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arXiv Computer Vision By Boa Jang, JunGyu Lee, Gwanho Lee, Jinwook Choi, Young-Gon Kim

Skeleton-Guided Progressive Test-Time Adaptation for Thin Curvilinear Structures

Read the original on arXiv Computer Vision →

The paper introduces Skeleton‑Guided Progressive Test‑Time Adaptation (SGP‑TTA) for accurately segmenting thin curvilinear structures such as vessels and roads. It combines Progressive Batch Normalization (ProgBN) to gradually shift normalization statistics toward target data, and Consensus Skeleton Recall (CSR) to enforce structural connectivity by updating only BN affine parameters. Experiments demonstrate that SGP‑TTA outperforms existing test‑time adaptation methods, especially under cross‑modality domain shifts.

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