Personalised aesthetics with residual adapters
This addresses the need for personalized aesthetic prediction in photography, enabling applications like picture enhancement and resource-efficient recommender systems, though it is incremental in adapting existing methods.
The paper tackled the problem of ignoring individual preferences in computational aesthetic evaluation of photos by proposing a residual learning model that learns user-specific aesthetic preferences, surpassing state-of-the-art methods with a limited number of user-specific parameters.
The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, user specific preferences over aesthetics in photography, while surpassing the state-of-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.