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...

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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

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