Video Object Segmentation under Aquatic Domain Shift: A Diagnostic Benchmark and Restoration Adapter
Hongshen Zhao, Jingkang Tai, Yuhang Wu, Wenkang Zhang, Xi Lan, Shangyan Wang, Tianyu Zhang, Wankou Yang
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.
cs.CV