{"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/video-object-segmentation-with-re","title":"Video Object Segmentation with Re-identification","arxiv_id":"1708.00197","date":"2017-08-01","proceeding":null,"authors":["Xiaoxiao Li","Yuankai Qi","Zhe Wang","Kai Chen","Ziwei Liu","Jianping Shi","Ping Luo","Xiaoou Tang","Chen Change Loy"],"abstract":"Conventional video segmentation methods often rely on temporal continuity to\npropagate masks. Such an assumption suffers from issues like drifting and\ninability to handle large displacement. To overcome these issues, we formulate\nan effective mechanism to prevent the target from being lost via adaptive\nobject re-identification. Specifically, our Video Object Segmentation with\nRe-identification (VS-ReID) model includes a mask propagation module and a ReID\nmodule. The former module produces an initial probability map by flow warping\nwhile the latter module retrieves missing instances by adaptive matching. With\nthese two modules iteratively applied, our VS-ReID records a global mean\n(Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017\nDAVIS Challenge.","url_abs":"http://arxiv.org/abs/1708.00197v1","url_pdf":"http://arxiv.org/pdf/1708.00197v1.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":"video-object-segmentation-with-re","repo_url":"https://github.com/birdman9390/MetaMaskTrack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"video-object-segmentation-with-re","repo_url":"https://github.com/lxx1991/VS-ReID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"video-object-segmentation-with-re","repo_url":"https://github.com/omkar13/MaskTrack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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-segmentation","task_name":"Video Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}