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Learn More Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation and member institutions. arxiv logo > cs > arXiv:2103.14030 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computer Vision and Pattern Recognition arXiv:2103.14030 (cs) [Submitted on 25 Mar 2021 (v1), last revised 17 Aug 2021 (this version, v2)] Title:Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Authors:Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo Download a PDF of the paper titled Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, by Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo Download PDF Abstract: This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with \textbf{S} hifted \textbf{win}dows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at~\url{this https URL }. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2103.14030 [cs.CV] (or arXiv:2103.14030v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2103.14030 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Han Hu [view email] [v1] Thu, 25 Mar 2021 17:59:31 UTC (1,064 KB) [v2] Tue, 17 Aug 2021 16:41:34 UTC (1,065 KB) Full-text links: Download: Download a PDF of the paper titled Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, by Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo * PDF * Other formats [by-4] Current browse context: cs.CV < prev | next > new | recent | 2103 Change to browse by: cs cs.LG References & Citations * NASA ADS * Google Scholar * Semantic Scholar 2 blog links (what is this?) DBLP - CS Bibliography listing | bibtex Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin ... a export BibTeX citation Loading... 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