Few-shot Anomaly Detection (FAD) poses significant challenges due to the limited availability of training samples and the frequent absence of abnormal samples. Previous approaches often rely on annotations or true abnormal samples to improve detection, but such textual or visual cues are not always accessible. To address this, we introduce AnoPLe, a multi-modal prompt learning method designed for anomaly detection without prior knowledge of anomalies. AnoPLe simulates anomalies and employs bidirectional coupling of textual and visual prompts to facilitate deep interaction between the two modalities. Additionally, we integrate a lightweight decoder with a learnable multi-view signal, trained on multi-scale images to enhance local semantic comprehension. To further improve performance, we align global and local semantics, enriching the image-level understanding of anomalies. The experimental results demonstrate that AnoPLe achieves strong FAD performance, recording 94.1% and 86.2% Image AUROC on MVTec-AD and VisA respectively, with only around a 1% gap compared to the SoTA, despite not being exposed to true anomalies. Code is available at https://github.com/YoojLee/AnoPLe.

本研究解决了在异常检测中由于缺乏异常样本和训练样本有限而带来的挑战。通过引入AnoPLe，这种多模态提示学习方法能够在没有异常样本的情况下模拟异常，并通过文本和视觉提示的双向结合促进深层次交互。实验结果表明，AnoPLe 在多个数据集上展示了卓越的少样本异常检测表现，实现了仅与当前最佳技术相差约1%的结果。

AnoPLe：基于双向提示学习的少样本异常检测，仅需正常样本