Multimodal Object Detection
Multimodal Object Detection is a computer vision task that integrates complementary information from multiple sensory modalities, such as visible light and infrared imagery or RGB and depth data, to localize and classify objects within a scene, thereby improving detection accuracy and robustness in complex environments.
171
Publications
10.4K
Citations
536
Authors
206
Institutions
Publications per year
2017–2026
142
Parents
171
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
Chunwei Wang, Chao Ma, Ming Zhu et al. · 2021 · 449 citations
nuScenes: A Multimodal Dataset for Autonomous Driving
Holger Caesar, Varun Bankiti, Alex Lang et al. · 2020 · 379 citations · Full text
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206

Beijing Institute of Technology
Beijing, China
8 h-index
9 publications
1.1K citations

Beihang University
Beijing, China
7 h-index
20 publications
1.5K citations
University of Science and Technology of China
Hefei, China
5 h-index
20 publications
1.5K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
7 publications
277 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
5 publications
579 citations

Peking University
Beijing, China
3 h-index
12 publications
443 citations

Anhui University
Hefei, China
3 h-index
5 publications
1.1K citations
Honda (Japan)
Tokyo, Japan
3 h-index
6 publications
166 citations
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Repository · 9 publications · 270 citations
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4 publications · 165 citations
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4 publications · 1K citations
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2 publications · 213 citations
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3 publications · 96 citations
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