Image Regression

Image regression focuses on predicting continuous values from image data, aiming to establish quantitative relationships between image features and target variables. Recent research emphasizes improving efficiency and accuracy through novel initialization strategies for kernel methods, leveraging neural ordinary differential equations to model complex temporal dynamics in image sequences, and adapting models to handle domain shifts and limited data. These advancements are crucial for applications ranging from medical image analysis (e.g., disease progression modeling, body composition assessment) to remote sensing (e.g., forest monitoring) and computer vision (e.g., image quality assessment), enabling more accurate and reliable predictions in diverse fields.