Multimodal Querying
Multimodal Querying is a computational framework that enables users to formulate information retrieval requests through the integrated use of multiple modalities—such as text, image, audio, and video—thereby allowing queries to be expressed and interpreted across heterogeneous data types in a unified semantic space.
88
Publications
3.6K
Citations
370
Authors
157
Institutions
Publications per year
2017–2026
28
Parents
88
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel · 2014 · 568 citations
Adding Semantics to Detectors for Video Retrieval
Cees G. M. Snoek, Bouke Huurnink, Laura Hollink et al. · IEEE Transactions on Multimedia · 2007 · 200 citations · Full text
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157
Carnegie Mellon University
Pittsburgh, United States
5 h-index
22 publications
763 citations
University of Maryland, College Park
College Park, United States
3 h-index
5 publications
129 citations
Dublin City University
Dublin, Ireland
3 h-index
14 publications
293 citations
Microsoft (United States)
Redmond, United States
2 h-index
5 publications
199 citations
National University of Singapore
Singapore, Singapore
2 h-index
5 publications
103 citations

Northeastern University
Shenyang, China
2 h-index
3 publications
74 citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
9 publications
434 citations
German Research Centre for Artificial Intelligence
Kaiserslautern, Germany
2 h-index
3 publications
34 citations
1–8 of 157
2 publications · 70 citations
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Repository · 2 publications · 65 citations
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TOMCCAP · 2 publications · 34 citations
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2 publications · 59 citations
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2 publications · 41 citations
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