A Hierarchical Flow for Few-shot Anomaly Detection via Global-local Aggregation Strategy

Abstract

In industrial scenarios, existing deep generative methods face the challenge of adapting to new domains with limited data for anomaly detection. Flow-based generative models are efficient but fail to perform well in out-of-distribution detection due to the inductive bias. To solve these problems, we utilize a hierarchical normalizing flow framework for few-shot anomaly detection and localization (FesFlow). First, we propose a multi-scale attention coupling block in each single flow with channel and spatial self-attention mechanism, which can model long-term dependencies of flows and obtain fine-grained anomalous feature distribution. Furthermore, to reduce the bias towards local-pixel correlation, we introduce a global and local aggregation module to capture semantic context and fuse with low-level detailed features. Compared to state-of-the-art methods, our approach demonstrates outstanding performance evaluated on MVTec-AD and BTAD datasets, achieving optimal balances in both detection and localization tasks in an end-to-end network.