Temporal Gap-Aware Attention Model for Temporal Action Proposal Generation.

Abstract

Temporal action proposal generation is a method for extracting temporal action instances or proposals from untrimmed videos. Existing methods often struggle to segment contiguous action proposals, which are a group of action boundaries with small temporal gaps. To address this limitation, we propose incorporating an attention mechanism to weigh the importance of each proposal within a contiguous group. This mechanism leverages the gap displacement between proposals to calculate attention scores, enabling a more accurate localization of action boundaries. We evaluate our method against a state-of-the-art boundary-based baseline on ActivityNet v1.3 and Thumos 2014 datasets. The experimental results demonstrate that our approach significantly improves the performance of short-duration and contiguous action proposals, achieving an average recall of 78.22%.