SAM Prior

"SAM Prior" refers to the incorporation of prior knowledge, often derived from pre-trained models like Segment Anything Model (SAM), into various machine learning tasks. Current research focuses on integrating these priors into diverse architectures, including diffusion models, transformers, and neural radiance fields, to improve performance in areas such as image generation, time series forecasting, and 3D scene reconstruction. This approach enhances model robustness, efficiency, and accuracy, particularly in scenarios with limited data or complex physical constraints, impacting fields ranging from medical imaging to autonomous driving.