Image Anomaly Detection

Image anomaly detection aims to identify deviations from normality within images, a crucial task across diverse fields like manufacturing quality control and medical imaging. Current research heavily emphasizes unsupervised and zero-shot learning approaches, leveraging powerful vision-language models (like CLIP) and transformer architectures, often incorporating techniques like contrastive learning and knowledge distillation to improve performance. These advancements are significantly impacting industrial applications by enabling automated defect detection and predictive maintenance, while also pushing the boundaries of explainability and robustness in anomaly detection algorithms.