{"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/simple-does-it-weakly-supervised-instance-and","title":"Simple Does It: Weakly Supervised Instance and Semantic Segmentation","arxiv_id":"1603.07485","date":"2016-03-24","proceeding":"CVPR 2017 7","authors":["Anna Khoreva","Rodrigo Benenson","Jan Hosang","Matthias Hein","Bernt Schiele"],"abstract":"Semantic labelling and instance segmentation are two tasks that require\nparticularly costly annotations. Starting from weak supervision in the form of\nbounding box detection annotations, we propose a new approach that does not\nrequire modification of the segmentation training procedure. We show that when\ncarefully designing the input labels from given bounding boxes, even a single\nround of training is enough to improve over previously reported weakly\nsupervised results. Overall, our weak supervision approach reaches ~95% of the\nquality of the fully supervised model, both for semantic labelling and instance\nsegmentation.","url_abs":"http://arxiv.org/abs/1603.07485v2","url_pdf":"http://arxiv.org/pdf/1603.07485v2.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":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-pascal-voc-2012","task":"Semantic Segmentation","dataset":"PASCAL VOC 2012 test","model":"SID","rank_in_archive_order":37,"of":51,"metrics":{"Mean IoU":"72.8%"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pascal-voc-2012-val","task":"Semantic Segmentation","dataset":"PASCAL VOC 2012 val","model":"SID","rank_in_archive_order":28,"of":29,"metrics":{"Mean IoU":"71.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.07485","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}