e-01 -8.63328110e-03
-4.88769352e-01 -8.08034241e-01 6.02044702e-01 7.09582046e-02
-2.11608961e-01 5.73479533e-01 -2.05305386e-02 -1.28360212e-01
1.07671760e-01 -8.82273197e-01 -6.87961280e-01 -8.04130971e-01
5.20513833e-01 -2.70816386e-01 3.01670104e-01 -2.60222465... | [11.21517276763916, -1.2293883562088013] |
aedadfd0-d8ea-46a6-a2d8-055950e8f746 | monoise-a-multi-lingual-and-easy-to-use | null | null | https://aclanthology.org/P19-3032 | https://aclanthology.org/P19-3032.pdf | MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool | In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages. We further improve this model by using features from the original word for every normalization candidate. For comparison with future work, we propose ... | ['Rob van der Goot'] | 2019-07-01 | null | null | null | acl-2019-7 | ['lexical-normalization'] | ['natural-language-processing'] | [-1.91923231e-01 -2.03705937e-01 -2.72546083e-01 -4.47029382e-01
-8.80163670e-01 -7.32174993e-01 9.85546887e-01 5.10895252e-01
-9.34731245e-01 5.66582084e-01 2.62173057e-01 -2.46158317e-01
-1.51184663e-01 -6.08174086e-01 -3.67884845e-01 -1.93713397e-01
1.67878792e-01 5.05308092e-01 3.94116759e-01 -4.81802821... | [10.181941032409668, 9.808755874633789] |
0fc9956f-ee11-467c-93a9-fe773fc2f304 | aspect-extraction-from-product-reviews-using | null | null | https://aclanthology.org/E17-2107 | https://aclanthology.org/E17-2107.pdf | Aspect Extraction from Product Reviews Using Category Hierarchy Information | Aspect extraction abstracts the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those fr... | ['Yinfei Yang', 'Cen Chen', 'Minghui Qiu', 'Forrest Bao'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['aspect-extraction'] | ['natural-language-processing'] | [-2.19419584e-01 5.15638828e-01 -6.89327359e-01 -6.95913553e-01
-5.27233958e-01 -8.06384325e-01 8.56195867e-01 4.25600976e-01
9.83311310e-02 3.16912919e-01 6.26269639e-01 -2.51352578e-01
2.85526067e-01 -9.09015298e-01 -3.57233703e-01 -6.94665492e-01
1.26666844e-01 4.37554091e-01 3.82825106e-01 6.86724409... | [11.35513973236084, 6.695321559906006] |
e26b7b43-4057-49e9-a70c-ff6aad6da0d5 | sepico-semantic-guided-pixel-contrast-for | 2204.08808 | null | https://arxiv.org/abs/2204.08808v2 | https://arxiv.org/pdf/2204.08808v2.pdf | SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation | Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain. In this work, we propose Semantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic ... | ['Guoren Wang', 'Gao Huang', 'Chi Harold Liu', 'Mingjia Li', 'Shuang Li', 'Binhui Xie'] | 2022-04-19 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.50609267e-01 5.54439239e-03 -3.64467174e-01 -6.97498441e-01
-8.08024526e-01 -6.27570808e-01 5.91931164e-01 8.19438249e-02
-2.30973125e-01 7.54632831e-01 -5.23843020e-02 -5.28822318e-02
-8.29564333e-02 -7.17930734e-01 -6.37522340e-01 -9.56285059e-01
2.76442915e-01 4.06727582e-01 3.24124277e-01 -1.25296356... | [9.67320442199707, 1.3594319820404053] |
1739466f-cbc6-45d7-81ff-27435dd4e36c | weakly-supervised-video-salient-object-1 | 2207.07269 | null | https://arxiv.org/abs/2207.07269v1 | https://arxiv.org/pdf/2207.07269v1.pdf | Weakly Supervised Video Salient Object Detection via Point Supervision | Video salient object detection models trained on pixel-wise dense annotation have achieved excellent performance, yet obtaining pixel-by-pixel annotated datasets is laborious. Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even... | ['Wenqiang Zhang', 'Qianyu Guo', 'Yan Wang', 'Wei zhang', 'Haozhe Xing', 'Shuyong Gao'] | 2022-07-15 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 2.83668548e-01 3.69956270e-02 -5.41129589e-01 -3.30316246e-01
-6.60366952e-01 -2.14102551e-01 4.04125333e-01 5.93686737e-02
-4.42219853e-01 9.26301718e-01 2.49231979e-01 6.07851967e-02
2.82192051e-01 -4.18553352e-01 -8.33014965e-01 -7.39192545e-01
5.42593971e-02 -1.45527780e-01 8.69482517e-01 -2.00870708... | [9.610840797424316, -0.33181050419807434] |
5132b0d3-5236-4f1d-b2e6-51b90bf53fe5 | evaluating-the-covid-19-identification-resnet | 2107.14549 | null | https://arxiv.org/abs/2107.14549v1 | https://arxiv.org/pdf/2107.14549v1.pdf | Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges | We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neural network originally designed to classify whether an individual is COVID-positive or COVID-neg... | ['Björn W. Schuller', 'Lyn Jones', 'Panagiotis Tzirakis', 'Alexander Gaskell', 'Harry Coppock', 'Alican Akman'] | 2021-07-30 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.07580878e-01 -4.31674808e-01 -9.48545411e-02 -1.81102410e-01
-9.84610558e-01 -6.96430385e-01 1.06473155e-01 1.07692853e-01
-7.23658919e-01 6.78238451e-01 2.24027902e-01 -4.39323708e-02
-8.04054439e-02 -3.33163053e-01 -3.37947726e-01 -2.38050118e-01
-2.45016366e-01 1.15981364e+00 -4.15288061e-01 1.36467263... | [14.568501472473145, 4.104015350341797] |
88876aad-d424-4cc3-9d3c-c76a1d6fe975 | automatic-sleep-stage-classification-with | 2008.09416 | null | https://arxiv.org/abs/2008.09416v1 | https://arxiv.org/pdf/2008.09416v1.pdf | Automatic sleep stage classification with deep residual networks in a mixed-cohort setting | Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We in... | ['Helge B. D. Sorensen', 'Poul Jennum', 'Emmanuel Mignot', 'Alexander Neergaard Olesen'] | 2020-08-21 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [-2.47752100e-01 -8.65070745e-02 -2.58346111e-01 -4.17677939e-01
-7.22617090e-01 -4.40961003e-01 -6.07495308e-02 4.31237400e-01
-7.25095093e-01 9.68131185e-01 7.99920186e-02 -3.64222080e-01
-3.73984605e-01 -7.62445092e-01 -5.33069789e-01 -2.31162727e-01
-5.61273515e-01 3.32473785e-01 1.08664289e-01 -7.50222653... | [13.457074165344238, 3.49280047416687] |
3301d5d8-9aed-42ef-bdce-af33b46910f2 | cx-db8-a-queryable-extractive-summarizer-and | 2012.03942 | null | https://arxiv.org/abs/2012.03942v1 | https://arxiv.org/pdf/2012.03942v1.pdf | CX DB8: A queryable extractive summarizer and semantic search engine | Competitive Debate's increasingly technical nature has left competitors looking for tools to accelerate evidence production. We find that the unique type of extractive summarization performed by competitive debaters - summarization with a bias towards a particular target meaning - can be performed using the latest inno... | ['Allen Roush'] | 2020-12-07 | null | null | null | null | ['query-based-extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.07159820e-02 5.70988178e-01 -7.26850569e-01 -2.41480153e-02
-1.26878703e+00 -9.24887061e-01 8.30353498e-01 6.96307659e-01
-5.65044820e-01 9.18645918e-01 1.25698745e+00 -5.21474898e-01
5.97418360e-02 -6.21560574e-01 -5.50466359e-01 -3.64723831e-01
4.50810939e-01 4.65457261e-01 1.09898515e-01 -5.58030605... | [12.334686279296875, 9.51749324798584] |
d1d6ea8a-5934-4687-a743-8819e5ecfe3a | model-based-clustering-with-hidden-markov | 1312.7024 | null | http://arxiv.org/abs/1312.7024v1 | http://arxiv.org/pdf/1312.7024v1.pdf | Model-based clustering with Hidden Markov Model regression for time series with regime changes | This paper introduces a novel model-based clustering approach for clustering
time series which present changes in regime. It consists of a mixture of
polynomial regressions governed by hidden Markov chains. The underlying hidden
process for each cluster activates successively several polynomial regimes
during time. The... | ['Gérard Govaert', 'Allou Samé', 'Faicel Chamroukhi', 'Patrice Aknin'] | 2013-12-25 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-1.07246563e-01 -3.29066366e-01 6.31469935e-02 -2.40822554e-01
