{"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/segflow-joint-learning-for-video-object","title":"SegFlow: Joint Learning for Video Object Segmentation and Optical Flow","arxiv_id":"1709.06750","date":"2017-09-20","proceeding":"ICCV 2017 10","authors":["Jingchun Cheng","Yi-Hsuan Tsai","Shengjin Wang","Ming-Hsuan Yang"],"abstract":"This paper proposes an end-to-end trainable network, SegFlow, for\nsimultaneously predicting pixel-wise object segmentation and optical flow in\nvideos. The proposed SegFlow has two branches where useful information of\nobject segmentation and optical flow is propagated bidirectionally in a unified\nframework. The segmentation branch is based on a fully convolutional network,\nwhich has been proved effective in image segmentation task, and the optical\nflow branch takes advantage of the FlowNet model. The unified framework is\ntrained iteratively offline to learn a generic notion, and fine-tuned online\nfor specific objects. Extensive experiments on both the video object\nsegmentation and optical flow datasets demonstrate that introducing optical\nflow improves the performance of segmentation and vice versa, against the\nstate-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1709.06750v1","url_pdf":"http://arxiv.org/pdf/1709.06750v1.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":"segflow-joint-learning-for-video-object","repo_url":"https://github.com/JingchunCheng/SegFlow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-video-object-segmentation","task_name":"Semi-Supervised Video Object 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-semantic-segmentation","task_name":"Video Semantic Segmentation"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-davis-2016","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS 2016","model":"SFL","rank_in_archive_order":67,"of":78,"metrics":{"F-measure (Decay)":"10.4","F-measure (Mean)":"76.0","F-measure (Recall)":"85.5","J&F":"76.05","Jaccard (Decay)":"12.1","Jaccard (Mean)":"76.1","Jaccard (Recall)":"90.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.06750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.06750"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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