[Submitted on 23 Dec 2020 (v1), revised 23 Feb 2022 (this version, v5), latest version 10 Jul 2023 (v6)]

Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, Dacheng Tao

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Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformer to computer vision tasks. In a variety of visual benchmarks, transformer-based models perform similar to or better than other types of networks such as convolutional and recurrent neural networks. Given its high performance and less need for vision-specific inductive bias, transformer is receiving more and more attention from the computer vision community. In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages. The main categories we explore include the backbone network, high/mid-level vision, low-level vision, and video processing. We also include efficient transformer methods for pushing transformer into real device-based applications. Furthermore, we also take a brief look at the self-attention mechanism in computer vision, as it is the base component in transformer. Toward the end of this paper, we discuss the challenges and provide several further research directions for vision transformers.

Submission history

From: Kai Han [view email]
[v1] Wed, 23 Dec 2020 09:37:54 UTC (2,147 KB)
[v2] Fri, 15 Jan 2021 07:09:36 UTC (2,460 KB)
[v3] Sat, 30 Jan 2021 09:33:55 UTC (2,578 KB)
[v4] Thu, 12 Aug 2021 12:05:39 UTC (5,057 KB)
[v5] Wed, 23 Feb 2022 05:15:28 UTC (2,992 KB)
[v6] Mon, 10 Jul 2023 13:54:04 UTC (3,097 KB)

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Donate > cs > arXiv:2012.12556v5 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search GO quick links * Login * Help Pages * About Computer Science > Computer Vision and Pattern Recognition arXiv:2012.12556v5 (cs) [Submitted on 23 Dec 2020 (v1), revised 23 Feb 2022 (this version, v5), latest version 10 Jul 2023 (v6)] Title: A Survey on Vision Transformer Authors: Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, Dacheng Tao Download a PDF of the paper titled A Survey on Vision Transformer, by Kai Han and 12 other authors Download PDF Abstract: Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformer to computer vision tasks. In a variety of visual benchmarks, transformer-based models perform similar to or better than other types of networks such as convolutional and recurrent neural networks. Given its high performance and less need for vision-specific inductive bias, transformer is receiving more and more attention from the computer vision community. In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages. The main categories we explore include the backbone network, high/mid-level vision, low-level vision, and video processing. We also include efficient transformer methods for pushing transformer into real device-based applications. Furthermore, we also take a brief look at the self-attention mechanism in computer vision, as it is the base component in transformer. Toward the end of this paper, we discuss the challenges and provide several further research directions for vision transformers. Comments: Accepted by TPAMI 2022 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2012.12556 [cs.CV] (or arXiv:2012.12556v5 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2012.12556 Focus to learn more arXiv-issued DOI via DataCite https://doi.org/10.1109/TPAMI.2022.3152247 Related DOI: Focus to learn more DOI(s) linking to related resources Submission history From: Kai Han [view email] [v1] Wed, 23 Dec 2020 09:37:54 UTC (2,147 KB) [v2] Fri, 15 Jan 2021 07:09:36 UTC (2,460 KB) [v3] Sat, 30 Jan 2021 09:33:55 UTC (2,578 KB) [v4] Thu, 12 Aug 2021 12:05:39 UTC (5,057 KB) [v5] Wed, 23 Feb 2022 05:15:28 UTC (2,992 KB) [v6] Mon, 10 Jul 2023 13:54:04 UTC (3,097 KB) Full-text links: Access Paper: Download a PDF of the paper titled A Survey on Vision Transformer, by Kai Han and 12 other authors * Download PDF * PostScript * Other Formats (view license) Current browse context: cs.CV < prev | next > new | recent | 2012 Change to browse by: cs cs.AI References & Citations * NASA ADS * Google Scholar * Semantic Scholar DBLP - CS Bibliography listing | bibtex Kai Han Yunhe Wang Hanting Chen Xinghao Chen Jianyuan Guo … a export BibTeX citation Loading... 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