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The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science
1985
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.,” Journal of Machine Learning Research
2010
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” NeurIPS
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
G. Christie, N. Fendley, J. Wilson, and R. Mukherjee, “Functional map of the world,” in CVPR
2018
Earlier work this paper cites.
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” in ECCV
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and f. Prabhat, “Deep learning and process understanding for data-driven earth system science,” Nature
2019
Earlier work this paper cites.
G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in IGARSS
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in ICLR
2019
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2019
Earlier work this paper cites.
W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao, H. Zhang, and L. Zhang, “Non-local meets global: An iterative paradigm for hyperspectral image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2020
Earlier work this paper cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in CVPR
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in ICML
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton, “Big self-supervised models are strong semi-supervised learners,” NeurIPS
2020
Cited alongside, same era.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” NeurIPS
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Xiong, M. Ren, and R. Urtasun, “Loco: Local contrastive representation learning,” NeurIPS
2020
Cited alongside, same era.
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” in ICLR
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in CVPR
2022
Later among the works it cites.
D. Wang, Q. Zhang, Y. Xu, J. Zhang, B. Du, D. Tao, and L. Zhang, “Advancing plain vision transformer towards remote sensing foundation model,” IEEE Transactions on Geoscience and Remote Sensing
2022
Later among the works it cites.
X. Sun, P. Wang, W. Lu, Z. Zhu, X. Lu, Q. He, J. Li, X. Rong, Z. Yang, H. Chang, et al
2022
Later among the works it cites.
Z. Tong, Y. Song, J. Wang, and L. Wang, “Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training,” NeurIPS
2022
Later among the works it cites.
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2020
Cited alongside, same era.
M. Neumann, A. S. Pinto, X. Zhai, and N. Houlsby, “In-domain representation learning for remote sensing,” in ICLR-AI for Earth Sciences Workshop
2020
Cited alongside, same era.
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
2021
Cited alongside, same era.
X. Liu, D. Hong, J. Chanussot, B. Zhao, and P. Ghamisi, “Modality translation in remote sensing time series,” IEEE Transactions on Geoscience and Remote Sensing
2021
Cited alongside, same era.
X. Chen and K. He, “Exploring simple siamese representation learning,” in CVPR
2021
Cited alongside, same era.
Z. Xie, Y. Lin, Z. Zhang, Y. Cao, S. Lin, and H. Hu, “Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,” in CVPR
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV
2021
Cited alongside, same era.
C. Feichtenhofer, Y. Li, K. He, et al
2022
Later among the works it cites.
Y. Cong, S. Khanna, C. Meng, P. Liu, E. Rozi, Y. He, M. Burke, D. Lobell, and S. Ermon, “Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,” NeurIPS
2022
Later among the works it cites.
M. Zhou, J. Huang, D. Hong, F. Zhao, C. Li, and J. Chanussot, “Rethinking pan-sharpening in closed-loop regularization,” IEEE Transactions on Neural Networks and Learning Systems
2023
Closest in time.
D. Hong, J. Yao, C. Li, D. Meng, N. Yokoya, and J. Chanussot, “Decoupled-and-coupled networks: Self-supervised hyperspectral image super-resolution with subpixel fusion,” IEEE Transactions on Geoscience and Remote Sensing
2023
Closest in time.
S. Mei, R. Jiang, M. Ma, and C. Song, “Rotation-invariant feature learning via convolutional neural network with cyclic polar coordinates convolutional layer,” IEEE Transactions on Geoscience and Remote Sensing
2023
Closest in time.
D. Hong, B. Zhang, H. Li, Y. Li, J. Yao, C. Li, M. Werner, J. Chanussot, A. Zipf, and X. X. Zhu, “Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks,” Remote Sensing of Environment
2023
Closest in time.
J. Tian, X. Sun, Y. Du, S. Zhao, Q. Liu, K. Zhang, W. Yi, W. Huang, C. Wang, X. Wu, et al
2023
Closest in time.
D. Du, Y. Gu, T. Liu, and X. Li, “Spectral reconstruction from satellite multispectral imagery using convolution and transformer joint network,” IEEE Transactions on Geoscience and Remote Sensing
2023
Closest in time.
D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, X. Jia, A. Plaza, P. Gamba, J. A. Benediktsson, and J. Chanussot, “SpectralGPT: The first remote sensing foundation model customized for spectral data,” Oct. 2023
2023
Closest in time.