Semantic Fusion Augmentation and Semantic Boundary Detection: A Novel Approach to Multi-Target Video Moment Retrieval

Given an untrimmed video and a natural language query, video moment retrieval (VMR) aims to retrieve video moments described by the query. However, most existing VMR methods assume a one-to-one mapping between the input query and the target video moment (single-target VMR), disregarding the possibility that a video may contain multiple target moments that match the query description (multi-target VMR). Previous methods tackle multi-target VMR by incorporating false negative moments with the original target moment for multi-target training. However, existing methods cannot properly work when no false negative moments exist in the video, or when the identified false negative moments are noisy but are still being utilized as pseudo-labels. In this paper, we propose to tackle multi-target VMR by Semantic Fusion Augmentation and Semantic Boundary Detection (SFABD). Specifically, we use feature-level augmentation to generate augmented target moments, along with an intra-video contrastive loss to ensure feature consistency. Meanwhile, we perform semantic boundary detection to adaptively remove all false negatives from the negative set of contrastive loss to avoid semantic confusion. Extensive experiments conducted on Charades-STA, ActivityNet Captions, and QVHighlights show that our method achieves state-of-the-art performance on multi-target metrics and single-target metrics. The source code is available at https://github.com/basiclab/SFABD.