# Neural Aesthetic Image Reviewer

## TL;DR

This paper introduces a neural model that assesses image aesthetics and generates textual explanations, enhancing understanding of aesthetic judgments through multi-task learning on a new dataset.

## Contribution

The paper proposes a multi-task neural architecture for aesthetic scoring and explanation generation, and introduces the AVA-Reviews dataset for research in aesthetic reasoning.

## Key findings

- The model outperforms baselines in aesthetic scoring accuracy.
- It generates human-like aesthetic comments.
- Multi-task learning improves both rating and explanation quality.

## 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, we 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, we propose two multi-task architectures 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, we 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, we demonstrate experimentally that our model can generate textual reviews related to aesthetics, which are consistent with human perception.