-6.11383080e-01 -1.55332223e-01 5.24367273e-01 -2.64022369e-02
-2.47702703e-01 4.16104883e-01 -3.20202738e-01 -3.91537398e-01
-5.75345159e-01 -5.38288593e-01 7.65178353e-02 -1.34465981e+00
-4.56136853e-01 1.06884396e+00 2.08821639e-01 1.07695453... | [7.154639720916748, 3.644670248031616] |
c861debf-401e-4f71-8300-9ba3b4b2a153 | high-accuracy-phishing-detection-based-on | 2004.03960 | null | https://arxiv.org/abs/2004.03960v1 | https://arxiv.org/pdf/2004.03960v1.pdf | High Accuracy Phishing Detection Based on Convolutional Neural Networks | The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-... | ['Suleiman Y. Yerima', 'Mohammed K. Alzaylaee'] | 2020-04-08 | null | null | null | null | ['phishing-website-detection'] | ['adversarial'] | [-1.56249434e-01 -2.62306005e-01 2.04520300e-01 -5.03828973e-02
-4.85843092e-01 -7.90986896e-01 8.80971909e-01 3.93959165e-01
-4.32133764e-01 4.97476161e-01 -3.37729305e-01 -5.50829828e-01
1.36124805e-01 -1.03963399e+00 -4.06504452e-01 -6.61765575e-01
-1.21547095e-01 2.99292296e-01 2.61324167e-01 -3.13292682... | [7.810333251953125, 9.98774242401123] |
9053a599-0189-4607-a714-42e92e50e8e0 | efficiently-measuring-the-cognitive-ability | 2306.10512 | null | https://arxiv.org/abs/2306.10512v1 | https://arxiv.org/pdf/2306.10512v1.pdf | Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective | Large language models (LLMs), like ChatGPT, have shown some human-like cognitive abilities. For comparing these abilities of different models, several benchmarks (i.e. sets of standard test questions) from different fields (e.g., Literature, Biology and Psychology) are often adopted and the test results under tradition... | ['Enhong Chen', 'Shijin Wang', 'Qingyang Mao', 'Zheng Zhang', 'Guanhao Zhao', 'Zhenya Huang', 'Rui Lv', 'Weizhe Huang', 'Yuting Ning', 'Qi Liu', 'Yan Zhuang'] | 2023-06-18 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-4.60682720e-01 4.36508991e-02 -1.73782527e-01 -2.38005802e-01
-6.18491769e-01 -7.68496215e-01 1.90228581e-01 5.88925064e-01
-5.16895533e-01 5.18983424e-01 -7.77177289e-02 -8.68763089e-01
-6.78661346e-01 -1.07972038e+00 -5.80881417e-01 -1.47850558e-01
2.62388259e-01 7.98624933e-01 5.55425763e-01 -3.26022923... | [10.02295207977295, 7.435790538787842] |
13b96357-1fd4-4d4e-83cd-3ff145298fe3 | s4nd-single-shot-single-scale-lung-nodule | 1805.02279 | null | http://arxiv.org/abs/1805.02279v2 | http://arxiv.org/pdf/1805.02279v2.pdf | S4ND: Single-Shot Single-Scale Lung Nodule Detection | The state of the art lung nodule detection studies rely on computationally
expensive multi-stage frameworks to detect nodules from CT scans. To address
this computational challenge and provide better performance, in this paper we
propose S4ND, a new deep learning based method for lung nodule detection. Our
approach use... | ['Naji Khosravan', 'Ulas Bagci'] | 2018-05-06 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.09174192e-01 2.96814024e-01 -8.19047764e-02 -4.89709750e-02
-8.63968432e-01 -2.37900466e-01 3.19503605e-01 4.73998711e-02
-6.98189735e-01 -1.26733929e-01 -2.57296443e-01 -7.36359656e-01
7.97930285e-02 -7.38266885e-01 -7.57376790e-01 -4.69925225e-01
-5.57213053e-02 6.06954753e-01 1.02402902e+00 4.80690673... | [15.35279369354248, -2.1580348014831543] |
e00ee139-19ea-4d3e-8036-dc2d53f0f4de | balanced-chamfer-distance-as-a-comprehensive | null | null | http://proceedings.neurips.cc/paper/2021/hash/f3bd5ad57c8389a8a1a541a76be463bf-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f3bd5ad57c8389a8a1a541a76be463bf-Paper.pdf | Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion | Chamfer Distance (CD) and Earth Mover’s Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mismatched local density, and EMD is usually dominated by global distribution while overlooks the fidelity of detailed structures. Besides, th... | ['Dahua Lin', 'Ziwei Liu', 'Tai Wang', 'Junzhe Zhang', 'Liang Pan', 'Tong Wu'] | 2021-12-01 | null | https://openreview.net/forum?id=B46BjXrLidN | https://openreview.net/pdf?id=B46BjXrLidN | neurips-2021-12 | ['point-cloud-completion'] | ['computer-vision'] | [-4.04434413e-01 -4.19805348e-01 -2.25631848e-01 -3.26858163e-01
-9.78195608e-01 -3.53549898e-01 7.11189508e-01 4.11387116e-01
-3.07162136e-01 4.81722564e-01 1.00758839e-02 -4.37681302e-02
-3.05839449e-01 -1.02118969e+00 -5.81584275e-01 -6.06043518e-01
-5.47427796e-02 6.27602935e-01 6.96628809e-01 -1.56735733... | [7.88663387298584, -3.1680209636688232] |
d61c90b1-180f-4f57-a068-e00e4f97a878 | alzheimers-disease-diagnostics-by-adaptation | 1607.00455 | null | http://arxiv.org/abs/1607.00455v1 | http://arxiv.org/pdf/1607.00455v1.pdf | Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network | Early diagnosis, playing an important role in preventing progress and
treating the Alzheimer\{'}s disease (AD), is based on classification of
features extracted from brain images. The features have to accurately capture
main AD-related variations of anatomical brain structures, such as, e.g.,
ventricles size, hippocamp... | ['Ayman El-Baz', 'Ehsan Hosseini-Asl', 'Robert Keynto'] | 2016-07-02 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-2.93543041e-01 1.42059952e-01 3.43639523e-01 -9.86131310e-01
-3.35074186e-01 -7.36355269e-03 3.23056847e-01 -9.97347161e-02
-4.53832746e-01 6.22972250e-01 1.31577954e-01 -8.26854482e-02
-2.64462739e-01 -7.53058612e-01 -5.57775676e-01 -4.43196595e-01
-7.73529589e-01 9.74235177e-01 5.03259003e-01 -5.73883541... | [14.195489883422852, -1.7570874691009521] |
5647069d-c24b-4b88-ab80-fceea2d35ae1 | fsgan-subject-agnostic-face-swapping-and | 1908.05932 | null | https://arxiv.org/abs/1908.05932v1 | https://arxiv.org/pdf/1908.05932v1.pdf | FSGAN: Subject Agnostic Face Swapping and Reenactment | We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number of technical contributions. We derive a novel recurrent neural network (RNN)-based app... | ['Yuval Nirkin', 'Yosi Keller', 'Tal Hassner'] | 2019-08-16 | fsgan-subject-agnostic-face-swapping-and-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Nirkin_FSGAN_Subject_Agnostic_Face_Swapping_and_Reenactment_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Nirkin_FSGAN_Subject_Agnostic_Face_Swapping_and_Reenactment_ICCV_2019_paper.pdf | iccv-2019-10 | ['face-reenactment', 'facial-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.73199457e-01 2.45579168e-01 2.41775736e-01 -5.91031194e-01
-7.67055631e-01 -6.12253964e-01 4.92502421e-01 -6.91407442e-01
-1.37686178e-01 6.06805801e-01 4.10253443e-02 1.00854971e-01
3.07915181e-01 -4.50071305e-01 -8.88569355e-01 -5.70071340e-01
1.79788828e-01 2.03309029e-01 -2.69410789e-01 -2.25176662... | [12.709162712097168, -0.17633309960365295] |
f4800640-50c1-4778-922b-f08b313fddea | 3d-graph-contrastive-learning-for-molecular | 2208.06360 | null | https://arxiv.org/abs/2208.06360v2 | https://arxiv.org/pdf/2208.06360v2.pdf | 3D Graph Contrastive Learning for Molecular Property Prediction | Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performa... | ['Sunyoung Kwon', 'Kisung Moon'] | 2022-05-31 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.43706775e-01 -1.53748840e-01 -1.16370916e+00 -3.12208951e-01
-4.44964260e-01 -5.12263119e-01 3.03950638e-01 7.26351678e-01
-2.06179217e-01 1.16521096e+00 2.06047408e-02 -5.47024310e-01
-1.20243207e-01 -8.94691348e-01 -8.60361636e-01 -9.32186007e-01
-2.30544642e-01 4.01791334e-01 2.68518776e-01 -3.18811178... | [5.131664752960205, 5.887186050415039] |
3edcd9f0-6f99-4567-82da-a04feddc93d6 | shape-interaction-matrix-revisited-and | 1509.02649 | null | http://arxiv.org/abs/1509.02649v2 | http://arxiv.org/pdf/1509.02649v2.pdf | Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data | The Shape Interaction Matrix (SIM) is one of the earliest approaches to
