Learning Action-guided Spatio-temporal Transformer for Group Activity Recognition

Abstract

Learning spatial and temporal relations among people plays an important role in recognizing group activity. Recently, transformer-based methods have become popular solutions due to the proposal of self-attention mechanism. However, the person-level features are fed directly into the self-attention module without any refinement. Moreover, group activity in a clip often involves unbalanced spatio-temporal interactions, where only a few persons with special actions are critical to identifying different activities. It is difficult to learn the spatio-temporal interactions due to the lack of elaborately modeling the action dependencies among all people. In this paper, a novel Action-guided Spatio-Temporal transFormer (ASTFormer) is proposed to capture the interaction relations for group activity recognition by learning action-centric aggregation and modeling spatio-temporal action dependencies. Specifically, ASTFormer starts with assigning all persons in each frame to the latent actions, while an action-centric aggregation strategy is performed by weighting the sum of residuals for each latent action under the supervision of global action information. Then, a dual-branch transformer is proposed to refine the inter- and intra-frame action-level features, where two encoders with the self-attention mechanism are employed to select important tokens. Next, a semantic action graph is explicitly devised to model the dynamic action-wise dependencies. Finally, our model is capable of boosting group activity recognition by fusing these important cues, while only requiring video-level action labels. Extensive experiments on two popular benchmarks (Volleyball and Collective Activity) demonstrate the superior performance of our method in comparison with the state-of-the-art methods using only raw RGB frames as input.

Topics & Concepts