{"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/progressive-mirror-detection","title":"Progressive Mirror Detection","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Jiaying Lin"," Guodong Wang"," Rynson W.H. Lau"],"abstract":"The mirror detection problem is important as mirrors can affect the performances of many vision tasks. It is a difficult problem as it requires an understanding of global scene semantics. Recently, a method was proposed to detect mirrors by learning multi-level contextual contrasts between inside and outside of mirrors, which helps locate mirror edges implicitly. We observe that the content of a mirror reflects the content of its surrounding, separated by the edge of the mirror. Hence, we propose a model in this paper to progressively learn the content similarity between the inside and outside of the mirror while explicitly detecting the mirror edges. Our work has two main contributions. First, we propose a new relational contextual contrasted local (RCCL) module to extract and compare the mirror features with its corresponding context features, and an edge detection and fusion (EDF) module to learn the features of mirror edges in complex scenes via explicit supervision. Second, we construct a challenging benchmark dataset of 6,461 mirror images. Unlike the existing MSD dataset, which has limited diversity, our dataset covers a variety of scenes and is much larger in scale. Experimental results show that our model outperforms relevant state-of-the-art methods.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Progressive_Mirror_Detection_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Progressive_Mirror_Detection_CVPR_2020_paper.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":"diversity","task_name":"Diversity"},{"task_slug":"edge-detection","task_name":"Edge Detection"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"mirror-detection","task_name":"Mirror Detection"}],"methods":[],"datasets_introduced":[{"slug":"pmd","name":"PMD","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-segmentation-on-msd-mirror-segmentation","task":"Image Segmentation","dataset":"MSD (Mirror Segmentation Dataset)","model":"PMD","rank_in_archive_order":3,"of":5,"metrics":{"F-measure":"0.892","IoU":"0.815","MAE":"0.047"},"uses_additional_data":false},{"leaderboard":"/sota/image-segmentation-on-pmd","task":"Image Segmentation","dataset":"PMD","model":"PMD","rank_in_archive_order":4,"of":5,"metrics":{"F-measure":"0.794","IoU":"0.660","MAE":"0.032"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}