{"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/single-stage-instance-shadow-detection-with","title":"Single-Stage Instance Shadow Detection with Bidirectional Relation Learning","arxiv_id":null,"date":"2021-06-19","proceeding":"CVPR 2021 1","authors":["Tianyu Wang","Xiaowei Hu","Chi-Wing Fu","Pheng-Ann Heng"],"abstract":"Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to form the final shadow-object pairs. In this paper, we present a new single-stage fully-convolutional network architecture with a bidirectional relation learning module to directly learn the relations of shadow and object instances in an end-to-end manner. Compared with the prior work, our method actively explores the internal relationship between shadows and objects to learn a better pairing between them, thus improving the overall performance for instance shadow detection. We evaluate our method on the benchmark dataset for instance shadow detection, both quantitatively and visually. The experimental results demonstrate that our method clearly outperforms the state-of-the-art method.","url_abs":"https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html","url_pdf":"https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_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":[{"paper_slug":"single-stage-instance-shadow-detection-with","repo_url":"https://github.com/stevewongv/SSIS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"instance-shadow-detection","task_name":"Instance Shadow Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"shadow-detection","task_name":"Shadow Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/instance-shadow-detection-on-soba","task":"Instance Shadow Detection","dataset":"SOBA","model":"SSIS (CVPR 2021)","rank_in_archive_order":2,"of":3,"metrics":{"Asso. AP_bbox":"59.2","Asso. AP_segm":"52.3","Bounding Box SOAP":"26.8","Instance AP_bbox":"41.5","Instance AP_segm":"43.5","mask SOAP":"29.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}