performing subspace clustering (i.e., separating points drawn from a union of
subspaces). In this paper, we revisit the SIM and reveal its connections to
several recent subspace clustering methods. Our analysis lets us derive a
simple, yet effectiv... | ['Mathieu Salzmann', 'Pan Ji', 'Hongdong Li'] | 2015-09-09 | shape-interaction-matrix-revisited-and-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Ji_Shape_Interaction_Matrix_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Ji_Shape_Interaction_Matrix_ICCV_2015_paper.pdf | iccv-2015-12 | ['face-clustering'] | ['computer-vision'] | [ 3.32566768e-01 -2.50257879e-01 4.19002026e-02 -5.57407886e-02
-8.46030295e-01 -9.12634134e-01 4.47076648e-01 -4.32375044e-01
-4.17916290e-02 2.96305299e-01 2.63863713e-01 -1.67282671e-01
-4.77769464e-01 -5.20889228e-03 -5.65138102e-01 -1.15673614e+00
-7.69979805e-02 5.77158153e-01 6.10076934e-02 6.25105053... | [7.70885705947876, 4.428779125213623] |
393acf08-c687-4eec-8d96-57a2f0979ecd | analysing-affective-behavior-in-the-second | 2106.15318 | null | https://arxiv.org/abs/2106.15318v2 | https://arxiv.org/pdf/2106.15318v2.pdf | Analysing Affective Behavior in the second ABAW2 Competition | The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second -- following the first very successful ABAW Competition held in conjunction with IEEE FG 2020- Competition that aims at automatically analyzing affect. ABAW2 is split into three Challenges, each one addressing one of the three main behav... | ['Stefanos Zafeiriou', 'Elnar Hajiyev', 'Irene Kotsia', 'Dimitrios Kollias'] | 2021-06-14 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [-3.57963261e-03 5.32251894e-02 -8.28009769e-02 -8.25196207e-01
-1.00079024e+00 -5.53563058e-01 4.63559896e-01 1.87563151e-01
-5.20851076e-01 7.51944423e-01 3.99062246e-01 3.80967498e-01
1.94255412e-01 3.52720777e-03 4.39141802e-02 -4.35365230e-01
-4.14082617e-01 2.11369216e-01 -2.46198937e-01 -5.98733366... | [13.57007884979248, 2.2381234169006348] |
afcc236e-b1a9-4c69-b67c-f10e9fe53046 | diffsinger-diffusion-acoustic-model-for | 2105.02446 | null | https://arxiv.org/abs/2105.02446v6 | https://arxiv.org/pdf/2105.02446v6.pdf | DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism | Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (e.g., mel-spectrogram) given a music score. Previous singing acoustic models adopt a simple loss (e.g., L1 and L2) or generative adversarial network (GAN)... | ['Zhou Zhao', 'Feiyang Chen', 'Yi Ren', 'Chengxi Li', 'Jinglin Liu'] | 2021-05-06 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-9.94959027e-02 6.57729730e-02 7.13031786e-03 1.86624229e-01
-1.22407091e+00 -5.90697169e-01 2.28502989e-01 -7.53268838e-01
1.73075527e-01 6.07766688e-01 3.63707781e-01 -2.20647842e-01
2.21613213e-01 -6.94138348e-01 -8.01672757e-01 -1.01149678e+00
3.24603468e-01 2.60474563e-01 7.17888996e-02 -1.85379028... | [15.480462074279785, 6.177434921264648] |
c3383294-54b7-43e7-80d2-8488f5c2f13d | building-advanced-dialogue-managers-for-goal | 1806.00780 | null | http://arxiv.org/abs/1806.00780v1 | http://arxiv.org/pdf/1806.00780v1.pdf | Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems | Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented
chatbots, help users achieve a predefined goal (e.g. book a movie ticket)
within a closed domain. A first step is to understand the user's goal by using
natural language understanding techniques. Once the goal is known, the bot must
manage a dialo... | ['Vladimir Ilievski'] | 2018-06-03 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-3.12900305e-01 5.68439305e-01 9.30574909e-03 -2.27202371e-01
-8.11754227e-01 -7.65000045e-01 5.72859883e-01 2.73775250e-01
-4.46282595e-01 1.22034740e+00 4.51520719e-02 -3.79308343e-01
4.84212041e-02 -8.18347156e-01 -3.58493656e-01 -4.63354886e-01
1.50096834e-01 9.39666867e-01 4.38351691e-01 -9.09539521... | [13.034567832946777, 8.044500350952148] |
d923fd28-c43a-468e-8129-ea2e5fe2ae83 | image-super-resolution-improved-by-edge | null | null | https://ieeexplore.ieee.org/document/8914550 | https://ieeexplore.ieee.org/document/8914550 | Image Super-Resolution Improved by Edge Information | As well as in other knowledge domains, deep learning techniques have revolutionized the development of image super-resolution approaches. State-of-the-art algorithms for this problem have employed convolutional neural networks in residual architectures with a number of layers and generic loss functions, such as L1 and ... | ['Helio Pedrini', 'Eldrey Galindo'] | 2019-10-06 | null | null | null | smc-2019-10 | ['ms-ssim'] | ['computer-vision'] | [ 6.01974905e-01 -1.85983330e-01 6.31682798e-02 -6.11825064e-02
-5.59165061e-01 -1.53148636e-01 4.62003767e-01 -2.12193653e-01
-4.06283289e-01 8.05772722e-01 3.03320646e-01 2.55669296e-01
-4.35685456e-01 -8.75759363e-01 -6.41212583e-01 -5.61975062e-01
-1.93176776e-01 -5.60270429e-01 5.59055805e-01 -5.47487736... | [11.101686477661133, -2.0299758911132812] |
4b3a39db-171d-44b3-aa00-c966e2c4c78f | pan-towards-efficient-and-accurate-end-to-end | 2105.00405 | null | https://arxiv.org/abs/2105.00405v4 | https://arxiv.org/pdf/2105.00405v4.pdf | PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text | Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text remains challenging. In this work, we propose an end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize te... | ['Chunhua Shen', 'Tong Lu', 'Zhibo Yang', 'Ding Liang', 'Xuebo Liu', 'Xiang Li', 'Enze Xie', 'Wenhai Wang'] | 2021-05-02 | null | null | null | null | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 5.34732342e-01 -4.42647010e-01 1.25257492e-01 -2.16288149e-01
-6.43610537e-01 -2.09023178e-01 5.11044323e-01 -1.87932495e-02
-2.59285927e-01 -2.92957146e-02 3.36733125e-02 -1.67737603e-01
3.32344890e-01 -6.97732508e-01 -6.35667324e-01 -6.73265040e-01
5.46439171e-01 1.81937963e-01 6.73917294e-01 1.94335684... | [12.040014266967773, 2.181122064590454] |
0cc5d1cb-884a-496f-8cb0-7b77cc3ee081 | siamese-masked-autoencoders | 2305.14344 | null | https://arxiv.org/abs/2305.14344v1 | https://arxiv.org/pdf/2305.14344v1.pdf | Siamese Masked Autoencoders | Establishing correspondence between images or scenes is a significant challenge in computer vision, especially given occlusions, viewpoint changes, and varying object appearances. In this paper, we present Siamese Masked Autoencoders (SiamMAE), a simple extension of Masked Autoencoders (MAE) for learning visual corresp... | ['Li Fei-Fei', 'Jia Deng', 'Jiajun Wu', 'Agrim Gupta'] | 2023-05-23 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.78716356e-01 2.34390974e-01 -1.85530782e-01 -3.38953376e-01
-3.63395661e-01 -2.64342457e-01 5.19034207e-01 -3.11779886e-01
-4.59308535e-01 4.27710146e-01 1.15120821e-01 9.30428579e-02
3.38770360e-01 -5.87398410e-01 -1.48029768e+00 -5.52000403e-01
-8.78716186e-02 4.95309711e-01 5.60274303e-01 -1.04448654... | [9.166678428649902, -0.01861005276441574] |
62361ac0-4123-487a-ba0c-505646833619 | label-inference-attack-against-split-learning | 2301.07284 | null | https://arxiv.org/abs/2301.07284v2 | https://arxiv.org/pdf/2301.07284v2.pdf | Label Inference Attack against Split Learning under Regression Setting | As a crucial building block in vertical Federated Learning (vFL), Split Learning (SL) has demonstrated its practice in the two-party model training collaboration, where one party holds the features of data samples and another party holds the corresponding labels. Such method is claimed to be private considering the sha... | ['Jiankai Sun', 'Taiqing Wang', 'Tianyi Liu', 'Yuanshun Yao', 'Xin Yang', 'Shangyu Xie'] | 2023-01-18 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 7.30352178e-02 6.00805394e-02 -5.56888878e-01 -3.06471854e-01
