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Foundation models refer to deep learning models pretrained on large unlabeled datasets through self-supervised algorithms.
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P. Iakubovskii, “Segmentation Models Pytorch,” GitHub repository , 2019. [Online]. Available: https://github.com/qubvel/segmentation_models.pytorch
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R. Gupta, B. Goodman, N. Patel, R. Hosfelt, S. Sajeev, E. Heim, J. Doshi, K. Lucas, H. Choset, and M. Gaston, “Creating xbd: A dataset for assessing building damage from satellite imagery,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2019
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An Image Is Worth 16X16 Words: Transformers for Image Recognition At Scale,” ICLR 2021 - 9th International Conference on Learning Representations , 2021
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Y. Cong, S. Khanna, C. Meng, P. Liu, E. Rozi, Y. He, M. Burke, D. B. Lobell, and S. Ermon, “SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery,” Advances in Neural Information Processing Systems , vol. 35, no. NeurIPS, pp. 1–24, 2022
2022
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K. He, X. Chen, S. Xie, Y. Li, P. Dollar, and R. Girshick, “Masked Autoencoders Are Scalable Vision Learners,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 2022-June, pp. 15 979–15 988, 2022
2022
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J. Jakubik, S. Roy, C. E. Phillips, P. Fraccaro, D. Godwin, B. Zadrozny, D. Szwarcman, C. Gomes, G. Nyirjesy, B. Edwards, D. Kimura, N. Simumba, L. Chu, S. K. Mukkavilli, D. Lambhate, K. Das, R. Bangalore, D. Oliveira, M. Muszynski, K. Ankur, M. Ramasubramanian, I. Gurung, S. Khallaghi, H. S. Li, M. Cecil, M. Ahmadi, F. Kordi, H. Alemohammad, M. Maskey, and R. Ganti, “Foundation Models for Generalist Geospatial Artificial Intelligence,” arXiv , pp. 1–26, 2023
2023
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2023
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2023
Cited alongside, same era.
L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model,” Proceedings of the 41 st International Conference on Machine Learning , vol. 235, pp. 62 429–62 442, 2024
2024
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2024
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H. Chen, J. Song, C. Han, J. Xia, and N. Yokoya, “ChangeMamba: Remote Sensing Change Detection with Spatiotemporal State Space Model,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–19, 2024
2024
Later among the works it cites.
2024
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2023
Cited alongside, same era.
J. Xia, N. Yokoya, B. Adriano, and C. Broni-Bediako, “OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping,” Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 , pp. 6243–6253, 2023
2023
Cited alongside, same era.
D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, X. Jia, A. Plaza, G. Paolo, J. A. Benediktsson, and J. Chanussot, “SpectralGPT: Spectral Remote Sensing Foundation Model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 8, pp. 5227–5244, 2024. [Online]. Available: 10.1109/TPAMI.2024.3362475
2024
Cited alongside, same era.
Z. Xiong, Y. Wang, F. Zhang, A. J. Stewart, D. Borth, I. Papoutsis, B. L. Saux, G. Camps-valls, and X. X. Zhu, “Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation,” arXiv , pp. 1–36, 2024
2024
Cited alongside, same era.
J. Ruan and S. Xiang, “VM-UNet: Vision Mamba UNet for Medical Image Segmentation.”
Cited in the paper.
2024
Later among the works it cites.
I. Corley, “Prithvi Pytorch,” GitHub repository , 2024. [Online]. Available: https://github.com/isaaccorley/prithvi-pytorch
2024
Later among the works it cites.