[5] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. Learning to prompt for continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 139-149, 2022.
[6] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge J. Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXIII. Springer, 2022.
[7] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
[8] Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Scholkopf, and Francesco Locatello. Assaying out-of-distribution generalization in transfer learning. In NeurIPS, 2022.
[9] Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, and Mingyuan Zhou. POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models. In ICML 2023: International Conference on Machine Learning, July 2023.
[10] Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. How can we know what language models know? Transactions of the Association for Computational Linguistics, 8:423-438, 2020.
[11] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1-35, 2023.
[12] Chengcheng Ma, Yang Liu, Jiankang Deng, Lingxi Xie, Weiming Dong, and Changsheng Xu. Understanding and mitigating overfitting in prompt tuning for vision-language models. IEEE Transactions on Circuits and Systems for Video Technology, pages 1-1, 2023. doi: 10.1109/TCSVT.2023.3245584.
[13] Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang. Prompt-aligned gradient for prompt tuning. arXiv preprint arXiv:2205.14865, 2022.
[14] Yue Zhang, Hongliang Fei, Dingcheng Li, Tan Yu, and Ping Li. Prompting through prototype: A prototype-based prompt learning on pretrained vision-language models. arXiv preprint arXiv:2210.10841, 2022.
[15] Feng Wang, Manling Li, Xudong Lin, Hairong Lv, Alex Schwing, and Heng Ji. Learning to decompose visual features with latent textual prompts. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=wtcud6HroZr.
[16] Nihal V. Nayak, Peilin Yu, and Stephen Bach. Learning to compose soft prompts for compositional zero-shot learning. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=S8-A2FXnIh.
[17] Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, and Kun Zhang. PLOT: Prompt learning with optimal transport for vision-language models. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=zqwryBoXYnh.
[18] Xinyang Liu, Dongsheng Wang, Miaoge Li, Zhibin Duan, Yishi Xu, Bo Chen, and Mingyuan Zhou. Patch-token aligned bayesian prompt learning for vision-language models. arXiv preprint arXiv:2303.09100, 2023.
[19] Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, and Xinmei Tian. Prompt distribution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5206-5215, 2022.