  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.
