Personality Driven Multi-task Learning for Image Aesthetic Assessment

With the prevalence of convolutional neural networks (CNNs), assessing the aesthetics of an image has gained great advances recently. Individual users often have different aesthetic preferences on images, which we believe are mainly affected by their personality traits. However, most of the current aesthetics models predict a generic aesthetic score based on handcrafted and/or learned feature representations, which are unified and thus cannot reflect the individual differences during image aesthetic rating. In this paper, we propose an end-to-end personality driven multi-task deep learning model to address this problem. Firstly, both image aesthetics and personality traits are learned from the proposed multi-task model. Then the personality features are employed to modulate the aesthetics features, producing the optimal generic image aesthetics scores. The experimental results on two public databases show that the proposed method is superior to the state-of-the-art approaches.