Exploring Spatio-Temporal Discriminative Cues for Group Activity Recognition Via Contrastive Learning

Abstract

Group activity recognition is a challenging task that involves multiple moving actors within a cluttered scene. Existing methods often rely on object detector to avoid individual bounding box labeling during testing, but are prone to false detections due to factors such as occlusion and background clutter. In addition, existing detector-free method based on Transformer attends to attention map that is too sparse, resulting in the loss of some important foreground information. In this paper, we introduce foreground-background contrast loss (FB-Loss) to help accurately seek discriminative cues in the foreground and eliminate noise interference in the background. Neither ground-truth bounding boxes nor object detectors are required during both training and testing. Experimental results on public datasets show that our proposed method achieves the state-of-the-art performance.