Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
This addresses the need for more adaptable anomaly segmentation in diverse industrial scenarios, offering a scalable solution, though it is incremental as it builds on existing text-guided models.
The paper tackles the problem of limited adaptability in zero-shot anomaly segmentation for industrial quality control by proposing IAP-AS, which uses image-aware prompt generation to enhance flexibility, resulting in up to a 10% improvement in the F1-max metric.
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