Multimodal Data Sampling
Multimodal Data Sampling is a methodological framework that systematically selects and integrates instances from multiple sensory modalities—such as text, audio, video, and sensor data—into a coherent dataset, distinguished from unimodal sampling by its explicit coordination of temporally and semantically aligned cross-modal signals for joint analysis.
15
Publications
1.1K
Citations
97
Authors
40
Institutions
Publications per year
2017–2026
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Harvard University
Cambridge, United States
2 h-index
4 publications
178 citations
Greenlane Clinical Centre
Auckland, New Zealand
1 h-index
5 publications
110 citations

Sun Yat-sen University
Guangzhou, China
1 h-index
1 publications
48 citations
Southwestern University of Finance and Economics
Chengdu, China
1 h-index
3 publications
612 citations
Queen Mary University of London
London, United Kingdom
1 h-index
2 publications
32 citations
University of Reading
Reading, United Kingdom
1 h-index
1 publications
38 citations
Qingdao University of Science and Technology
Qingdao, China
1 h-index
4 publications
120 citations
META Health
Marina del Rey, United States
1 h-index
1 publications
48 citations
1–8 of 40
JCGS · 1 publications · 12 citations
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1 publications · 226 citations
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Conference · 1 publications · 48 citations
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1 publications · 30 citations
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1 publications · 152 citations
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