Multi-derivative physical and geometric convolutional embedding networks for skeleton-based action recognition

DOI10.1016/j.cagd.2021.101964zbMath1468.65021OpenAlexW3134816637MaRDI QIDQ2020327

Guoli Yan, Michelle Hua, Zichun Zhong

Publication date: 23 April 2021

Published in: Computer Aided Geometric Design (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cagd.2021.101964

zbMATH Keywords

multi-task learningmulti-derivativephysical and geometric embeddingskeleton-based action recognition

Mathematics Subject Classification ID

Computer science aspects of computer-aided design (68U07) Computational issues in computer and robotic vision (65D19)

Related Items (max. 100)

GMP2021 - 15th international conference on geometric modeling and processing

Uses Software

• Adam
• NTU RGB+D

Cites Work

• Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
• Learning Clip Representations for Skeleton-Based 3D Action Recognition

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