{"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/separating-self-expression-and-visual-content","title":"Separating Self-Expression and Visual Content in Hashtag Supervision","arxiv_id":"1711.09825","date":"2017-11-27","proceeding":"CVPR 2018 6","authors":["Andreas Veit","Maximilian Nickel","Serge Belongie","Laurens van der Maaten"],"abstract":"The variety, abundance, and structured nature of hashtags make them an\ninteresting data source for training vision models. For instance, hashtags have\nthe potential to significantly reduce the problem of manual supervision and\nannotation when learning vision models for a large number of concepts. However,\na key challenge when learning from hashtags is that they are inherently\nsubjective because they are provided by users as a form of self-expression. As\na consequence, hashtags may have synonyms (different hashtags referring to the\nsame visual content) and may be ambiguous (the same hashtag referring to\ndifferent visual content). These challenges limit the effectiveness of\napproaches that simply treat hashtags as image-label pairs. This paper presents\nan approach that extends upon modeling simple image-label pairs by modeling the\njoint distribution of images, hashtags, and users. We demonstrate the efficacy\nof such approaches in image tagging and retrieval experiments, and show how the\njoint model can be used to perform user-conditional retrieval and tagging.","url_abs":"http://arxiv.org/abs/1711.09825v1","url_pdf":"http://arxiv.org/pdf/1711.09825v1.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":"separating-self-expression-and-visual-content","repo_url":"https://github.com/weiyinwei/GCN_PHR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.09825","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}