MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have been widely used and have achieved remarkable results in skeleton-based action recognition. We note that existing GCN-based approaches rely on local context information of the skeleton joints to construct adaptive graphs for feature aggregation, limiting thei...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Kowovi Comivi Alowonou, Ji-Hyeong Han
Format:	Article
Language:	English
Published:	IEEE 2024-01-01
Subjects:	Skeleton-based action recognition GCN dynamic graph topology multi-scale temporal processing
Online Access:	https://ieeexplore.ieee.org/document/10807218/