Anomaly Localization

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Overview

Anomaly localization studies how to find the specific pixels, regions, frames, time intervals, or variables responsible for an unusual observation, so that detection can support inspection and diagnosis. Common approaches learn normal patterns with reconstruction models, pretrained visual features, or memory banks, then produce anomaly maps; weakly supervised video models and vision-language systems use image- or video-level labels, prompts, or reasoning to ground their predictions. In time-series and infrastructure settings, forecasting, causal graphs, and counterfactual analysis help locate affected variables or likely root causes. A central challenge is making localization reliable under domain shift and limited labels, while evaluating spatial or temporal precision separately from image- or video-level detection scores.

Latest papers

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147 papers16 papers in the last four weeks, up 7% on the four weeks before. 0.1% of all new papers.

  1. Flownomaly: Single-Step Conditional-Mean Recovery for Few-Shot Anomaly Detection

    Oct 23, 2026Bernardo Perrone Ribeiro, Erkut Akdag, Egor BondarevFew-Shot Anomaly DetectionIndustrial Anomaly Detection

    AIMS Group, Department of Electrical Engineering, Eindhoven University of Technology

  2. Adaptive World-Models for Anomaly Recognition (AWARE): A Framework for Robot Dynamics Prediction and Introspection

    Oct 23, 2026Luke Beddow, George Mavroghenis, Rodrigo Chacon Quesada +5Robotic Anomaly DetectionRobot Failure Detection

    BOLD, Imperial College London

  3. Guided Zero-Shot Anomaly Explanation for Anonymized Crowd Video Monitoring: Context, Categorization, and Spatial Localization

    Oct 23, 2026Mulugeta Weldezgina Asres, Lei Jiao, Christian Walter OmlinVideo Anomaly DetectionAnomaly Localization

    University of Agder (UiA), Norway

  4. LACToS: Local Anomaly Detection via Conditional Typicality of Superpixels

    Oct 22, 2026Jihoon Hong, Alireza Kheirandish, Rohan Sanda +1Superpixel SegmentationAnomaly Detection

    Georgia Institute of Technology, Atlanta, GA, USA · Stanford University, Stanford, CA, USA · Cohere

  5. Knowledge-Guided Distributional Prompt Learning with Visual Hierarchy for Zero-Shot Anomaly Detection

    Oct 16, 2026Jiyul Ham, Seongho Yoon, Yonggon Jung +2Zero-Shot Anomaly DetectionPrompt Learning

    Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea

  6. InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection

    Oct 10, 2026Łukasz Marcjan, Dorota Wilk-Kołodziejczyk, Roberto CorizzoTask-Incremental LearningContinual Anomaly Detection

    AGH University of Krakow, Krakow, Poland · American University, Washington, DC, USA

  7. Is In-Domain Training Enough for Fine-Grained Industrial Anomaly Understanding?

    Oct 8, 2026Xingwu Zhang, Duanyang Du, Huiling Zhu +5Industrial Anomaly DetectionMultimodal Anomaly Detection

    Hunan University · University of Aberdeen · South China Normal University +1

  8. Anchor and Adapt: Asymmetric Prompt Adaptation for Few-Shot Industrial Anomaly Detection

    Oct 4, 2026Mengyang Zhao, Teng Fu, Haiyang Yu +3Prompt Learning for VLMsFew-Shot Anomaly Detection

    Shanghai Key Lab of Intelligent Information Processing and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China

  9. Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)

    Oct 3, 2026Lars Heckler-Kram, Dorian Henning, Ashwin Vaidya +6Anomaly DetectionIndustrial Anomaly Detection

    MVTec Software GmbH · Technical University of Munich · Amazon +2

  10. MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI

    Oct 1, 2026Negin Kafee Hernashki, Soumick ChatterjeeAnomaly LocalizationUnsupervised Anomaly Detection

    Università degli Studi di Milano, Milan, Italy · Politecnico di Milano, Milan, Italy · Human Technopole, Milan, Italy +1

  11. Anomaly Detection and Localization for the Pantograph-Catenary System

    Oct 1, 2026Francesco Vitale, Hangli Ge, Francesco FlamminiIndustrial Anomaly DetectionAnomaly Localization

    DIETI University of Naples Federico II Naples, Italy · Interfaculty Initiative in Information Studies The University of Tokyo Tokyo, Japan · DIMAI University of Florence Florence, Italy +1

  12. Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

    Oct 1, 2026Roberto Stanzione, Jules Barbe, Magali Parrino +2Interpretable Anomaly DetectionAnomaly Localization

    Inria, ENS, CNRS, PSL Paris, France · Scality Paris, France · Inria, ENS, CNRS, PSL, EDF Paris, France

