  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.
