Active Factor Graph Network for Group Activity Recognition

Group activity recognition aims to identify a consistent group activity from different actions performed by respective individuals. Most existing methods focus on learning the interaction between each two individuals (i.e., second-order interaction). In this work, we argue that the second-order interactive relation is insufficient to address this task. We propose a third-order active factor graph network, which models the third-order interaction in each pair of three active individuals. At first, to alleviate the noisy individual actions, we select active individuals by measuring each individual’s influence. The individuals with the top-k largest influence weights are selected as active individuals. Then, for each three-individuals pair, we build a new factor node and contact the factor node with these individual nodes. In other words, we extend the base second-order interactive graph to a new third-order interactive graph, which is defined as factor graph. Next, we design a two-branch factor graph network, in which one branch is to consider all individuals (denoted as full factor graph) and the other one takes the active individuals into consideration (denoted as active factor graph). We leverage both the active and full factor graphs comprehensively for group activity recognition. Besides, to enforce group consistency, a consistency-aware reasoning module is designed with two penalty terms, which describe the inconsistency between individual actions and group activity respectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance on four benchmark datasets, i.e., Volleyball, Collective Activity, Collective Activity Extended, and SoccerNet-v3 datasets. Visualization results further validate the interpretability of our method.