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arXiv Computer Vision By Hannaneh Kalantari, Javad Khoramdel

When More References Hurt: Contamination-Aware DINOv2 Memory Banks for Few-Shot Steel Defect Detection

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The paper investigates how to improve patch‑memory anomaly detectors for steel defect detection when additional industrial images may contain unseen defects. By filtering out the most suspicious 20% of patches from a contaminated reference bank and merging the remaining patches with a clean seed bank, the authors reduce contamination from 9.46% to 2.59% and achieve higher AUPRC scores compared to naive expansion or random removal. The method demonstrates that reference purity is a critical design factor and that unverified images can be beneficial only after explicit filtering.

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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