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

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

@article{park2025_2504.13560,
  title={ Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation },
  author={ SoYoung Park and Hyewon Lee and Mingyu Choi and Seunghoon Han and Jong-Ryul Lee and Sungsu Lim and Tae-Ho Kim },
  journal={arXiv preprint arXiv:2504.13560},
  year={ 2025 }
}