Lightweight Relational Proposal Network with Dual-Branch Distillation for Video Moment Retrieval

Video Moment Retrieval (VMR) aims to localize specific temporal segments in untrimmed videos that correspond to given natural language queries. However, existing proposal-based methods often fail to effectively model inter-proposal relationships and typically involve large parameter overheads. To address these issues, we propose a Lightweight Relational Proposal Network (LRPN) for efficient video moment retrieval. LRPN adopts a dual-branch slow-transfer distillation framework comprising a teacher and a student branch, reflecting the real-world characteristics of both roles. Specifically, we first introduce a semantic relation-aware module that mines relationships between video snippets and queries. Besides, in the teacher branch, we design a knowledge-enhanced relational module to leverage the teacher's knowledge capacity for modeling proposal relationships. In contrast, the student branch incorporates a compact relational modeling module, enabling efficient proposal relationship modeling with less parameters to meet the demand for rapid inference. Extensive experiments on TACoS, ActivityNet-Captions, and Charades-STA demonstrate that LRPN achieves state-of-the-art performance while maintaining a highly compact model design.