mlsanityData to 7 Oct 2026

Dual-path Frequency Discriminators for Few-shot Anomaly Detection

arXiv 2403.04151 ↗First posted 7 Mar 2024cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

Few-shot anomaly detection (FSAD) plays a crucial role in industrial manufacturing. However, existing FSAD methods encounter difficulties leveraging a limited number of normal samples, frequently failing to detect and locate inconspicuous anomalies in the spatial domain. We have further discovered that these subtle anomalies would be more noticeable in the frequency domain. In this paper, we propose a Dual-Path Frequency Discriminators (DFD) network from a frequency perspective to tackle these issues. The original spatial images are transformed into multi-frequency images, making them more conducive to the tailored discriminators in detecting anomalies. Additionally, the discriminators learn a joint representation with forms of pseudo-anomalies. Extensive experiments conducted on MVTec AD and VisA benchmarks demonstrate that our DFD surpasses current state-of-the-art methods. The code is available at urlhttps://github.com/yuhbai/DFD.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 44 of its 67 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

44 of 67 references.

References · 44

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2016Wide Residual Networkscited by 3,057
  4. 2013Describing Textures in the Wildcited by 2,110
  5. 2021Transformers in Vision: A Surveycited by 1,009
  6. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  7. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  8. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  9. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  10. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  11. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  12. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
32 more
  1. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  2. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  3. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  4. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  5. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  6. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  7. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  8. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  9. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  10. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  11. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  12. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  13. 2021TransFG: A Transformer Architecture for Fine-grained Recognitioncited by 131
  14. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  15. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  16. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  17. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  18. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  19. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  20. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  21. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
  22. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  23. 2019Learning What and Where to Transfercited by 43
  24. 2018One-Shot Unsupervised Cross Domain Translationcited by 35
  25. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  26. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  27. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  28. 2019Revisiting Fine-tuning for Few-shot Learningcited by 29
  29. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  30. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 9
  31. 2021N-Shot Learning for Augmenting Task-Oriented Dialogue State Trackingcited by 6
  32. 2018Localized Multiple Kernel Learning for Anomaly Detection: One-class Classificationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  2. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  3. 2025Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Conceptscited by 1
  4. 2025Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detectioncited by 0
  5. 2026A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detectioncited by 0
  6. 2026SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detectioncited by 0
  7. 2026CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detectioncited by 0