{"total":214,"rows":[{"id":"2310.18961","title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","created":"2023-10-29","cited_by_count":271,"area":null},{"id":"2301.11514","title":"Deep Industrial Image Anomaly Detection: A Survey","created":"2023-01-27","cited_by_count":148,"area":null},{"id":"2403.12580","title":"Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection","created":"2024-03-19","cited_by_count":139,"area":null},{"id":"2403.05897","title":"RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection","created":"2024-03-09","cited_by_count":116,"area":null},{"id":"2405.14325","title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","created":"2024-05-23","cited_by_count":93,"area":null},{"id":"2309.13226","title":"Real3D-AD: A Dataset of Point Cloud Anomaly Detection","created":"2023-09-23","cited_by_count":87,"area":null},{"id":"2407.09359","title":"A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization","created":"2024-07-12","cited_by_count":74,"area":null},{"id":"2503.06661","title":"AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP","created":"2025-03-09","cited_by_count":73,"area":null},{"id":"2310.14228","title":"Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection","created":"2023-10-22","cited_by_count":66,"area":null},{"id":"2301.13359","title":"IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing","created":"2023-01-31","cited_by_count":64,"area":null},{"id":"2306.02602","title":"ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction","created":"2023-06-05","cited_by_count":63,"area":null},{"id":"2404.13671","title":"FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization","created":"2024-04-21","cited_by_count":51,"area":null},{"id":"2503.02424","title":"Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection","created":"2025-03-04","cited_by_count":48,"area":null},{"id":"2312.07495","title":"Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection","created":"2023-12-12","cited_by_count":47,"area":null},{"id":"2310.07716","title":"PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection","created":"2023-10-11","cited_by_count":46,"area":null},{"id":"2307.13925","title":"EasyNet: An Easy Network for 3D Industrial Anomaly Detection","created":"2023-07-26","cited_by_count":43,"area":null},{"id":"2308.02983","title":"Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection","created":"2023-08-06","cited_by_count":32,"area":null},{"id":"2401.01010","title":"Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt","created":"2024-01-02","cited_by_count":31,"area":null},{"id":"2410.00320","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","created":"2024-10-01","cited_by_count":29,"area":null},{"id":"2505.09926","title":"AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection","created":"2025-05-15","cited_by_count":29,"area":null},{"id":"2403.13349","title":"Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection","created":"2024-03-20","cited_by_count":28,"area":null},{"id":"2412.03342","title":"UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection","created":"2024-12-04","cited_by_count":28,"area":null},{"id":"2404.04518","title":"MedIAnomaly: A comparative study of anomaly detection in medical images","created":"2024-04-06","cited_by_count":26,"area":null},{"id":"2412.04867","title":"MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects","created":"2024-12-06","cited_by_count":26,"area":null},{"id":"2503.18325","title":"Towards Training-free Anomaly Detection with Vision and Language Foundation Models","created":"2025-03-24","cited_by_count":26,"area":null},{"id":"2312.08917","title":"An Incremental Unified Framework for Small Defect Inspection","created":"2023-12-14","cited_by_count":25,"area":null},{"id":"2410.20047","title":"ResAD: A Simple Framework for Class Generalizable Anomaly Detection","created":"2024-10-26","cited_by_count":25,"area":null},{"id":"2304.02216","title":"Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction","created":"2023-04-05","cited_by_count":23,"area":null},{"id":"2403.09303","title":"Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective","created":"2024-03-14","cited_by_count":23,"area":null},{"id":"2406.01078","title":"Unseen Visual Anomaly Generation","created":"2024-06-03","cited_by_count":23,"area":null},{"id":"2505.01969","title":"MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection","created":"2025-05-04","cited_by_count":23,"area":null},{"id":"2405.19882","title":"PixOOD: Pixel-Level Out-of-Distribution Detection","created":"2024-05-30","cited_by_count":20,"area":null},{"id":"2211.13968","title":"MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection","created":"2022-11-25","cited_by_count":19,"area":null},{"id":"2403.11561","title":"Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection","created":"2024-03-18","cited_by_count":19,"area":null},{"id":"2406.11507","title":"Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection","created":"2024-06-17","cited_by_count":19,"area":null},{"id":"2210.14485","title":"Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection","created":"2022-10-26","cited_by_count":18,"area":null},{"id":"2310.18677","title":"Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach","created":"2023-10-28","cited_by_count":18,"area":null},{"id":"2505.09264","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","created":"2025-05-14","cited_by_count":18,"area":null},{"id":"2412.17458","title":"Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection","created":"2024-12-23","cited_by_count":17,"area":null},{"id":"2402.18293","title":"Continuous Memory Representation for Anomaly Detection","created":"2024-02-28","cited_by_count":16,"area":null},{"id":"2404.10760","title":"Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark","created":"2024-04-16","cited_by_count":16,"area":null},{"id":"2505.07375","title":"Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection","created":"2025-05-12","cited_by_count":16,"area":null},{"id":"2208.01188","title":"Curved Geometric Networks for Visual Anomaly Recognition","created":"2022-08-02","cited_by_count":15,"area":null},{"id":"2412.04769","title":"Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection","created":"2024-12-06","cited_by_count":15,"area":null},{"id":"2312.15911","title":"Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection","created":"2023-12-26","cited_by_count":14,"area":null},{"id":"2403.20236","title":"Long-Tailed Anomaly Detection with Learnable Class Names","created":"2024-03-29","cited_by_count":14,"area":null},{"id":"2401.09793","title":"PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection","created":"2024-01-18","cited_by_count":12,"area":null},{"id":"2401.14432","title":"A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams","created":"2024-01-25","cited_by_count":12,"area":null},{"id":"2412.11802","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","created":"2024-12-16","cited_by_count":12,"area":null},{"id":"2412.17297","title":"Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective","created":"2024-12-23","cited_by_count":12,"area":null},{"id":"2504.14221","title":"Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection","created":"2025-04-19","cited_by_count":12,"area":null},{"id":"2307.12540","title":"UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection","created":"2023-07-24","cited_by_count":11,"area":null},{"id":"2308.15939","title":"Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization","created":"2023-08-30","cited_by_count":10,"area":null},{"id":"2407.11935","title":"Learning