Shot Anomaly Detection

Shot anomaly detection focuses on identifying deviations from normal patterns in data, particularly when limited labeled examples ("shots") of anomalies are available. Current research emphasizes leveraging large vision-language models (like CLIP) and diffusion models, along with techniques like prompt engineering, contrastive learning, and meta-learning, to improve the accuracy and efficiency of anomaly detection in various modalities (images, videos, graphs, and text). This field is crucial for applications like industrial quality control, medical image analysis, and cybersecurity, where obtaining extensive labeled anomaly data is often impractical or impossible. The development of robust few-shot anomaly detection methods significantly advances the capabilities of automated systems to identify rare and critical events.