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Title
Japanese: 
English:Integrating Generative and Contrastive Approaches for Human Action Recognition 
Author
Japanese: CERVANTES BAQUE Pablo Alberto, 関川 雄介, 佐藤育郎, 篠田 浩一.  
English: Pablo Cervantes, Yusuke Sekikawa, Ikuro Sato, Koichi Shinoda.  
Language English 
Journal/Book name
Japanese: 
English:IEEE Access 
Volume, Number, Page vol. 13        pp. 100095-100104
Published date June 2, 2025 
Publisher
Japanese: 
English:IEEE 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Official URL https://ieeexplore.ieee.org/document/11020639
 
Abstract This study introduces a novel approach to unsupervised skeleton-based human action recognition by integrating generative and contrastive learning methods. We propose a decomposition of representations, allowing for the preservation of detailed motion information for the generative learning objective while also extracting action features for the contrastive learning objective. By swapping contrastive representations between positive pairs (coining the name SwapCLR), we ensure that the generative and contrastive representations are complementary and both objectives contribute to learning a strong representation for downstream tasks like action recognition. Additionally, we address the challenge of noisy data in skeleton-based action recognition with a new saturating reconstruction loss, significantly reducing the impact of noise common to key-point detections. Our method demonstrates state-of-the-art performance in unsupervised action recognition on the NTU and PKU-MMD datasets, while also enabling generative downstream tasks such as motion in-painting and motion generation. Overall, these experimental results confirm the method’s effectiveness and suggest its applicability to a variety of action analysis tasks.

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