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原文摘要

Underwater video object segmentation (VOS) requires temporally consistent dense segmentation in scenes where water acts as a participating optical medium. Wavelength-dependent attenuation, scattering, turbidity, and unstable illumination alter object appearance, weaken mask-level localization cues, and destabilize temporal association, yet existing VOS benchmarks provide limited support for diagnosing these aquatic factors. We introduce UW-VOS, a large-scale diagnostic benchmark with 1,431 video sequences, 307,267 mask annotations, 411 semantic categories, and 16 fine-grained attributes. UW-VOS supports both standard VOS evaluation and factorized analysis of aquatic appearance degradation, spatial localization ambiguity, and temporal identity discontinuity. We further propose the Aquatic Restoration Adapter (ARA), a physics-derived parameter-efficient module that transfers the inverse structure of underwater image formation into bottleneck feature space and can be instantiated in different VOS backbones. Experiments show that representative open-air VOS methods suffer an average 13-point $\mathcal{J}\&\mathcal{F}$ drop on UW-VOS. ARA matches or surpasses full fine-tuning in both Transformer- and memory-based VOS backbones, and outperforms standard PEFT counterparts under a matched setting. These results establish UW-VOS as a diagnostic platform for underwater video perception and ARA as a compact feature-restoration module for aquatic domain adaptation. Project repository: https://github.com/hszh/UW-VOS.