mlsanityData to 7 Oct 2026

Dual Distillation for Few-Shot Anomaly Detection

arXiv 2603.01713 ↗First posted 2 Mar 2026cs.CVcited by 3

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Abstract (arXiv)

Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. While recent unsupervised anomaly detection approaches show promise, they require substantial normal training data and struggle to generalize across anatomical contexts. We introduce D24FAD, a novel dual distillation framework for few-shot anomaly detection that identifies anomalies in previously unseen tasks using only a small number of normal reference images. Our approach leverages a pre-trained encoder as a teacher network to extract multi-scale features from both support and query images, while a student decoder learns to distill knowledge from the teacher on query images and self-distill on support images. We further propose a learn-to-weight mechanism that dynamically assesses the reference value of each support image conditioned on the query, optimizing anomaly detection performance. To evaluate our method, we curate a comprehensive benchmark dataset comprising 13,084 images across four organs, four imaging modalities, and five disease categories. Extensive experiments demonstrate that D24FAD significantly outperforms existing approaches, establishing a new state-of-the-art in few-shot medical anomaly detection. Code is available at https://github.com/ttttqz/D24FAD.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 39 of its 52 references that are in the map. First posted on arXiv 2 Mar 2026.

Builds on

39 of 52 references.

References · 39

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  2. 2016Wide Residual Networkscited by 3,057
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  10. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  11. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  12. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
27 more
  1. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  2. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  3. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  4. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  5. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  6. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  7. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  8. 2020Deep Learning for Medical Anomaly Detection -- A Surveycited by 127
  9. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
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  14. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  15. 2019Deep Weakly-supervised Anomaly Detectioncited by 59
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  20. 2023Anomaly Detection under Distribution Shiftcited by 35
  21. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
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  27. 2025Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detectioncited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detectioncited by 0
  2. 2026Beyond Normal References: Discriminative Few-Shot Anomaly Detectioncited by 0
  3. 2026On Revisiting Entropy for Identifying Mislabeled Imagescited by 0