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CentralNet: A Multilayer Approach for Multimodal Fusion
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“The training load of conventional multimodal backbone grows as the number of modalities increases since the backbone usually consists of modalityspecific sub-models that need to be trained independently for each modality [27, 41]. Instead, Transformer models process all modalities simultaneously using a single model [18,24], which greatly reduces the training load.”
From Are Multimodal Transformers Robust to Missing Modality? (2022) · cites this paper as [41]
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Multimodal intelligence: Representation learning, information fusion, and applications
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“We follow the training and validation splits provided in the previous work (Vielzeuf et al. 2018 (Vielzeuf et al. 2018) consists of an independent image and audio modalities.”
BackgroundMethodInfluentialMulti-Modal Learning with Missing Modality via Shared-Specific Feature Modelling
Wang, H., Chen, Y., Ma, C., Avery, J., Hull, M. L., & Carneiro, G. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01524 · Cited by 262
Rahate, A., Walambe, R., Ramanna, S., & Kotecha, K. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.12.003 · Cited by 214
Metadata
- Authors
- Vielzeuf, V., Lechervy, A., Pateux, S., & Jurie, F.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Orange (France) (FR)
- Corresponding author
- Vielzeuf, Valentin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 575-589
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888471892
- Cited by
- 166
- References
- 30
- Citations by year
- 2026: 16 · 2025: 28 · 2024: 29 · 2023: 25 · 2022: 20 · 2021: 16 · 2020: 22 · 2019: 8
- FWCI
- 5.38
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- multimodal fusion, multi-task learning, regularization, computer vision tasks
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