{"total":386,"rows":[{"id":"2106.08265","title":"Towards Total Recall in Industrial Anomaly Detection","created":"2021-06-15","cited_by_count":776,"area":null},{"id":"2104.04015","title":"CutPaste: Self-Supervised Learning for Anomaly Detection and Localization","created":"2021-04-08","cited_by_count":531,"area":null},{"id":"2108.07610","title":"DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection","created":"2021-08-17","cited_by_count":514,"area":null},{"id":"2201.10703","title":"Anomaly Detection via Reverse Distillation from One-Class Embedding","created":"2022-01-26","cited_by_count":421,"area":"distill"},{"id":"2207.14315","title":"SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation","created":"2022-07-28","cited_by_count":400,"area":null},{"id":"2107.12571","title":"CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows","created":"2021-07-27","cited_by_count":321,"area":null},{"id":"2303.14814","title":"WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation","created":"2023-03-26","cited_by_count":315,"area":null},{"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":"2206.03687","title":"A Unified Model for Multi-class Anomaly Detection","created":"2022-06-08","cited_by_count":268,"area":null},{"id":"2303.15140","title":"SimpleNet: A Simple Network for Image Anomaly Detection and Localization","created":"2023-03-27","cited_by_count":256,"area":null},{"id":"2101.10030","title":"Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning","created":"2021-01-25","cited_by_count":230,"area":null},{"id":"2104.10036","title":"VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization","created":"2021-04-20","cited_by_count":223,"area":null},{"id":"2112.09045","title":"The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization","created":"2021-12-16","cited_by_count":178,"area":null},{"id":"2303.14535","title":"EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies","created":"2023-03-25","cited_by_count":160,"area":null},{"id":"2110.14051","title":"A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges","created":"2021-10-26","cited_by_count":154,"area":null},{"id":"2211.11317","title":"DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection","created":"2022-11-21","cited_by_count":154,"area":null},{"id":"2301.11514","title":"Deep Industrial Image Anomaly Detection: A Survey","created":"2023-01-27","cited_by_count":148,"area":null},{"id":"2103.04257","title":"Student-Teacher Feature Pyramid Matching for Anomaly Detection","created":"2021-03-07","cited_by_count":146,"area":null},{"id":"2206.04325","title":"CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization","created":"2022-06-09","cited_by_count":135,"area":null},{"id":"2111.09099","title":"Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection","created":"2021-11-17","cited_by_count":119,"area":null},{"id":"2210.07829","title":"Asymmetric Student-Teacher Networks for Industrial Anomaly Detection","created":"2022-10-14","cited_by_count":117,"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":"2404.06564","title":"MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection","created":"2024-04-09","cited_by_count":113,"area":"ssm"},{"id":"2203.14506","title":"Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection","created":"2022-03-28","cited_by_count":104,"area":null},{"id":"2203.03962","title":"Generative Cooperative Learning for Unsupervised Video Anomaly Detection","created":"2022-03-08","cited_by_count":102,"area":null},{"id":"2104.06064","title":"Mixed supervision for surface-defect detection: from weakly to fully supervised learning","created":"2021-04-13","cited_by_count":101,"area":null},{"id":"2404.05231","title":"PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection","created":"2024-04-08","cited_by_count":98,"area":null},{"id":"2203.05550","title":"Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection","created":"2022-03-10","cited_by_count":93,"area":null},{"id":"2208.01521","title":"DSR -- A dual subspace re-projection network for surface anomaly detection","created":"2022-08-02","cited_by_count":90,"area":null},{"id":"2403.06495","title":"Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts","created":"2024-03-11","cited_by_count":89,"area":null},{"id":"2306.11876","title":"BMAD: Benchmarks for Medical Anomaly Detection","created":"2023-06-20","cited_by_count":82,"area":null},{"id":"2203.00259","title":"Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection","created":"2022-03-01","cited_by_count":80,"area":null},{"id":"2303.02595","title":"PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow","created":"2023-03-05","cited_by_count":74,"area":null},{"id":"2207.01463","title":"Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection","created":"2022-07-04","cited_by_count":72,"area":null},{"id":"2108.00462","title":"Explainable Deep Few-shot Anomaly Detection with Deviation Networks","created":"2021-08-01","cited_by_count":69,"area":null},{"id":"2211.12634","title":"PNI : Industrial Anomaly Detection using Position and Neighborhood Information","created":"2022-11-22","cited_by_count":69,"area":null},{"id":"2403.12570","title":"Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images","created":"2024-03-19","cited_by_count":69,"area":null},{"id":"2207.10298","title":"Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey","created":"2022-07-21","cited_by_count":68,"area":null},{"id":"2101.10043","title":"Deep One-Class Classification via Interpolated Gaussian Descriptor","created":"2021-01-25","cited_by_count":67,"area":null},{"id":"2112.10063","title":"Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation","created":"2021-12-19","cited_by_count":67,"area":"distill"},{"id":"2303.02389","title":"Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation","created":"2023-03-04","cited_by_count":67,"area":null},{"id":"2311.14897","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","created":"2023-11-25","cited_by_count":67,"area":null},{"id":"2202.11660","title":"Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors","created":"2022-02-23","cited_by_count":66,"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":"2305.10724","title":"Segment Any Anomaly without Training via Hybrid Prompt