Registration based few-shot anomaly detection

Chaoqin Huang*, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, Yan-Feng Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

24 Citations (Scopus)


This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training. So far, existing FSAD studies follow the one-model-per-category learning paradigm used for standard AD, and the inter-category commonality has not been explored. Inspired by how humans detect anomalies, i.e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model. During testing, the anomalies are identified by comparing the registered features of the test image and its corresponding support (normal) images. As far as we know, this is the first FSAD method that trains a single generalizable model and requires no re-training or parameter fine-tuning for new categories. Experimental results have shown that the proposed method outperforms the state-of-the-art FSAD methods by 3%–8% in AUC on the MVTec and MPDD benchmarks. Source code is available at:

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 - 17th European Conference, Proceedings
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
Number of pages17
ISBN (Print)9783031200526
Publication statusPublished - 2022
EventEuropean Conference on Computer Vision - Tel Aviv, Israel
Duration: 23 Oct 202227 Oct 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13684 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Computer Vision
Abbreviated titleECCV
CityTel Aviv
Internet address


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