Visual aesthetic assessment has been an active research field for decades.
Although latest methods have achieved promising performance on benchmark
datasets, they typically rely on a large number of manual annotations including
both aesthetic labels and related image attributes. In this paper, we revisit
the problem of image aesthetic assessment from the self-supervised feature
learning perspective. Our motivation is that a suitable feature representation
for image aesthetic assessment should be able to distinguish different
expert-designed image manipulations, which have close relationships with
negative aesthetic effects. To this end, we design two novel pretext tasks to
identify the types and parameters of editing operations applied to synthetic
instances. The features from our pretext tasks are then adapted for a one-layer
linear classifier to evaluate the performance in terms of binary aesthetic
classification. We conduct extensive quantitative experiments on three
benchmark datasets and demonstrate that our approach can faithfully extract
aesthetics-aware features and outperform alternative pretext schemes. Moreover,
we achieve comparable results to state-of-the-art supervised methods that use
10 million labels from ImageNet.

本文通过自监督特征学习的视角重新探讨了图像审美评估问题，并针对编辑操作设计了两个新颖的预训练任务，实现了从图像中提取审美感知特征。在三个基准数据集上进行的实验表明，该方法能够有效地提取特征并且超越了其他预训练模式，并达到了使用 ImageNet 1000 万标签的监督方法的可比较结果。