AI RESEARCH
Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning
arXiv CS.CV
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ArXi:2604.04467v1 Announce Type: new 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.