特征挖掘与区域增强的弱监督时序动作定位.

Weakly supervised temporal action localization (WTAL) aims to locate the start and end boundaries of action instances and identify the corresponding actions. Although the existing methods have made great progress, there are still problems of incomplete localization and missing detection of shorter motions. To this end, this paper proposed a localization method of feature mining and region enhancement (FMRE) Firstly it calculated the similarity score between video segments through the base branch, and aggregated the context information with this score to obtain a more differentiated segment classification score, further realizing the complete positioning of the action. Then, it added a enhance branch to dynamically up-sample action proposals with a shorter duration in the initial localization along the temporal dimension, and then utilized the multi-head self-attention mechanism to explicitly model the temporal structure between action proposals, which facilitated action localization with temporal dependencies and prevented missing detection of short actions. Finally, it constructed pseudo-labels of mutual supervision between the two branches to gradually improve the quality of action proposals during the training process. The algorithm achieves mAP of 70.3% and 40.7% detection performances on the THUMOS14 and ActivityNet1.3 datasets respectively, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]