-1.01270270e+00 -1.14989769e+00 3.62868011e-01 1.59082174e-01
-2.72468895e-01 7.90325046e-01 -2.20501199e-01 -7.57963955e-01
-7.06589743e-02 -9.15751815e-01 -9.03339565e-01 -1.09070432e+00
-3.24798971e-01 -1.63292177e-02 -6.51605874e-02 9.36905667... | [5.823479175567627, 6.782739162445068] |
c1e6916b-dc24-4590-bcbe-7a9b387b80a9 | sgaligner-3d-scene-alignment-with-scene | 2304.14880 | null | https://arxiv.org/abs/2304.14880v1 | https://arxiv.org/pdf/2304.14880v1.pdf | SGAligner : 3D Scene Alignment with Scene Graphs | Building 3D scene graphs has recently emerged as a topic in scene representation for several embodied AI applications to represent the world in a structured and rich manner. With their increased use in solving downstream tasks (eg, navigation and room rearrangement), can we leverage and recycle them for creating 3D map... | ['Iro Armeni', 'Daniel Barath', 'Marc Pollefeys', 'Ondrej Miksik', 'Sayan Deb Sarkar'] | 2023-04-28 | null | null | null | null | ['point-cloud-registration', '3d-scene-graph-alignment'] | ['computer-vision', 'computer-vision'] | [ 3.26393515e-01 8.38311091e-02 3.21248919e-01 -3.78930002e-01
-4.49788302e-01 -9.84647334e-01 9.90901887e-01 1.87375590e-01
-3.52199197e-01 4.54208493e-01 6.29818201e-01 -2.23215058e-01
-1.40501663e-01 -6.02283239e-01 -9.07995462e-01 -4.37433273e-01
-1.79177120e-01 6.64543748e-01 2.47372672e-01 -5.26584744... | [4.687088489532471, 0.44569772481918335] |
691803b4-7838-4561-9066-6ebe6bce4dd1 | hybridization-of-filter-and-wrapper | 2210.16496 | null | https://arxiv.org/abs/2210.16496v1 | https://arxiv.org/pdf/2210.16496v1.pdf | Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images | The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and redundant bands. And consequently, increase the classification accuracy. However, identification of... | ['Chafik Nacir', 'Ahmed Hammouch', 'Elkebir Sarhrouni', 'Maria Merzouqi', 'Asma Elmaizi'] | 2022-10-29 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 7.73956478e-01 -4.69929338e-01 3.17868501e-01 -1.95885733e-01
-3.35590571e-01 -5.08146942e-01 2.99672693e-01 1.01073347e-01
-1.37658954e-01 9.59770143e-01 -1.66941926e-01 -1.01952836e-01
-1.14286375e+00 -9.41833675e-01 2.44372822e-02 -1.06563711e+00
-2.67409801e-01 5.67746796e-02 -1.12707488e-01 -5.29857464... | [9.755059242248535, -1.8168561458587646] |
6741f150-f03c-4afc-b58f-dc7b9f279989 | a-case-study-on-the-impact-of-dynamic-time | 2010.05270 | null | https://arxiv.org/abs/2010.05270v1 | https://arxiv.org/pdf/2010.05270v1.pdf | A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression | It is well understood that Dynamic Time Warping (DTW) is effective in revealing similarities between time series that do not align perfectly. In this paper, we illustrate this on spectroscopy time-series data. We show that DTW is effective in improving accuracy on a regression task when only a single wavelength is cons... | ['Pádraig Cunningham', 'Vivek Mahato'] | 2020-10-11 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 4.25623059e-01 -7.23364830e-01 1.11108549e-01 -2.30587482e-01
-5.76106489e-01 -8.97305906e-01 7.51471817e-01 2.57680446e-01
-5.36214054e-01 7.64014602e-01 2.61205614e-01 -3.14949900e-01
-8.93228829e-01 -5.70570648e-01 -1.92163169e-01 -1.10980976e+00
-6.48290038e-01 9.69221517e-02 2.23894671e-01 -3.94716352... | [7.255536079406738, 3.3409440517425537] |
c6d29958-54bd-438f-9df5-295e9197d972 | monitored-distillation-for-positive-congruent | 2203.16034 | null | https://arxiv.org/abs/2203.16034v2 | https://arxiv.org/pdf/2203.16034v2.pdf | Monitored Distillation for Positive Congruent Depth Completion | We propose a method to infer a dense depth map from a single image, its calibration, and the associated sparse point cloud. In order to leverage existing models (teachers) that produce putative depth maps, we propose an adaptive knowledge distillation approach that yields a positive congruent training process, wherein ... | ['Alex Wong', 'Byung-Woo Hong', 'Allison Chen', 'Parth Agrawal', 'Tian Yu Liu'] | 2022-03-30 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 2.52682716e-01 6.22119904e-01 -1.04109876e-01 -4.02346581e-01
-1.29511750e+00 -5.53995311e-01 5.74084997e-01 1.00598842e-01
-4.64709878e-01 5.64164221e-01 5.00100628e-02 -1.25640616e-01
-2.38126982e-02 -5.95229864e-01 -1.13132322e+00 -8.84764910e-01
2.75629282e-01 6.53636932e-01 2.84049153e-01 4.89532292... | [8.59000015258789, -2.699378490447998] |
a3c95671-bcb2-4d85-8fa4-2a8f1323bbc8 | norm-guided-latent-space-exploration-for-text | 2306.08687 | null | https://arxiv.org/abs/2306.08687v1 | https://arxiv.org/pdf/2306.08687v1.pdf | Norm-guided latent space exploration for text-to-image generation | Text-to-image diffusion models show great potential in synthesizing a large variety of concepts in new compositions and scenarios. However, their latent seed space is still not well understood and has been shown to have an impact in generating new and rare concepts. Specifically, simple operations like interpolation an... | ['Gal Chechik', 'Haggai Maron', 'Nir Darshan', 'Rami Ben-Ari', 'Dvir Samuel'] | 2023-06-14 | null | null | null | null | ['long-tail-learning'] | ['methodology'] | [ 4.34030056e-01 -1.68838263e-01 -1.09417036e-01 -2.46471420e-01
-6.58270180e-01 -5.75024068e-01 1.13947809e+00 2.15919957e-01
-4.49335843e-01 6.46031857e-01 2.81416118e-01 7.19577745e-02
-4.46309485e-02 -1.02895069e+00 -6.13611460e-01 -8.38924885e-01
3.95338759e-02 4.79334384e-01 4.10935879e-01 -4.64835852... | [11.226889610290527, -0.18234364688396454] |
ebf51e98-12bb-4050-b5e6-e3851cdcdfc1 | neural-representations-reveal-distinct-modes | 2212.00771 | null | https://arxiv.org/abs/2212.00771v1 | https://arxiv.org/pdf/2212.00771v1.pdf | Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks | We leverage probabilistic models of neural representations to investigate how residual networks fit classes. To this end, we estimate class-conditional density models for representations learned by deep ResNets. We then use these models to characterize distributions of representations across learned classes. Surprising... | ['Marcin Kurdziel', 'Michał Jamroż'] | 2022-12-01 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 4.34689641e-01 3.03272277e-01 -2.07137186e-02 -3.16961288e-01
-5.20858347e-01 -8.77771020e-01 1.11828971e+00 1.72900334e-01
-3.26561570e-01 8.04857314e-01 2.25900769e-01 -6.75929710e-02
-5.57870448e-01 -8.32500756e-01 -1.00762546e+00 -8.85095596e-01
-4.32097949e-02 1.23514093e-01 2.34454617e-01 -1.74511120... | [9.556265830993652, 2.697054147720337] |
5ada126c-240f-429f-8f2e-02dec58583fd | learned-tree-search-for-long-horizon-social | 2304.01428 | null | https://arxiv.org/abs/2304.01428v1 | https://arxiv.org/pdf/2304.01428v1.pdf | Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace | The fast-growing demand for fully autonomous aerial operations in shared spaces necessitates developing trustworthy agents that can safely and seamlessly navigate in crowded, dynamic spaces. In this work, we propose Social Robot Tree Search (SoRTS), an algorithm for the safe navigation of mobile robots in social domain... | ['Jean Oh', 'Sebastian Scherer', 'Ian Higgins', 'Rohan Baijal', 'Joao P. A. Dantas', 'Jay Patrikar', 'Ingrid Navarro'] | 2023-04-04 | null | null | null | null | ['trajectory-prediction', 'social-navigation', 'robot-navigation'] | ['computer-vision', 'robots', 'robots'] | [-2.12921008e-01 3.85339737e-01 3.25472683e-01 -2.81123519e-01
