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Anomaly detection is a crucial task across different domains and data types.However, existing anomaly detection models are often designed for specificdomains and modalities. This study explores the use of GPT-4V(ision), apowerful visual-linguistic model, to address anomaly detection tasks in ageneric manner. We investigate the application of GPT-4V in multi-modality,multi-domain anomaly detection tasks, including image, video, point cloud, andtime series data, across multiple application areas, such as industrial,medical, logical, video, 3D anomaly detection, and localization tasks. Toenhance GPT-4V's performance, we incorporate different kinds of additional cuessuch as class information, human expertise, and reference images asprompts.Based on our experiments, GPT-4V proves to be highly effective indetecting and explaining global and fine-grained semantic patterns inzero/one-shot anomaly detection. This enables accurate differentiation betweennormal and abnormal instances. Although we conducted extensive evaluations inthis study, there is still room for future evaluation to further exploitGPT-4V's generic anomaly detection capacity from different aspects. Theseinclude exploring quantitative metrics, expanding evaluation benchmarks,incorporating multi-round interactions, and incorporating human feedback loops.Nevertheless, GPT-4V exhibits promising performance in generic anomalydetection and understanding, thus opening up a new avenue for anomalydetection.

Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead