Anomaly Localization

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
Apr 26, 2026Simone Mosco, Daniel Fusaro, Alberto PrettoPoint Cloud Anomaly Detection 3D Point Cloud Segmentation
University of Padova, Italy
Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography
Apr 24, 2026Tania Haghighi, Sina Gholami, Hamed Tabkhi +1Medical Imaging Medical Image Anomaly Detection
University of North Carolina at Charlotte Charlotte, NC, USA
Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Apr 22, 2026Burak Karabulut, Carlo Manna, Chris DevelderGraph Attention Networks Graph Neural Networks
Ghent University – imec · VITO
Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
Apr 20, 2026Pooyan Khosravinia, João Gama, Bruno VelosoAnomaly Localization Time-Series Anomaly Detection
ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
Apr 20, 2026Qiuhui Chen, Jiaxiang Song, Shuai Tan +1Multimodal Grounding Interpretable Anomaly Detection
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
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 · Université de Toulouse, IRIT, CNRS · UT2J, IRIT-SIG, IRIT +2
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
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
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
Auditing Frame-Level AUC in Weakly Supervised Video Anomaly Detection: Granularity, Resolution, and Scene Bias
Date pendingSara Abdulaziz, Egor BondarevAnomaly Localization Algorithmic Bias
Eindhoven University of Technology, 5612 AE Eindhoven, Netherlands

Apr 26, 2026, cs.CVJ/K move · Enter open · S save

Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation

Simone Mosco, Daniel Fusaro, Alberto Pretto

University of Padova, Italy

Point Cloud Anomaly Detection 3D Point Cloud Segmentation Autonomous Driving Perception Anomaly Localization OOD Detection

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Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial in real-world environments, as done in Anomaly Segmentation. However, research in the 3D field remains limited, with most existing approaches applying post-processing techniques from 2D vision. To cover this lack, we propose a new efficient approach that directly operates in the feature space, modeling the feature distribution of inlier classes to constrain anomalous samples. Moreover, the only publicly available 3D LiDAR anomaly segmentation dataset contains simple scenarios, with few anomaly instances, and exhibits a severe domain gap due to its sensor resolution. To bridge this gap, we introduce a set of mixed real-synthetic datasets for 3D LiDAR anomaly segmentation, built upon established semantic segmentation benchmarks, with multiple out-of-distribution objects and diverse, complex environments. Extensive experiments demonstrate that our approach achieves state-of-the-art and competitive results on the existing real-world dataset and the newly introduced mixed datasets, respectively, validating the effectiveness of our method and the utility of the proposed datasets. Code and datasets are available at https://simom0.github.io/lido-page/.

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