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Learning Semantic-Aware Spatial-Temporal Attention for Interpretable Action Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 8, 页码: 5213-5224
作者:  Fu, Jie;  Gao, Junyu;  Xu, Changsheng
收藏  |  浏览/下载：239/0  |  提交时间：2022/09/19
Visualization  Semantics  Task analysis  Three-dimensional displays  Feature extraction  Solid modeling  Predictive models  Semantic-aware  spatial-temporal attention  interpretable  action recognition  
Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 5, 页码: 1915-1925
作者:  Song, Yi-Fan;  Zhang, Zhang;  Shan, Caifeng;  Wang, Liang
Adobe PDF(3381Kb)  |  收藏  |  浏览/下载：328/54  |  提交时间：2021/06/15
Skeleton  Robustness  Noise measurement  Three-dimensional displays  Degradation  Standards  Feature extraction  Action recognition  skeleton  activation map  graph convolutional network  occlusion  jittering  
HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2335-2346
作者:  Chen, Chaofan;  Qian, Shengsheng;  Fang, Quan;  Xu, Changsheng
收藏  |  浏览/下载：171/0  |  提交时间：2021/11/02
Three-dimensional displays  Feature extraction  Task analysis  Layout  Logic gates  Machine learning  Two dimensional displays  Point cloud segmentation  hierarchical graph pooling  gated graph attention network  
Learning to Learn Cropping Models for Different Aspect Ratio Requirements 会议论文
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, Virtual, 14-19, June, 2020
作者:  Li, Debang;  Zhang, Junge;  Huang, Kaiqi
Adobe PDF(1065Kb)  |  收藏  |  浏览/下载：178/56  |  提交时间：2021/05/31
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：306/40  |  提交时间：2019/09/30
Visual tracking  deep learning  sampling and regression