A Training-Free Correlation-Weighted Model for Zero-/Few-Shot Industrial Anomaly Detection with Retrieval Augmentation

Obtaining labeled data in the field of industrial anomaly detection is challenging, which necessitates the development of label-free frameworks. However, current methods mainly focus on the unsupervised paradigm, which uses a large number of normal samples of the same category to train the model, and distinguish anomalies during testing. This training approach necessitates retraining when new datasets or object categories are encountered. Recently, studies have suggested using large pre-trained multimodal vision-language models, such as CLIP, for zero-shot and few-shot anomaly detection, yielding promising outcomes. However, the lack of spatial awareness of these models results in less effectiveness in dense prediction tasks such as anomaly localization. To mitigate this issue, various fine-tuning methods using additional labeled anomaly data have been employed. In other words, substantial data and extensive training efforts are still necessary to ensure optimal model performance on specific datasets. In this paper, we introduce a training-free, CLIP-based model that utilizes patch correlations and prototype guidance to enable zero-shot and few-shot anomaly detection. Specifically, we first use a self-supervised pre-trained model to capture patch correlations within a single image, enhancing the model's regional awareness of defects. Then, we dynamically construct prototypes using a retrieval-enhanced method to alleviate domain gap in general domain models for anomaly detection. Extensive experiments on the popular benchmarks MVTec and VisA demonstrate that our approach achieves state-of-the-art performance across nearly all metrics. Furthermore, we validate the generalization of our method on collected real industrial data.