-2.77453274e-01 -8.86959195e-01 4.83184457e-01 -2.48145938e-01
-6.36201024e-01 1.12836754e+00 6.63295612e-02 -6.33649051e-01
-5.11056244e-01 -4.86307353e-01 -3.58346760e-01 -3.44051003e-01
-7.56231725e-01 3.90958607e-01 4.68625605e-01 -8.91599715... | [4.74060583114624, 1.0471702814102173] |
5ded187b-0f47-4e0a-b2da-a31b51d5fed5 | 6d-camera-relocalization-in-ambiguous-scenes | 2004.04807 | null | https://arxiv.org/abs/2004.04807v2 | https://arxiv.org/pdf/2004.04807v2.pdf | 6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference | We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses. In highly ambiguous environments, which can easily arise due to symmetries and repetitive structures in the scene, computing one plausible solution ... | ['Tolga Birdal', 'Mai Bui', 'Haowen Deng', 'Slobodan Ilic', 'Nassir Navab', 'Leonidas Guibas', 'Shadi Albarqouni'] | 2020-04-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2942_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630137.pdf | eccv-2020-8 | ['camera-localization', 'camera-relocalization'] | ['computer-vision', 'computer-vision'] | [ 4.26043123e-02 -7.27500692e-02 1.81007579e-01 -4.22217309e-01
-9.71878469e-01 -9.12530899e-01 7.24849761e-01 -1.57004625e-01
-3.84673834e-01 5.68910301e-01 3.21980193e-02 -1.71030283e-01
-3.15970600e-01 -2.79420912e-01 -9.61184800e-01 -7.25306988e-01
1.94698691e-01 8.51589799e-01 9.77548771e-03 3.55630443... | [7.746932506561279, -2.1897871494293213] |
041e5e15-42e7-4d3d-a01e-aa55e88e6725 | preserving-fine-grain-feature-information-in | 2208.03684 | null | https://arxiv.org/abs/2208.03684v1 | https://arxiv.org/pdf/2208.03684v1.pdf | Preserving Fine-Grain Feature Information in Classification via Entropic Regularization | Labeling a classification dataset implies to define classes and associated coarse labels, that may approximate a smoother and more complicated ground truth. For example, natural images may contain multiple objects, only one of which is labeled in many vision datasets, or classes may result from the discretization of a ... | ['Vincent Gripon', 'Lucas Drumetz', 'Raphael Baena'] | 2022-08-07 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 4.50011760e-01 3.59214813e-01 -1.95379332e-01 -6.20848715e-01
-8.42788339e-01 -4.29048330e-01 6.06044233e-01 2.70980150e-01
-1.59724340e-01 1.03511441e+00 -3.52313928e-02 2.65975714e-01
-3.93269747e-01 -9.21237290e-01 -5.77512622e-01 -8.74665737e-01
-2.09170617e-02 6.27199471e-01 5.96935600e-02 3.42717201... | [9.509827613830566, 2.9193203449249268] |
e72b27aa-15e6-4b3f-976e-f03522e1f19e | vrkitchen2-0-indoorkit-a-tutorial-for | 2206.11887 | null | https://arxiv.org/abs/2206.11887v1 | https://arxiv.org/pdf/2206.11887v1.pdf | VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse | With the recent progress of simulations by 3D modeling software and game engines, many researchers have focused on Embodied AI tasks in the virtual environment. However, the research community lacks a platform that can easily serve both indoor scene synthesis and model benchmarking with various algorithms. Meanwhile, c... | ['Song-Chun Zhu', 'Wensi Ai', 'Xiaofeng Gao', 'Steven Gong', 'Yizhou Zhao'] | 2022-06-23 | null | null | null | null | ['indoor-scene-synthesis'] | ['computer-vision'] | [-5.43386877e-01 -4.20562446e-01 4.17519271e-01 -2.04849571e-01
-3.24064493e-02 -7.20338106e-01 5.00939310e-01 -5.09118557e-01
-7.25684986e-02 3.68862599e-01 7.22867325e-02 -5.92363477e-01
4.59187269e-01 -1.03555727e+00 -5.16205132e-01 -6.09109044e-01
1.07327841e-01 1.83274701e-01 2.68217534e-01 -3.85851413... | [4.525620460510254, 0.6779904365539551] |
78e7ea69-0fcf-4796-9686-d4cc99f9a04e | but-fit-at-semeval-2019-task-7-determining | 1902.10126 | null | http://arxiv.org/abs/1902.10126v2 | http://arxiv.org/pdf/1902.10126v2.pdf | BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers | This paper describes our system submitted to SemEval 2019 Task 7: RumourEval
2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell
et al., 2019). The challenge focused on classifying whether posts from Twitter
and Reddit support, deny, query, or comment a hidden rumour, truthfulness of
which is ... | ['Lukáš Burget', 'Martin Fajcik', 'Pavel Smrz'] | 2019-02-25 | but-fit-at-semeval-2019-task-7-determining-1 | https://aclanthology.org/S19-2192 | https://aclanthology.org/S19-2192.pdf | semeval-2019-6 | ['rumour-detection'] | ['natural-language-processing'] | [-1.04397759e-01 6.17261171e-01 -3.97987038e-01 -2.11032256e-01
-5.86977124e-01 -3.04182708e-01 1.31356299e+00 5.47354400e-01
-2.47003227e-01 9.45101559e-01 6.15438938e-01 -4.90559280e-01
4.53883737e-01 -3.32175404e-01 -5.06226480e-01 -2.78504610e-01
-1.10850379e-01 5.36270618e-01 3.94546926e-01 -6.94502294... | [8.230732917785645, 10.116455078125] |
07d63608-3e56-45ae-b73d-4066dcb97851 | face-sketch-synthesis-via-semantic-driven | 2106.15121 | null | https://arxiv.org/abs/2106.15121v1 | https://arxiv.org/pdf/2106.15121v1.pdf | Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network | Face sketch synthesis has made significant progress with the development of deep neural networks in these years. The delicate depiction of sketch portraits facilitates a wide range of applications like digital entertainment and law enforcement. However, accurate and realistic face sketch generation is still a challengi... | ['Caifeng Shan', 'Qi Li', 'Xiaoxiao Dong', 'Weining Wang', 'Muyi Sun', 'Xingqun Qi'] | 2021-06-29 | null | null | null | null | ['face-sketch-synthesis', 'face-parsing'] | ['computer-vision', 'computer-vision'] | [ 2.41858318e-01 -3.71102691e-02 5.02092466e-02 -4.88100886e-01
-2.30176806e-01 -3.56372714e-01 6.45285964e-01 -6.47893190e-01
1.73023283e-01 5.76402366e-01 1.78129539e-01 1.42173573e-01
1.16209224e-01 -9.11741972e-01 -7.80510187e-01 -6.02686524e-01
4.95608330e-01 -5.98548912e-02 2.65795648e-01 -3.86755139... | [12.536463737487793, -0.15038596093654633] |
92f73e67-9a26-4767-9258-7bf7b6697ce6 | scalable-modular-synthetic-data-generation | 2211.05335 | null | https://arxiv.org/abs/2211.05335v2 | https://arxiv.org/pdf/2211.05335v2.pdf | Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy | One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world data collection through deploying drones, there has been an increasing shift towards using synthetic data for training models in drone applicat... | ['Sakshi Mishra', 'Praveen Palanisamy', 'Mehrnaz Sabet'] | 2022-11-10 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.89957136e-01 -1.47544548e-01 3.49414527e-01 -3.45123023e-01
-3.58637035e-01 -1.20245433e+00 5.98070323e-01 1.35116756e-01
-2.31070310e-01 5.95168710e-01 -7.61222187e-03 -4.42530841e-01
-9.67381522e-02 -1.05784023e+00 -6.28121555e-01 -1.05317466e-01
-2.24165723e-01 7.09923565e-01 2.56993681e-01 -6.23856306... | [7.7163004875183105, -1.1988612413406372] |
4c78acbe-ca67-4e4c-8aa1-e29a13e34a7f | hierachial-protein-function-prediction-with | 2007.12804 | null | https://arxiv.org/abs/2007.12804v1 | https://arxiv.org/pdf/2007.12804v1.pdf | Hierachial Protein Function Prediction with Tails-GNNs | Protein function prediction may be framed as predicting subgraphs (with certain closure properties) of a directed acyclic graph describing the hierarchy of protein functions. Graph neural networks (GNNs), with their built-in inductive bias for relational data, are hence naturally suited for this task. However, in contr... | ['Mladen Nikolić', 'Jovana Kovačević', 'Petar Veličković', 'Stefan Spalević'] | 2020-07-24 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 6.17036760e-01 7.72546828e-01 -1.40017658e-01 -4.79227930e-01
