From Interference to Stability: Adversarial Reliability Correction for Video Moment Retrieval with Relevance Feedback

Video Moment Retrieval (VMR) aims to retrieve target video moments that correspond to natural language queries. Most existing methods rely on a positive-only assumption that the queried moment always exists within the video, which limits their reliability in practical scenarios. Departing from this restrictive setting, we study Video Moment Retrieval with Relevance Feedback (VMR-RF), which requires models to both retrieve relevant moments and reject irrelevant queries. This task remains challenging due to the following issues: 1) Intrinsic Semantic Interference caused by visually similar but irrelevant moments, and 2) Propagative Decision Irreversibility induced by unidirectional relevance prediction. In light of these, we introduce AdversaRial Reliability cOrrection netWork (ARROW) for VMR-RF. ARROW adopts an active discriminative strategy through two synergetic components: a Gradient-induced Semantic Adversary (GSA) that probes model vulnerabilities by actively amplifying semantic interference, and an Adversarial Reliability Predictor (ARP) that quantifies prediction stability under such interference to effectively suppress unreliable decisions. Extensive experiments validate the effectiveness of ARROW.