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VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization

arXiv 2104.10036 ↗First posted 20 Apr 2021cs.CV cs.AI cs.LGcited by 222

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Abstract (arXiv)

We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps to preserve the spatial information of the embedded patches, which are later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.

Key facts. Cited by 222 papers in the map (data through 7 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

15 of 31 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  4. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  5. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  6. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  7. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 151
  8. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  9. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  10. 2018Generative Probabilistic Novelty Detection with Adversarial Autoencoderscited by 99
  11. 2018Anomaly Detection using One-Class Neural Networkscited by 82
  12. 2018Learning Deep Features for One-Class Classificationcited by 76
3 more
  1. 2018Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representationscited by 17
  2. 2020Image Anomaly Detection by Aggregating Deep Pyramidal Representationscited by 4
  3. 2019Image anomaly detection with capsule networks and imbalanced datasetscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 24; 2023: 34; 2024: 57; 2025: 63; 2026: 41.

222 papers.

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  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  2. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  3. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  4. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  5. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  6. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  7. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  8. 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
  9. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  10. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  11. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  12. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 92
38 more
  1. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  2. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  3. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  4. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 72
  5. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  6. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  7. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  8. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  9. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  10. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  11. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  12. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  13. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  14. 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationcited by 49
  15. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  16. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  17. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  18. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  19. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  20. 2022ADTR: Anomaly Detection Transformer with Feature Reconstructioncited by 31
  21. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  22. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31
  23. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  24. 2024Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detectioncited by 28
  25. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 28
  26. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  27. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  28. 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25
  29. 2023VISION Datasets: A Benchmark for Vision-based InduStrial InspectiONcited by 25
  30. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 24
  31. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  32. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  33. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  34. 2023PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentationcited by 21
  35. 2022MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detectioncited by 19
  36. 2024Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detectioncited by 19
  37. 2024Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detectioncited by 19
  38. 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18

50 most cited of 222 All 222 (JSON)