[1] 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 International Conference on Learning Representations, 2020.
[2] K. He, X. Chen, S. Xie, Y. Li, P. Dollar, and R. Girshick, "Masked autoencoders are scalable vision learners," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000-16009.
[3] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning. PMLR, 2021, pp. 8748–8763.
[4] X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer, "Scaling vision transformers," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12-14, 12-13.
[5] K. Zhou, J. Yang, C. C. Loy, and Z. Liu, "Conditional prompt learning for vision-language models," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16816-16825.
[6] M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in Proceedings of the European conference on computer vision (ECCV). Springer, 2022, pp. 709–727.
[7] N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning. PMLR, 2019, pp. 2790–2799.
[8] S. Chen, C. Ge, Z. Tong, J. Wang, Y. Song, J. Wang, and P. Luo, "Adaptformer: Adapting vision transformers for scalable visual recognition," in Advances in Neural Information Processing Systems (NeurIPS), 2022.
[9] D. Lian, D. Zhou, J. Feng, and X. Wang, "Scaling & shifting your features: A new baseline for efficient model tuning," in Advances in Neural Information Processing Systems (NeurIPS), 2022.
[10] G. Luo, M. Huang, Y. Zhou, X. Sun, G. Jiang, Z. Wang, and R. Ji, "Towards efficient visual adaption via structural re-parameterization," arXiv preprint arXiv:2302.08106, 2023.
[11] 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 Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10012-10022.
[12] F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proceedings of the IEEE, vol. 109, no. 1, pp. 43–76, 2020.
[13] S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345–1359, 2010.
[14] M. Iman, H. R. Arabnia, and K. Rasheed, “A review of deep transfer learning and recent advancements,” Technologies, vol. 11, no. 2, p. 40, 2023.
[15] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2009, pp. 248-255.
[16] T. Ridnik, E. Ben-Baruch, A. Noy, and L. Zelnik-Manor, "Imagenet-21k pretraining for the masses," in Advances in Neural Information Processing Systems (NeurIPS), 2021.
[17] C. Sun, A. Shrivastava, S. Singh, and A. Gupta, "Revisiting unreasonable effectiveness of data in deep learning era," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 843-852.