Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition

Obtaining discriminative joint features is crucial for skeleton-based human action recognition. Current models mainly focus on the research of skeleton topology encoding. However, their predefined topology is the same and fixed for all action samples, making it challenging to obtain discriminative j...

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Detalles Bibliográficos

Autores principales:	Xiao-Wei Han, Xing-Yu Chen, Ying Cui, Qiu-Yang Guo, Wen Hu
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2024-09-01
Colección:	Applied Sciences
Materias:	human action recognition adaptive graph convolutional neural network channel attention skeleton data
Acceso en línea:	https://www.mdpi.com/2076-3417/14/18/8185
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