中国科学院自动化研究所机构知识库

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-Refner and SEG-Refner, enhancing model training. These networks, connected to residual textual features of standard vision-language pre-trained models, focus on amplifying adjectives’ signifcance 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 classifcation 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.