  Few-shot anomaly detection (FSAD) is essential in industrial manufacturing.
However, existing FSAD methods struggle to effectively leverage a limited
number of normal samples, and they may fail to detect and locate inconspicuous
anomalies in the spatial domain. We further discover that these subtle
anomalies would be more noticeable in the frequency domain. In this paper, we
propose a Dual-Path Frequency Discriminators (DFD) network from a frequency
perspective to tackle these issues. Specifically, we generate anomalies at both
image-level and feature-level. Differential frequency components are extracted
by the multi-frequency information construction module and supplied into the
fine-grained feature construction module to provide adapted features. We
consider anomaly detection as a discriminative classification problem,
wherefore the dual-path feature discrimination module is employed to detect and
locate the image-level and feature-level anomalies in the feature space. The
discriminators aim to learn a joint representation of anomalous features and
normal features in the latent space. Extensive experiments conducted on MVTec
AD and VisA benchmarks demonstrate that our DFD surpasses current
state-of-the-art methods. Source code will be available.
