{"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/the-2017-davis-challenge-on-video-object","title":"The 2017 DAVIS Challenge on Video Object Segmentation","arxiv_id":"1704.00675","date":"2017-04-03","proceeding":null,"authors":["Jordi Pont-Tuset","Federico Perazzi","Sergi Caelles","Pablo Arbeláez","Alex Sorkine-Hornung","Luc van Gool"],"abstract":"We present the 2017 DAVIS Challenge on Video Object Segmentation, a public\ndataset, benchmark, and competition specifically designed for the task of video\nobject segmentation. Following the footsteps of other successful initiatives,\nsuch as ILSVRC and PASCAL VOC, which established the avenue of research in the\nfields of scene classification and semantic segmentation, the DAVIS Challenge\ncomprises a dataset, an evaluation methodology, and a public competition with a\ndedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on\nthe recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which\nhas fostered the development of several novel state-of-the-art video object\nsegmentation techniques. In this paper we describe the scope of the benchmark,\nhighlight the main characteristics of the dataset, define the evaluation\nmetrics of the competition, and present a detailed analysis of the results of\nthe participants to the challenge.","url_abs":"http://arxiv.org/abs/1704.00675v3","url_pdf":"http://arxiv.org/pdf/1704.00675v3.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":"scene-classification","task_name":"Scene Classification"},{"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":[{"slug":"davis-2017","name":"DAVIS 2017","full_name":"DAVIS 2017"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.00675","atlas_url":"https://app.syntology.ai/?focus=1704.00675","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}