Research Advances in Deep Learning for Image Semantic Segmentation Techniques
Image semantic segmentation represents a significant area of research within the field of computer vision. With the advent of deep learning, image semantic segmentation techniques that integrate deep learning have demonstrated superior accuracy compared to traditional image semantic segmentation met...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10750790/ |
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| author | Zhiguo Xiao Tengfei Chai Nianfeng Li Xiangfeng Shen Tong Guan Jia Tian Xinyuan Li |
| author_facet | Zhiguo Xiao Tengfei Chai Nianfeng Li Xiangfeng Shen Tong Guan Jia Tian Xinyuan Li |
| author_sort | Zhiguo Xiao |
| collection | DOAJ |
| description | Image semantic segmentation represents a significant area of research within the field of computer vision. With the advent of deep learning, image semantic segmentation techniques that integrate deep learning have demonstrated superior accuracy compared to traditional image semantic segmentation methods. Recently, the Mamba architecture has demonstrated superior semantic segmentation performance compared to the Transformer architecture, and has consequently become a research focus in this field. Nevertheless, the specifics of the Mamba architecture have remained underexplored in the extant literature. This review provides a comprehensive overview of the latest research progress in deep learning techniques for semantic segmentation. It offers a systematic review of traditional convolutional neural network (CNN)-based architectures and focuses on a series of emerging architectures, including the Transformer architecture, the Mamba architecture, and cutting-edge approaches such as self-supervised learning strategies. For each category, a detailed account is provided of the principal algorithms and techniques employed, together with a report on the performance achieved using datasets commonly used in the field. |
| format | Article |
| id | doaj-art-36a7ff6153c84c5daedf30c4d6e8e7ea |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36a7ff6153c84c5daedf30c4d6e8e7ea2024-11-29T00:02:10ZengIEEEIEEE Access2169-35362024-01-011217571517574110.1109/ACCESS.2024.349672310750790Research Advances in Deep Learning for Image Semantic Segmentation TechniquesZhiguo Xiao0https://orcid.org/0000-0001-6719-0652Tengfei Chai1Nianfeng Li2https://orcid.org/0000-0003-2450-5217Xiangfeng Shen3https://orcid.org/0000-0002-2461-5419Tong Guan4Jia Tian5Xinyuan Li6College of Computer Science and Technology, Changchun University, Changchun, ChinaCollege of Computer Science and Technology, Changchun University, Changchun, ChinaCollege of Computer Science and Technology, Changchun University, Changchun, ChinaCollege of Computer Science and Technology, Changchun University, Changchun, ChinaCollege of Computer Science and Technology, Changchun University, Changchun, ChinaCollege of Cyber Security, Changchun University, Changchun, ChinaCollege of Computer Science and Technology, Changchun University, Changchun, ChinaImage semantic segmentation represents a significant area of research within the field of computer vision. With the advent of deep learning, image semantic segmentation techniques that integrate deep learning have demonstrated superior accuracy compared to traditional image semantic segmentation methods. Recently, the Mamba architecture has demonstrated superior semantic segmentation performance compared to the Transformer architecture, and has consequently become a research focus in this field. Nevertheless, the specifics of the Mamba architecture have remained underexplored in the extant literature. This review provides a comprehensive overview of the latest research progress in deep learning techniques for semantic segmentation. It offers a systematic review of traditional convolutional neural network (CNN)-based architectures and focuses on a series of emerging architectures, including the Transformer architecture, the Mamba architecture, and cutting-edge approaches such as self-supervised learning strategies. For each category, a detailed account is provided of the principal algorithms and techniques employed, together with a report on the performance achieved using datasets commonly used in the field.https://ieeexplore.ieee.org/document/10750790/Image segmentationsemantic segmentationdeep learningimage processing |
| spellingShingle | Zhiguo Xiao Tengfei Chai Nianfeng Li Xiangfeng Shen Tong Guan Jia Tian Xinyuan Li Research Advances in Deep Learning for Image Semantic Segmentation Techniques IEEE Access Image segmentation semantic segmentation deep learning image processing |
| title | Research Advances in Deep Learning for Image Semantic Segmentation Techniques |
| title_full | Research Advances in Deep Learning for Image Semantic Segmentation Techniques |
| title_fullStr | Research Advances in Deep Learning for Image Semantic Segmentation Techniques |
| title_full_unstemmed | Research Advances in Deep Learning for Image Semantic Segmentation Techniques |
| title_short | Research Advances in Deep Learning for Image Semantic Segmentation Techniques |
| title_sort | research advances in deep learning for image semantic segmentation techniques |
| topic | Image segmentation semantic segmentation deep learning image processing |
| url | https://ieeexplore.ieee.org/document/10750790/ |
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