Repetitive Action Counting with Motion Feature Learning.

Repetitive action counting aims to count the number of repetitive actions in a video. The critical challenge of this task is to uncover the periodic pattern between repetitive actions by computing feature similarity between frames. However, existing methods only rely on the RGB feature of each frame to compute the feature similarity while neglecting the background change of repetitive actions. The abrupt background change may cause feature discrepancies of the same action moment and lead to errors in counting. To this end, we propose a two-branch framework, i.e., RGB and motion branches, with the motion branch complementing the RGB branch to enhance the foreground motion feature learning. Specifically, foreground motion features are highlighted with flow-guided attention on frame features. In addition, to alleviate the noise from moving background distractors and reinforce the periodic pattern, we propose a temporal self-similarity matrix reconstruction loss to improve the temporal correspondence between the same motion feature from different frames. Lastly, to make the motion feature effectively supplement the RGB feature, we present a novel variance-prompted loss weights generation technique to automatically generate dynamic loss weights for two branches in collaborative training. Extensive experiments are conducted on the RepCount and UCFRep datasets to verify our proposed method with state-of-the-art performance. Our method also achieves the best performance on the cross-dataset generalization experiment.