Image Aesthetic

Image aesthetic assessment aims to computationally understand and predict human judgments of visual appeal, bridging the gap between objective image properties and subjective perception. Current research focuses on leveraging large language models (LLMs) and multimodal models to incorporate contextual understanding and align AI evaluations with human preferences, often employing techniques like reinforcement learning and knowledge distillation within convolutional neural networks (CNNs) and diffusion models. This field is significant for applications in e-commerce, urban planning, and creative content generation, offering efficient and potentially more nuanced methods for evaluating and improving visual quality and appeal.