Estate: Expert-Guided State Text Enhancement for Zero-Shot Industrial Anomaly Detection

Abstract

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The Expert-Guided State Text Enhancement Anomaly Detection (ESTATE) framework addresses the challenges in industrial anomaly detection arising from diverse product categories and limited defective samples. This framework, integrating expert insights through comparative state prompts, leverages two innovative text-guided networks, CLS-Refiner and SEG-Refiner, enhancing model training. These networks, connected to residual textual features of standard vision-language pre-trained models, focus on amplifying adjectives’ significance in text for improved image block and pixel-level alignment. ESTATE’s effectiveness is demonstrated through evaluations on MVTecAD and VisA datasets, achieving AUROC scores of 89.6%/89.6% for classification and 95.1%/85.0% for segmentation tasks, alongside setting new benchmarks in F1Max and PRO metrics. The AUC-cls on MVTecAD and VisA demonstrated an enhancement of 5.06% and 8.97%, respectively, compared to the APRIL-GAN approach.