Bi-Causal: Group Activity Recognition via Bidirectional Causality

Youliang Zhang, Wenxuan Liu, Danni Xu, Zhuo Zhou, Zheng Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 1450-1459

Current approaches in Group Activity Recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Interactions (HOIs). This study prioritizes the consideration of both HRs and HOIs emphasizing their interdependence. Notably employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight we propose a Bidirectional-Causal GAR network. This network establishes a causality communication channel while modeling relations and interactions enabling reciprocal enhancement between human-object interactions and human relations ensuring their mutual consistency. Additionally an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches.