In this paper, we propose a new, simple, and effective Self-supervised
Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition
(GAR) using unlabeled video data. Given a video, we create local and global
Spatio-temporal views with varying spatial patch sizes and frame rates. The
proposed self-supervised objective aims to match the features of these
contrasting views representing the same video to be consistent with the
variations in spatiotemporal domains. To the best of our knowledge, the
proposed mechanism is one of the first works to alleviate the weakly supervised
setting of GAR using the encoders in video transformers. Furthermore, using the
advantage of transformer models, our proposed approach supports long-term
relationship modeling along spatio-temporal dimensions. The proposed SPARTAN
approach performs well on two group activity recognition benchmarks, including
NBA and Volleyball datasets, by surpassing the state-of-the-art results by a
significant margin in terms of MCA and MPCA metrics.

本文提出了一种新的、简单、有效的自我监督空间时间变换器（SPARTAN）方法来使用未标记的视频数据对小组活动识别（GAR）进行识别。该方法采用 encoder 提取视频特征，并进行长期关系建模，通过处理不同的空间尺度和帧率来进行自我监督训练，最终在 NBA 和排球数据集上显著优于现有的最先进算法。