[1] J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds, et al. Flamingo: A visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022.
[2] H. Bao, L. Dong, S. Piao, and F. Wei. BEiT: BERT pre-training of image Transformers. arXiv preprint arXiv:2106.08254, 2021.
[3] C. Beattie, J. Z. Leibo, D. Teplayashin, T. Ward, M. Wainwright, H. Kuttler, A. Lefrancq, S. Green, V. Valdés, A. Sadik, et al. Deepmind lab. arXiv preprint arXiv:1612.03801, 2016.
[4] L. Bossard, M. Guillaumin, and L. Van Gool. Food-101 – mining discriminative components with random forests. In Eur. Conf. Comput. Vis., 2014.
[5] 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. In Adv. Neural Inform. Process. Syst., 2020.
[6] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko. End-to-end object detection with Transformers. In Eur. Conf. Comput. Vis., pages 213-229, 2020.
[7] S. Chen, C. Ge, Z. Tong, J. Wang, Y. Song, J. Wang, and P. Luo. AdaptFormer: Adapting vision Transformers for scalable visual recognition. In Adv. Neural Inform. Process. Syst., 2022.
[8] G. Cheng, J. Han, and X. Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 2017.
[9] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi. Describing textures in the wild. In IEEE Conf. Comput. Vis. Pattern Recog., 2014.
[10] M. Dehghani, J. Djolonga, B. Mustafa, P. Padlewski, J. Heek, J. Gilmer, A. Steiner, M. Caron, R. Geirhos, I. Alabdulmohsin, et al. Scaling Vision Transformers to 22 billion parameters. arXiv preprint arXiv:2302.05442, 2023.
[11] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248–255, 2009.
[12] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of deep bidirectional Transformers for language understanding. North Am. Chap. Assoc. Comput. Linguist., 2018.
[13] 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. In Int. Conf. Learn. Represent., 2020.
[14] Y. Fang, W. Wang, B. Xie, Q. Sun, L. Wu, X. Wang, T. Huang, X. Wang, and Y. Cao. Eva: Exploring the limits of masked visual representation learning at scale. arXiv preprint arXiv:2211.07636, 2022.
[15] T. Gebru, J. Krause, Y. Wang, D. Chen, J. Deng, and L. Fei-Fei. Fine-grained car detection for visual census estimation. In Assoc. Adv. Artif. Intell., 2017.
[16] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The KITTI dataset. Int. J. Robotics Research, 2013.
[17] Y. Gong, Y.-A. Chung, and J. Glass. Ast: Audio spectrogram transformer. arXiv preprint arXiv:2104.01778, 2021.
[18] B. Graham. Kaggle diabetic retinopathy detection competition report., 2015.
[19] J. He, C. Zhou, X. Ma, T. Berg-Kirkpatrick, and G. Neubig. Towards a unified view of parameter-efficient transfer learning. In Int. Conf. Learn. Represent., 2022.
[20] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 770-778, 2016.
[21] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision learners. In IEEE Conf. Comput. Vis. Pattern Recog., pages 16000-16009, 2022.
[22] 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.