[Submitted on 29 Oct 2026]
Title:AquaProbe: Efficient Adaptation of Frozen Vision Models for Underwater Images
View a PDF of the paper titled AquaProbe: Efficient Adaptation of Frozen Vision Models for Underwater Images, by Md Shihabul Islam Shovo and 2 other authors
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Abstract:Reliable visual perception is essential for safe autonomous underwater exploration. Yet vision models pretrained on terrestrial images become less reliable underwater, where turbidity, backscatter, and wavelength-dependent attenuation alter scene appearance. Existing adaptation methods either spread adapters equally across the encoder depth or select layers using the divergence between two fixed domains. In this paper, we introduce AquaProbe for measuring how the location of the domain gap changes with water type and imaging range. AquaProbe separates newly introduced divergence from that accumulated through a frozen vision encoder using paired clean and physically rendered images and allocates a fixed LoRA budget according to the measured profile. The segmentation mean Intersection-over-Union (mIoU) for the proportional LoRA rank allocation, based on the measured profile, is 70.1; this is a better result than uniform LoRA rank allocation by 1.9 mIoU points while having the same parameter budget of 0.59 million adapter parameters. The three-way router, conditioned on estimated condition clusters, achieves 70.6 mIoU on the standard SUIM split and 63.4 AUDRC under held-out degradation.
Comments: 10 pages, 4 figures, 3 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
Cite as: arXiv:2610.40900 [cs.CV]
(or arXiv:2610.40900v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.40900
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shihabul Islam Shovo [view email] [v1] Thu, 29 Oct 2026 19:22:03 UTC (1,700 KB)
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