  This paper introduces a novel approach to Social Group Activity Recognition
(SoGAR) using Self-supervised Transformers network that can effectively utilize
unlabeled video data. To extract spatio-temporal information, we created local
and global views with varying frame rates. Our self-supervised objective
ensures that features extracted from contrasting views of the same video were
consistent across spatio-temporal domains. Our proposed approach is efficient
in using transformer-based encoders to alleviate the weakly supervised setting
of group activity recognition. By leveraging the benefits of transformer
models, our approach can model long-term relationships along spatio-temporal
dimensions. Our proposed SoGAR method achieved state-of-the-art results on
three group activity recognition benchmarks, namely JRDB-PAR, NBA, and
Volleyball datasets, surpassing the current numbers in terms of F1-score, MCA,
and MPCA metrics.