Multi-view Anomaly Detection with Efficient Adaptive Selection","created":"2024-07-16","cited_by_count":9,"area":null},{"id":"2501.13864","title":"Autoencoders for Anomaly Detection are Unreliable","created":"2025-01-23","cited_by_count":9,"area":null},{"id":"2505.06603","title":"ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection","created":"2025-05-10","cited_by_count":9,"area":null},{"id":"2501.10067","title":"FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization","created":"2025-01-17","cited_by_count":8,"area":null},{"id":"2506.09237","title":"PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies","created":"2025-06-10","cited_by_count":8,"area":null},{"id":"2507.15905","title":"Foundation Models and Transformers for Anomaly Detection: A Survey","created":"2025-07-21","cited_by_count":8,"area":null},{"id":"2302.06670","title":"Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos","created":"2023-02-13","cited_by_count":7,"area":null},{"id":"2403.04151","title":"Dual-path Frequency Discriminators for Few-shot Anomaly Detection","created":"2024-03-07","cited_by_count":7,"area":null},{"id":"2403.14213","title":"Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference","created":"2024-03-21","cited_by_count":7,"area":null},{"id":"2405.06875","title":"LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization","created":"2024-05-11","cited_by_count":7,"area":null},{"id":"2406.03262","title":"A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection","created":"2024-06-05","cited_by_count":7,"area":null},{"id":"2411.17217","title":"Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning","created":"2024-11-26","cited_by_count":7,"area":null},{"id":"2501.12295","title":"Towards Accurate Unified Anomaly Segmentation","created":"2025-01-21","cited_by_count":7,"area":null},{"id":"2502.16412","title":"A Survey on Industrial Anomaly Synthesis","created":"2025-02-23","cited_by_count":7,"area":null},{"id":"2505.17551","title":"Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection","created":"2025-05-23","cited_by_count":7,"area":null},{"id":"2506.03660","title":"INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning","created":"2025-06-04","cited_by_count":7,"area":null},{"id":"2506.21398","title":"FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection","created":"2025-06-26","cited_by_count":7,"area":null},{"id":"2306.03492","title":"Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers","created":"2023-06-06","cited_by_count":6,"area":null},{"id":"2405.11238","title":"SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection","created":"2024-05-18","cited_by_count":6,"area":null},{"id":"2510.17611","title":"Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection","created":"2025-10-20","cited_by_count":6,"area":null},{"id":"2305.15652","title":"Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision","created":"2023-05-25","cited_by_count":5,"area":null},{"id":"2308.10155","title":"Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection","created":"2023-08-20","cited_by_count":5,"area":null},{"id":"2310.07511","title":"Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery","created":"2023-10-11","cited_by_count":5,"area":null},{"id":"2312.03005","title":"Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations","created":"2023-12-04","cited_by_count":5,"area":null},{"id":"2403.12362","title":"DMAD: Dual Memory Bank for Real-World Anomaly Detection","created":"2024-03-19","cited_by_count":5,"area":null},{"id":"2407.21351","title":"Small Object Few-shot Segmentation for Vision-based Industrial Inspection","created":"2024-07-31","cited_by_count":5,"area":null},{"id":"2409.00942","title":"VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization","created":"2024-09-02","cited_by_count":5,"area":null},{"id":"2410.00713","title":"RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations","created":"2024-10-01","cited_by_count":5,"area":null},{"id":"2411.16049","title":"ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift","created":"2024-11-25","cited_by_count":5,"area":null},{"id":"2503.14910","title":"Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift","created":"2025-03-19","cited_by_count":5,"area":null},{"id":"2504.02775","title":"TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection","created":"2025-04-03","cited_by_count":5,"area":null},{"id":"2504.03306","title":"Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection","created":"2025-04-04","cited_by_count":5,"area":null},{"id":"2505.01476","title":"CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering","created":"2025-05-02","cited_by_count":5,"area":null},{"id":"2510.01934","title":"Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors","created":"2025-10-02","cited_by_count":5,"area":null},{"id":"2405.04782","title":"Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection","created":"2024-05-08","cited_by_count":4,"area":null},{"id":"2408.13516","title":"Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection","created":"2024-08-24","cited_by_count":4,"area":null},{"id":"2502.01216","title":"Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2506.00956","title":"Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection","created":"2025-06-01","cited_by_count":4,"area":null},{"id":"2601.05552","title":"One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection","created":"2026-01-09","cited_by_count":4,"area":null},{"id":"2603.02629","title":"Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective","created":"2026-03-03","cited_by_count":4,"area":null},{"id":"2302.14696","title":"Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2307.03243","title":"That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering","created":"2023-07-06","cited_by_count":3,"area":null},{"id":"2405.09933","title":"MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection","created":"2024-05-16","cited_by_count":3,"area":null},{"id":"2503.11088","title":"Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion","created":"2025-03-14","cited_by_count":3,"area":null},{"id":"2504.14919","title":"GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection","created":"2025-04-21","cited_by_count":3,"area":null},{"id":"2508.12931","title":"Towards High-Resolution Industrial Image Anomaly Detection","created":"2025-08-18","cited_by_count":3,"area":null},{"id":"2510.21296","title":"An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination","created":"2025-10-24","cited_by_count":3,"area":null}],"next_cursor":"100"}