Movie tag prediction: An extreme multi-label multi-modal transformer-based solution with explanation-Reference-Cited by-同舟云学术

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Movie tag prediction: An extreme multi-label multi-modal transformer-based solution with explanation

Published:2024-01-06 Issue:4 Volume:62 Page:1021-1043
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Guarascio Massimo^ORCID,Minici Marco^ORCID,Pisani Francesco Sergio^ORCID,De Francesco Erika,Lambardi Pasquale

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10844-023-00836-7.pdf

Reference34 articles.

1. Abavisani, M., Wu, L., Hu, S., et al. (2020). Multimodal categorization of crisis events in social media. In: 2020 IEEE/CVF conf. on computer vision and pattern recognition, CVPR 2020. Computer Vision Foundation/IEEE, pp 14,667–14,677, https://doi.org/10.1109/CVPR42600.2020.01469

2. Arevalo, J., Solorio, T., Montes-y Gómez, M., et al. (2017). Gated multimodal units for information fusion. arXiv:1702.01992

3. Audebert, N., Herold, C., Slimani, K., et al. (2020). Multimodal deep networks for text and image-based document classification. In I. Part (Ed.), Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings (pp. 427–443). Springer.

4. Choi, J. H., & Lee, J. S. (2019). Embracenet: A robust deep learning architecture for multimodal classification. Information Fusion, 51, 259–270. https://doi.org/10.1016/j.inffus.2019.02.010

5. Cui, Y., Jia, M., Lin, T.Y., et al. (2019). Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9268–9277, https://doi.org/10.1109/CVPR.2019.00949

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