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Category-Level Multi-Attention based Boundary Refinement for Action Detection

Temporal action localization has attracted considerable attention in recent years. Most previous work focused on a fully supervised setting leveraging frame-level action labels. However, it is usually expensive and time-consuming to acquire precise temporal annotations. In this paper, we focus on a weakly supervised setting using only video-level labels to address the temporal action localization task. A category-level multi-attention based method is proposed to provide a comprehensive representation for each action category and obtain much more precise temporal intervals. Our method is evaluated on the untrimmed THUMOS14 video dataset, and the experiments have provided very positive results.