arXiv Computer Vision September 7, 2026 By Changyi Li, Miao Yu, Kai Dong, Yu Xiao

Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion

Read the original on arXiv Computer Vision →

The paper introduces a training‑free anomaly detector that simultaneously handles structural and logical defects by calibrating heterogeneous anomaly cues with statistics from normal images. This calibration aligns frozen representations, allowing their fusion without extra training or part‑level supervision. The resulting method achieves state‑of‑the‑art AUROC scores on MVTec‑LOCO and remains competitive on MVTec‑AD.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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