Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition-Reference-Cited by-同舟云学术

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Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Multimedia
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Author:

Song Yi-Fan¹,Zhang Zhang¹,Shan Caifeng²,Wang Liang³

Affiliation:

1. University of Chinese Academy of Sciences & Institute of Automation, Chinese Academy of Sciences, Beijing, China

2. Shandong University of Science and Technology & Artificial Intelligence Research, Chinese Academy of Sciences, Qingdao, China

3. University of Chinese Academy of Sciences & Institute of Automation, Chinese Academy of Sciences & Anhui University, Beijing, China

Funder

National Key R&D Program of China

Shandong Provincial Key Research and Development Program

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394171.3413802

Reference38 articles.

1. Human activity analysis

2. Human Action Recognition: Pose-Based Attention Draws Focus to Hands

3. Skeleton Image Representation for 3D Action Recognition Based on Tree Structure and Reference Joints

4. SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition

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1. Joint-Partition Group Attention for skeleton-based action recognition;Signal Processing;2024-11

2. GRA: Graph Representation Alignment for Semi-Supervised Action Recognition;IEEE Transactions on Neural Networks and Learning Systems;2024-09

3. Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation;Signal Processing;2024-08

4. Decomposed gate attention graph convolutional networks for skeleton-based action recognition;Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024);2024-07-05

5. Spatial–temporal hypergraph based on dual-stage attention network for multi-view data lightweight action recognition;Pattern Recognition;2024-07

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