mlsanityData to 14 Oct 2026

APRIL-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 AD

arXiv 2305.17382 ↗First posted 27 May 2023cs.CVcited by 130

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Cited by 23 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this technical report, we briefly introduce our solution for the Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. For industrial visual inspection, building a single model that can be rapidly adapted to numerous categories without or with only a few normal reference images is a promising research direction. This is primarily because of the vast variety of the product types. For the zero-shot track, we propose a solution based on the CLIP model by adding extra linear layers. These layers are used to map the image features to the joint embedding space, so that they can compare with the text features to generate the anomaly maps. Besides, when the reference images are available, we utilize multiple memory banks to store their features and compare them with the features of the test images during the testing phase. In this challenge, our method achieved first place in the zero-shot track, especially excelling in segmentation with an impressive F1 score improvement of 0.0489 over the second-ranked participant. Furthermore, in the few-shot track, we secured the fourth position overall, with our classification F1 score of 0.8687 ranking first among all participating teams.

Key facts. Cited by 130 papers in the map (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 27 May 2023.

Builds on

16 of 21 references.

References · 16

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  4. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,173
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  6. 2017Focal Loss for Dense Object Detectioncited by 752
  7. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  8. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  9. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
  10. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  11. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  12. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
4 more
  1. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  2. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  3. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  4. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 15; 2024: 36; 2025: 47; 2026: 32.

130 papers.

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  1. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  2. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 171
  3. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  4. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
  5. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 111
  6. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  7. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 73
  8. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  9. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  10. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  11. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  12. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
38 more
  1. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  2. 2024FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localizationcited by 51
  3. 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationcited by 50
  4. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  5. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  6. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detectioncited by 42
  7. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  8. 2023CLIP in Medical Imaging: A Surveycited by 40
  9. 2023Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detectioncited by 39
  10. 2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detectioncited by 36
  11. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  12. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  13. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  14. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31
  15. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  16. 2024UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detectioncited by 28
  17. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 26
  18. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 25
  19. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  20. 2023GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detectioncited by 23
  21. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  22. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  23. 2024Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detectioncited by 15
  24. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  25. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detectioncited by 13
  26. 2025MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learningcited by 12
  27. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  28. 2026VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformercited by 11
  29. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  30. 2023Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localizationcited by 10
  31. 2025MadCLIP: Few-shot Medical Anomaly Detection with CLIPcited by 10
  32. 2025DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookupcited by 10
  33. 2025LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detectioncited by 9
  34. 2025LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoningcited by 9
  35. 2023Zero-Shot Anomaly Detection with Pre-trained Segmentation Modelscited by 8
  36. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  37. 2025FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localizationcited by 8
  38. 2025Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Processcited by 8

50 most cited of 130 All 130 (JSON)