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.

翻译：少样本异常检测在工业制造中至关重要。然而，现有少样本异常检测方法难以有效利用有限的正常样本，并且可能无法在空间域中检测和定位不明显的异常。我们进一步发现，这些细微异常在频率域中会更为显著。本文从频率视角提出一种双路径频率判别器（DFD）网络来解决上述问题。具体而言，我们同时在图像级和特征级生成异常。通过多频信息构建模块提取差分频率分量，并将其输入细粒度特征构建模块以提供自适应特征。我们将异常检测视为判别性分类问题，因此采用双路径特征判别模块在特征空间中检测和定位图像级与特征级异常。该判别器旨在学习隐空间中异常特征与正常特征的联合表征。在MVTec AD和VisA基准数据集上的大量实验表明，我们的DFD方法超越了当前最先进的方法。源代码将公开。