Research Progress on Vision–Language Multimodal Pretraining Model Technology

Because the pretraining model is not limited by the scale of data annotation and can learn general semantic information, it performs well in tasks related to natural language processing and computer vision. In recent years, more and more attention has been paid to research on the multimodal pretrain...

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Bibliographic Details
Published inElectronics (Basel) Vol. 11; no. 21; p. 3556
Main Authors Wang, Huansha, Huang, Ruiyang, Zhang, Jianpeng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
Subjects
Annotations
Computational linguistics
Computer vision
Datasets
Electronic data processing
Language
Language processing
Learning
Machine learning
Machine vision
Methods
Natural language interfaces
Natural language processing
Regions
Semantics
Two dimensional analysis
China
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Summary:Because the pretraining model is not limited by the scale of data annotation and can learn general semantic information, it performs well in tasks related to natural language processing and computer vision. In recent years, more and more attention has been paid to research on the multimodal pretraining model. Many vision–language multimodal datasets and related models have been proposed one after another. In order to better summarize and analyze the development status and future trend of vision–language multimodal pretraining model technology, firstly this paper comprehensively combs the category system and related tasks of vision–language multimodal pretraining. Secondly, research progress on vision–language multimodal pretraining is summarized and analyzed from the two dimensions of image–language and video–language models. Finally, problems with and development trends in vision–language multimodal pretraining are discussed.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11213556