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arXiv · 2504.13560

Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation

SoYoung Park·Hyewon Lee·Mingyu Choi·Seunghoon Han·Jong-Ryul Lee·Sungsu Lim·Tae-Ho Kim
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Abstract

Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse industrial scenarios. This highlights the need for flexible, context-aware prompting strategies. We propose Image-Aware Prompt Anomaly Segmentation (IAP-AS), which enhances anomaly segmentation by generating dynamic, context-aware prompts using an image tagging model and a large language model (LLM). IAP-AS extracts object attributes from images to generate context-aware prompts, improving adaptability and generalization in dynamic and unstructured industrial environments. In our experiments, IAP-AS improves the F1-max metric by up to 10%, demonstrating superior adaptability and generalization. It provides a scalable solution for anomaly segmentation across industries

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SoYoung Park, Hyewon Lee, Mingyu Choi, Seunghoon Han, Jong-Ryul Lee, Sungsu Lim, Tae-Ho Kim. 2025-04-18. Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation. https://arxiv.org/abs/2504.13560

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