Anomaly Localization

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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

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

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Apr 19, 2026Shashank Mishra, Karan Patil, Cedric Schockaert +2Feature Attribution Interpretable Anomaly Detection
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Germany · Paul Wurth S.A, Luxembourg
AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving
Apr 16, 2026Fabrizio Genilotti, Arianna Stropeni, Gionata Grotto +4Autonomous Driving Anomaly Localization
University of Padova, Italy
Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling
Apr 14, 2026Pierre Lotte, André Péninou, Olivier TesteTime Series Generation Anomaly Localization
EPE UT, IRIT · UT2J, IRIT-SIG, IRIT · IRIT-SIG, IRIT, UT2J, Comue de Toulouse
MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
Apr 13, 2026Xincheng Yao, Zefeng Qian, Chao Shi +2Anomaly Detection Industrial Anomaly Detection
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University · MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University
ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions
Apr 9, 2026Zihao Liu, Xiaoyu Wu, Wenna Li +2Video Anomaly Detection Anomaly Localization
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
Apr 7, 2026Sakshi Agarwal, Aishik Konwer, Ankit Parag ShahVideo Anomaly Detection Interpretable Anomaly Detection
Center of Advanced AI, Accenture.
Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection
Apr 5, 2026Xueyang Kang, Zizhao Li, Tian Lan +3Point Cloud Anomaly Detection Anomaly Detection
The University of Melbourne, Parkville, Victoria 3010, Australia · Tsinghua University, Haidian District, Beijing 100871, China · The University of New South Wales, Sydney, NSW 2052, Australia +1
GenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation
Apr 2, 2026Elisa Motta, Marta Lorenzini, Clara Mouawad +3Gait Analysis Anomaly Localization
HRI2 Laboratory, Istituto Italiano di Tecnologia (IIT), Genoa, Italy · Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Rome, Italy · Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
mmAnomaly: Leveraging Visual Context for Robust Anomaly Detection in the Non-Visual World with mmWave Radar
Apr 1, 2026Tarik Reza Toha, Shao-Jung, Lu +2Anomaly Detection Anomaly Localization
Louie · University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision
Mar 28, 2026Yizhou Jin, Yuezhu Feng, Jinjin Zhang +3Anomaly Localization Multimodal Anomaly Detection
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China · Hangzhou Innovation Institute, Beihang University, Hangzhou, China
ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality
Mar 26, 2026Wenzhuo Qian, Hailiang Zhao, Ziqi Wang +4Root Cause Analysis Multimodal Anomaly Detection
Zhejiang University, Hangzhou, China
Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection
Mar 26, 2026Md Awsafur Rahman, Chandrakanth Gudavalli, Hardik Prajapati +1Anomaly Localization Transformer Attention
UC Santa Barbara, USA
Template-Based Feature Aggregation Network for Industrial Anomaly Detection
Mar 24, 2026Wei Luo, Haiming Yao, Wenyong YuIndustrial Anomaly Detection Reconstruction-Based Anomaly Detection
State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, 100084, China · State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
Mar 24, 2026Wei Luo, Peng Xing, Yunkang Cao +3Unsupervised Anomaly Detection Industrial Anomaly Detection
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Multimodal Industrial Anomaly Detection via Geometric Prior
Mar 24, 2026Min Li, Jinghui He, Gang Li +3Industrial Anomaly Detection Anomaly Localization
Faculty of Data Science, City University of Macau, Macau, China. · Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China. · Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China.
Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection
Mar 20, 2026Hantao Zheng, Ning Han, Yawen Zeng +1Video Anomaly Detection Weakly Supervised Video Anomaly Detection
School of Computer Science, Hunan University, China · School of Computer Science, Xiangtan University, China
Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Mar 20, 2026Chaoqin Huang, Zi Zeng, Aofan Jiang +6Electrocardiogram Classification Anomaly Detection
Shanghai Jiao Tong University, Shanghai, China · Shanghai AI Laboratory, Shanghai, China · National University of Defense Technology, Wuhan, China +2
FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement
Mar 20, 2026Ming Hu, Yongsheng Huo, Mingyu Dou +6Zero-Shot Anomaly Detection Anomaly Detection
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Xi’an Jiaotong University +1
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
Mar 14, 2026Xi Jiang, Yue Guo, Jian Li +7Industrial Anomaly Detection Industrial Inspection
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China · Macau University of Science and Technology, Macau, China · Tencent YouTu Lab, Shenzhen and Shanghai, China +3
Integration of deep generative Anomaly Detection algorithm in high-speed industrial line
Mar 8, 2026Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli +2Industrial Anomaly Detection Online Anomaly Detection
Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy. · Bonfiglioli Engineering, Via Amerigo Vespucci 20, Ferrara, 44124, Italy.
GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module
Mar 8, 2026Niccolò Ferrari, Michele Fraccaroli, Evelina LammaIndustrial Anomaly Detection Anomaly Localization
Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy · Bonfiglioli Engineering, Via Amerigo Vespucci 20, 44124 Ferrara, Italy
Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
Feb 28, 2026Yu Wang, Shengjie ZhaoVideo Anomaly Detection Weakly Supervised Video Anomaly Detection
School of Computer Science and Technology, Tongji University, Shanghai, China
RAID: Retrieval-Augmented Anomaly Detection
Feb 23, 2026Mingxiu Cai, Zhe Zhang, Gaochang Wu +2Unsupervised Anomaly Detection Anomaly Detection
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University · University of Surrey
Explainable Visual Anomaly Detection via Concept Bottleneck Models
Nov 25, 2025Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti +3Interpretable Anomaly Detection Anomaly Localization
University of Padova, Italy
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
Oct 18, 2025Dongchan Cho, Jiho Han, Keumyeong Kang +3Time-Series Anomaly Detection Unsupervised Anomaly Detection
Industrial AI Lab, SimPlatform Co. Ltd. Affiliate Research Institute
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
Aug 27, 2025Luhu Li, Bin Liu, Bowen Lin +3Prototype Learning Medical Image Anomaly Detection
School of Mathematics, Shandong University, Jinan 250100, China · Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University, Jinan 250033, China · Department of Neurosurgery, The Second Qilu Hospital of Shandong University,2026 Jinan 250033, China
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
Aug 6, 2025Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6VLM Adaptation Industrial Anomaly Detection
1Nanyang Technological University · Institute for Infocomm Research (I2R), A*STAR · 3South China University of Technology
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
Jul 25, 2025Nicolas Pinon, Robin Trombetta, Carole LartizienMedical Image Anomaly Detection Unsupervised Representation Learning
MYRIAD

