{"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/segmentation-guided-image-to-image","title":"Segmentation Guided Image-to-Image Translation with Adversarial Networks","arxiv_id":"1901.01569","date":"2019-01-06","proceeding":null,"authors":["Songyao Jiang","Zhiqiang Tao","Yun Fu"],"abstract":"Recently image-to-image translation has received increasing attention, which\naims to map images in one domain to another specific one. Existing methods\nmainly solve this task via a deep generative model, and focus on exploring the\nrelationship between different domains. However, these methods neglect to\nutilize higher-level and instance-specific information to guide the training\nprocess, leading to a great deal of unrealistic generated images of low\nquality. Existing methods also lack of spatial controllability during\ntranslation. To address these challenge, we propose a novel Segmentation Guided\nGenerative Adversarial Networks (SGGAN), which leverages semantic segmentation\nto further boost the generation performance and provide spatial mapping. In\nparticular, a segmentor network is designed to impose semantic information on\nthe generated images. Experimental results on multi-domain face image\ntranslation task empirically demonstrate our ability of the spatial\nmodification and our superiority in image quality over several state-of-the-art\nmethods.","url_abs":"http://arxiv.org/abs/1901.01569v2","url_pdf":"http://arxiv.org/pdf/1901.01569v2.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":"segmentation-guided-image-to-image","repo_url":"https://github.com/jackyjsy/SGGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}