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Knowledge-driven Egocentric Multimodal Activity Recognition 期刊论文
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2020, 卷号: 16, 期号: 4, 页码: 21
作者:  Huang, Yi;  Yang, Xiaoshan;  Gao, Junyu;  Sang, Jitao;  Xu, Changsheng
Adobe PDF(1875Kb)  |  收藏  |  浏览/下载:422/66  |  提交时间:2021/03/08
Egocentric videos  wearable sensors  graph neural networks  
Part-based Structured Representation Learning for Person Re-identification 期刊论文
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2020, 卷号: 16, 期号: 4, 页码: 22
作者:  Li, Yaoyu;  Yao, Hantao;  Zhang, Tianzhu;  Xu, Changsheng
Adobe PDF(19052Kb)  |  收藏  |  浏览/下载:310/46  |  提交时间:2021/03/08
Person re-identification  representation learning  graph convolutional network  
Toward Improving Engagement in Neural Rehabilitation: Attention Enhancement Based on Brain-Computer Interface and Audiovisual Feedback 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2020, 卷号: 12, 期号: 4, 页码: 787-796
作者:  Wang, Jiaxing;  Wang, Weiqun;  Hou, Zeng-Guang
Adobe PDF(2031Kb)  |  收藏  |  浏览/下载:335/72  |  提交时间:2021/03/02
Avatars  Electroencephalography  Training  Games  Real-time systems  Task analysis  Stroke (medical condition)  Attention enhancement  brain–  computer interface (BCI)  electroencephalogram (EEG)  neural rehabilitation  virtual reality  
High-speed rail pole number recognition through deep representation and temporal redundancy 期刊论文
NEUROCOMPUTING, 2020, 卷号: 415, 页码: 201-214
作者:  Yang, Yang;  Zhang, Wensheng;  He, Zewen;  Li, Ding
Adobe PDF(2751Kb)  |  收藏  |  浏览/下载:318/58  |  提交时间:2021/01/07
Region-based convolutional neural network  Context information  Object detection  Number recognition  Pole number  High-speed rail  
CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection 期刊论文
Pattern Recognition, 2020, 卷号: 109, 期号: 0, 页码: 10
作者:  Zou W(邹伟)
浏览  |  Adobe PDF(1839Kb)  |  收藏  |  浏览/下载:175/50  |  提交时间:2020/10/22
Weakly supervised learning, Automated surface inspection, Defect detection, Knowledge distillation  
High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN 期刊论文
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2020, 卷号: 6, 页码: 830-842
作者:  Wang, Yunlong;  Liu, Fei;  Zhang, Kunbo;  Wang, Zilei;  Sun, Zhenan;  Tan, Tieniu
Adobe PDF(7054Kb)  |  收藏  |  浏览/下载:211/40  |  提交时间:2020/09/28
View synthesis  light field reconstruction  end-to-end  structure preserving  extended pseudo 4DCNN  
Joint Learning in the Spatio-Temporal and Frequency Domains for Skeleton-Based Action Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2020, 卷号: 22, 期号: 9, 页码: 2207-2220
作者:  Guyue, Hu;  Bo, Cui;  Shan, Yu
Adobe PDF(4803Kb)  |  收藏  |  浏览/下载:326/64  |  提交时间:2020/09/28
Skeleton-based Action Recognition  Frequency Attention  Synchronous Local and Non-local Learning  Soft-margin Focal Loss  Pesudo Multi-task Learning  
Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network 期刊论文
PATTERN RECOGNITION, 2020, 卷号: 107, 期号: 107511, 页码: 12
作者:  Si, Chenyang;  Jing, Ya;  Wang, Wei;  Wang, Liang;  Tan, Tieniu
Adobe PDF(2378Kb)  |  收藏  |  浏览/下载:382/72  |  提交时间:2020/08/31
Skeleton-based action recognition  Hierarchical spatial reasoning  Temporal stack learning  Clip-based incremental loss  
Self-Attention Based Visual-Tactile Fusion Learning for Predicting Grasp Outcomes 期刊论文
IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 卷号: 5, 期号: 4, 页码: 5827-5834
作者:  Cui, Shaowei;  Wang, Rui;  Wei, Junhang;  Hu, Jingyi;  Wang, Shuo
Adobe PDF(1535Kb)  |  收藏  |  浏览/下载:350/62  |  提交时间:2020/08/31
Grasping  perception for grasping and manipulation  multi-modal perception  force and tactile sensing  
Improve Person Re-Identification With Part Awareness Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7468-7481
作者:  Huang, Houjing;  Yang, Wenjie;  Lin, Jinbin;  Huang, Guan;  Xu, Jiamiao;  Wang, Guoli;  Chen, Xiaotang;  Huang, Kaiqi
Adobe PDF(3927Kb)  |  收藏  |  浏览/下载:342/65  |  提交时间:2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning