{"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/bilateral-space-video-segmentation","title":"Bilateral Space Video Segmentation","arxiv_id":null,"date":"2016-06-01","proceeding":"CVPR 2016 6","authors":["Nicolas Maerki","Federico Perazzi","Oliver Wang","Alexander Sorkine-Hornung"],"abstract":"In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatio-temporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2016/html/Maerki_Bilateral_Space_Video_CVPR_2016_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2016/papers/Maerki_Bilateral_Space_Video_CVPR_2016_paper.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":"segmentation","task_name":"Segmentation"},{"task_slug":"semi-supervised-video-object-segmentation","task_name":"Semi-Supervised Video Object Segmentation"},{"task_slug":"video-segmentation","task_name":"Video Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-davis-2016","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS 2016","model":"BVS","rank_in_archive_order":76,"of":78,"metrics":{"F-measure (Decay)":"21.3","F-measure (Mean)":"58.8","F-measure (Recall)":"67.9","J&F":"59.4","Jaccard (Decay)":"28.9","Jaccard (Mean)":"60.0","Jaccard (Recall)":"66.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}