{"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/palettenet-image-recolorization-with-given","title":"Palettenet: Image recolorization with given color palette","arxiv_id":null,"date":"2017-07-21","proceeding":"The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017 7","authors":["Junho Cho","Sangdoo Yun","Kyoung Mu Lee","Jin Young Choi"],"abstract":"Image recolorization enhances the visual perception of an image for design and artistic purposes. In this work, we present a deep neural network, referred to as PaletteNet, which recolors an image according to a given target color palette that is useful to express the color concept of an image. PaletteNet takes two inputs: a source image to be recolored and a target palette. PaletteNet is then designed to change the color concept of a source image so that the palette of the output image is close to the target palette. To train PaletteNet, the proposed multi-task loss is composed of Euclidean loss and adversarial loss. The experimental results show that the proposed method outperforms the existing recolorization methods. Human experts with a commercial software take on average 18 minutes to recolor an image, while PaletteNet automatically recolors plausible results in less than a second.","url_abs":"https://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/html/Cho_PaletteNet_Image_Recolorization_CVPR_2017_paper.html","url_pdf":"https://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Cho_PaletteNet_Image_Recolorization_CVPR_2017_paper.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":"palettenet-image-recolorization-with-given","repo_url":"https://github.com/yongzx/PyTorch-PaletteNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}