Pixel Level Anomaly

Pixel-level anomaly detection focuses on identifying and precisely locating anomalous pixels within images, crucial for various applications like industrial quality control and autonomous driving. Current research emphasizes unsupervised methods, leveraging architectures like transformers and autoencoders, along with techniques such as reconstruction-based anomaly scoring, prompt engineering for vision-language models, and diffusion models for generating synthetic anomalies or counterfactuals. These advancements improve the accuracy and efficiency of anomaly detection, particularly in complex scenarios with limited labeled data, impacting fields ranging from medical image analysis to infrastructure monitoring.