Deformable Convolutional Detection
Deformable Convolutional Detection is a detection framework in object detection that extends standard convolutional networks by integrating deformable convolutional layers, which enable adaptive sampling of feature maps through learnable offsets, thereby improving the modeling of geometric transformations and object deformations.
69
Publications
4.1K
Citations
321
Authors
123
Institutions
Publications per year
2017–2026
64
Parents
69
DeepID-Net: Deformable deep convolutional neural networks for object detection
Wanli Ouyang, Xiaogang Wang, Xingyu Zeng et al. · 2015 · 438 citations
A New Steel Defect Detection Algorithm Based on Deep Learning
Weidong Zhao, Feng Chen, Hancheng Huang et al. · Computational Intelligence and Neuroscience · 2021 · 256 citations · Full text
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
Yuwen Xiong, Zhiqi Li, Yuntao Chen et al. · 2024 · 221 citations
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Chinese University of Hong Kong
Hong Kong, Hong Kong
5 h-index
53 publications
10.7K citations
University of Kaiserslautern
Kaiserslautern, Germany
4 h-index
14 publications
736 citations
German Research Centre for Artificial Intelligence
Kaiserslautern, Germany
4 h-index
15 publications
823 citations
Wuhan University
Wuhan, China
3 h-index
11 publications
377 citations

Xi'an Jiaotong University
Xi'an, China
3 h-index
6 publications
464 citations

Beihang University
Beijing, China
3 h-index
5 publications
418 citations

Hefei University of Technology
Hefei, China
2 h-index
6 publications
118 citations
Luleå University of Technology
Luleå, Sweden
2 h-index
2 publications
32 citations
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7 publications · 305 citations
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9 publications · 412 citations
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Repository · 3 publications · 242 citations
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2 publications · 82 citations
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2 publications · 47 citations
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