arXiv:2608.29112v1 Announce Type: new
Abstract: Recent advances in anomaly detection (AD) for industrial inspection have pushed performance on standard benchmarks toward saturation. However, strong b...
By Dat Cao, Son Nghiem, Phan Nguyen, Jun Rekimoto, Jhih-Ciang Wu
Vision foundation models such as DINOv2 enable strong few‑shot anomaly detection (FSAD) using a frozen backbone and a non‑parametric k‑nearest‑neighbor (k‑NN) scorer. The paper introduces a white‑box attack framework for k‑NN memory‑bank detectors and proposes an adversarially robust FSAD method that is training‑free with respect to the backbone. The defense combines a PatchShift input augmentation and a FeaturePurifier residual MLP, achieving significant gains in image‑level AUROC (~27%) and pixel‑level PRO (~51%) on MVTec‑AD, VisA, and MPDD while maintaining clean accuracy within 3%.
whyItMatters:"The work addresses the previously unstudied few‑shot regime for robust anomaly detection, providing a practical, training‑free defense that matches full‑shot adversarially‑trained baselines and improves performance under adversarial attacks."
By Akib Mohammed Khan, Bartosz Krawczyk
The paper introduces PL‑SCEA, a method that reconfigures the attention mechanism of frozen Vision Foundation Models to better detect and localize anomalies in industrial images with few training examples. PL‑SCEA preserves the semantic context of pretrained query‑key attention while adding token‑adaptive self‑correlations over contextualized value features, then applies positive‑correlation filtering and power‑law reweighting to highlight task‑relevant relationships. The resulting features are fed into a lightweight variational autoencoder to produce reconstruction‑based anomaly scores, achieving competitive image‑level detection and strong pixel‑level localization on MVTec AD and VisA datasets.
By Xiaoyu Yang, Qixing Wu, Huixian Zhao, Changlong Jin
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.
By Changyi Li, Miao Yu, Kai Dong, Yu Xiao
arXiv:2606. 07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.
By Qiaoyu Chen, Bing Zhang
arXiv:2608.23723v1 Announce Type: new
Abstract: Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual f...
By Wenyang Liu, Tianyi Liu, Dongshuo Zhang, Kejun Wu, Adams Wai-Kin Kong