Abstract
In the area of fewshot anomaly detection (FSAD), efficient visual feature
plays an essential role in memory bank M-based methods. However, these methods
do not account for the relationship between the visual feature and its rotated
visual feature, drastically limiting the anomaly detection performance. To push
the limits, we reveal that rotation-invariant feature property has a
significant impact in industrial-based FSAD. Specifically, we utilize graph
representation in FSAD and provide a novel visual isometric invariant feature
(VIIF) as anomaly measurement feature. As a result, VIIF can robustly improve
the anomaly discriminating ability and can further reduce the size of redundant
features stored in M by a large amount. Besides, we provide a novel model
GraphCore via VIIFs that can fast implement unsupervised FSAD training and can
improve the performance of anomaly detection. A comprehensive evaluation is
provided for comparing GraphCore and other SOTA anomaly detection models under
our proposed fewshot anomaly detection setting, which shows GraphCore can
increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%,
22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively.