mlsanityData to 7 Oct 2026

A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

arXiv 2401.16402 ↗First posted 29 Jan 2024cs.CV cs.AIcited by 69

Not on the map. Why?

Abstract (arXiv)

Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspection, and medical lesion detection. This survey comprehensively examines recent advancements in VAD by identifying three primary challenges: 1) scarcity of training data, 2) diversity of visual modalities, and 3) complexity of hierarchical anomalies. Starting with a brief overview of the VAD background and its generic concept definitions, we progressively categorize, emphasize, and discuss the latest VAD progress from the perspective of sample number, data modality, and anomaly hierarchy. Through an in-depth analysis of the VAD field, we finally summarize future developments for VAD and conclude the key findings and contributions of this survey.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). Builds on 54 of its 67 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

54 of 67 references.

References · 54

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Segment Anythingcited by 8,158
  3. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  5. 2022MaPLe: Multi-modal Prompt Learningcited by 685
  6. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
  7. 2021The Medical Segmentation Decathloncited by 620
  8. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 591
  9. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  10. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  11. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  12. 2022Multimodal Learning with Transformers: A Surveycited by 399
38 more
  1. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  2. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  3. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  4. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  5. 2023Sequential Modeling Enables Scalable Learning for Large Vision Modelscited by 212
  6. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  7. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  8. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  9. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  10. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  11. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 128
  12. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  13. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  14. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  15. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  16. 2022Anomaly Detection in Autonomous Driving: A Surveycited by 87
  17. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  18. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  19. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  20. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  21. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  22. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  23. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  24. 2023Few-Shot Defect Image Generation via Defect-Aware Feature Manipulationcited by 66
  25. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  26. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  27. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
  28. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  29. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  30. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  31. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  32. 2020Safe Robot Navigation via Multi-Modal Anomaly Detectioncited by 33
  33. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  34. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  35. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  36. 2023GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detectioncited by 23
  37. 2023Component-aware anomaly detection framework for adjustable and logical industrial visual inspectioncited by 22
  38. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 27; 2025: 24; 2026: 18.

69 papers.

Sort:

By area:

  1. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  2. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  3. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  4. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  5. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 47
  6. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  7. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  8. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  9. 2024MemoryMamba: Memory-Augmented State Space Model for Defect Recognitioncited by 24
  10. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  11. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 24
  12. 2024Unseen Visual Anomaly Generationcited by 23
38 more
  1. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  2. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  3. 2024Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoningcited by 19
  4. 2024LogiCode: an LLM-Driven Framework for Logical Anomaly Detectioncited by 18
  5. 2025Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?cited by 17
  6. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 15
  7. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  8. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  9. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  10. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 9
  11. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  12. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  13. 2024Dual-path Frequency Discriminators for Few-shot Anomaly Detectioncited by 7
  14. 2024GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detectioncited by 7
  15. 2024A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detectioncited by 7
  16. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  17. 2025INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learningcited by 7
  18. 2026MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptationcited by 6
  19. 2024ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shiftcited by 5
  20. 2025TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detectioncited by 5
  21. 2025CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filteringcited by 5
  22. 2024Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detectioncited by 4
  23. 2025Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusioncited by 3
  24. 2025RoBiS: Robust Binary Segmentation for High-Resolution Industrial Imagescited by 3
  25. 2025CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environmentscited by 3
  26. 2024Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detectioncited by 2
  27. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  28. 2025MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detectioncited by 2
  29. 2025ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learningcited by 2
  30. 2026RAID: Retrieval-Augmented Anomaly Detectioncited by 2
  31. 2025IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domaincited by 1
  32. 2025Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathologycited by 1
  33. 2025Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Conceptscited by 1
  34. 2025Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignmentcited by 1
  35. 2025AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRIcited by 1
  36. 2026AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detectioncited by 1
  37. 2026Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detectioncited by 1
  38. 2024On The Relationship between Visual Anomaly-free and Anomalous Representationscited by 0

50 most cited of 69 All 69 (JSON)