{"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/mmed-a-multi-domain-and-multi-modality-event","title":"MMED: A Multi-domain and Multi-modality Event Dataset","arxiv_id":"1904.02354","date":"2019-04-04","proceeding":null,"authors":["Zhenguo Yang","Zehang Lin","Min Cheng","Qing Li","Wenyin Liu"],"abstract":"In this work, we construct and release a multi-domain and multi-modality\nevent dataset (MMED), containing 25,165 textual news articles collected from\nhundreds of news media sites (e.g., Yahoo News, Google News, CNN News.) and\n76,516 image posts shared on Flickr social media, which are annotated according\nto 412 real-world events. The dataset is collected to explore the problem of\norganizing heterogeneous data contributed by professionals and amateurs in\ndifferent data domains, and the problem of transferring event knowledge\nobtained from one data domain to heterogeneous data domain, thus summarizing\nthe data with different contributors. We hope that the release of the MMED\ndataset can stimulate innovate research on related challenging problems, such\nas event discovery, cross-modal (event) retrieval, and visual question\nanswering, etc.","url_abs":"http://arxiv.org/abs/1904.02354v2","url_pdf":"http://arxiv.org/pdf/1904.02354v2.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":[],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[{"slug":"mmed","name":"MMED","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}