  In industrial scenarios, it is crucial not only to identify anomalous items
but also to classify the type of anomaly. However, research on anomaly
multi-classification remains largely unexplored. This paper proposes a novel
and valuable research task called anomaly multi-classification. Given the
challenges in applying few-shot learning to this task, due to limited training
data and unique characteristics of anomaly images, we introduce a baseline
model that combines RelationNet and PatchCore. We propose a data generation
method that creates pseudo classes and a corresponding proxy task, aiming to
bridge the gap in transferring few-shot learning to industrial scenarios.
Furthermore, we utilize contrastive learning to improve the vanilla baseline,
achieving much better performance than directly fine-tune a ResNet. Experiments
conducted on MvTec AD and MvTec3D AD demonstrate that our approach shows
superior performance in this novel task.
