One potential limitation of the decoupled aesthetic feedback learning approach is the challenge of effectively capturing and representing the diverse dimensions of aesthetic preferences. An improvement could involve refining the annotation process for collecting aesthetic preference data to ensure a more comprehensive coverage of different aesthetic aspects. Additionally, incorporating more advanced machine learning techniques, such as multi-task learning or reinforcement learning, could help in better modeling the complex relationships between different aesthetic dimensions. Furthermore, exploring the use of advanced neural network architectures, such as transformer models, could enhance the capability of the decoupled feedback learning approach to capture and optimize aesthetic qualities more effectively.