CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based Action Recognition
Author:
Affiliation:
1. School of Electrical and Information Engineering, Tianjin University,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10008391/10008793/10008837.pdf?arnumber=10008837
Reference35 articles.
1. Learning spatiotemporal features with 3d convolutional networks;tran;Proceedings of the IEEE International Conference on Computer Vision,2015
2. Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition
3. iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis
4. Cross-view action mod-eling, learning and recognition;wang;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2014
5. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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2. End-to-end Action Quality Assessment with Action Parsing Transformer;2023 IEEE International Conference on Visual Communications and Image Processing (VCIP);2023-12-04
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