This work addresses the problem of Social Activity Recognition (SAR), a
critical component in real-world tasks like surveillance and assistive
robotics. Unlike traditional event understanding approaches, SAR necessitates
modeling individual actors' appearance and motions and contextualizing them
within their social interactions. Traditional action localization methods fall
short due to their single-actor, single-action assumption. Previous SAR
research has relied heavily on densely annotated data, but privacy concerns
limit their applicability in real-world settings. In this work, we propose a
self-supervised approach based on multi-actor predictive learning for SAR in
streaming videos. Using a visual-semantic graph structure, we model social
interactions, enabling relational reasoning for robust performance with minimal
labeled data. The proposed framework achieves competitive performance on
standard group activity recognition benchmarks. Evaluation on three publicly
available action localization benchmarks demonstrates its generalizability to
arbitrary action localization.

在这项研究中，我们提出了一种基于多演员预测学习的自监督方法，用于流媒体视频中的社交活动识别。通过使用视觉语义图结构，我们对社交互动进行建模，从而实现了关系推理，使其在具有最少标记数据的情况下具有鲁棒性表现。该方法在标准群体活动识别基准上取得了竞争性的性能，并通过三个公开可用的动作定位基准的评估证明了其可泛化到任意动作定位的能力。