ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection

ArXi:2604.17949v1 Announce Type: new Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evidence. We propose ZSG-IAD, a multimodal vision-language framework for zero-shot grounded industrial anomaly detection. Given RGB images, sensor images, and 3D point clouds, ZSG-IAD generates structured anomaly reports and pixel-level anomaly masks.