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而备受关注，特别是在工业缺陷检测领域。为解决数据采集的挑战，研究者引入了每类仅需少量正常图像的零样本/少样本异常检测技术。然而，复杂的工业场景常涉及多个物体，这带来了显著挑战。为此，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。该方法采用全局记忆库捕捉整个图像的特征，同时通过个体记忆库聚焦于包含单个物体的简化场景。其有效性通过视觉异常与新颖性检测（VAND）竞赛中的出色表现得到验证：在零样本赛道获得第4名，在少样本赛道获得第2名。