arXiv Computer Vision
Aug 25

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

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.

By Hannaneh Kalantari, Javad Khoramdel
arXiv Machine Learning
Aug 19

Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

The paper introduces a training‑free, human‑in‑the‑loop anomaly detection framework that allows a domain expert to correct a PatchCore detector by editing its memory bank, without retraining or using gradients. Using only ten golden samples, operator corrections close a median 66% of the performance gap to a fully trained bank, improving 12 of 15 MVTec AD categories while harming none. The approach is evaluated with a rigorous held‑out protocol and shows that passive and active querying yield statistically indistinguishable gains, with a defect‑memory extension failing decisively.

By Ayusha Abbas, Saram Abbas, Kabita Adhikari
arXiv Computer Vision
5d ago

ShiftSplit-AD: Separating Domain Shift from Defects in Foundation-Feature Visual Anomaly Detection

ShiftSplit-AD is a method that separates domain shift from defects in visual anomaly detection by decomposing the residual matrix of DINOv2 features into low‑rank and row‑sparse components. The sparse component is used for scoring anomalies, optionally fused with the low‑rank part. Experiments on AeBAD‑S show that sparse‑only scoring raises image AUROC from 0.6780 to 0.7294 and AUPRC from 0.8052 to 0.8465, but it also lowers clean AUROC on MVTec categories and hurts Bottle localization, highlighting a trade‑off between filtering shift and preserving defect information.

By Muhamathu Ameer Ali Aacaas Muhamath