Aesthetic Assessment of Packaging Design Based on Con-Transformer

Abstract

Different from the traditional natural images' aesthetic assessment task, the aesthetic assessment of packaging design should not only pay attention to artistic beauty, but also pay attention to functional beauty, that is, the attraction of the packaging design to consumers. In this paper, the authors propose a con-transformer packaging design aesthetic assessment method, which takes advantage of convolutional operations and self-attention mechanisms for enhanced representation learning, resulting in an effective aesthetic assessment of the packaging design images. Specifically, con-transformer integrates convolution network branch and transformer network branch to extract local representation features and global representation features of the packaging design images respectively. Finally, the fused representation features are used for aesthetic assessment. Experimental results show that the proposed method can not only effectively assess the aesthetic of packaging design images, but also be applied to the aesthetic assessment of natural images.