| Summary: | Modeling the interactions among individuals in a group is essential for group activity recognition (GAR). Various graph neural networks (GNNs) are regarded as popular modeling methods for GAR, as they can characterize the interaction among individuals at a low computational cost. The performance of the current GNN-based modeling methods is affected by two factors. Firstly, their local receptive field in the mapping layer limits their ability to characterize the global interactions among individuals in spatial–temporal dimensions. Secondly, GNN-based GAR methods do not have an efficient mechanism to use global activity consistency and individual action consistency. In this paper, we argue that the global interactions among individuals, as well as the constraints of global activity and individual action consistencies, are critical to group activity recognition. We propose new convolutional operations to capture the interactions among individuals from a global perspective. We use contrastive learning to maximize the global activity consistency and individual action consistency for more efficient recognition. Comprehensive experiments show that our method achieved better GAR performance than the state-of-the-art methods on two popular GAR benchmark datasets.
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