[27] Shu Manli, Nie Weili, Huang De-An, Yu Zhiding, Goldstein Tom, Anandkumar Anima, and Xiao Chaowei. Test-time prompt tuning for zero-shot generalization in vision-language models. In NeurIPS, 2022.
[28] 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.
[29] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 2008.
[30] Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, and Stephen H. Bach. TAGLETS: A system for automatic semi-supervised learning with auxiliary data. In Conference on Machine Learning and Systems (MLSys), 2022.
[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021.
[32] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019.
[33] Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations, 2022.
[34] Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai, Joseph E. Gonzalez, Kurt Keutzer, and Trevor Darrell. Multitask vision-language prompt tuning. arXiv preprint arXiv:2211.11720, 2022.
[35] Weiwei Shi, Yihong Gong, C. Ding, Zhiheng Ma, Xiaoyu Tao, and Nanning Zheng. Transductive semi-supervised deep learning using min-max features. In European Conference on Computer Vision, 2018.
[36] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, 2020.
[37] Xineng Sun, Ping Hu, and Kate Saenko. Dualcoop: Fast adaptation to multi-label recognition with limited annotations. ArXiv, 2022.
[38] Xudong Wang, Zhi-Li Wu, Long Lian, and Stella X. Yu. Debiased learning from naturally imbalanced pseudo-labels. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[39] Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-shot learning — the good, the bad and the ugly. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[40] Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Unsupervised data augmentation for consistency training. In Advances in Neural Information Processing Systems, 2020.
[41] Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, and Rong Jin. Dash: Semi-supervised learning with dynamic thresholding. In International Conference on Machine Learning, 2021.