CASIA OpenIR

浏览/检索结果: 共10条,第1-10条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
Global Instance Tracking: Locating Target More Like Humans 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 1, 页码: 576-592
作者:  Hu, Shiyu;  Zhao, Xin;  Huang, Lianghua;  Huang, Kaiqi
Adobe PDF(15055Kb)  |  收藏  |  浏览/下载:247/55  |  提交时间:2023/02/22
Global instance tracking  single object tracking  benchmark dataset  performance evaluation  human tracking ability  
Joint Expression Synthesis and Representation Learning for Facial Expression Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 3, 页码: 1681-1695
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(4827Kb)  |  收藏  |  浏览/下载:260/1  |  提交时间:2022/06/06
Face recognition  Task analysis  Generative adversarial networks  Image synthesis  Image recognition  Faces  Training  Facial expression recognition  facial image synthesis  generative adversarial network  representation learning  
Unsupervised Video Summarization via Relation-Aware Assignment Learning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3203-3214
作者:  Gao, Junyu;  Yang, Xiaoshan;  Zhang, Yingying;  Xu, Changsheng
Adobe PDF(3649Kb)  |  收藏  |  浏览/下载:342/67  |  提交时间:2021/11/03
Feature extraction  Training  Optimization  Semantics  Recurrent neural networks  Task analysis  Graph neural network  unsupervised learning  video summarization  
Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2386-2397
作者:  Wang, Wei;  Gao, Junyu;  Yang, Xiaoshan;  Xu, Changsheng
Adobe PDF(2165Kb)  |  收藏  |  浏览/下载:351/46  |  提交时间:2021/11/02
Feature extraction  Encoding  Task analysis  Semantics  Data models  Cognition  Focusing  Video-text retrieval  graph neural network  coarse-to-fine strategy  
Learning Aligned Image-Text Representations Using Graph Attentive Relational Network 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 期号: 30, 页码: 1840-1852
作者:  Jing, Ya;  Wang, Wei;  Wang, Liang;  Tan, Tieniu
Adobe PDF(4532Kb)  |  收藏  |  浏览/下载:357/60  |  提交时间:2021/03/08
Graph neural networks  Visualization  Semantics  Task analysis  Feature extraction  Annotations  Recurrent neural networks  Image-text matching  cross-modal retrieval  person search  graph neural network  
Recurrent Prediction with Spatio-temporal Attention for Crowd Attribute Recognition 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2019, 卷号: 30, 期号: Early Access, 页码: 1 - 1
作者:  Li, Qiaozhe;  Zhao, Xin;  He, Ran;  Huang, Kaiqi
浏览  |  Adobe PDF(2648Kb)  |  收藏  |  浏览/下载:417/107  |  提交时间:2020/01/14
Crowd video understanding , Attribute recognition , Attention mechanism , Multi-label classification  
Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2019, 卷号: 21, 期号: 9, 页码: 2419-2431
作者:  Ma, Xinhong;  Zhang, Tianzhu;  Xu, Changsheng
Adobe PDF(2142Kb)  |  收藏  |  浏览/下载:374/48  |  提交时间:2019/12/16
Unsupervised domain adaptation  triplet loss  stacked attention  multi-modality  social event recognition  
GaitNet: An end-to-end network for gait based human identification 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 96, 期号: 106988, 页码: 11
作者:  Song, Chunfeng;  Huang, Yongzhen;  Huang, Yan;  Jia, Ning;  Wang, Liang
Adobe PDF(3015Kb)  |  收藏  |  浏览/下载:633/165  |  提交时间:2019/12/16
Gait recognition  Video-based human identification  End-to-end CNN  Joint learning  
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
Adobe PDF(9244Kb)  |  收藏  |  浏览/下载:364/46  |  提交时间:2019/09/30
Visual tracking  deep learning  sampling and regression  
Part-aligned pose-guided recurrent network for action recognition 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 92, 期号: 1, 页码: 165-176
作者:  Huang, Linjiang;  Huang, Yan;  Ouyang, Wanli;  Wang, Liang
Adobe PDF(2555Kb)  |  收藏  |  浏览/下载:553/125  |  提交时间:2019/07/11
Action recognition  Part alignment  Auto-transformer attention