Image style migration method based on transformer generative adversarial network combined with contrast learning strategy
Year: 2024
Type: conference-paper
Abstract: Aiming at the problems of large loss of image patch embedding information and lack of stylized features in the generated image during transformer style migration, a style migration model combining contrast learning with transformer backbone generative adversarial network is established. Firstly, overlapping patch embedding is used to serialize the input images to get richer features; then the style and content contrast loss of contrast learning is used to mine the non-significant information between stylized images to reduce the style and content loss; finally, a multi-scale discriminator discriminates the true and false of stylized images to optimize the generator to get more stylistic features. The results on MS-COCO and WikiArt datasets show that the model improves the effectiveness and transformation efficacy of stylized images in style migration.
Author: Xing Chen
Institution: Shanghai Maritime University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401517511