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Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition
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“The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement patterns.”
From Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition (2022) · cites this paper as Rao et al. 2021
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“As shown in Table 4, for a single stream (i.e., joint stream), our AimCLR outperforms all other methods (Zheng et al. 2018; Lin et al. 2020; Rao et al. 2021; Su, Liu, and Shlizerman 2020; Nie, Liu, and Liu 2020; Li et al. 2021).”
From Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition (2022) · cites this paper as Rao et al. 2021
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“As shown in Table 6, TSRJI [2] supervisedly utilizes attention LSTM, ASCAL [39] adopts LSTM for skeleton modeling, and our method defeats the other unsupervised method and some of the supervised methods.”
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“Different from traditional contrastive learning methods (Rao et al. 2021; Li et al. 2021) which directly use normally augmented view, a novel framework proposed in our work is based on the extreme augmentations and the drop mechanism which obtain diverse positive samples and bring abundant…”
BackgroundMethodInfluential
Metadata
- Authors
- Rao, H., Xu, S., Hu, X., Cheng, J., & Hu, B.
- Affiliations
- Chinese Academy of Sciences (CN)
- Lanzhou University (CN)
- Corresponding author
- Hu, Xiping
- Cheng, Jun
- Hu, Bin
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 569
- Pages
- 90-109
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W3156509901
- Cited by
- 235
- References
- 57
- Citations by year
- 2026: 21 · 2025: 35 · 2024: 66 · 2023: 63 · 2022: 36 · 2021: 14
- FWCI
- 16.09
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Information Sciences (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lanzhou University Institutional Repository (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
- Keywords
- 3D skeleton data, contrastive learning
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