Regularization","created":"2023-05-18","cited_by_count":60,"area":null},{"id":"2210.04570","title":"The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization","created":"2022-10-10","cited_by_count":57,"area":null},{"id":"2302.08769","title":"Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization","created":"2023-02-17","cited_by_count":57,"area":null},{"id":"2212.02031","title":"Prototypical Residual Networks for Anomaly Detection and Localization","created":"2022-12-05","cited_by_count":56,"area":null},{"id":"2305.15956","title":"Anomaly Detection with Conditioned Denoising Diffusion Models","created":"2023-05-25","cited_by_count":56,"area":"diffusion"},{"id":"2403.14233","title":"SoftPatch: Unsupervised Anomaly Detection with Noisy Data","created":"2024-03-21","cited_by_count":55,"area":null},{"id":"2502.07601","title":"Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models","created":"2025-02-11","cited_by_count":54,"area":"lr"},{"id":"2305.19643","title":"Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models","created":"2023-05-31","cited_by_count":50,"area":"diffusion"},{"id":"2401.12665","title":"ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation","created":"2024-01-23","cited_by_count":49,"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":"2303.08730","title":"DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection","created":"2023-03-15","cited_by_count":46,"area":"diffusion"},{"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":"2205.06568","title":"Self-Supervised Masking for Unsupervised Anomaly Detection and Localization","created":"2022-05-13","cited_by_count":43,"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":"2311.09999","title":"TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection","created":"2023-11-16","cited_by_count":43,"area":"diffusion"},{"id":"2312.04521","title":"Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping","created":"2023-12-07","cited_by_count":42,"area":null},{"id":"2406.07487","title":"GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection","created":"2024-06-11","cited_by_count":42,"area":"diffusion"},{"id":"2306.12041","title":"Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors","created":"2023-06-21","cited_by_count":37,"area":null},{"id":"2312.08880","title":"GenDet: Towards Good Generalizations for AI-Generated Image Detection","created":"2023-12-12","cited_by_count":36,"area":null},{"id":"2407.21794","title":"Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey","created":"2024-07-31","cited_by_count":36,"area":"mllm"},{"id":"2101.03064","title":"One-Class Classification: A Survey","created":"2021-01-08","cited_by_count":35,"area":null},{"id":"2303.13845","title":"Anomaly Detection under Distribution Shift","created":"2023-03-24","cited_by_count":35,"area":null},{"id":"2111.13495","title":"SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection","created":"2021-11-26","cited_by_count":34,"area":null},{"id":"2209.12148","title":"Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection","created":"2022-09-25","cited_by_count":34,"area":null},{"id":"2110.04538","title":"Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization","created":"2021-10-09","cited_by_count":32,"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":"2209.01816","title":"ADTR: Anomaly Detection Transformer with Feature Reconstruction","created":"2022-09-05","cited_by_count":31,"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":"2305.16713","title":"ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection","created":"2023-05-26","cited_by_count":30,"area":null},{"id":"2503.21622","title":"The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection","created":"2025-03-27","cited_by_count":30,"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":"2410.16215","title":"Pre-training Distillation for Large Language Models: A Design Space Exploration","created":"2024-10-21","cited_by_count":27,"area":"distill"},{"id":"2105.14737","title":"Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation","created":"2021-05-31","cited_by_count":26,"area":null},{"id":"2308.04268","title":"Teacher-Student Architecture for Knowledge Distillation: A Survey","created":"2023-08-08","cited_by_count":26,"area":"distill"},{"id":"2401.15123","title":"Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection","created":"2024-01-26","cited_by_count":26,"area":"distill"},{"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":"2112.04185","title":"Transformaly -- Two (Feature Spaces) Are Better Than One","created":"2021-12-08","cited_by_count":25,"area":"distill"},{"id":"2308.15300","title":"MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection","created":"2023-08-29","cited_by_count":25,"area":null},{"id":"2310.12790","title":"Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection","created":"2023-10-19","cited_by_count":25,"area":null},{"id":"2404.06832","title":"SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection","created":"2024-04-10","cited_by_count":25,"area":null},{"id":"2408.04604","title":"Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning","created":"2024-08-08","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":"2303.00609","title":"Unsupervised Pathology Detection: A Deep Dive Into the State of the Art","created":"2023-03-01","cited_by_count":24,"area":null},{"id":"2403.07532","title":"Open-World Semantic Segmentation Including Class Similarity","created":"2024-03-12","cited_by_count":24,"area":null},{"id":"2109.01303","title":"Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images","created":"2021-09-03","cited_by_count":23,"area":null},{"id":"2210.17332","title":"Teacher-Student Architecture for Knowledge Learning: A Survey","created":"2022-10-28","cited_by_count":23,"area":"distill"},{"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":"2311.02612","title":"GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection","created":"2023-11-05","cited_by_count":23,"area":"mllm"},{"id":"2305.08509","title":"Component-aware anomaly detection framework for adjustable and logical industrial visual inspection","created":"2023-05-15","cited_by_count":22,"area":null},{"id":"2308.06748","title":"Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval","created":"2023-08-13","cited_by_count":22,"area":null},{"id":"2410.10289","title":"Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection","created":"2024-10-14","cited_by_count":22,"area":null},{"id":"2110.03396","title":"AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning","created":"2021-10-07","cited_by_count":21,"area":null}],"next_cursor":"100"}