{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attention-aware-multi-stroke-style-transfer","title":"Attention-aware Multi-stroke Style Transfer","arxiv_id":"1901.05127","date":"2019-01-16","proceeding":"CVPR 2019 6","authors":["Yuan Yao","Jianqiang Ren","Xuansong Xie","Weidong Liu","Yong-Jin Liu","Jun Wang"],"abstract":"Neural style transfer has drawn considerable attention from both academic and\nindustrial field. Although visual effect and efficiency have been significantly\nimproved, existing methods are unable to coordinate spatial distribution of\nvisual attention between the content image and stylized image, or render\ndiverse level of detail via different brush strokes. In this paper, we tackle\nthese limitations by developing an attention-aware multi-stroke style transfer\nmodel. We first propose to assemble self-attention mechanism into a\nstyle-agnostic reconstruction autoencoder framework, from which the attention\nmap of a content image can be derived. By performing multi-scale style swap on\ncontent features and style features, we produce multiple feature maps\nreflecting different stroke patterns. A flexible fusion strategy is further\npresented to incorporate the salient characteristics from the attention map,\nwhich allows integrating multiple stroke patterns into different spatial\nregions of the output image harmoniously. We demonstrate the effectiveness of\nour method, as well as generate comparable stylized images with multiple stroke\npatterns against the state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1901.05127v1","url_pdf":"http://arxiv.org/pdf/1901.05127v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attention-aware-multi-stroke-style-transfer","repo_url":"https://github.com/JianqiangRen/AAMS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.05127","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}