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<p>Video Moment Retrieval (VMR) serves as a fundamental task in video understanding, bridging vision and language by localizing the most relevant temporal segments in untrimmed videos according to a textual query. However, existing approaches excel at fine-grained alignment but often fail to capture global temporal context effectively, particularly in long-form videos. To address this challenge, we propose Hybrid Mamba Network (HM-Net), a two-level fusion architecture which unifying the strengths of attention and sequence modeling. Especially, its core lies in the Hybrid Modulated Bi-Mamba (HMB) Block, which integrates the powerful temporal modeling capability of Mamba into the VMR framework to achieve effective long-range temporal reasoning. Extensive experiments on the challenging TACoS and QVHighlights benchmarks show that HM-Net consistently outperforms existing approaches, achieving 3.84% improvement in R1@0.5 (TACoS) and 1.65% in mAP (QVHighlights), demonstrating notable gains in localization accuracy, particularly on long-form videos.</p>

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Mamba-based modulated fusion model for video moment retrieval

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Bing Yu,
  • Jingyu Li,
  • Youxian Di,
  • Yingran Liu,
  • Youdong Ding,
  • Dongjin Huang

摘要

Video Moment Retrieval (VMR) serves as a fundamental task in video understanding, bridging vision and language by localizing the most relevant temporal segments in untrimmed videos according to a textual query. However, existing approaches excel at fine-grained alignment but often fail to capture global temporal context effectively, particularly in long-form videos. To address this challenge, we propose Hybrid Mamba Network (HM-Net), a two-level fusion architecture which unifying the strengths of attention and sequence modeling. Especially, its core lies in the Hybrid Modulated Bi-Mamba (HMB) Block, which integrates the powerful temporal modeling capability of Mamba into the VMR framework to achieve effective long-range temporal reasoning. Extensive experiments on the challenging TACoS and QVHighlights benchmarks show that HM-Net consistently outperforms existing approaches, achieving 3.84% improvement in R1@0.5 (TACoS) and 1.65% in mAP (QVHighlights), demonstrating notable gains in localization accuracy, particularly on long-form videos.