Current methods for Video Moment Retrieval (VMR) struggle to align complex
situations involving specific environmental details, character descriptions,
and action narratives. To tackle this issue, we propose a Large Language
Model-guided Moment Retrieval (LMR) approach that employs the extensive
knowledge of Large Language Models (LLMs) to improve video context
representation as well as cross-modal alignment, facilitating accurate
localization of target moments. Specifically, LMR introduces a context
enhancement technique with LLMs to generate crucial target-related context
semantics. These semantics are integrated with visual features for producing
discriminative video representations. Finally, a language-conditioned
transformer is designed to decode free-form language queries, on the fly, using
aligned video representations for moment retrieval. Extensive experiments
demonstrate that LMR achieves state-of-the-art results, outperforming the
nearest competitor by up to 3.28\% and 4.06\% on the challenging QVHighlights
and Charades-STA benchmarks, respectively. More importantly, the performance
gains are significantly higher for localization of complex queries.

通过引入大型语言模型（LLMs）的广泛知识，我们提出了一种大型语言模型引导的时刻检索（LMR）方法，以改善视频上下文表示和跨模态对齐，从而实现准确的目标时刻定位。