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GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection

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arxiv 2311.02612 v2 pith:QTABQPJR submitted 2023-11-05 cs.CV

Jiangning Zhang , Haoyang He , Xuhai Chen , Zhucun Xue , Yabiao Wang , Chengjie Wang , Lei Xie , Yong Liu This is my paper
classification cs.CV
keywords gpt-4vzero-shotcertaingpt-4v-adtasktextbftextitvisual
verification ladder T0 review T1 audit T2 compute T3 formal
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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}.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts

    cs.CV 2025-07 conditional novelty 5.0 of 10

    The paper proposes the long-tailed online anomaly detection (LTOAD) benchmark and a class-agnostic concept-based framework that outperforms class-aware baselines in most offline settings and in the online setting.

  2. IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning

    cs.CV 2025-08 reject novelty 4.0 of 10

    The advertised IADGPT framework and its anomaly-detection results are absent from the supplied full text, which instead reports a RAG-based cybersecurity incident-response system.

  3. Foundation Models and Transformers for Anomaly Detection: A Survey

    cs.LG 2025-07 reject novelty 4.0 of 10

    A taxonomy and literature review of Transformer-based visual anomaly detection, compromised by fabricated citations with dummy arXiv IDs.

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