{"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/unsupervised-video-object-segmentation-with-1","title":"Unsupervised Video Object Segmentation with Motion-based Bilateral Networks","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV 2018 9","authors":["Siyang Li","Bryan Seybold","Alexey Vorobyov","Xuejing Lei","C. -C. Jay Kuo"],"abstract":"In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false positive regions by accurately identifying background objects. Then, we integrate the background estimate from the bilateral network with instance embeddings into a graph, which allows multiple frame reasoning with graph edges linking pixels from different frames. We classify graph nodes by defining and minimizing a cost function, and segment the video frames based on the node labels. The proposed method outperforms previous state-of-the-art unsupervised video object segmentation methods against the DAVIS 2016 and the FBMS-59 datasets.","url_abs":"http://openaccess.thecvf.com/content_ECCV_2018/html/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ECCV_2018/papers/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_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":"object","task_name":"Object"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-video-object-segmentation","task_name":"Unsupervised Video Object Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-salient-object-detection","task_name":"Video Salient Object Detection"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-salient-object-detection-on-davis-2016","task":"Video Salient Object Detection","dataset":"DAVIS-2016","model":"MBNM","rank_in_archive_order":4,"of":11,"metrics":{"AVERAGE MAE":"0.031","MAX E-MEASURE":"0.966","MAX F-MEASURE":"0.862","S-Measure":"0.887"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-davsod-1","task":"Video Salient Object Detection","dataset":"DAVSOD-Normal25","model":"MBNM","rank_in_archive_order":4,"of":8,"metrics":{"Average MAE":"0.127","S-Measure":"0.597","max E-measure":"0.665"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-davsod","task":"Video Salient Object Detection","dataset":"DAVSOD-easy35","model":"MBNM","rank_in_archive_order":5,"of":9,"metrics":{"Average MAE":"0.109","S-Measure":"0.646","max E-Measure":"0.694","max F-Measure":"0.506"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-fbms-59","task":"Video Salient Object Detection","dataset":"FBMS-59","model":"MBNM","rank_in_archive_order":5,"of":16,"metrics":{"AVERAGE MAE":"0.047","MAX E-MEASURE":"0.892","MAX F-MEASURE":"0.816","S-Measure":"0.857"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-mcl","task":"Video Salient Object Detection","dataset":"MCL","model":"MBNM","rank_in_archive_order":3,"of":8,"metrics":{"AVERAGE MAE":"0.119","MAX E-MEASURE":"0.858","MAX F-MEASURE":"0.698","S-Measure":"0.755"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-segtrack-v2","task":"Video Salient Object Detection","dataset":"SegTrack v2","model":"MBNM","rank_in_archive_order":4,"of":8,"metrics":{"AVERAGE MAE":"0.026","MAX F-MEASURE":"0.716","S-Measure":"0.809","max E-measure":"0.878"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-uvsd","task":"Video Salient Object Detection","dataset":"UVSD","model":"MBNM","rank_in_archive_order":4,"of":8,"metrics":{"Average MAE":"0.079","S-Measure":"0.698","max E-measure":"0.776"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-vos-t","task":"Video Salient Object Detection","dataset":"VOS-T","model":"MBNM","rank_in_archive_order":4,"of":9,"metrics":{"Average MAE":"0.099","S-Measure":"0.742","max E-measure":"0.797"},"uses_additional_data":true},{"leaderboard":"/sota/video-salient-object-detection-on-visal","task":"Video Salient Object Detection","dataset":"ViSal","model":"MBNM","rank_in_archive_order":6,"of":10,"metrics":{"Average MAE":"0.047","S-Measure":"0.857","max E-measure":"0.892"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}