Survey of Deformable Convolutional Networks
In recent years, with the rapid development of deep learning, deformable convolutional networks have received extensive attention because of their powerful feature extraction capabilities, overcoming some problems that are difficult to solve in convolutional neural networks, and have played an impor...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2023-07-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2209076.pdf |
| _version_ | 1797786634411835392 |
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| author | LIU Weiguang, LIU Dong, WANG Lu |
| author_facet | LIU Weiguang, LIU Dong, WANG Lu |
| author_sort | LIU Weiguang, LIU Dong, WANG Lu |
| collection | DOAJ |
| description | In recent years, with the rapid development of deep learning, deformable convolutional networks have received extensive attention because of their powerful feature extraction capabilities, overcoming some problems that are difficult to solve in convolutional neural networks, and have played an important role in computer vision, natural language processing and other related fields. Since there is a little research on systematic summary of the deformable convolutional network, in order to provide a detailed reference for subsequent research, this paper summarizes the related work since the introduction of the deformable convolutional network. Firstly, this paper reviews the high-quality literature in recent years, and introduces the core technologies such as deformable convolution and deformable region of interest pooling in deformable convolutional networks from the perspective of invariant features. Secondly, the collected relevant literature is classified according to different research fields, and the appli-cation of deformable convolutional networks in image recognition and classification, target detection, image seg-mentation, target tracking and other research fields is comprehensively summarized. At the same time, the perfor-mance, advantages and disadvantages of important network models are listed. Thirdly, by combing the literature, the advantages and disadvantages of the deformable convolutional network are analyzed, and the possible future research trends of the deformable convolutional network are discussed according to some problems existing at the present stage. Finally, the deformable convolutional networks are summarized and prospected based on invariant feature extraction. |
| first_indexed | 2024-03-13T01:10:37Z |
| format | Article |
| id | doaj.art-5fb633b557434646b804fc53eda5f6f6 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-13T01:10:37Z |
| publishDate | 2023-07-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-5fb633b557434646b804fc53eda5f6f62023-07-06T01:16:31ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182023-07-011771549156410.3778/j.issn.1673-9418.2209076Survey of Deformable Convolutional NetworksLIU Weiguang, LIU Dong, WANG Lu01. School of Software, Zhongyuan University of Technology, Zhengzhou 450000, China 2. School of Computer, Zhongyuan University of Technology, Zhengzhou 451100, ChinaIn recent years, with the rapid development of deep learning, deformable convolutional networks have received extensive attention because of their powerful feature extraction capabilities, overcoming some problems that are difficult to solve in convolutional neural networks, and have played an important role in computer vision, natural language processing and other related fields. Since there is a little research on systematic summary of the deformable convolutional network, in order to provide a detailed reference for subsequent research, this paper summarizes the related work since the introduction of the deformable convolutional network. Firstly, this paper reviews the high-quality literature in recent years, and introduces the core technologies such as deformable convolution and deformable region of interest pooling in deformable convolutional networks from the perspective of invariant features. Secondly, the collected relevant literature is classified according to different research fields, and the appli-cation of deformable convolutional networks in image recognition and classification, target detection, image seg-mentation, target tracking and other research fields is comprehensively summarized. At the same time, the perfor-mance, advantages and disadvantages of important network models are listed. Thirdly, by combing the literature, the advantages and disadvantages of the deformable convolutional network are analyzed, and the possible future research trends of the deformable convolutional network are discussed according to some problems existing at the present stage. Finally, the deformable convolutional networks are summarized and prospected based on invariant feature extraction.http://fcst.ceaj.org/fileup/1673-9418/PDF/2209076.pdffeature extraction; deformable convolution; invariance feature; region of interest |
| spellingShingle | LIU Weiguang, LIU Dong, WANG Lu Survey of Deformable Convolutional Networks Jisuanji kexue yu tansuo feature extraction; deformable convolution; invariance feature; region of interest |
| title | Survey of Deformable Convolutional Networks |
| title_full | Survey of Deformable Convolutional Networks |
| title_fullStr | Survey of Deformable Convolutional Networks |
| title_full_unstemmed | Survey of Deformable Convolutional Networks |
| title_short | Survey of Deformable Convolutional Networks |
| title_sort | survey of deformable convolutional networks |
| topic | feature extraction; deformable convolution; invariance feature; region of interest |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2209076.pdf |
| work_keys_str_mv | AT liuweiguangliudongwanglu surveyofdeformableconvolutionalnetworks |