Neural aesthetic image reviewer

Abstract

Recently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high‐ or low‐aesthetic score should be assigned to an image. From such a point of view, the authors propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, they propose three models based on shared aesthetically semantic layers and task‐specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, they collect the AVA‐Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi‐task learning, the proposed models can rate aesthetic images as well as produce comments in an end‐to‐end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA‐Reviews dataset. Moreover, they demonstrate experimentally that the authors’ model can generate textual reviews related to aesthetics, which are consistent with human perception.

Keywords

• Neural aesthetic image reviewer
• image aesthetics
• high- score
• low-aesthetic score
• Neural Aesthetic Image Reviewer
• plausible rating score