  Existing industrial anomaly detection (IAD) methods predict anomaly scores
for both anomaly detection and localization. However, they struggle to perform
a 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 perception
abilities on multiple vision tasks such as image captioning, visual
understanding, visual reasoning, etc., making it a competitive potential choice
for more comprehensible anomaly detection. However, the knowledge about anomaly
detection is absent in existing general LMMs, while training a specific LMM for
anomaly detection requires a tremendous amount of annotated data and massive
computation resources. In this paper, we propose a novel large multi-modal
model by applying vision experts for industrial anomaly detection (dubbed
Myriad), which leads to definite anomaly detection and high-quality anomaly
description. Specifically, we adopt MiniGPT-4 as the base LMM and design an
Expert Perception module to embed the prior knowledge from vision experts as
tokens which are intelligible to Large Language Models (LLMs). To compensate
for the errors and confusions of vision experts, we introduce a domain adapter
to bridge the visual representation gaps between generic and industrial images.
Furthermore, we propose a Vision Expert Instructor, which enables the Q-Former
to generate IAD domain vision-language tokens according to vision expert prior.
Extensive experiments on MVTec-AD and VisA benchmarks demonstrate that our
proposed method not only performs favorably against state-of-the-art methods
under the 1-class and few-shot settings, but also provide definite anomaly
prediction along with detailed descriptions in IAD domain.
