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.

翻译：在少样本异常检测（FSAD）领域，高效的视觉特征在基于记忆库M的方法中扮演着关键角色。然而，这些方法未考虑视觉特征与其旋转后视觉特征之间的关系，严重限制了异常检测性能。为突破这一极限，我们揭示了旋转不变特征属性在工业场景的FSAD中具有重要影响。具体而言，我们在FSAD中引入图表示，并提出一种新型视觉等距不变特征（VIIF）作为异常度量特征。实验表明，VIIF能稳健提升异常判别能力，并显著压缩存储于记忆库M中的冗余特征规模。此外，我们基于VIIF设计了一种新型模型GraphCore，该模型可快速实现无监督FSAD训练并提升异常检测性能。在提出的少样本异常检测设定下，我们开展了GraphCore与其他先进异常检测模型的全面对比评估。结果显示，在MVTec AD数据集上，对于1、2、4、8样本场景，GraphCore的平均AUC分别提升5.8%、4.1%、3.4%和1.6%；在MPDD数据集上，相应提升幅度达25.5%、22.0%、16.9%和14.1%。