mlsanityData to 7 Oct 2026

MAEDAY: MAE for few and zero shot AnomalY-Detection

arXiv 2211.14307 ↗First posted 25 Nov 2022cs.CVcited by 30

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Abstract (arXiv)

We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to reconstruct compared with normal regions. MAEDAY is the first image-reconstruction-based anomaly detection method that utilizes a pre-trained model, enabling its use for Few-Shot Anomaly Detection (FSAD). We also show the same method works surprisingly well for the novel tasks of Zero-Shot AD (ZSAD) and Zero-Shot Foreign Object Detection (ZSFOD), where no normal samples are available. Code is available at https://github.com/EliSchwartz/MAEDAY .

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

20 of 36 references.

References · 20

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  4. 2016Matching Networks for One Shot Learningcited by 2,246
  5. 2015Variational Inference with Normalizing Flowscited by 2,161
  6. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  7. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  8. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  9. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  10. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  11. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  12. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
8 more
  1. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  2. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  3. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  4. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  5. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  6. 2021FOD-A: A Dataset for Foreign Object Debris in Airportscited by 5
  7. 2019MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classificationcited by 2
  8. 2022Foreign Object Debris Detection for Airport Pavement Images based on Self-supervised Localization and Vision Transformercited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 9; 2025: 6; 2026: 7.

30 papers.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  2. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  3. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  4. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  5. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  6. 2023Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localizationcited by 10
  7. 2025DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookupcited by 9
  8. 2023FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networkscited by 8
  9. 2023What makes a good data augmentation for few-shot unsupervised image anomaly detection?cited by 8
  10. 2024PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptationcited by 8
  11. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  12. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
18 more
  1. 2024Dual-path Frequency Discriminators for Few-shot Anomaly Detectioncited by 7
  2. 2024Towards Universal Unsupervised Anomaly Detection in Medical Imagingcited by 6
  3. 2025AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentationcited by 4
  4. 2023Aerial Image Object Detection With Vision Transformer Detector (ViTDet)cited by 3
  5. 2024UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Drivingcited by 3
  6. 2024Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modelingcited by 2
  7. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  8. 2023Zero-Shot Anomaly Detection via Batch Normalizationcited by 1
  9. 2026Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversioncited by 1
  10. 2024LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Spacecited by 0
  11. 2025Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Datacited by 0
  12. 2025Quantized FCA: Efficient Zero-Shot Texture Anomaly Detectioncited by 0
  13. 2026HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detectioncited by 0
  14. 2026InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentationcited by 0
  15. 2026Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detectioncited by 0
  16. 2026Identifying the Unknown: Prompt-Free Open Vocabulary Anomaly Recognition for Robot-Object Interactioncited by 0
  17. 2026GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Modelscited by 0
  18. 2026PADFormer: Pose-agnostic Anomaly Detection from Sparse View Imagescited by 0