Abstract
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.
Abstract (translated)
在几个样本异常检测(FSAD)领域,高效的视觉特征在基于记忆库M的方法中发挥着至关重要的作用。然而,这些方法并未考虑到视觉特征与其旋转视觉特征之间的关系,这大大限制了异常检测性能。为了突破极限,我们揭示了旋转不变特征性质在工业级FSAD中的重要性。具体而言,我们在FSAD中使用图形表示,并提供了一种新的视觉等效不变特征(VIIF)作为异常检测测量特征。VIIF能够 robustly 改善异常区分能力,并进一步减少存储在M中的冗余特征的数量。此外,我们提供了VIIF的新方法GraphCore,它可以通过VIIF快速实现无监督FSAD训练,并提高异常检测性能。我们提供了一份全面的评估,用于比较GraphCore和其他SOTA异常检测模型在我们的建议的几个样本异常检测设置下的表现,这表明GraphCore可以在MVTec AD中提高平均AUC值,分别增加5.8%、4.1%、3.4%和1.6%,在1、2、4和8个样本情况下MPDD中增加25.5%、22.0%、16.9%和14.1%。
URL
https://arxiv.org/abs/2301.12082
