DAAN: Mitigating Information Asymmetries for Point-Supervised Video Moment Retrieval

Video Moment Retrieval (VMR), the task of identifying a specific moment in an untrimmed video from a natural language query, is a key capability for advanced video understanding. As an emerging research direction, point-supervised VMR offers a cost-effective solution by requiring minimal annotation efforts. In this paper, we explore the information asymmetries within this task: a semantic-positional asymmetry between the clarity of the visual cues and the imprecision of the annotated frame's position, and an instance-duration asymmetry between the certainty of a moment's occurrence and the ambiguity of its duration. To mitigate these asymmetries, we propose a novel framework, Dual-Asymmetry Aligned Network (DAAN), featuring two specialized components: (1) the Center-Guided Calibration module (CGC) which mitigates positional imprecision by dynamically generating a pseudo temporal center and leveraging a decomposed cross-modal attention strategy to recalibrate the feature distribution to align semantic attention with the estimated event centroid; and (2) the Duration-Aware Proposal Discriminator (DPD) which alleviates duration ambiguity by distinguishing the most plausible temporal spans. It infers missing duration information by learning query-conditioned structural compatibility between proposal content and boundary representations. Extensive experiments on TACoS, ActivityNet Captions, and Charades-STA validate the effectiveness of our proposed framework in point-supervised VMR.