Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey Article Swipe
Xiao Wang
,
Guangyao Chen
,
Guangwu Qian
,
Pengcheng Gao
,
Xiao-Yong Wei
,
Yaowei Wang
,
Yonghong Tian
,
Wen Gao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s11633-022-1410-8
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s11633-022-1410-8
With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT), generative pre-trained transformers (GPT), etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models: https://github.com/wangxiao5791509/MultiModal_BigModels_Survey .
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11633-022-1410-8
- https://link.springer.com/content/pdf/10.1007/s11633-022-1410-8.pdf
- OA Status
- hybrid
- Cited By
- 154
- References
- 242
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379929801
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4379929801Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s11633-022-1410-8Digital Object Identifier
- Title
-
Large-scale Multi-modal Pre-trained Models: A Comprehensive SurveyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-06Full publication date if available
- Authors
-
Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiao-Yong Wei, Yaowei Wang, Yonghong Tian, Wen GaoList of authors in order
- Landing page
-
https://doi.org/10.1007/s11633-022-1410-8Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11633-022-1410-8.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s11633-022-1410-8.pdfDirect OA link when available
- Concepts
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Computer science, Modal, Transformer, Deep learning, Artificial intelligence, Machine learning, Generative grammar, Visualization, Process (computing), Data science, Engineering, Operating system, Electrical engineering, Polymer chemistry, Chemistry, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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154Total citation count in OpenAlex
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2025: 64, 2024: 71, 2023: 19Per-year citation counts (last 5 years)
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242Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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