PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition
Skeleton-based human action recognition has become an active research area in recent years. The key to this task is to fully explore both spatial and temporal features. Recently, GCN-based methods modeling the human body skeletons as spatial-temporal graphs, have achieved remarkable performances. Ho...
Main Authors: | Hongye Yang, Yuzhang Gu, Jianchao Zhu, Keli Hu, Xiaolin Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8950167/ |
Similar Items
-
Part-Wise Adaptive Topology Graph Convolutional Network for Skeleton-Based Action Recognition
by: Jiale Wang, et al.
Published: (2023-04-01) -
Skeleton Action Recognition Based on Temporal Gated Unit and Adaptive Graph Convolution
by: Qilin Zhu, et al.
Published: (2022-09-01) -
Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition
by: Chaoyue Li, et al.
Published: (2021-09-01) -
Multi-scale Gated Graph Convolutional Network for Skeleton-based Action Recognition
by: GAN Chuang, WU Gui-xing, ZHAN Qing-yuan, WANG Peng-kun, PENG Zhi-lei
Published: (2022-01-01) -
Temporal‐enhanced graph convolution network for skeleton‐based action recognition
by: Yulai Xie, et al.
Published: (2022-04-01)