With the continuous development of social software and multimedia technology,
images have become a kind of important carrier for spreading information and
socializing. How to evaluate an image comprehensively has become the focus of
recent researches. The traditional image aesthetic assessment methods often
adopt single numerical overall assessment scores, which has certain
subjectivity and can no longer meet the higher aesthetic requirements. In this
paper, we construct an new image attribute dataset called aesthetic mixed
dataset with attributes(AMD-A) and design external attribute features for
fusion. Besides, we propose a efficient method for image aesthetic attribute
assessment on mixed multi-attribute dataset and construct a multitasking
network architecture by using the EfficientNet-B0 as the backbone network. Our
model can achieve aesthetic classification, overall scoring and attribute
scoring. In each sub-network, we improve the feature extraction through ECA
channel attention module. As for the final overall scoring, we adopt the idea
of the teacher-student network and use the classification sub-network to guide
the aesthetic overall fine-grain regression. Experimental results, using the
MindSpore, show that our proposed method can effectively improve the
performance of the aesthetic overall and attribute assessment.

本研究通过构建一个新的图像属性数据集 AMD-A，设计外在属性特征来融合，提出了一种高效的混合多属性图像美学属性评估方法，并利用 EfficientNet-B0 作为骨干网络构建了一个多任务网络体系结构，能够实现美学分类、总体评分和属性评分。实验结果表明，我们提出的方法能够有效提高美学整体和属性方面的表现。