Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding

Author:

Sun Shengkai1ORCID,Liu Daizong2ORCID,Dong Jianfeng3ORCID,Qu Xiaoye4ORCID,Gao Junyu5ORCID,Yang Xun6ORCID,Wang Xun3ORCID,Wang Meng7ORCID

Affiliation:

1. Zhejiang Gongshang University, Hangzhou, China

2. Peking University, Beijing, China

3. Zhejiang Gongshang University & Zhejiang Key Lab of E-Commerce, Hangzhou, China

4. Huazhong University of Science and Technology, Wuhan, China

5. Institute of automation, Chinese Academy of Sciences, Beijing, China

6. University of Science and Technology of China, Hefei, China

7. Hefei University of Technology, Hefei, China

Funder

Young Elite Scientists Sponsorship Program by CAST

Fundamental Research Funds for the Provincial Universities of Zhejiang

National Natural Science Foundation of China

Pioneer and Leading Goose R&D Program of Zhejiang

open research fund of The State Key Laboratory of Multimodal Artificial Intelligence Systems

Public Welfare Technology Research Project of Zhejiang Province

Publisher

ACM

Reference64 articles.

1. Adrien Bardes , Jean Ponce , and Yann Lecun . 2022 . VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In International Conference on Learning Representations. 1--1. Adrien Bardes, Jean Ponce, and Yann Lecun. 2022. VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In International Conference on Learning Representations. 1--1.

2. Artistic style transfer with internal-external learning and contrastive learning;Chen Haibo;Advances in Neural Information Processing Systems,2021

3. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International Conference on Machine Learning. 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning. 1597--1607.

4. Hierarchically Self-supervised Transformer for Human Skeleton Representation Learning

5. Hierarchical Transformer: Unsupervised Representation Learning for Skeleton-Based Human Action Recognition

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