{"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/fast-user-guided-video-object-segmentation-by","title":"Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks","arxiv_id":"1904.09791","date":"2019-04-22","proceeding":"CVPR 2019 6","authors":["Seoung Wug Oh","Joon-Young Lee","Ning Xu","Seon Joo Kim"],"abstract":"We present a deep learning method for the interactive video object\nsegmentation. Our method is built upon two core operations, interaction and\npropagation, and each operation is conducted by Convolutional Neural Networks.\nThe two networks are connected both internally and externally so that the\nnetworks are trained jointly and interact with each other to solve the complex\nvideo object segmentation problem. We propose a new multi-round training scheme\nfor the interactive video object segmentation so that the networks can learn\nhow to understand the user's intention and update incorrect estimations during\nthe training. At the testing time, our method produces high-quality results and\nalso runs fast enough to work with users interactively. We evaluated the\nproposed method quantitatively on the interactive track benchmark at the DAVIS\nChallenge 2018. We outperformed other competing methods by a significant margin\nin both the speed and the accuracy. We also demonstrated that our method works\nwell with real user interactions.","url_abs":"http://arxiv.org/abs/1904.09791v2","url_pdf":"http://arxiv.org/pdf/1904.09791v2.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":"fast-user-guided-video-object-segmentation-by","repo_url":"https://github.com/seoungwugoh/ivs-demo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"interactive-video-object-segmentation","task_name":"Interactive Video Object Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/interactive-video-object-segmentation-on","task":"Interactive Video Object Segmentation","dataset":"DAVIS 2017","model":"FUGVOS","rank_in_archive_order":7,"of":7,"metrics":{"AUC-J":"0.691","J@60s":"0.734"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.09791","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}