The paper introduces Anomaly‑LR, a defect‑grounded latent reasoning framework for industrial anomaly detection that builds a global understanding of an image and then refines anomaly‑relevant representations directly in visual latent space. It also presents IAD‑LR‑22K, a new instruction dataset with 22,228 image‑question pairs and detailed annotations. Experiments demonstrate that Anomaly‑LR outperforms comparable‑scale methods on multiple IAD benchmarks without needing external references or tools.
By Jaron Yeh, Yen-Wei Chang, Jiang Liu, Shao-Yuan Lo
arXiv:2607. 18850v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning.
By Shuimu Chen, Jing Jin, Nan Su, Hongbo Xu, Zebang Cheng, Wenming Yang, Fei Ma, Guijin Wang
arXiv:2610.15032v1 Announce Type: cross
Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in whi...
By {\L}ukasz Marcjan, Dorota Wilk-Ko{\l}odziejczyk, Roberto Corizzo
arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.
By Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang
arXiv:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.
By Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, Yang Liu, Min Xu
arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.
By Zekai Zhang, Jinglin Zhang, Qinghui Chen, Gang Li, Da Chen, Shuainan Jing, He Wang, Dagang Li, Cong Liu, Cong Bai, Shengyong Chen
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
Cog-VADU is a training‑free framework that transforms video anomaly detection into a sequential cognitive reasoning task. It uses Chain‑of‑Anomaly Detection Thought Prompting (CoADTP) to create a recurrent reasoning chain across video segments, preserving temporal memory and distinguishing complex anomalies from high‑motion normal activities. A cross‑modal re‑ranking stage aligns textual rationales with visual embeddings to enforce semantic consistency and temporal coherence, yielding competitive zero‑shot performance on multiple VAD benchmarks.
By Mohd Ubaid Wani, Sara Atito, Josef Kittler, Muhammad Awais
arXiv:2607. 10666v1 Announce Type: cross Abstract: Deploying AI-based visual inspection in manufacturing is hard because requirements change often, new defect types appear, and large labeled datasets are rarely available.
By Shubham Rao
arXiv:2606. 07965v1 Announce Type: new Abstract: Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks.
By Zekai Zhang, Qinghui Chen, Maomao Xiong, Shijiao Ding, Zhanzhi Su, Xinjie Yao, Yiming Sun, Cong Bai, Jinglin Zhang
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:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.
By Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti