  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.
