Aesthetic Score

Aesthetic score prediction aims to quantify the perceived beauty or visual appeal of images, a subjective task tackled using computational methods. Current research focuses on developing robust models, often employing convolutional neural networks (CNNs) and incorporating multiple aesthetic attributes (e.g., composition, color harmony) to improve prediction accuracy, sometimes leveraging techniques like multi-task learning, self-supervised learning, and reinforcement learning. This field is significant for advancing computer vision, enabling applications like personalized image recommendations, automated image quality control, and a deeper understanding of human aesthetic perception.