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的方法中起着至关重要的作用。然而，这些方法未考虑视觉特征与其旋转视觉特征之间的关系，从而极大地限制了异常检测性能。为突破这一极限，我们发现旋转不变特征属性在工业场景下的FSAD中具有显著影响。具体而言，我们利用图表示方法于FSAD中，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能够稳健提升异常判别能力，并大幅减少存储于M中的冗余特征数量。此外，我们基于VIIF提出了一种新型模型GraphCore，该模型可快速实现无监督FSAD训练，并提升异常检测性能。在本文提出的少样本异常检测设置下，我们进行了全面评估，将GraphCore与其他最先进（SOTA）异常检测模型进行对比。结果表明，在MVTec AD数据集上，GraphCore在1、2、4和8样本情况下平均AUC分别提升了5.8%、4.1%、3.4%和1.6%；在MPDD数据集上，对应提升幅度分别为25.5%、22.0%、16.9%和14.1%。