ST2SI: Image Style Transfer via Vision Transformer using Spatial Interaction

Abstract

While retaining the original content structure, image style transfer uses style image to render it to obtain stylized images with artistic features. Because the content image contains different detail units and the style image has various style patterns, it is easy to cause the distortion of the stylized image. We proposes a new Style Transfer based on Vision Transformer using Spatial Interaction (ST2SI), which takes advantage of Spatial Interactive Convolution (SIC) and Spatial Unit Attention (SUA) to further enhance the content and style representation, so that the encoder can not only better learn the features of the content domain and the style domain, but also maintain the structural integrity of the image content and the effective integration of style features. Concretely, the high-order spatial interaction ability of Spatial Interactive Convolution can capture complex style patterns, and Spatial Unit Attention can balance the content information of different detail units through the change of attention weight, thus solving the problem of image distortion. Comprehensive qualitative and quantitative experiments prove the efficacy of our approach.