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Holographic Feature Learning of Egocentric-Exocentric Videos for Multi-Domain Action Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 2273-2286
作者:  Huang, Yi;  Yang, Xiaoshan;  Gao, Junyun;  Xu, Changsheng
Adobe PDF(2409Kb)  |  收藏  |  浏览/下载：344/68  |  提交时间：2022/07/25
Videos  Feature extraction  Visualization  Task analysis  Computational modeling  Target recognition  Prototypes  Egocentric videos  exocentric videos  holographic feature  multi-domain  action recognition  
Multi-Target Multi-Camera Tracking With Optical-Based Pose Association 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 8, 页码: 3105-3117
作者:  You, Sisi;  Yao, Hantao;  Xu, Changsheng
收藏  |  浏览/下载：220/0  |  提交时间：2021/11/02
Target tracking  Trajectory  Cameras  Visualization  Feature extraction  Proposals  Object detection  Multi-target multi-camera tracking  pose estimation  optical flow  pose matching  
PEN: Pose-Embedding Network for Pedestrian Detection 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 3, 页码: 1150-1162
作者:  Jiao, Yifan;  Yao, Hantao;  Xu, Changsheng
收藏  |  浏览/下载：170/0  |  提交时间：2021/05/10
Visualization  Proposals  Detectors  Feature extraction  Object detection  Pose estimation  Fuses  Pedestrian detection  pedestrian recognization network  pose-embedding  pose information  
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：293/42  |  提交时间：2021/03/08
Person re-identification  representation learning  graph convolutional network  
SMART: Joint Sampling and Regression for Visual Tracking 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 8, 页码: 3923-3935
作者:  Gao, Junyu;  Zhang, Tianzhu;  Xu, Changsheng
浏览  |  Adobe PDF(9244Kb)  |  收藏  |  浏览/下载：345/43  |  提交时间：2019/09/30
Visual tracking  deep learning  sampling and regression