Motion Attention-Guided Relational Reasoning for Weakly Supervised Group Activity Recognition

The existing attention-based label-free weakly supervised group activity recognition methods can automatically learn tokens related to the actors. And they have difficulties generating sufficiently diverse token embeddings. To address these issues, we automatically obtain the grayscale motion mask of all the moving objects based on the motion direction not the motion amplitude. A Motion-Guided Mask Generator module (MGMG) is proposed to estimate the attention region mask under the supervision of the grayscale motion mask. MGMG involves four parts. A correlation layer measures the relative displacement between two adjacent feature maps. A cosine attention mechanism is designed to reduce the module's sensitivity to feature amplitude changes. A mask generator is built to generate the attention region mask. And a specifically designed activation function is used to refine the attention region mask and to enhance its focus on actor motion regions. We also customize a normalized relative error loss function for MGMG module. This loss can address the value range mismatch problem for the estimated attention mask as well as the grayscale motion mask. Furthermore, a Motion Attention-Guided Relational Reasoning (MAGRR) framework is presented for the weakly supervised condition. It uses the MGMG module to estimate the attention region automatically, and a Spatial-temporal Aggregation Stack (SAS) module to activate the attention regions of the features at the spatial level, then transform them into multiple tokens, which are further captured by the attention mechanism for their temporal dependencies and interrelationships. MAGRR is experimented on the Collective Activity dataset and the Collective Activity Extension dataset, achieving state-of-the-art performance and competitive performance on the Volleyball and the NBA datasets.