  13. PCB-MC: Missing Component Analysis in Printed Circuit Boards

    Sep 30, 2026Betsy Villa Brochero, Ian Gibson, Estefania TalaveraIndustrial InspectionAnomaly Localization

    EEMCS-CS-DMB University of Twente Enschede, The Netherlands · ET-DPM-AMSPES University of Twente Enschede, The Netherlands

  14. TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization

    Sep 30, 2026JinYoung Kim, Geonho Kim, GiJeong Park +2Domain GeneralizationAnomaly Localization

    Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea · SNUAILAB Co., Ltd., Seoul, Korea

  15. LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

    Sep 30, 2026Jahyeob Koo, Kio Yun, Byoungmo Koo +1LLM-Based Time Series Anomaly DetectionAnomaly Localization

    Department of Industrial and Management Engineering, Korea University Seoul, Republic of Korea

  16. Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detection

    Sep 28, 2026Zhiqiang Qin, He Xie, Junfei Yi +5Point Cloud Anomaly DetectionIndustrial Anomaly Detection

    School of Artificial Intelligence and Robotics, Hunan University · National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University

  17. Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space

    Sep 24, 2026Jaron Yeh, Yen-Wei Chang, Jiang Liu +1Industrial Anomaly DetectionAnomaly Localization

    National Taiwan University · AMD GenAI

  18. AT3D-AD: Anomaly Type-Aware 3D Anomaly Detection via Hierarchical Point-Language Alignment

    Sep 22, 2026Jingyu Zeng, Haoquan Lu, Can GaoPoint Cloud Anomaly DetectionIndustrial Anomaly Detection

    College of Computer Science and Software Engineering and the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China

  19. Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior

    Sep 21, 2026Hugues Roy, Reuben Dorent, Ninon BurgosMedical Image Anomaly DetectionAnomaly Localization

    Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France

  20. SAFe: Segment-guided Aggregation of Feature Densities for Anomaly-aware Segmentation

    Sep 21, 2026Anja Delić, Jurica Runtas, Marin Oršić +2Anomaly LocalizationDensity Estimation

    University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb, Croatia

  21. Earth Surface Immune System for Rapid Monitoring of Unknown Anomalies

    Sep 17, 2026Jingtao Li, Qian Zhu, Xinyu Wang +3Remote Sensing Change DetectionAnomaly Localization

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, P. R. China. · School of Remote Sensing and Information Engineering, Wuhan University, P. R. China.

  22. A Non-Linear Neuron Based Detection of Isolated Pixels in Binary and Grayscale Images using Contrast Sensitive Receptive Fields

    Sep 16, 2026Nassir MohammadAnomaly Localization

    Cyber Innovation Lab, VCX, Airbus, Newport, UK

  23. PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection

    Sep 15, 2026Xuezhi Xiang, Guanghao Wu, Heqi Xiang +4Promptable Image SegmentationAnomaly Localization

    Information and Communication Engineering, Harbin Engineering University, Harbin, China · Key Laboratory of Advanced Marine Communication and Information Technology, Harbin, China · Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada +1

  24. Variational Template Matching with Statistical Fusion for Anomaly Detection in Patterned Structures

    Sep 10, 2026Qinwu Xu, Yifan JiangAnomaly DetectionInterpretable Anomaly Detection

  25. GaugeDefect: Detecting Surface Anomalies by Curvature of Feature Transport

    Sep 8, 2026Yefan WangIndustrial Anomaly DetectionAnomaly Localization

    University of Shanghai for Science and Technology, Shanghai, China

  26. SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection

    Sep 8, 2026Kehan Yan, Yue Tan, Qingfeng Chen +3Anomaly LocalizationAnomaly Detection

    Guangxi University, Guangxi, China · Griffith University, Queensland, Australia

  27. Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection

    Sep 7, 2026Manwen Yang, Leqian Ding, Yu Guo +1VLM AdaptationAnomaly Localization

    School of Software Engineering, Xi’an Jiaotong University · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University

  28. Spatial Attention Supervision for Defect Localization: Exploiting Ground-Truth Masks as Training Signal in Diffusion-Augmented Defect Detection

    Sep 5, 2026Sajjad Rezvani Boroujeni, Muskan Saraf, Gnana Tulasi Makineni +2Industrial Anomaly DetectionAnomaly Localization

    Data Science Department, Actual Reality Technologies, OH, USA.

  29. PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection

    Sep 3, 2026Xiaoyu Yang, Qixing Wu, Huixian Zhao +1Few-Shot Anomaly DetectionVision Foundation Model Adaptation

    School of Airspace Science and Engineering, Shandong University, Shandong, China

  30. Neural-Collapse-guided Task-Free Continual Anomaly Detection

    Sep 3, 2026Xiaotong Kong, Chaoyang Song, Ziai Zhou +3Continual Anomaly DetectionContinual Learning

    Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China