{"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/spa-former-transformer-image-shadow-detection","title":"SpA-Former: Transformer image shadow detection and removal via spatial attention","arxiv_id":"2206.10910","date":"2022-06-22","proceeding":null,"authors":["Xiao Feng Zhang","Chao Chen Gu","Shan Ying Zhu"],"abstract":"In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection. Thus, SpA-former is adaptable to real image de-shadowing for shadows projected on different semantic regions. SpA-Former consists of transformer layer and a series of joint Fourier transform residual blocks and two-wheel joint spatial attention. The network in this paper is able to handle the task while achieving a very fast processing efficiency. Our code is relased on https://github.com/zhangbaijin/SpA-Former-shadow-removal","url_abs":"https://arxiv.org/abs/2206.10910v3","url_pdf":"https://arxiv.org/pdf/2206.10910v3.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":"spa-former-transformer-image-shadow-detection","repo_url":"https://github.com/zhangbaijin/spa-former-shadow-removal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"spa-former-transformer-image-shadow-detection","repo_url":"https://github.com/zhangbaijin/spatial-transformer-shadow-removal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"shadow-detection","task_name":"Shadow Detection"},{"task_slug":"shadow-detection-and-removal","task_name":"Shadow Detection And Removal"},{"task_slug":"shadow-removal","task_name":"Shadow Removal"},{"task_slug":"single-particle-analysis","task_name":"Single Particle Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/shadow-removal-on-istd","task":"Shadow Removal","dataset":"ISTD","model":"Zhang et al.","rank_in_archive_order":7,"of":10,"metrics":{"MAE":"6.62"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.10910","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}