Image Based Score

Image-based scoring aims to automatically assess the perceived quality or aesthetic appeal of images, often leveraging machine learning to predict human preferences. Current research focuses on improving the accuracy and generalizability of these scores, employing techniques like masked image modeling, diffusion models, and prompt engineering to address data scarcity and diverse assessment criteria. These advancements have applications in e-commerce (improving product image ranking and sales), content moderation, and even scientific domains like plant disease diagnosis, where image-based scores can aid in automated analysis and decision-making.