  Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding
capabilities, making it possible to handle certain tasks through the Visual
Question Answering (VQA) paradigm. This paper explores the potential of
VQA-oriented GPT-4V in the recently popular visual Anomaly Detection (AD) and
is the first to conduct qualitative and quantitative evaluations on the popular
MVTec AD and VisA datasets. Considering that this task requires both
image-/pixel-level evaluations, the proposed GPT-4V-AD framework contains three
components: \textbf{\textit{1)}} Granular Region Division, \textbf{\textit{2)}}
Prompt Designing, \textbf{\textit{3)}} Text2Segmentation for easy quantitative
evaluation, and have made some different attempts for comparative analysis. The
results show that GPT-4V can achieve certain results in the zero-shot AD task
through a VQA paradigm, such as achieving image-level 77.1/88.0 and pixel-level
68.0/76.6 AU-ROCs on MVTec AD and VisA datasets, respectively. However, its
performance still has a certain gap compared to the state-of-the-art zero-shot
method, \eg, WinCLIP and CLIP-AD, and further researches are needed. This study
provides a baseline reference for the research of VQA-oriented LMM in the
zero-shot AD task, and we also post several possible future works. Code is
available at \url{https://github.com/zhangzjn/GPT-4V-AD}.
