Cross-Modal Guidance Learning for Zero-Shot Industrial Anomaly Detection
Zero-shot industrial anomaly detection (ZIAD) aims to develop a unified model capable of directly identifying unseen anomaly categories in images without requiring reference samples. Recently, large-scale Vision-Language Models (VLMs) such as CLIP have shown great potential for solving this task. However, existing meth...