IAFormer: A Transformer Network for Image Aesthetic Evaluation and Cropping

Abstract

Aesthetic quality evaluation of images has an important role in the field of visual analysis, and the widespread use of high-quality image editing has gradually increased the importance of image aesthetic evaluation in automatic image processing tasks. Previous researchers have mostly explored the mapping relationship between images and labeled scores using convolutional neural networks, but the aesthetic features of different regions on images have not been explored sufficiently, when an image is rich in background information and it is necessary to correlate the aesthetic features of different regions to evaluate the image, convolutional neural networks often cannot extract the aesthetic features of the image adequately due to the lack of the advantage of global feature modeling. We introduce a novel Transformer architecture for image aesthetic quality assessment(IAFormer), IAFormer can model the global aesthetic features of an image, and it is a framework that unifies the aesthetic quality assessment of images and the aesthetic cropping of images, while the aesthetic quality of the image is evaluated, the aesthetic weights on different patches within the image can be calculated to give valid reference information for the aesthetic cropping task.