Visual Anomaly Detection

Visual anomaly detection aims to identify deviations from normality in images, crucial for applications like industrial quality control and medical diagnosis. Current research emphasizes developing robust methods that handle diverse anomaly types and limited labeled data, focusing on architectures like autoencoders, transformers, and increasingly, multimodal models incorporating language for improved zero-shot capabilities. This field is significant due to its potential for automating inspection processes across various industries, improving efficiency and safety while reducing reliance on extensive manual labeling.