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Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

Shashank Mishra, Karan Patil, Cedric Schockaert, Didier Stricker+1

German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Germany · Paul Wurth S.A, Luxembourg

Feature Attribution Interpretable Anomaly Detection Anomaly Localization Root Cause Analysis Time-Series Anomaly Detection

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Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unrealistic feature perturbations and ignore temporal and cross-feature dependencies, leading to unreliable attributions. We propose a conditional attribution framework that explains anomalies relative to contextually similar normal system states. Instead of using marginal or randomly sampled baselines, our method retrieves representative normal instances conditioned on the anomalous observation, enabling dependency-preserving and operationally meaningful explanations. To support high-dimensional time-series data, contextual retrieval is performed in learned low-dimensional representations using both variational autoencoder latent spaces and UMAP manifold embeddings. By grounding the retrieval process in the system's learned manifold, this strategy avoids out-of-distribution artifacts and ensures attribution fidelity while maintaining computational efficiency. We further introduce confidence-aware and temporal evaluation metrics for assessing explanation reliability and responsiveness. Experiments on the SWaT and MSDS benchmarks demonstrate that the proposed approach consistently improves root-cause identification accuracy, temporal localization, and robustness across multiple anomaly detection models. These results highlight the practical utility of conditional attribution for explainable anomaly diagnosis in complex time-series systems. Code and models are available at: https://github.com/dfki-av/Conditional-Attribution-for-Root-Cause-Analysis-in-Time-Series-Anomaly-Detection.