MetaMP: Metalearning-Based Multipatch Image Aesthetics Assessment
提出一种基于元学习的多块图像美学评估方法，通过元学习训练网络快速适应不同主题的美学评估任务，并设计完整信息块选择方案和多块网络来协调细节与整体印象，在AVA基准数据集上表现优于现有模型。
Image aesthetics assessment (IAA) is a subjective and complex task. The aesthetics of different themes vary greatly in content and aesthetic results, whether they are in the same aesthetic community or not. In aesthetic evaluation tasks, the pretrained network with direct fine-tune may not be able to quickly adapt to tasks on various themes. This article introduces a metalearning-based multipatch (MetaMP) IAA method to adapt to various thematic tasks quickly. The network is trained based on metalearning to obtain content-oriented aesthetic expression. In addition, we design a complete-information patch selection scheme and a multipatch (MP) network to make the fine details fit the overall impression. Experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art models based on aesthetic visual analysis (AVA) benchmark datasets. In addition, the evaluation of the dataset shows the effectiveness of our metalearning training model, which not only improves MetaMP assessment accuracy but also provides valuable guidance for network initialization of IAA.