The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
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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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Deep Canonical Correlation Analysis
Andrew, G., Arora, R., Bilmes, J., & Livescu, K. (2013) · Cited by 1,603
Piczak, K. J. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806390 · Cited by 1,572
YouTube-8M: A Large-Scale Video Classification BenchmarkPreprint
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G. D., Varadarajan, B., & Vijayanarasimhan, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.08675 · Cited by 1,382
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296
Multimodal fusion for multimedia analysis: a survey
Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010) · Multimedia Systems · doi:10.1007/s00530-010-0182-0 · Cited by 1,279
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Multimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511
MMTM: Multimodal Transfer Module for CNN Fusion
Joze, H. R. V., Shaban, A., Iuzzolino, M. L., & Koishida, K. (2020) · 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/cvpr42600.2020.01330 · Cited by 337
SMIL: Multimodal Learning with Severely Missing Modality
Ma, M., Ren, J., Zhao, L., Tulyakov, S., Wu, C., & Peng, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16330 · Cited by 332
“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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