{"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/scops-self-supervised-co-part-segmentation","title":"SCOPS: Self-Supervised Co-Part Segmentation","arxiv_id":"1905.01298","date":"2019-05-03","proceeding":"CVPR 2019 6","authors":["Wei-Chih Hung","Varun Jampani","Sifei Liu","Pavlo Molchanov","Ming-Hsuan Yang","Jan Kautz"],"abstract":"Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations. Existing works on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and can not generalize to unseen object categories. We propose a self-supervised deep learning approach for part segmentation, where we devise several loss functions that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances. Extensive experiments on different types of image collections demonstrate that our approach can produce part segments that adhere to object boundaries and also more semantically consistent across object instances compared to existing self-supervised techniques.","url_abs":"https://arxiv.org/abs/1905.01298v1","url_pdf":"https://arxiv.org/pdf/1905.01298v1.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":"scops-self-supervised-co-part-segmentation","repo_url":"https://github.com/NVlabs/SCOPS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"unsupervised-facial-landmark-detection","task_name":"Unsupervised Facial Landmark Detection"},{"task_slug":"unsupervised-human-pose-estimation","task_name":"Unsupervised Human Pose Estimation"},{"task_slug":"unsupervised-keypoint-estimation","task_name":"Unsupervised Keypoint Estimation"},{"task_slug":null,"task_name":"Unsupervised Keypoints"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-facial-landmark-detection-on-5","task":"Unsupervised Facial Landmark Detection","dataset":"MAFL Unaligned","model":"SCOPS","rank_in_archive_order":6,"of":9,"metrics":{"NME":"15.01"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.01298","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}