TFC-GCN: Lightweight Temporal Feature Cross-Extraction Graph Convolutional Network for Skeleton-Based Action Recognition

For skeleton-based action recognition, graph convolutional networks (GCN) have absolute advantages. Existing state-of-the-art (SOTA) methods tended to focus on extracting and identifying features from all bones and joints. However, they ignored many new input features which could be discovered. More...

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Detalles Bibliográficos
Autores principales: Wang, Kaixuan, Deng, Hongmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301807/
https://www.ncbi.nlm.nih.gov/pubmed/37420759
http://dx.doi.org/10.3390/s23125593

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301807/
https://www.ncbi.nlm.nih.gov/pubmed/37420759
http://dx.doi.org/10.3390/s23125593

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