Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection

TL;DR

This paper introduces Generate Aligned Anomaly (GAA), a novel few-shot framework leveraging pretrained diffusion models to produce realistic, diverse, and well-aligned anomaly image-mask pairs for industrial inspection, improving data augmentation.

Contribution

GAA is the first to combine region-guided, few-shot anomaly synthesis with semantic clustering and mask alignment using pretrained diffusion models, enhancing realism and generalization.

Findings

GAA outperforms existing methods in anomaly synthesis quality.

GAA improves downstream localization and classification performance.

GAA demonstrates robustness with limited anomaly samples.

Abstract

Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as localization and classification. While several anomaly synthesis approaches have been introduced for data augmentation, they often struggle with low realism, inaccurate mask alignment, and poor generalization. To overcome these limitations, we propose Generate Aligned Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework. GAA leverages the strong priors of a pretrained latent diffusion model to generate realistic, diverse, and semantically aligned anomalies using only a small number of samples. The framework first employs Localized Concept Decomposition to jointly model the semantic features and spatial information of anomalies, enabling flexible control over the type and location of anomalies. It then utilizes Adaptive Multi-Round Anomaly Clustering to perform fine-grained semantic clustering of anomaly concepts, thereby enhancing the consistency of anomaly representations. Subsequently, a region-guided mask generation strategy ensures precise alignment between anomalies and their corresponding masks, while a low-quality sample filtering module is introduced to further improve the overall quality of the generated samples. Extensive experiments on the MVTec AD and LOCO datasets demonstrate that GAA achieves superior performance in both anomaly synthesis quality and downstream tasks such as localization and classification.