-5.57946414e-02 -5.88785529e-01 3.00201684e-01 4.01566476e-01
-9.71498042e-02 6.83946431e-01 3.09174061e-01 -6.56575084e-01
-3.37941915e-01 -1.03700650e+00 -1.17674494e+00 -6.11327946e-01
-4.15279567e-01 6.71044171e-01 1.17700100e-01 -2.79534906... | [6.8167877197265625, 6.280070781707764] |
e9328f16-78df-444c-8b9c-392e276e8f01 | mastering-atari-go-chess-and-shogi-by | 1911.08265 | null | https://arxiv.org/abs/1911.08265v2 | https://arxiv.org/pdf/1911.08265v2.pdf | Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model | Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics gove... | ['Laurent SIfre', 'Edward Lockhart', 'Thore Graepel', 'Karen Simonyan', 'Ioannis Antonoglou', 'David Silver', 'Timothy Lillicrap', 'Thomas Hubert', 'Simon Schmitt', 'Demis Hassabis', 'Julian Schrittwieser', 'Arthur Guez'] | 2019-11-19 | null | null | null | null | ['game-of-go', 'game-of-shogi', 'game-of-chess'] | ['playing-games', 'playing-games', 'playing-games'] | [-3.26898247e-02 2.48847842e-01 2.03029085e-02 2.07366183e-01
-3.30555499e-01 -6.92248881e-01 7.29035378e-01 4.03922871e-02
-5.47093689e-01 1.12850308e+00 -1.28893673e-01 -3.50825220e-01
-4.32071239e-01 -7.49454319e-01 -4.41531867e-01 -5.96161485e-01
-6.10427737e-01 1.04387224e+00 6.77021205e-01 -8.82531166... | [3.907331943511963, 1.4090818166732788] |
a8279d6e-bd05-4d39-bbbc-e6cd01cecaf5 | sentence-to-label-generation-framework-for | 2306.15978 | null | https://arxiv.org/abs/2306.15978v1 | https://arxiv.org/pdf/2306.15978v1.pdf | Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition | Information extraction(IE) is a crucial subfield within natural language processing. In this study, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG... | ['Tatsunori Mori', 'Qinghao Zhang', 'Chengguang Gan'] | 2023-06-28 | null | null | null | null | ['multi-task-learning', 'sentence-classification', 'named-entity-recognition-ner', 'cg'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.18340707e-01 3.77327353e-01 3.98371480e-02 -6.07765973e-01
-1.28545511e+00 -7.40674615e-01 6.85518503e-01 3.47952634e-01
-6.62657201e-01 1.06062663e+00 4.68605220e-01 -2.27003798e-01
1.41189590e-01 -7.77767658e-01 -5.50029874e-01 1.73888162e-01
4.83381689e-01 2.81702310e-01 -1.11058675e-01 -2.10526884... | [9.737373352050781, 9.44758415222168] |
1903758d-3b77-4a80-acbd-05c722e0ce2e | sparse2dense-from-direct-sparse-odometry-to | 1903.09199 | null | http://arxiv.org/abs/1903.09199v1 | http://arxiv.org/pdf/1903.09199v1.pdf | Sparse2Dense: From direct sparse odometry to dense 3D reconstruction | In this paper, we proposed a new deep learning based dense monocular SLAM
method. Compared to existing methods, the proposed framework constructs a dense
3D model via a sparse to dense mapping using learned surface normals. With
single view learned depth estimation as prior for monocular visual odometry, we
obtain both... | ['Patric Jensfelt', 'Jiexiong Tang', 'John Folkesson'] | 2019-03-21 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.27304801e-01 -2.02931929e-02 -3.72879058e-01 -5.11086702e-01
-4.03368920e-01 -2.86763638e-01 7.17284322e-01 -3.42286885e-01
-1.72128215e-01 8.48458827e-01 5.29451333e-02 -6.66091510e-04
3.00135523e-01 -8.12267601e-01 -9.83058572e-01 -3.48197073e-01
2.57047921e-01 8.88247669e-01 2.57289290e-01 1.82536766... | [8.031820297241211, -2.2456493377685547] |
c9e0fec2-496c-438a-a8ad-001f1138a913 | shallow-semantic-reasoning-from-an-incomplete | null | null | https://aclanthology.org/W16-0512 | https://aclanthology.org/W16-0512.pdf | Shallow Semantic Reasoning from an Incomplete Gold Standard for Learner Language | null | ['Markus Dickinson', 'Levi King'] | 2016-06-01 | null | null | null | ws-2016-6 | ['grammatical-error-detection'] | ['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... | [-7.238481044769287, 3.7889959812164307] |
78884a1a-611f-4e6f-bf57-902431a30765 | openhps-an-open-source-hybrid-positioning | 2101.05198 | null | https://arxiv.org/abs/2101.05198v1 | https://arxiv.org/pdf/2101.05198v1.pdf | OpenHPS: An Open Source Hybrid Positioning System | Positioning systems and frameworks use various techniques to determine the position of an object. Some of the existing solutions combine different sensory data at the time of positioning in order to compute more accurate positions by reducing the error introduced by the used individual positioning techniques. We presen... | ['Beat Signer', 'Maxim Van de Wynckel'] | 2020-12-29 | null | null | null | null | ['hybrid-positioning'] | ['computer-vision'] | [-2.08638132e-01 -2.64375303e-02 1.86486647e-01 -3.39109778e-01
-4.26897943e-01 -1.08627045e+00 5.06937087e-01 1.81561753e-01
-4.04279858e-01 5.00382364e-01 -2.00437099e-01 -3.34443599e-01
-5.07128358e-01 -8.43888044e-01 -8.31666231e-01 -4.57664609e-01
2.78551560e-02 6.53643608e-01 9.55257297e-01 -5.13594925... | [7.312210559844971, -2.0051138401031494] |
367d9b92-4470-481f-a6f3-ab4692d3a84c | forecasting-directional-movements-of-stock | 2004.10178 | null | https://arxiv.org/abs/2004.10178v2 | https://arxiv.org/pdf/2004.10178v2.pdf | Forecasting directional movements of stock prices for intraday trading using LSTM and random forests | We employ both random forests and LSTM networks (more precisely CuDNNLSTM) as training methodologies to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500 from January 1993 till December 2018 for intraday trading. We introduce a multi-feature setting cons... | ['Jajati Keshari Sahoo', 'Ariel Neufeld', 'Pushpendu Ghosh'] | 2020-04-21 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.79204738e-01 2.85822153e-02 -5.60278535e-01 -3.42774838e-02
-5.13815224e-01 -9.00398016e-01 1.02102280e+00 -1.27841532e-01
-5.18400609e-01 1.20619833e+00 -1.78533923e-02 -7.38201082e-01
-2.03170523e-01 -1.29653776e+00 -9.39523876e-01 -4.63505238e-01
-4.20119107e-01 3.12547684e-01 -7.85648748e-02 -6.19322294... | [4.668651580810547, 4.038506984710693] |
5ff54e8c-8f15-408a-b351-9777920600f7 | timedial-temporal-commonsense-reasoning-in | 2106.04571 | null | https://arxiv.org/abs/2106.04571v1 | https://arxiv.org/pdf/2106.04571v1.pdf | TIMEDIAL: Temporal Commonsense Reasoning in Dialog | Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely u... | ['Manaal Faruqui', 'Yejin Choi', 'Luheng He', 'Shyam Upadhyay', 'Aditya Gupta', 'Lianhui Qin'] | 2021-06-08 | null | https://aclanthology.org/2021.acl-long.549 | https://aclanthology.org/2021.acl-long.549.pdf | acl-2021-5 | ['timedial'] | ['natural-language-processing'] | [-2.58718014e-01 2.17854545e-01 -2.50036925e-01 -5.09674788e-01
-6.85485780e-01 -9.86044645e-01 1.17004263e+00 1.23333961e-01
-3.82997185e-01 6.60764337e-01 8.43846381e-01 -5.41049659e-01
1.57034937e-02 -2.67627776e-01 -1.29300794e-02 -6.22573197e-02
-8.84501711e-02 7.81150043e-01 1.96012378e-01 -6.90936446... | [12.649280548095703, 8.01131534576416] |
fe8e379f-1c73-46af-ab98-360b45fbc792 | schema-encoding-for-transferable-dialogue-1 | 2210.02351 | null | https://arxiv.org/abs/2210.02351v1 | https://arxiv.org/pdf/2210.02351v1.pdf | Schema Encoding for Transferable Dialogue State Tracking | Dialogue state tracking (DST) is an essential sub-task for task-oriented dialogue systems. Recent work has focused on deep neural models for DST. However, the neural models require a large dataset for training. Furthermore, applying them to another domain needs a new dataset because the neural models are generally trai... | ['Gary Geunbae Lee', 'Hyunmin Jeon'] | 2022-10-05 | null | https://aclanthology.org/2022.coling-1.28 | https://aclanthology.org/2022.coling-1.28.pdf | coling-2022-10 | ['dialogue-state-tracking', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.93420488e-01 6.63556635e-01 6.77247904e-03 -8.07454050e-01
