What is it about?
skeleton action recognition method based graph convolution can calculate the motion relationship between skeleton joints in non-Euclidean space. To improve the modeling capability for the skeleton graph convolution, we propose the channel attention and multi-scale graph convolution network. This network can improve the recognition accuracy on the NTU60 and NTU120 dataset with a small number of parameters introduced.
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Why is it important?
Currently, the high-precision skeleton action recognition methods all endure a huge amount of calculation. Our method can achieve competitive accuracy with light calculation.
Perspectives
Writing this paper makes me learn more deep learning knowledge and we are pleased to contribute to action recognition.
Ronghao Dang
Tongji University
Read the Original
This page is a summary of: Channel attention and multi-scale graph neural networks for skeleton-based action recognition, AI Communications, September 2022, IOS Press,
DOI: 10.3233/aic-210250.
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