Multi-Modality Adaptive Feature Fusion Graph Convolutional Network for Skeleton-Based Action Recognition
Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that...
Main Authors: | Haiping Zhang, Xinhao Zhang, Dongjin Yu, Liming Guan, Dongjing Wang, Fuxing Zhou, Wanjun Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-06-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/12/5414 |
Similar Items
-
Structure-Feature Fusion Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
by: Zhitao Zhang, et al.
Published: (2020-01-01) -
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition
by: Han Chen, et al.
Published: (2022-01-01) -
Cross-Channel Graph Convolutional Networks for Skeleton-Based Action Recognition
by: Jun Xie, et al.
Published: (2021-01-01) -
Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition
by: Di Liu, et al.
Published: (2021-10-01) -
Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
by: Zhiyun Zheng, et al.
Published: (2022-01-01)