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Personalized Image Aesthetics Assessment with Rich Attributes

Yuzhe Yang1, Liwu Xu1, Leida Li2, Nan Qie1, Yaqian Li1, Peng Zhang1, Yandong Guo1

1OPPO Research Institute,  2Xidian University



Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image characteristics and subject characters. The existing PIAA databases are limited in terms of annotation diversity, especially the subject aspect, which can no longer meet the increasing demands of PIAA research. To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31,220 images with annotations by 438 subjects. PARA features wealthy annotations, including 9 image-oriented objective attributes and 4 human-oriented subjective attributes. In addition, desensitized subject information, such as personality traits, is also provided to support study of PIAA and user portraits. A comprehensive analysis of the annotation data is provided and statistic study indicates that the aesthetic preferences can be mirrored by proposed subjective attributes. We also propose a conditional PIAA model by utilizing subject information as conditional prior. Experiment results indicate that the conditional PIAA model can outperform the control group, which is also the first attempt to demonstrate how image aesthetics and subject characters interact to produce the intricate personalized tastes on image aesthetics. We believe the database and the associated analysis would be useful for conducting next-generation PIAA study. 


Download:   Google Drive     Baidu Cloud

The dataset is password protected. Please send a download application email to ippllewis@gmail.com (Yuzhe Yang) together with your name and affiliation, we will send you the password.  


If you use the database in your research, please cite our paper:

  • Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, Yandong Guo, "Personalized Image Aesthetics Assessment with Rich Attributes",  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [PDF]