Hierarchical intertwined graph representation learning for skeleton-based action recognition

Abstract Graph Convolutional Networks (GCNs) have emerged as a leading approach for human skeleton-based action recognition, owing to their capacity to represent skeletal joints as adaptive graphs that effectively capture complex spatial relationships for feature aggregation. However, existing metho...

Full description

Bibliographic Details

Published in:	Scientific Reports
Main Authors:	Xi Zhang, Caiyan Tan, Yuan Yuan, Jiexing Yan
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-10-01
Subjects:	Human skeleton-based action recognition Skeleton-based graph Graph convolutional network
Online Access:	https://doi.org/10.1038/s41598-025-19399-4