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Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
Earlier work this paper cites.
Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Cited alongside, same era.
Lomar: A local defense against poisoning attack on federated learning
Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, and Yao Liu · 2021
Cited alongside, same era.
Prompt-aligned gradient for prompt tuning
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang · 2022
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Lasp: Text-to-text optimization for language-aware soft prompting of vision & language models
Adrian Bulat and Georgios Tzimiropoulos · 2023
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Spatial-temporal prompt learning for federated weather forecasting
Shengchao Chen, Guodong Long, Tao Shen, Tianyi Zhou, and Jing Jiang · 2023
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pfedprompt: Learning personalized prompt for vision-language models in federated learning
Tao Guo, Song Guo, and Junxiao Wang · 2023
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Hepco: Data-free heterogeneous prompt consolidation for continual federated learning
Shaunak Halbe, James Seale Smith, Junjiao Tian, and Zsolt Kira · 2023
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Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
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 · 2021
Cited alongside, same era.
On the convergence of multi-server federated learning with overlapping area
Zhe Qu, Xingyu Li, Jie Xu, Bo Tang, Zhuo Lu, and Yao Liu · 2022
Cited alongside, same era.
Cross-domain federated adaptive prompt tuning for clip
Shangchao Su, Mingzhao Yang, Bin Li, and Xiangyang Xue · 2022
Cited alongside, same era.
Unified vision and language prompt learning
Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy · 2022
Cited alongside, same era.
Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model
Tao Guo, Song Guo, Junxiao Wang, Xueyang Tang, and Wenchao Xu
Cited in the paper.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al
Cited in the paper.
Closest in time.
Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2023
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Fedclip: Fast generalization and personalization for clip in federated learning
Wang Lu, Xixu Hu, Jindong Wang, and Xing Xie · 2023
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Cooperative hardware-prompt learning for snapshot compressive imaging
Jiamian Wang, Zongliang Wu, Yulun Zhang, Xin Yuan, Tao Lin, and Zhiqiang Tao · 2023
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Efficient model personalization in federated learning via client-specific prompt generation
Fu-En Yang, Chien-Yi Wang, and Yu-Chiang Frank Wang · 2023
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Visual-language prompt tuning with knowledge-guided context optimization
Hantao Yao, Rui Zhang, and Changsheng Xu · 2023
Closest in time.