Anomaly Map

Anomaly maps visually represent deviations from expected patterns in data, aiming to pinpoint anomalies within images or other data structures. Current research focuses on improving anomaly map generation through various techniques, including student-teacher networks, diffusion models, and large language models, often incorporating multimodal data (e.g., RGB images and 3D point clouds) and addressing both structural and logical anomalies. This field is significant for its applications in diverse areas such as industrial quality control, medical diagnosis, and urban infrastructure monitoring, enabling automated detection of defects and improving efficiency in various sectors.