The paper introduces DPA, a diffusion-based framework that decouples product-agnostic anomaly representations to enable zero-shot anomaly generation. By reusing real anomalies from existing source products and filtering them for plausibility, DPA learns product-irrelevant anomaly embeddings that can be transferred across products. An adaptive mask-guided pipeline and a training-free labeling module further refine the realism and localization of generated anomalies, leading to improved performance on MVTec-AD, VisA, and a new anomaly-transfer benchmark.
By Hang Yao, Yansheng Fu, Ming Liu, Zifei Yan, Yanli Ji, Hongzhi Zhang, Wangmeng Zuo
arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).
By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.
arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.
By Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar, Alberto Crivellaro, Matteo Matteucci
Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the key challenge is to estimate an episode-specific boundary for an unseen target category from a small support set.
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
arXiv:2608. 00442v2 Announce Type: replace-cross Abstract: Medical anomaly detection identifies abnormal images and localizes lesions under scarce supervision while generalizing across organs and modalities.
By Yibo Wan, Jinyu Cai, See-kiong Ng
arXiv:2609.36968v1 Announce Type: new
Abstract: Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods...
By Doyun Choi, Dooho Lee, Jaemin Yoo
This paper introduces IDEAL, a framework for few-shot anomaly detection that uses both normal and anomalous reference examples. IDEAL suppresses irrelevant normal variations and encodes intrinsic deviation vectors that capture discriminative anomaly directions. Experiments on eight real-world datasets show that IDEAL generalizes to unseen anomalies and outperforms existing methods.
By Huan Wang, Jun Shen, Jun Yan, Guansong Pang
arXiv:2609.16785v1 Announce Type: new
Abstract: Zero-shot anomaly detection aims to localize anomalies without target-domain samples. Existing CLIP-based methods suffer from coarse anomaly maps and l...
By Xuezhi Xiang, Guanghao Wu, Heqi Xiang, Jiayao Liu, Xiaoheng Li, Yiming Chen, Shanjun Zhang
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data.
Zero-shot anomaly detection aims to localize anomalies without target-domain samples. Existing CLIP-based methods suffer from coarse anomaly maps and limited semantic prompts. We propose PSMP-CLIP, in...