← 研踪 PaperTrailarXiv:2603.24006 · v2
Video Object Segmentation under Aquatic Domain Shift: A Diagnostic Benchmark and Restoration Adapter
首次提交 2026-03-25 15:10
版本更新 2026-11-07 19:49
雷达观测 2026-11-10 11:14
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.
作者备注：Accepted by Pattern Recognition. Project repository: https://github.com/hszh/UW-VOS
期刊信息：Pattern Recognition 183 (2027) 115218
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v2 · 2026-11-07 19:49 · 最新观测版本
Video Object Segmentation under Aquatic Domain Shift: A Diagnostic Benchmark and Restoration Adapter
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.
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