Multimodal Machine Learning is a computational framework that integrates and analyzes multiple modalities of data—such as textual, visual, auditory, and sensor inputs—within a unified model architecture to enable machines to learn complex representations and perform tasks requiring cross-modal understanding.
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Publications
131.7K
Citations
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Authors
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Institutions
Publications per year
2017–2026
1.7K
Variants
Multimodal Ml
Parents
Publications
2K
Jiquan Ngiam, Aditya Khosla, Mingyu Kim et al. · 2011 · 2.3K citations
Flamingo: a Visual Language Model for Few-Shot Learning
arXiv (Cornell University) · 2022 · 1.2K citations · Full text
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9.2K
- LM
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- PP
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- HH
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- AZ
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Institutions
1.5K
Chinese Academy of Sciences
Beijing, China
37 h-index
195 publications
18.6K citations
Tsinghua University
Beijing, China
36 h-index
175 publications
15.5K citations
Nanyang Technological University
Singapore, Singapore
36 h-index
120 publications
13.7K citations
Carnegie Mellon University
Pittsburgh, United States
31 h-index
129 publications
23K citations
University of Chinese Academy of Sciences
Beijing, China
30 h-index
132 publications
10.4K citations
Sun Yat-sen University
Guangzhou, China
28 h-index
114 publications
7.5K citations
Institute of Automation
Beijing, China
25 h-index
92 publications
6.2K citations
National University of Singapore
Singapore, Singapore
24 h-index
77 publications
4.1K citations
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- arXiv (Cornell University)
Repository · 288 publications · 16.6K citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 58 publications · 4.1K citations
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- IEEE Transactions on Multimedia
36 publications · 2.7K citations
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- Information Fusion
23 publications · 2.9K citations
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- IEEE Access
32 publications · 1.8K citations
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