Year: 2026
Type: article
Abstract: 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 [email protected] (TACoS) and 1.65% in mAP (QVHighlights), demonstrating notable gains in localization accuracy, particularly on long-form videos.
Source: Scientific Reports
Institution: Shanghai University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G579253222
OpenAlex ID: W7148628826