Abstract
Existing multi-modal fusion methods typically apply static frame-based image fusion techniques directly to video fusion tasks, neglecting inherent temporal dependencies and leading to inconsistent results across frames. To address this limitation, we propose the first video fusion framework that explicitly incorporates temporal modeling with visual-semantic collaboration to simultaneously ensure visual fidelity, semantic accuracy, and temporal consistency. First, we introduce a visual-semantic interaction module consisting of a semantic branch and a visual branch, with Dinov2 and VGG19 employed for targeted distillation, allowing simultaneous enhancement of both the visual and semantic representations. Second, we pioneer integrate the video degradation enhancement task into the video fusion pipeline by constructing a temporal cooperative module, which leverages temporal dependencies to facilitate weak information recovery. Third, to ensure temporal consistency, we embed a temporal-enha