SAMVSR: Leveraging Semantic Priors to Zone-Focused Mamba for Video Snow Removal

The outdoor vision systems are frequently degraded by snow particles, which obscure scene content and impair the performance of downstream vision tasks. While previous methods rely on physical priors, their performance often deteriorates under real-world conditions. Recently, semantic priors have proven effective in guiding image restoration, especially with the advent of the Segment Anything Model (SAM), which provides robust segmentation masks under adverse weather. However, leveraging SAM in video restoration remains underexplored due to the temporal inconsistency of inter-frame segmentation. In this work, we carefully construct the first framework to incorporate SAM-derived semantic priors into video snow removal, called SAMVSR. Specifically, to address temporal SAM label misalignment, we introduce an Entropy-wise Zone Propagation technique, which selects a reliable reference mask and semantically aligns instances across different frames via an entropy-guided label matching mechanism. Based on the aligned SAM semantic priors, we propose a Zone-Focused Mamba module, a novel Mamba-based architecture that restricts its scanning scope to semantically coherent zones, effectively mitigating irrelevant interactions and enhancing temporal-spatial consistency. Extensive experiments on both synthetic and real-world benchmarks finely validate the superiority of our proposed SAMVSR over existing state-of-the-art video desnowing techniques.