-4.67199147e-01 -7.92332411e-01 6.30789697e-01 -1.50957450e-01
-4.26076382e-01 9.97267723e-01 8.14908743e-02 -2.00829431e-01
2.81338662e-01 -8.15284133e-01 -6.29546404e-01 -9.58294049e-02
2.50745118e-01 1.08039963e+00 4.24432516e-01 -1.05099392... | [12.81657600402832, 7.995871543884277] |
c5a629e0-8f67-41c0-ba3e-68e74b644380 | efficient-cnns-via-passive-filter-pruning | 2304.02319 | null | https://arxiv.org/abs/2304.02319v1 | https://arxiv.org/pdf/2304.02319v1.pdf | Efficient CNNs via Passive Filter Pruning | Convolutional neural networks (CNNs) have shown state-of-the-art performance in various applications. However, CNNs are resource-hungry due to their requirement of high computational complexity and memory storage. Recent efforts toward achieving computational efficiency in CNNs involve filter pruning methods that elimi... | ['Mark D. Plumbley', 'Arshdeep Singh'] | 2023-04-05 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 2.59558707e-01 1.95408612e-01 5.51709235e-01 -3.52706492e-01
1.31002069e-01 -2.59314567e-01 1.31137684e-01 2.95997024e-01
-8.53246391e-01 7.58943856e-01 5.82211800e-02 -2.88040459e-01
-4.87842262e-01 -1.11104262e+00 -5.35613418e-01 -4.38376069e-01
1.00891732e-01 -3.73569846e-01 8.24014544e-01 -3.00291747... | [8.54523754119873, 3.0131213665008545] |
39153f62-f768-4192-b53f-c038ba46c751 | neural-decipherment-via-minimum-cost-flow | 1906.06718 | null | https://arxiv.org/abs/1906.06718v1 | https://arxiv.org/pdf/1906.06718v1.pdf | Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B | In this paper we propose a novel neural approach for automatic decipherment of lost languages. To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change documented in historical linguistics. The model utilizes an expressive sequence-to-sequence model to capture... | ['Regina Barzilay', 'Jiaming Luo', 'Yuan Cao'] | 2019-06-16 | neural-decipherment-via-minimum-cost-flow-1 | https://aclanthology.org/P19-1303 | https://aclanthology.org/P19-1303.pdf | acl-2019-7 | ['decipherment'] | ['natural-language-processing'] | [ 4.27806914e-01 2.57438481e-01 7.67911300e-02 -3.75812948e-01
-6.42321110e-01 -6.48448527e-01 4.67928648e-01 -5.71130514e-02
-8.28886628e-01 8.03963959e-01 4.12975043e-01 -7.13288307e-01
2.67346501e-01 -6.92013562e-01 -9.26665306e-01 -2.18398854e-01
7.08020627e-02 4.66553688e-01 -2.23201513e-01 -5.86805642... | [10.842726707458496, 10.02560806274414] |
b5e8a39f-e884-4b34-8d8b-6199621f411a | a-computational-framework-of-human-values-for | 2305.02748 | null | https://arxiv.org/abs/2305.02748v1 | https://arxiv.org/pdf/2305.02748v1.pdf | A computational framework of human values for ethical AI | In the diverse array of work investigating the nature of human values from psychology, philosophy and social sciences, there is a clear consensus that values guide behaviour. More recently, a recognition that values provide a means to engineer ethical AI has emerged. Indeed, Stuart Russell proposed shifting AI's focus ... | ["Mark d'Inverno", 'Nardine Osman'] | 2023-05-04 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 4.86140132e-01 5.63430250e-01 -4.43433464e-01 -4.32684034e-01
1.51737064e-01 -3.29184592e-01 7.51223743e-01 5.01531780e-01
-5.55400014e-01 4.07332003e-01 9.98918056e-01 -3.41790348e-01
-7.92159677e-01 -5.10474682e-01 -2.19491601e-01 -5.97411335e-01
5.20272136e-01 1.95797950e-01 -2.89476454e-01 -4.78307188... | [9.069456100463867, 6.343607425689697] |
19fe5e4f-be69-4acb-acac-0e7a321fb9fa | bert-based-clinical-knowledge-extraction-for | 2304.10996 | null | https://arxiv.org/abs/2304.10996v1 | https://arxiv.org/pdf/2304.10996v1.pdf | BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis | Background : Knowledge is evolving over time, often as a result of new discoveries or changes in the adopted methods of reasoning. Also, new facts or evidence may become available, leading to new understandings of complex phenomena. This is particularly true in the biomedical field, where scientists and physicians are ... | ['Bouchra El Asri', 'Zineb Elkaimbillah', 'Siham Yousfi', 'Mounia Mikram', 'Maryem Rhanoui', 'Ayoub Harnoune'] | 2023-04-21 | null | null | null | null | ['graph-construction', 'clinical-knowledge', 'named-entity-recognition-ner'] | ['graphs', 'miscellaneous', 'natural-language-processing'] | [ 2.68280953e-01 5.54728210e-01 -9.36243534e-02 -2.50001550e-01
-3.93938750e-01 -2.89029241e-01 3.91459763e-01 1.11801434e+00
-4.13436890e-01 9.75184977e-01 4.17987108e-01 -4.62177336e-01
-5.38154066e-01 -1.22266459e+00 -3.38267505e-01 -4.00938541e-01
-2.83330590e-01 7.61729658e-01 -1.86551129e-04 -4.76018339... | [8.469648361206055, 8.58399486541748] |
9779b6ee-beb1-4dce-bcd9-e4854dd1a51b | text2mesh-text-driven-neural-stylization-for | 2112.03221 | null | https://arxiv.org/abs/2112.03221v1 | https://arxiv.org/pdf/2112.03221v1.pdf | Text2Mesh: Text-Driven Neural Stylization for Meshes | In this work, we develop intuitive controls for editing the style of 3D objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a ... | ['Rana Hanocka', 'Sagie Benaim', 'Richard Liu', 'Roi Bar-On', 'Oscar Michel'] | 2021-12-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Michel_Text2Mesh_Text-Driven_Neural_Stylization_for_Meshes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Michel_Text2Mesh_Text-Driven_Neural_Stylization_for_Meshes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['neural-stylization'] | ['computer-vision'] | [ 3.08022410e-01 3.10243219e-01 2.10152328e-01 -1.32551834e-01
-4.10413235e-01 -1.02941716e+00 7.68365562e-01 -2.15812460e-01
4.02391225e-01 4.72576767e-01 1.28309116e-01 -8.94931033e-02
2.66982168e-01 -9.96126294e-01 -9.36838329e-01 -2.44929761e-01
1.10793017e-01 7.53338814e-01 -4.31104563e-02 -3.15950811... | [9.066080093383789, -3.5486972332000732] |
bb7d8c64-8492-4eb0-9128-c718d3e79ad2 | deep-shape-analysis-on-abdominal-organs-for | 1808.01946 | null | http://arxiv.org/abs/1808.01946v1 | http://arxiv.org/pdf/1808.01946v1.pdf | Deep Shape Analysis on Abdominal Organs for Diabetes Prediction | Morphological analysis of organs based on images is a key task in medical
imaging computing. Several approaches have been proposed for the quantitative
assessment of morphological changes, and they have been widely used for the
analysis of the effects of aging, disease and other factors in organ
morphology. In this wor... | ['Sergios Gatidis', 'Benjamin Gutierrez-Becker', 'Christopher Schlett Fabian Bamberg', 'Annette Peters', 'Daniel Gutmann', 'Christian Wachinger'] | 2018-08-06 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [-5.41228950e-02 -1.27692893e-01 -6.73585236e-02 -6.97056293e-01
-3.22983056e-01 -1.96659669e-01 2.10007623e-01 8.75173390e-01
-4.57737625e-01 1.58866018e-01 -3.25612316e-04 -7.34513253e-02
-1.52916312e-01 -1.06280363e+00 -5.15099704e-01 -5.27864695e-01
-2.54354179e-01 7.46290684e-01 -2.22368374e-01 -2.52697486... | [14.211413383483887, -2.4599101543426514] |
1779cdc9-7d11-4eee-b7e0-1ce38b831e01 | dialogue-based-relation-extraction | 2004.08056 | null | https://arxiv.org/abs/2004.08056v1 | https://arxiv.org/pdf/2004.08056v1.pdf | Dialogue-Based Relation Extraction | We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that spea... | ['Kai Sun', 'Dian Yu', 'Dong Yu', 'Claire Cardie'] | 2020-04-17 | dialogue-based-relation-extraction-1 | https://aclanthology.org/2020.acl-main.444 | https://aclanthology.org/2020.acl-main.444.pdf | acl-2020-6 | ['dialog-relation-extraction'] | ['natural-language-processing'] | [ 4.53137197e-02 5.21383822e-01 -2.07602337e-01 -5.03430068e-01
