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Foundation models, i.e., very large deep learning models, have demonstrated impressive performances in various language and vision tasks that are otherwise difficult to reach using smaller-size models.
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2015
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T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proc. of the 22nd acm sigkdd intl. conf. on knowledge discovery and data mining , 2016, pp. 785–794
2016
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P. G. Curtis, C. M. Slay, N. L. Harris et al. , “Classifying drivers of global forest loss,” Science , vol. 361, no. 6407, pp. 1108–1111, 2018
2018
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L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proc. of the European conf. on computer vision (ECCV) , 2018, pp. 801–818
2018
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2020
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2020
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D. Bonafilia, B. Tellman, T. Anderson, and E. Issenberg, “Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops , 2020, pp. 210–211
2020
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F. Wei, Y. Gao, Z. Wu, H. Hu, and S. Lin, “Aligning pretraining for detection via object-level contrastive learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 22 682–22 694, 2021
2021
Cited alongside, same era.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in neural information processing systems , vol. 34, pp. 12 077–12 090, 2021
2021
Cited alongside, same era.
K. He, X. Chen, S. Xie et al. , “Masked autoencoders are scalable vision learners,” in Proc. of the IEEE/CVF conf. on computer vision and pattern recognition , 2022, pp. 16 000–16 009
2022
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2022
Cited alongside, same era.
J. Jakubik, M. Muszynski, M. Vössing, N. Kühl, and T. Brunschwiler, “Toward foundation models for earth monitoring: Generalizable deep learning models for natural hazard segmentation,” in IEEE IGARSS 2023 , 2023, pp. 5638–5641
2023
Later among the works it cites.
P. Dias, A. Potnis, S. Guggilam, L. Yang, A. Tsaris, H. Medeiros, and D. Lunga, “An agenda for multimodal foundation models for earth observation,” in IGARSS 2023-2023 IEEE Intl. Geoscience and Remote Sensing Symposium . IEEE, 2023, pp. 1237–1240
2023
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2023
Later among the works it cites.
J. Jakubik, L. Chu, P. Fraccaro et al. , “Prithvi-100M,” Aug. 2023
2023
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X. Sun, P. Wang, W. Lu et al. , “Ringmo: A remote sensing foundation model with masked image modeling,” IEEE Trans. on Geoscience and Remote Sensing , 2022
2022
Cited alongside, same era.
L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why do tree-based models still outperform deep learning on typical tabular data?” Advances in neural information processing systems , vol. 35, pp. 507–520, 2022
2022
Cited alongside, same era.
R. Lam, A. Sanchez-Gonzalez, M. Willson et al. , “Learning skillful medium-range global weather forecasting,” Science , vol. 382, no. 6677, pp. 1416–1421, 2023
2023
Cited alongside, same era.
Y. Xie, Z. Wang, G. Mai, Y. Li, X. Jia, S. Gao, and S. Wang, “Geo-foundation models: Reality, gaps and opportunities,” in Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems , 2023
2023
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M. Thomas, E. Tellman, D. E. Osgood et al. , “A framework to assess remote sensing algorithms for satellite-based flood index insurance,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 16, pp. 2589–2604, 2023
2023
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GLAD, “Global forest change,” https://glad.umd.edu/dataset , 2024, accessed: 04/10/2024
2024
Closest in time.