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.
翻译:随着社会软件和多媒体技术的持续发展,图像已成为一种传播信息和社会化的重要载体。如何全面评价图像已成为最近研究的重点。传统的图像审美评估方法往往采用单一的数值总体评分,具有一定主观性,无法再满足较高的审美要求。在本文中,我们建立了一个新的图像属性数据集,称为具有属性的审美混合数据集(AMD-A),并设计了融合的外部属性特征。此外,我们提出了一个有效的方法,用于对混合多属性数据集进行图像审美学属性评估,并用高效网-B0作为主干网构建多任务网络结构。我们的模型可以实现审美分类、总体评分和分分分。在每一个子网络中,我们通过非洲经委会的频道关注模块改进特征提取。关于最后的总体评分,我们采用师生网络的构想,并使用分类子网络来指导审美总体精度回归。使用MindSpore的实验结果显示,我们提出的方法可以有效地改进审美总体和属性评估的绩效。