Spatial-Temporal Asynchronous Normalization for Unsupervised 3D Action Representation Learning
Author:
Affiliation:
1. School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
2. Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China
3. Peng Cheng Laboratory, Shenzhen, China
Funder
Youth Program of National Natural Science Foundation of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/97/9686799/09691879.pdf?arnumber=9691879
Reference28 articles.
1. Unsupervised representation learning with long-term dynamics for skeleton based action recognition;zheng;Proc AAAI,0
2. Unsupervised human 3D pose representation with viewpoint and pose disentanglement;nie;Proc ECCV,0
3. PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
4. Reducing the Dimensionality of Data with Neural Networks
5. Augmented skeleton based contrastive action learning with momentum LSTM for unsupervised action recognition;rao;Inf Sci,2020
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