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 create local
and global views with varying frame rates. Our self-supervised objective
ensures that features extracted from contrasting views of the same video are
consistent across spatio-temporal domains. Our proposed approach is efficient
in using transformer-based encoders for alleviating 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 achieves state-of-the-art
results on three group activity recognition benchmarks, namely JRDB-PAR, NBA,
and Volleyball datasets, surpassing the current state-of-the-art in terms of
F1-score, MCA, and MPCA metrics.

本文提出了一种使用自我监督变压器网络的社交组活动识别（SoGAR）的新方法，可以有效利用未标记的视频数据，通过利用变化的帧率创建局部和全局视图来提取空时信息，并通过提取来自同一视频的对比视图的特征的自我监督目标，确保跨空时域中的特征是一致的。该方法在使用变压器模型方面具有高效性，可以沿着空时维度建模长期关系，并在三个小组活动识别基准测试中取得了最新的成果，在 F1-score，MCA 和 MPCA 指标方面超越了当前最新技术。