Abstract: Few-shot anomaly detection involves using a limited number of normal samples to detect and localize anomalies. While existing methods for few-shot anomaly detection are primarily applied within the same domain, research on cross-domain scenarios remains limited. In this paper, we address the challenging yet practical problem of cross-domain few-shot anomaly detection, where training and test data are drawn from different distributions. A key challenge in this context is transferring knowledge learned in the source domain to the target domain for effective few-shot anomaly detection. To address this challenge, we propose a two-module approach. The first module defines domain differences in terms of differences in style features and employs a style memory module to store intra-domain statistical features from the source domain. These features are then integrated into the target domain to mitigate the impact of domain differences and enhance performance. The second module introduces a contrastive learning strategy to differentiate between enhanced query features for different categories, thereby improving the model’s ability to represent different categories in the source domain and enhancing few-shot detection performance. Our approach achieves state-of-the-art performance on cross-domain few-shot anomaly detection setting involving MVTec AD, MPDD, and MVTec LOCO-AD datasets, with image AUROC improvements ranging from 3% to 5% compared to baseline methods. Moreover, the proposed model still has the best performance on the few-shot anomaly detection task under the intra-domain setting.