Beyond Caption-Based Queries in Video Moment Retrieval

Current Video Moment Retrieval (VMR) models are trained on videos paired with captions, which are written by annotators after watching the videos. These captions are used as textual queries---which we term caption-based queries. This annotation process induces a visual bias, leading to overly descriptive and fine-grained queries, which significantly differ from the more general search queries that users are likely to employ in practice. In this work, we investigate the degradation of existing VMR methods, particularly of DETR architectures, when trained on caption-based queries but evaluated on search queries. For this, we introduce three benchmarks by modifying the textual queries in three public VMR datasets---i.e., HD-EPIC, YouCook2 and ActivityNet-Captions. Our analysis reveals two key generalization challenges: (i) A language gap, arising from the linguistic under-specification of search-queries, and (ii) a multi-moment gap, caused by the shift from single moment to multi-moment queries. We also identify a critical issue in these architectures---an active decoder-query collapse---as a primary cause of the poor generalization to multi-moment instances. We mitigate this issue with architectural modifications that effectively increase the number of active decoder queries. Extensive experiments demonstrate that our approach improves performance on search queries by up to 14.82% $mAP_m$, and up to 21.83% $mAP_m$ on multi-moment search queries.