Adaptive Feature Selection With Reinforcement Learning for Skeleton-Based Action Recognition

Adaptive Feature Selection With Reinforcement Learning for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted extensive attention recently in the computer vision community. Previous studies, especially GCN-based methods, have presented remarkable improvements for this task. However, in existing GCN-based methods, global average pooling is applied to the extrac...

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Bibliographic Details
Main Authors: Zheyuan Xu, Yingfu Wang, Jiaqin Jiang, Jian Yao, Liang Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Action recognition
skeleton
feature selection
reinforcement learning
graph convolutional network
Online Access:https://ieeexplore.ieee.org/document/9260250/

Internet

https://ieeexplore.ieee.org/document/9260250/

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