Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915917728841728 |
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| author | Tezuka, Ryuki Nakatani, Chihiro Ukita, Norimichi |
| author_facet | Tezuka, Ryuki Nakatani, Chihiro Ukita, Norimichi |
| contents | This paper proposes Group Activity Feature (GAF) learning without group activity annotations. Unlike prior work, which uses low-level static local features to learn GAFs, we propose leveraging dynamics-aware and group-aware pretext tasks, along with local and global features provided by DINO, for group-dynamics-aware GAF learning. To adapt DINO and GAF learning to local dynamics and global group features, our pretext tasks use person flow estimation and group-relevant object location estimation, respectively. Person flow estimation is used to represent the local motion of each person, which is an important cue for understanding group activities. In contrast, group-relevant object location estimation encourages GAFs to learn scene context (e.g., spatial relations of people and objects) as global features. Comprehensive experiments on public datasets demonstrate the state-of-the-art performance of our method in group activity retrieval and recognition. Our ablation studies verify the effectiveness of each component in our method. Code: https://github.com/tezuka0001/Group-DINOmics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04467 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning Tezuka, Ryuki Nakatani, Chihiro Ukita, Norimichi Computer Vision and Pattern Recognition This paper proposes Group Activity Feature (GAF) learning without group activity annotations. Unlike prior work, which uses low-level static local features to learn GAFs, we propose leveraging dynamics-aware and group-aware pretext tasks, along with local and global features provided by DINO, for group-dynamics-aware GAF learning. To adapt DINO and GAF learning to local dynamics and global group features, our pretext tasks use person flow estimation and group-relevant object location estimation, respectively. Person flow estimation is used to represent the local motion of each person, which is an important cue for understanding group activities. In contrast, group-relevant object location estimation encourages GAFs to learn scene context (e.g., spatial relations of people and objects) as global features. Comprehensive experiments on public datasets demonstrate the state-of-the-art performance of our method in group activity retrieval and recognition. Our ablation studies verify the effectiveness of each component in our method. Code: https://github.com/tezuka0001/Group-DINOmics. |
| title | Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.04467 |