Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection-Reference-Cited by-同舟云学术

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE)
language:
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Author:

Wang Liangliang¹,Huang Chengxi²,Chen Xinwei³

Affiliation:

1. Wuhan University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China,430070

2. Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology,Quanzhou,China,362008

3. Minjiang University,Fujian Provincial Key Laboratory of Information Processing and Intelligent Control,Fuzhou,China,350121

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10208269/10208123/10208414.pdf?arnumber=10208414

Reference20 articles.

1. Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation

2. Part-Activated Deep Reinforcement Learning for Action Prediction

3. Simple statistical gradient-following algorithms for connectionist reinforcement learning

4. R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

5. G-TAD: Sub-Graph Localization for Temporal Action Detection

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