Boundary-Aware Noise-Resistant Video Moment Retrieval

Video Moment Retrieval (VMR) is a critical task that aims to retrieve relevant video segments based on textual queries from untrimmed videos. This paper introduces a Boundary-aware variant of the 2D Temporal Adjacency Network (BA-TAN) to address VMR challenges. The proposed model leverages cross-modal interactions and emphasizes video moment boundary in the temporal network to improve retrieval accuracy. Attention mechanisms are integrated to suppress background interference and highlight query-relevant video features. Additionally, we introduce a Time Proximity Mask Convolution to learn video features that are adjacent to the current time segment. This enhances the model’s ability to capture temporal context and improves moment retrieval accuracy. Experimental results demonstrate the effectiveness of our approach BA-TAN in enhancing VMR performance. This work provides valuable insights for further research and applications in related fields.