Deep Template Matching
Deep Template Matching is a template matching paradigm that integrates deep learning architectures to learn discriminative feature representations for matching templates in complex visual data, distinguishing itself from traditional methods by leveraging hierarchical neural network models to capture semantic and contextual patterns.
69
Publications
2.7K
Citations
320
Authors
152
Institutions
Publications per year
2017–2026
56
Parents
69
Learning Deep Features for One-Class Classification
IEEE Transactions on Image Processing · 2019 · 289 citations · Full text
A practical system for road marking detection and recognition
Tao Wu, Ananth Ranganathan · 2012 · 119 citations
QATM: Quality-Aware Template Matching for Deep Learning
Jiaxin Cheng, Yue Wu, Wael AbdAlmageed et al. · 2019 · 91 citations
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152

Chinese Academy of Sciences
Beijing, China
3 h-index
11 publications
473 citations
Tsinghua University
Beijing, China
3 h-index
4 publications
106 citations

Johns Hopkins University
Baltimore, United States
3 h-index
4 publications
620 citations
Nanjing University of Science and Technology
Nanjing, China
2 h-index
8 publications
157 citations

National Taiwan University
Taipei, Taiwan
2 h-index
8 publications
161 citations
China Academy of Railway Sciences
Beijing, China
2 h-index
9 publications
1.1K citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
6 publications
169 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
5 publications
140 citations
1–8 of 152
Repository · 3 publications · 32 citations
3
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2 publications · 105 citations
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2 publications · 37 citations
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GRSL · 2 publications · 84 citations
2
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2 publications · 192 citations
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