Anchor-Guided Discriminative Semantic Expansion for Point-Supervised Video Moment Localization

Abstract

With the rapid growth of real-world untrimmed videos, video moment localization (VML) has become a fundamental task in query-guided video understanding, aiming to identify the temporal moment that semantically matches a natural language query. Although fully supervised methods have achieved promising performance, their reliance on precise start–end annotations makes them difficult to scale to open-domain video scenarios, where visual contents are diverse, distracting contexts are common, and annotation resources are limited. Point-supervised VML provides a more data-efficient alternative by requiring only a single annotated point inside the target moment. However, such sparse supervision makes it challenging to infer the complete query-relevant interval and to suppress semantically similar distractors. To address these challenges, we propose an anchor-guided discriminative semantic expansion (ADSE) framework. ADSE treats the annotated point as a reliable semantic anchor, learns anchor-centered cross-modal alignment to generate a temporal relevance curve, and adaptively expands the anchor into a coherent target moment by integrating query relevance and temporal semantic continuity. Meanwhile, an anchor-guided discriminative learning strategy mines high-confidence anchor-excluded intervals as hard negatives, and an inside–outside separation objective further distinguishes target moments from surrounding contexts. Extensive experiments on public benchmarks demonstrate the effectiveness of ADSE and show consistent improvements over existing point-supervised methods under sparse point-level supervision.