[18] D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. Van Der Maaten, "Exploring the limits of weakly supervised pretraining," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 181-196.
[19] W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev et al., "The kinetics human action video dataset," arXiv preprint arXiv:1705.06950, 2017.
[20] K. Han, A. Xiao, E. Wu, J. Guo, C. Xu, and Y. Wang, "Transformer in transformer," Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 15908-15919, 2021.
[21] D. Zhou, B. Kang, X. Jin, L. Yang, X. Lian, Z. Jiang, Q. Hou, and J. Feng, “Deepvit: Towards deeper vision transformer,” arXiv preprint arXiv:2103.11886, 2021.
[22] 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 (NeurIPS), vol. 33, pp. 1877-1901, 2020.
[23] X. Chen, S. Xie, and K. He, "An empirical study of training self-supervised vision transformers," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 9640-9649.
[24] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, "Lora: Low-rank adaptation of large language models," arXiv preprint arXiv:2106.09685, 2021.
[25] B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021, pp. 3045–3059.
[26] X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190, 2021.
[27] P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, "Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing," ACM Computing Surveys, vol. 55, no. 9, pp. 1-35, 2023.
[28] T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh, "Autoprompt: Eliciting knowledge from language models with automatically generated prompts," arXiv preprint arXiv:2010.15980, 2020.
[29] Y.-L. Sung, J. Cho, and M. Bansal, "Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5227-5237.
[30] Y. Zhang, K. Zhou, and Z. Liu, “Neural prompt search,” arXiv preprint arXiv:2206.04673, 2022.
[31] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011.
[32] G. Van Horn, S. Branson, R. Farrell, S. Haber, J. Barry, P. Ipeirotis, P. Perona, and S. Belongie, "Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 595-604.
[33] M.-E. Nilsback and A. Zisserman, "Automated flower classification over a large number of classes," in 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. IEEE, 2008, pp. 722-729.
[34] A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li, “Novel dataset for fine-grained image categorization: Stanford dogs,” in Proc. CVPR workshop on fine-grained visual categorization (FGVC), vol. 2, no. 1. CiteSeer, 2011.