[1] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33:1877-1901, 2020.
[2] P. J. Burt and E. H. Adelson. The laplacian pyramid as a compact image code. In Readings in Computer Vision, pages 671-679. Elsevier, 1987.
[3] M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9650-9660, 2021.
[4] E. Catanzariti and A. Mackworth. Forests and pyramids: Using image hierarchies to understand landsat images. Technical Report 78-2, The University of British Columbia, Vancouver, British Columbia, 1978. https://www.cs.ubc.ca/sites/default/files/tr/1978/TR-78-02.pdf.
[5] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):834-848, 2017.
[6] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for contrastive learning of visual representations. In H. D. III and A. Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 1597-1607. Pmlr, 13-18 Jul 2020. doi: 10.48550/arxiv.2002.05709. URL https://proceedings.mlr.press/v119/chen20j.html.
[7] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, pages 1597-1607. PMLR, 2020.
[8] T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton. Big self-supervised models are strong semi-supervised learners. Advances in Neural Information Processing Systems, 33:22243-22255, 2020.
[9] X. Chen and K. He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15750-15758, 2021.
[10] G. Cheng, J. Han, and X. Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 105(10):1865-1883, 2017.
[11] G. Christie, N. Fendley, J. Wilson, and R. Mukherjee. Functional map of the world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6172–6180, 2018.
[12] Y. Cong, S. Khanna, C. Meng, P. Liu, E. Rozi, Y. He, M. Burke, D. B. Lobell, and S. Ermon. Satmae: Pretraining transformers for temporal and multi-spectral satellite imagery. arXiv preprint arXiv:2207.08051, 2022.
[13] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[14] P. Dollár and C. L. Zitnick. Fast edge detection using structured forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8):1558-1570, 2014.
[15] S. Dong, G. Hangel, W. Bogner, G. Widhalm, K. Rössler, S. Trattnig, C. You, R. de Graaf, J. A. Onofrey, and J. S. Duncan. Multi-scale super-resolution magnetic resonance spectroscopic imaging with adjustable sharpness. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 410–420. Springer, 2022.
[16] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[17] T. E. S. A. (ESA). Sentinel 2. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2_overview, 2015.