{"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/minimizing-supervision-for-free-space","title":"Minimizing Supervision for Free-space Segmentation","arxiv_id":"1711.05998","date":"2017-11-16","proceeding":null,"authors":["Satoshi Tsutsui","Tommi Kerola","Shunta Saito","David J. Crandall"],"abstract":"Identifying \"free-space,\" or safely driveable regions in the scene ahead, is\na fundamental task for autonomous navigation. While this task can be addressed\nusing semantic segmentation, the manual labor involved in creating pixelwise\nannotations to train the segmentation model is very costly. Although weakly\nsupervised segmentation addresses this issue, most methods are not designed for\nfree-space. In this paper, we observe that homogeneous texture and location are\ntwo key characteristics of free-space, and develop a novel, practical framework\nfor free-space segmentation with minimal human supervision. Our experiments\nshow that our framework performs better than other weakly supervised methods\nwhile using less supervision. Our work demonstrates the potential for\nperforming free-space segmentation without tedious and costly manual\nannotation, which will be important for adapting autonomous driving systems to\ndifferent types of vehicles and environments","url_abs":"http://arxiv.org/abs/1711.05998v3","url_pdf":"http://arxiv.org/pdf/1711.05998v3.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":"minimizing-supervision-for-free-space","repo_url":"https://github.com/apple2373/min-seg-road","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"weakly-supervised-segmentation","task_name":"Weakly supervised 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}