Anomaly Segmentation

Anomaly segmentation aims to identify and delineate regions in an image that deviate from a learned representation of "normal" data, a crucial task in various applications like autonomous driving and industrial quality control. Current research heavily emphasizes unsupervised and few/zero-shot approaches, leveraging powerful vision-language models (like CLIP) and incorporating techniques such as prototype learning, diffusion models, and contrastive learning to improve accuracy and reduce reliance on extensive labeled datasets. These advancements are significantly impacting fields requiring robust real-time anomaly detection, enhancing safety and efficiency in applications ranging from autonomous vehicle navigation to medical image analysis.