{"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/annotation-order-matters-recurrent-image","title":"Annotation Order Matters: Recurrent Image Annotator for Arbitrary Length Image Tagging","arxiv_id":"1604.05225","date":"2016-04-18","proceeding":null,"authors":["Jiren Jin","Hideki Nakayama"],"abstract":"Automatic image annotation has been an important research topic in\nfacilitating large scale image management and retrieval. Existing methods focus\non learning image-tag correlation or correlation between tags to improve\nannotation accuracy. However, most of these methods evaluate their performance\nusing top-k retrieval performance, where k is fixed. Although such setting\ngives convenience for comparing different methods, it is not the natural way\nthat humans annotate images. The number of annotated tags should depend on\nimage contents. Inspired by the recent progress in machine translation and\nimage captioning, we propose a novel Recurrent Image Annotator (RIA) model that\nforms image annotation task as a sequence generation problem so that RIA can\nnatively predict the proper length of tags according to image contents. We\nevaluate the proposed model on various image annotation datasets. In addition\nto comparing our model with existing methods using the conventional top-k\nevaluation measures, we also provide our model as a high quality baseline for\nthe arbitrary length image tagging task. Moreover, the results of our\nexperiments show that the order of tags in training phase has a great impact on\nthe final annotation performance.","url_abs":"http://arxiv.org/abs/1604.05225v3","url_pdf":"http://arxiv.org/pdf/1604.05225v3.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":"annotation-order-matters-recurrent-image","repo_url":"https://github.com/jinjiren/recurrent-image-annotator-web-demo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"management","task_name":"Management"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"tag","task_name":"TAG"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.05225","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}