Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
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arXiv
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| Format: | Preprint |
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2025
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| author | Park, SoYoung Lee, Hyewon Choi, Mingyu Han, Seunghoon Lee, Jong-Ryul Lim, Sungsu Kim, Tae-Ho |
| author_facet | Park, SoYoung Lee, Hyewon Choi, Mingyu Han, Seunghoon Lee, Jong-Ryul Lim, Sungsu Kim, Tae-Ho |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation Park, SoYoung Lee, Hyewon Choi, Mingyu Han, Seunghoon Lee, Jong-Ryul Lim, Sungsu Kim, Tae-Ho Computer Vision and Pattern Recognition Artificial Intelligence 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 |
| title | Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13560 |