Deep Learning For Localization
Deep Learning for Localization is a methodological framework within indoor localization that employs deep neural networks to learn complex spatial representations from raw sensor data, distinguishing itself by automatically extracting hierarchical features for precise position estimation without relying on hand-crafted models.
705
Publications
53.1K
Citations
2.6K
Authors
755
Institutions
Publications per year
2017–2026
652
Parents
705
Learning Deep Features for Discriminative Localization
Bolei Zhou, Aditya Khosla, Àgata Lapedriza et al. · 2016 · 10.8K citations · Full text
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Mihai Dusmanu, Ignacio Rocco, Tomáš Pajdla et al. · 2019 · 1.1K citations
DSAC — Differentiable RANSAC for Camera Localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin et al. · 2017 · 615 citations
1–5 of 705
2.6K
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Egypt-Japan University of Science and Technology, Egypt
10 publications · 706 citations
10
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10
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8
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1–5 of 2.6K
755

Zhejiang University
Hangzhou, China
13 h-index
51 publications
2.6K citations

Chinese Academy of Sciences
Beijing, China
13 h-index
50 publications
4.5K citations

Alexandria University
Alexandria, Egypt
12 h-index
25 publications
2.3K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
12 h-index
38 publications
3.3K citations

Centre National de la Recherche Scientifique
Paris, France
11 h-index
21 publications
1.1K citations
Auburn University
Auburn, United States
11 h-index
29 publications
6.8K citations
Tsinghua University
Beijing, China
11 h-index
36 publications
2.4K citations

Shanghai Jiao Tong University
Shanghai, China
11 h-index
41 publications
1.5K citations
1–8 of 755
36 publications · 2.1K citations
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33 publications · 1.3K citations
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24 publications · 1.1K citations
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Repository · 41 publications · 2K citations
20
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15 publications · 789 citations
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