Spatio-Temporal Action Detector with Self-Attention
Xurui Ma, Zhigang Luo, Xiang Zhang, Qing Min Liao, Xingyu Shen, Mengzhu Wang · 2021
In the field of spatio-temporal action detection, some current studies attempt to solve the problem of action detection by using the one-stage object detectors based on anchor-free. Albeit efficiency, more performance boosts are expected. Towards this goal, a Self-Attention MovingCenter Detector (SAMOC) is proposed, which is blessed with two attractive aspects: 1) to effectively capture motion cues, a spatio-temporal self-attention block is explored to reinforce feature representation by aggregating motion-dependent global contexts, and 2) a link branch serves to model the frame-level object dependency, which promotes the confidence scores of correct actions. Experiments on two benchmark datasets show that SAMOC with the proposed two aspects achieves the state-of-the-art and works in real-time as well.