[45] X. Liu, K. Ji, Y. Fu, Z. Du, Z. Yang, and J. Tang. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602, 2021.
[46] X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang. GPT understands, too. arXiv preprint arXiv:2103.10385, 2021.
[47] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
[48] 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 Int. Conf. Comput. Vis., 2021.
[49] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. A convnet for the 2020s. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11976-11986, 2022.
[50] I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In Int. Conf. Learn. Represent., 2019.
[51] S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013.
[52] L. Matthey, I. Higgins, D. Hassabis, and A. Lerchner. dSprites: Disentanglement testing sprites dataset, 2017.
[53] T. Meinhardt, A. Kirillov, L. Leal-Taixe, and C. Feichtenhofer. Trackformer: Multi-object tracking with transformers. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8844-8854, 2022.
[54] Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. In Adv. Neural Inform. Process. Syst. Worksh., 2011.
[55] M.-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Ind. Conf. Comput. Vis. Graph. Image Process., pages 722-729, 2008.
[56] O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar. Cats and dogs. In IEEE Conf. Comput. Vis. Pattern Recog., 2012.
[57] W. Peebles and S. Xie. Scalable diffusion models with Transformers. arXiv preprint arXiv:2212.09748, 2022.
[58] J. Pfeiffer, A. Kamath, A. Rückle, K. Cho, and I. Gurevych. AdapterFusion: Non-destructive task composition for transfer learning. In Eur. Chap. Assoc. Comput. Linguist., pages 487-503, 2020.
[59] 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 Int. Conf. Mach. Learn., pages 8748-8763, 2021.
[60] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. Exploring the limits of transfer learning with a unified text-to-text Transformer. J. Mach. Learn. Res., 21(140):1-67, 2020.
[61] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125, 2022.
[62] B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. DoImagenet classifiers generalize toImagenet? In Int. Conf. Mach. Learn., 2019.
[63] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10684-10695, 2022.
[64] O. Ronneberger, P. Fischer, and T. Brox. U-Net: Convolutional networks for biomedical image segmentation. In Med. Image Comput. Computer-Assisted Interv., pages 234–241, 2015.
[65] N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In IEEE Conf. Comput. Vis. Pattern Recog., 2022.
[66] R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In Conf. Empirical Methods NLP, pages 1631-1642, 2013.