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Existing industrial anomaly detection (IAD) methods predict anomaly scoresfor both anomaly detection and localization. However, they struggle to performa multi-turn dialog and detailed descriptions for anomaly regions, e.g., color,shape, and categories of industrial anomalies. Recently, large multimodal(i.e., vision and language) models (LMMs) have shown eminent perceptionabilities on multiple vision tasks such as image captioning, visualunderstanding, visual reasoning, etc., making it a competitive potential choicefor more comprehensible anomaly detection. However, the knowledge about anomalydetection is absent in existing general LMMs, while training a specific LMM foranomaly detection requires a tremendous amount of annotated data and massivecomputation resources. In this paper, we propose a novel large multi-modalmodel by applying vision experts for industrial anomaly detection (dubbedMyriad), which leads to definite anomaly detection and high-quality anomalydescription. Specifically, we adopt MiniGPT-4 as the base LMM and design anExpert Perception module to embed the prior knowledge from vision experts astokens which are intelligible to Large Language Models (LLMs). To compensatefor the errors and confusions of vision experts, we introduce a domain adapterto bridge the visual representation gaps between generic and industrial images.Furthermore, we propose a Vision Expert Instructor, which enables the Q-Formerto generate IAD domain vision-language tokens according to vision expert prior.Extensive experiments on MVTec-AD and VisA benchmarks demonstrate that ourproposed method not only performs favorably against state-of-the-art methodsunder the 1-class and few-shot settings, but also provide definite anomalyprediction along with detailed descriptions in IAD domain.

Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection