Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

Using skeletal information to model and recognize human actions is currently a hot research subject in the realm of Human Action Recognition (HAR). Graph Convolutional Networks (GCN) have gained popularity in this discipline due to their capacity to efficiently process graph-structured data. However...

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Hlavní autoři:	Zhiyun Zheng, Qilong Yuan, Huaizhu Zhang, Yizhou Wang, Junfeng Wang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Tsinghua University Press 2025-04-01
Edice:	Big Data Mining and Analytics
Témata:	human action recognition (har) skeleton data graph convolutional network (gcn) attention mechanism
On-line přístup:	https://www.sciopen.com/article/10.26599/BDMA.2024.9020095
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