Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
Author:
Affiliation:
1. The University of Melbourne,School of Computing and Information Systems
2. Monash University,Department of Data Science & AI
3. The University of Melbourne,School of Mathematics and Statistics
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10030081/10030084/10030878.pdf?arnumber=10030878
Reference41 articles.
1. NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
2. Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
3. Adaptive RNN Tree for Large-Scale Human Action Recognition
4. Matching networks for one shot learning;vinyals;Adv Neural Inform Process Syst,2016
5. Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
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