{"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/weakly-supervised-domain-specific-color","title":"Weakly Supervised Domain-Specific Color Naming Based on Attention","arxiv_id":"1805.04385","date":"2018-05-11","proceeding":null,"authors":["Lu Yu","Yongmei Cheng","Joost Van de Weijer"],"abstract":"The majority of existing color naming methods focuses on the eleven basic\ncolor terms of the English language. However, in many applications, different\nsets of color names are used for the accurate description of objects. Labeling\ndata to learn these domain-specific color names is an expensive and laborious\ntask. Therefore, in this article we aim to learn color names from weakly\nlabeled data. For this purpose, we add an attention branch to the color naming\nnetwork. The attention branch is used to modulate the pixel-wise color naming\npredictions of the network. In experiments, we illustrate that the attention\nbranch correctly identifies the relevant regions. Furthermore, we show that our\nmethod obtains state-of-the-art results for pixel-wise and image-wise\nclassification on the EBAY dataset and is able to learn color names for various\ndomains.","url_abs":"http://arxiv.org/abs/1805.04385v1","url_pdf":"http://arxiv.org/pdf/1805.04385v1.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":"weakly-supervised-domain-specific-color","repo_url":"https://github.com/yulu0724/AttentionColorName","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}