Context-Enhanced Video Moment Retrieval with Large Language Models

• URL: http://arxiv.org/abs/2405.12540v1
• Date: Tue, 21 May 2024 07:12:27 GMT
• Title: Context-Enhanced Video Moment Retrieval with Large Language Models
• Authors: Weijia Liu, Bo Miao, Jiuxin Cao, Xuelin Zhu, Bo Liu, Mehwish Nasim, Ajmal Mian,
• Abstract summary: Current methods for Video Moment Retrieval (VMR) struggle to align complex situations involving specific environmental details, character descriptions, and action narratives. 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. 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.
• Score: 22.283367604425916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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