{"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/videograph-recognizing-minutes-long-human","title":"VideoGraph: Recognizing Minutes-Long Human Activities in Videos","arxiv_id":"1905.05143","date":"2019-05-13","proceeding":null,"authors":["Noureldien Hussein","Efstratios Gavves","Arnold W. M. Smeulders"],"abstract":"Many human activities take minutes to unfold. To represent them, related works opt for statistical pooling, which neglects the temporal structure. Others opt for convolutional methods, as CNN and Non-Local. While successful in learning temporal concepts, they are short of modeling minutes-long temporal dependencies. We propose VideoGraph, a method to achieve the best of two worlds: represent minutes-long human activities and learn their underlying temporal structure. VideoGraph learns a graph-based representation for human activities. The graph, its nodes and edges are learned entirely from video datasets, making VideoGraph applicable to problems without node-level annotation. The result is improvements over related works on benchmarks: Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to learn the temporal structure of human activities in minutes-long videos.","url_abs":"https://arxiv.org/abs/1905.05143v2","url_pdf":"https://arxiv.org/pdf/1905.05143v2.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":"long-video-activity-recognition","task_name":"Long-video Activity Recognition"},{"task_slug":"video-classification","task_name":"Video Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/long-video-activity-recognition-on-breakfast","task":"Long-video Activity Recognition","dataset":"Breakfast","model":"VideoGraph (I3D-K400-Pretrain-feature)","rank_in_archive_order":6,"of":8,"metrics":{"mAP":"63.14"},"uses_additional_data":false},{"leaderboard":"/sota/video-classification-on-breakfast","task":"Video Classification","dataset":"Breakfast","model":"VideoGraph","rank_in_archive_order":9,"of":9,"metrics":{"Accuracy (%)":"69.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.05143","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}