-8.97839010e-01 -7.44733334e-01 1.35379553e+00 4.24842715e-01
-4.60303128e-01 7.78149247e-01 8.87341797e-01 -4.41722035e-01
-9.37032476e-02 -3.84845704e-01 -1.00913003e-01 -1.28621869e-02
3.52718458e-02 6.92512155e-01 2.99124748e-01 -9.48488176... | [12.483976364135742, 8.051324844360352] |
3bdaaa81-e7ca-4ade-aaf6-47a17c988e51 | deep-learning-based-end-to-end-diagnosis | 2002.05536 | null | https://arxiv.org/abs/2002.05536v2 | https://arxiv.org/pdf/2002.05536v2.pdf | Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head | As the first diagnostic imaging modality of avascular necrosis of the femoral head (AVNFH), accurately staging AVNFH from a plain radiograph is critical yet challenging for orthopedists. Thus, we propose a deep learning-based AVNFH diagnosis system (AVN-net). The proposed AVN-net reads plain radiographs of the pelvis, ... | ['Yang Li', 'Yan Li', 'Hua Tian'] | 2020-02-12 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-2.88544953e-01 3.75687480e-01 -4.92778957e-01 -3.25693160e-01
-1.04536533e+00 -6.27546534e-02 -2.27381274e-01 1.94043964e-01
-2.73689091e-01 6.64568663e-01 4.70446348e-02 -7.66836226e-01
-4.78985548e-01 -8.20348859e-01 -6.40206873e-01 -4.74796385e-01
-2.29211658e-01 1.08221185e+00 1.22253388e-01 1.44764721... | [15.158407211303711, -2.1124579906463623] |
e4a025e5-b168-4792-8a97-1d9113d0f152 | vehicle-position-estimation-with-aerial | 2004.08206 | null | https://arxiv.org/abs/2004.08206v2 | https://arxiv.org/pdf/2004.08206v2.pdf | Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles | The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording multiple objects simultaneously and overcomes limitations such as occlusions. However, there are only few data sets available. This work descr... | ['Eduardo Sánchez Morales', 'Friedrich Kruber', 'Samarjit Chakraborty', 'Michael Botsch'] | 2020-04-17 | null | null | null | null | ['robust-object-detection', 'drone-based-object-tracking', 'traffic-classification'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.54846027e-01 -3.40242684e-01 -4.69721071e-02 -4.14620131e-01
-4.61207092e-01 -2.67648220e-01 4.52755392e-01 -2.46595025e-01
-6.18769407e-01 7.58969188e-01 -6.14457428e-01 -2.40924045e-01
-9.35318917e-02 -7.41657555e-01 -7.21381307e-01 -9.23961043e-01
-2.74354190e-01 4.38460022e-01 3.96118551e-01 -2.06661314... | [8.04917049407959, -1.2697261571884155] |
1ab37c0a-8b8b-4768-8590-1cbdd2fdf692 | time-aware-prompting-for-text-generation | 2211.02162 | null | https://arxiv.org/abs/2211.02162v1 | https://arxiv.org/pdf/2211.02162v1.pdf | Time-aware Prompting for Text Generation | In this paper, we study the effects of incorporating timestamps, such as document creation dates, into generation systems. Two types of time-aware prompts are investigated: (1) textual prompts that encode document timestamps in natural language sentences; and (2) linear prompts that convert timestamps into continuous v... | ['Lu Wang', 'Shuyang Cao'] | 2022-11-03 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 1.12353235e-01 6.91525489e-02 -2.45728642e-01 -4.23114955e-01
-6.72462523e-01 -7.79957294e-01 1.33751845e+00 6.81401134e-01
-5.99210620e-01 9.42836046e-01 1.03710854e+00 -8.05083364e-02
-4.98228483e-02 -8.84020329e-01 -7.89074659e-01 -4.68513779e-02
-3.40232462e-01 4.58876520e-01 2.27708608e-01 -5.02458692... | [12.245514869689941, 9.102269172668457] |
92d57a44-3114-43c6-a837-e1af1cfe6c80 | siamese-object-tracking-for-unmanned-aerial | 2205.04281 | null | https://arxiv.org/abs/2205.04281v2 | https://arxiv.org/pdf/2205.04281v2.pdf | Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis | Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its versatility and effectiveness. As an emerging force in the revolutionary trend of deep learning, Siamese networks shine... | ['Geng Lu', 'Bowen Li', 'Ziang Cao', 'Junjie Ye', 'Guangze Zheng', 'Kunhan Lu', 'Changhong Fu'] | 2022-05-09 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-5.66432714e-01 -6.76765859e-01 -2.94498920e-01 3.40262562e-01
7.29738027e-02 -7.37169325e-01 3.73033375e-01 -3.86124969e-01
-4.11750555e-01 4.90741789e-01 -6.71454430e-01 -1.50418580e-01
-1.17059849e-01 -4.51396465e-01 -6.23487234e-01 -8.09274077e-01
-4.30840403e-01 1.79960579e-01 4.03863758e-01 -3.75304490... | [6.569521427154541, -2.01816725730896] |
b73af49d-c88c-4bdd-b8b5-c9c8991dbcb8 | using-pre-trained-transformer-for-better-lay | null | null | https://aclanthology.org/2020.sdp-1.38 | https://aclanthology.org/2020.sdp-1.38.pdf | Using Pre-Trained Transformer for Better Lay Summarization | In this paper, we tack lay summarization tasks, which aim to automatically produce lay summaries for scientific papers, to participate in the first CL-LaySumm 2020 in SDP workshop at EMNLP 2020. We present our approach of using Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS; Zhang et a... | ['Seungwon Kim'] | null | null | null | null | emnlp-sdp-2020-11 | ['lay-summarization'] | ['natural-language-processing'] | [ 2.83330411e-01 8.84836435e-01 -1.85738593e-01 1.36347534e-02
-1.28283477e+00 -6.25641525e-01 7.62624204e-01 5.07891476e-01
-1.19592749e-01 1.35970259e+00 1.25770557e+00 -1.89803436e-01
-2.66252905e-01 -6.46356463e-01 -8.80800903e-01 -1.34737656e-01
3.29897553e-01 2.92686403e-01 -3.16734165e-01 -7.31720105... | [12.549968719482422, 9.576931953430176] |
d1478abc-8886-48d8-a1b0-4bbbd5a09152 | neuralreg-an-end-to-end-approach-to-referring | 1805.08093 | null | http://arxiv.org/abs/1805.08093v1 | http://arxiv.org/pdf/1805.08093v1.pdf | NeuralREG: An end-to-end approach to referring expression generation | Traditionally, Referring Expression Generation (REG) models first decide on
the form and then on the content of references to discourse entities in text,
typically relying on features such as salience and grammatical function. In
this paper, we present a new approach (NeuralREG), relying on deep neural
networks, which ... | ['Sander Wubben', 'Ákos Kádár', 'Thiago Castro Ferreira', 'Emiel Krahmer', 'Diego Moussallem'] | 2018-05-21 | neuralreg-an-end-to-end-approach-to-referring-1 | https://aclanthology.org/P18-1182 | https://aclanthology.org/P18-1182.pdf | acl-2018-7 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.93631524e-01 8.34944010e-01 -3.37703496e-01 -5.36839008e-01
-9.40438569e-01 -7.91456044e-01 1.22668874e+00 2.43815169e-01
-5.71343839e-01 9.20162380e-01 8.19644809e-01 -3.40647936e-01
3.86236459e-01 -9.77695286e-01 -6.93714738e-01 -1.75548673e-01
2.97819316e-01 4.94874984e-01 -3.08289519e-03 -6.55049384... | [10.846491813659668, 9.199474334716797] |
66ee6379-9b6e-4f2b-a1ea-a2bc73842028 | scene-aware-learning-network-for-radar-object | 2107.01469 | null | https://arxiv.org/abs/2107.01469v1 | https://arxiv.org/pdf/2107.01469v1.pdf | Scene-aware Learning Network for Radar Object Detection | Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in self-driving. However, cameras tend to fail in bad driving conditions, e.g. bad weather or weak lighting, while LiDAR scanners are too expens... | ['Alberto Sangiovanni Vincentelli', 'Kurt Keutzer', 'Xiangyu Yue', 'Zangwei Zheng'] | 2021-07-03 | null | null | null | null | ['robust-object-detection', 'radar-object-detection'] | ['computer-vision', 'robots'] | [ 2.58387387e-01 -6.02050543e-01 1.46113887e-01 -5.07019401e-01
-7.61357188e-01 -3.17022920e-01 7.23155737e-01 -4.31693951e-03
-5.61017692e-01 3.51801187e-01 -3.20518345e-01 -1.81734413e-01
1.57598421e-01 -8.65107119e-01 -6.28334999e-01 -6.65419757e-01
1.50291532e-01 2.37869903e-01 4.14347947e-01 -2.33609155... | [7.944762706756592, -1.3919123411178589] |
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