{"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/videomatch-matching-based-video-object","title":"VideoMatch: Matching based Video Object Segmentation","arxiv_id":"1809.01123","date":"2018-09-04","proceeding":"ECCV 2018 9","authors":["Yuan-Ting Hu","Jia-Bin Huang","Alexander G. Schwing"],"abstract":"Video object segmentation is challenging yet important in a wide variety of\napplications for video analysis. Recent works formulate video object\nsegmentation as a prediction task using deep nets to achieve appealing\nstate-of-the-art performance. Due to the formulation as a prediction task, most\nof these methods require fine-tuning during test time, such that the deep nets\nmemorize the appearance of the objects of interest in the given video. However,\nfine-tuning is time-consuming and computationally expensive, hence the\nalgorithms are far from real time. To address this issue, we develop a novel\nmatching based algorithm for video object segmentation. In contrast to\nmemorization based classification techniques, the proposed approach learns to\nmatch extracted features to a provided template without memorizing the\nappearance of the objects. We validate the effectiveness and the robustness of\nthe proposed method on the challenging DAVIS-16, DAVIS-17, Youtube-Objects and\nJumpCut datasets. Extensive results show that our method achieves comparable\nperformance without fine-tuning and is much more favorable in terms of\ncomputational time.","url_abs":"http://arxiv.org/abs/1809.01123v1","url_pdf":"http://arxiv.org/pdf/1809.01123v1.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":"memorization","task_name":"Memorization"},{"task_slug":"object","task_name":"Object"},{"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":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-davis-2017","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS 2017 (val)","model":"VideoMatch","rank_in_archive_order":69,"of":81,"metrics":{"F-measure (Mean)":"68.2","J&F":"62.4","Jaccard (Mean)":"56.5"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1809.01123","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}