  Most existing anomaly detection methods require a dedicated model for each
category. Such a paradigm, despite its promising results, is computationally
expensive and inefficient, thereby failing to meet the requirements for
real-world applications. Inspired by how humans detect anomalies, by comparing
a query image to known normal ones, this paper proposes a novel few-shot
anomaly detection (FSAD) framework. Using a training set of normal images from
various categories, registration, aiming to align normal images of the same
categories, is leveraged as the proxy task for self-supervised
category-agnostic representation learning. At test time, an image and its
corresponding support set, consisting of a few normal images from the same
category, are supplied, and anomalies are identified by comparing the
registered features of the test image to its corresponding support image
features. Such a setup enables the model to generalize to novel test
categories. It is, to our best knowledge, the first FSAD method that requires
no model fine-tuning for novel categories: enabling a single model to be
applied to all categories. Extensive experiments demonstrate the effectiveness
of the proposed method. Particularly, it improves the current state-of-the-art
for FSAD by 11.3% and 8.3% on the MVTec and MPDD benchmarks, respectively. The
source code is available at https://github.com/Haoyan-Guan/CAReg.
