{"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/deep-learning-methods-for-efficient-large","title":"Deep Learning Methods for Efficient Large Scale Video Labeling","arxiv_id":"1706.04572","date":"2017-06-14","proceeding":null,"authors":["Miha Skalic","Marcin Pekalski","Xingguo E. Pan"],"abstract":"We present a solution to \"Google Cloud and YouTube-8M Video Understanding\nChallenge\" that ranked 5th place. The proposed model is an ensemble of three\nmodel families, two frame level and one video level. The training was performed\non augmented dataset, with cross validation.","url_abs":"http://arxiv.org/abs/1706.04572v1","url_pdf":"http://arxiv.org/pdf/1706.04572v1.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":"deep-learning-methods-for-efficient-large","repo_url":"https://github.com/mpekalski/Y8M","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"video-understanding","task_name":"Video Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}