The DOI matches its Crossref record; 236 of the 236 references it cites were found in a registry or an index.
Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions
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“Concurrently, the integration of expert capacity management strategies, emerges as a crucial approach in MoE models to regulate the processing abilities of individual experts by setting thresholds on the number of tokens each can handle, effectively averting bottlenecks and ensuring a more efficient and streamlined model operation, leading to improved training processes and heightened performance”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [303]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
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
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Research works printed but not linked: 3
- Martin, 2001, On the Annotation of Multimodal Behavior and Computation of Cooperation Between Modalities, Auton, Agent ’01, 1
- Cheng, 2016, Semi-supervised multimodal deep learning for RGB-D object recognition, IJCAI Int. Jt. Conf. Artif. Intell. 2016-Janua, 3345
- Wang, 2020, Generalizing from a few examples, ACM Comput. Surv., 53, 1
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Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
Gawde, S., Patil, S., Kumar, S., Kamat, P. V., Kotecha, K. V., & Abraham, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106139 · Cited by 327
Self-citationDomain Adaptation: Challenges, Methods, Datasets, and Applications
Singhal, P., Walambe, R. A., Ramanna, S., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3237025 · Cited by 276
Self-citationBayoudh, K. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102217 · Cited by 180
Ghalkhani, M., & Habibi, S. R. (2022) · Energies · doi:10.3390/en16010185 · Cited by 171
Metadata
- Authors
- Rahate, A., Walambe, R., Ramanna, S., & Kotecha, K.
- Affiliations
- Symbiosis International University (IN)
- The University of Winnipeg (CA)
- Corresponding author
- Kotecha, Ketan
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 81
- Pages
- 203-239
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W3184679245
- Cited by
- 214
- References
- 226
- Citations by year
- 2026: 31 · 2025: 70 · 2024: 49 · 2023: 44 · 2022: 20
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Information Fusion (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- multimodal learning, cross-modal knowledge transfer, missing modalities, cross-modal representation learning, cross-modal alignment, cross-modal fusion, multi-view learning
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