mlsanityData to 14 Oct 2026

PNI : Industrial Anomaly Detection using Position and Neighborhood Information

arXiv 2211.12634 ↗First posted 22 Nov 2022cs.CVcited by 69

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Abstract (arXiv)

Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution. However, these methods neglect the impact of position and neighborhood information on the distribution of normal features. To overcome this, we propose a new algorithm, PNI, which estimates the normal distribution using conditional probability given neighborhood features, modeled with a multi-layer perceptron network. Moreover, position information is utilized by creating a histogram of representative features at each position. Instead of simply resizing the anomaly map, the proposed method employs an additional refine network trained on synthetic anomaly images to better interpolate and account for the shape and edge of the input image. We conducted experiments on the MVTec AD benchmark dataset and achieved state-of-the-art performance, with 99.56% and 98.98% AUROC scores in anomaly detection and localization, respectively.

Key facts. Cited by 69 papers in the map (data through 14 Oct 2026). Builds on 27 of its 38 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

27 of 38 references.

References · 27

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  8. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  9. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  10. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
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15 more
  1. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  2. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
  3. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  4. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  5. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
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  7. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  8. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
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  10. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  11. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  12. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 24; 2025: 25; 2026: 16.

69 papers.

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  1. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  2. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
  3. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  4. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  5. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  6. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  7. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
  8. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  9. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  10. 2024PixOOD: Pixel-Level Out-of-Distribution Detectioncited by 20
  11. 2025One-for-More: Continual Diffusion Model for Anomaly Detectioncited by 19
  12. 2024Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detectioncited by 17
38 more
  1. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  2. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  3. 2024CSAD: Unsupervised Component Segmentation for Logical Anomaly Detectioncited by 12
  4. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  5. 2024AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarioscited by 9
  6. 2024FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Datacited by 8
  7. 2024Unlocking the Potential of Reverse Distillation for Anomaly Detectioncited by 8
  8. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  9. 2025Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detectioncited by 7
  10. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  11. 2025Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detectioncited by 5
  12. 2025CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filteringcited by 5
  13. 2025Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detectioncited by 4
  14. 2025Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detectioncited by 4
  15. 2025Towards High-Resolution Industrial Image Anomaly Detectioncited by 3
  16. 2023Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detectioncited by 2
  17. 2024Distillation-based fabric anomaly detectioncited by 2
  18. 2024SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detectioncited by 2
  19. 2024ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinementcited by 2
  20. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  21. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  22. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categoriescited by 2
  23. 2026GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detectioncited by 2
  24. 2026Is Task-Specific Training Necessary for Anomaly Detection?cited by 2
  25. 2026Integration of deep generative Anomaly Detection algorithm in high-speed industrial linecited by 2
  26. 2026TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detectioncited by 2
  27. 2024Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detectioncited by 1
  28. 2025FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detectioncited by 1
  29. 2025Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detectioncited by 1
  30. 2025PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detectioncited by 1
  31. 2025Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignmentcited by 1
  32. 2026Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimizationcited by 1
  33. 2026Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detectioncited by 1
  34. 2023How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detectioncited by 0
  35. 2024Separating Novel Features for Logical Anomaly Detection: A Straightforward yet Effective Approachcited by 0
  36. 2024SRTFD: Scalable Real-Time Fault Diagnosis through Online Continual Learningcited by 0
  37. 2024Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturingcited by 0
  38. 2024Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tunerscited by 0

50 most cited of 69 All 69 (JSON)