Multimodal Retrieval Models
Multimodal Retrieval Models is a retrieval framework that integrates and aligns information from multiple modalities—such as text, image, audio, and video—to enable cross-modal or multimodal query and document matching, distinguishing itself from unimodal retrieval models by leveraging heterogeneous data representations within a shared semantic space.
405
Publications
29K
Citations
2K
Authors
420
Institutions
Publications per year
2017–2026
270
Parents
405
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
Junhua Mao, Wei Xu, Yi Yang et al. · arXiv (Cornell University) · 2014 · 653 citations · Full text
Cross-modal Retrieval with Correspondence Autoencoder
Fangxiang Feng, Xiaojie Wang, Ruifan Li · 2014 · 599 citations
1–5 of 405
2K
University of Electronic Science and Technology of China, China
10 publications · 298 citations
10
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University of Electronic Science and Technology of China, China
10 publications · 279 citations
10
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7
h-index
7
h-index
7
h-index
1–5 of 2K
420

University of Electronic Science and Technology of China
Chengdu, China
15 h-index
59 publications
1.8K citations
National University of Singapore
Singapore, Singapore
14 h-index
33 publications
3K citations

Chinese Academy of Sciences
Beijing, China
13 h-index
44 publications
1.4K citations

Peking University
Beijing, China
12 h-index
28 publications
2.3K citations
Peng Cheng Laboratory
Shenzhen, China
11 h-index
15 publications
537 citations
University of Chinese Academy of Sciences
Beijing, China
10 h-index
18 publications
737 citations
Tsinghua University
Beijing, China
10 h-index
24 publications
2.2K citations
Nanyang Technological University
Singapore, Singapore
9 h-index
20 publications
826 citations
1–8 of 420
Repository · 31 publications · 3.5K citations
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17 publications · 985 citations
16
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15 publications · 609 citations
15
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Conference · 13 publications · 1.2K citations
13
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TOMCCAP · 10 publications · 299 citations
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