Masked Autoencoders in Computer Vision: A Comprehensive Survey
Masked autoencoders (MAE) is a deep learning method based on Transformer. Originally used for images, it has now been extended to video, audio, and some other temporal prediction tasks. In the field of computer vision, MAE performs well in classification, prediction, and target detection tasks. In t...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10278410/ |
| _version_ | 1797655648967589888 |
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| author | Zexian Zhou Xiaojing Liu |
| author_facet | Zexian Zhou Xiaojing Liu |
| author_sort | Zexian Zhou |
| collection | DOAJ |
| description | Masked autoencoders (MAE) is a deep learning method based on Transformer. Originally used for images, it has now been extended to video, audio, and some other temporal prediction tasks. In the field of computer vision, MAE performs well in classification, prediction, and target detection tasks. In terms of specific application, MAE has made many achievements in medical treatment, geography, 3D point cloud and machine troubleshooting. Since its introduction at the end of 2021, there have been more than 300 related preprints, and MAE has been significantly performed in tier one computer vision conferences during 2022 and 2023. In view of the current popularity of MAE and its future development prospects, we conduct a relatively comprehensive survey of MAE mainly covering officially published articles so far. We comb through and classify the improvements in MAE, demonstrating relatively representative applications in computer vision. Finally, as a summary, we discuss the possible future research directions and development areas based on the characteristics of MAE, hoping our work could be a reference for the future work of MAE. |
| first_indexed | 2024-03-11T17:17:31Z |
| format | Article |
| id | doaj.art-76f70a1ab61048a9a49f27e20e803fc4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:31Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-76f70a1ab61048a9a49f27e20e803fc42023-10-19T23:01:40ZengIEEEIEEE Access2169-35362023-01-011111356011357910.1109/ACCESS.2023.332338310278410Masked Autoencoders in Computer Vision: A Comprehensive SurveyZexian Zhou0https://orcid.org/0000-0001-5948-2102Xiaojing Liu1https://orcid.org/0000-0002-5571-4735Department of Computer Technology and Application, Qinghai University, Xining, ChinaDepartment of Computer Technology and Application, Qinghai University, Xining, ChinaMasked autoencoders (MAE) is a deep learning method based on Transformer. Originally used for images, it has now been extended to video, audio, and some other temporal prediction tasks. In the field of computer vision, MAE performs well in classification, prediction, and target detection tasks. In terms of specific application, MAE has made many achievements in medical treatment, geography, 3D point cloud and machine troubleshooting. Since its introduction at the end of 2021, there have been more than 300 related preprints, and MAE has been significantly performed in tier one computer vision conferences during 2022 and 2023. In view of the current popularity of MAE and its future development prospects, we conduct a relatively comprehensive survey of MAE mainly covering officially published articles so far. We comb through and classify the improvements in MAE, demonstrating relatively representative applications in computer vision. Finally, as a summary, we discuss the possible future research directions and development areas based on the characteristics of MAE, hoping our work could be a reference for the future work of MAE.https://ieeexplore.ieee.org/document/10278410/Computer vision surveyMAEmasked autoencodersmasked image modeling |
| spellingShingle | Zexian Zhou Xiaojing Liu Masked Autoencoders in Computer Vision: A Comprehensive Survey IEEE Access Computer vision survey MAE masked autoencoders masked image modeling |
| title | Masked Autoencoders in Computer Vision: A Comprehensive Survey |
| title_full | Masked Autoencoders in Computer Vision: A Comprehensive Survey |
| title_fullStr | Masked Autoencoders in Computer Vision: A Comprehensive Survey |
| title_full_unstemmed | Masked Autoencoders in Computer Vision: A Comprehensive Survey |
| title_short | Masked Autoencoders in Computer Vision: A Comprehensive Survey |
| title_sort | masked autoencoders in computer vision a comprehensive survey |
| topic | Computer vision survey MAE masked autoencoders masked image modeling |
| url | https://ieeexplore.ieee.org/document/10278410/ |
| work_keys_str_mv | AT zexianzhou maskedautoencodersincomputervisionacomprehensivesurvey AT xiaojingliu maskedautoencodersincomputervisionacomprehensivesurvey |