Anomaly detection has gained considerable attention due to its broad range of applications, particularly in industrial defect detection. To address the challenges of data collection, researchers have introduced zero-/few-shot anomaly detection techniques that require minimal normal images for each category. However, complex industrial scenarios often involve multiple objects, presenting a significant challenge. In light of this, we propose a straightforward yet powerful multi-scale memory comparison framework for zero-/few-shot anomaly detection. Our approach employs a global memory bank to capture features across the entire image, while an individual memory bank focuses on simplified scenes containing a single object. The efficacy of our method is validated by its remarkable achievement of 4th place in the zero-shot track and 2nd place in the few-shot track of the Visual Anomaly and Novelty Detection (VAND) competition.

翻译：异常检测因其广泛的应用场景（尤其在工业缺陷检测中）而备受关注。为解决数据收集的挑战，研究人员引入了零样本/少样本异常检测技术，该技术对每个类别仅需极少量正常图像。然而，复杂的工业场景往往涉及多个目标，这构成了重大挑战。鉴于此，我们提出了一种简洁而强大的多尺度记忆比较框架，用于零样本/少样本异常检测。我们的方法采用全局记忆库来捕获整幅图像的特征，同时利用个体记忆库聚焦于包含单个目标的简化场景。该方法在视觉异常与新奇检测（VAND）竞赛的零样本赛道中获得第四名、少样本赛道中获得第二名的优异成绩，充分验证了其有效性。