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Recently, despite the unprecedented success of large pre-trained visual-language models (VLMs) on a wide range of downstream tasks, the real-world unsupervised domain adaptation (UDA) problem is still not well explored.
Learning how to propagate messages in graph neural networks
Xiao, T.; Chen, Z.; Wang, D.; and Wang, S. 2021 · 1903
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Adapting visual category models to new domains
Saenko, K.; Kulis, B.; Fritz, M.; and Darrell, T. 2010 · 2010
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Unsupervised domain adaptation by backpropagation
Ganin, Y.; and Lempitsky, V. 2015 · 2015
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Learning transferable features with deep adaptation networks
Long, M.; Cao, Y.; Wang, J.; and Jordan, M. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Return of Frustratingly Easy Domain Adaptation
Sun, B.; Feng, J.; and Saenko, K. 2016 · 2016
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Deep transfer learning with joint adaptation networks
Long, M.; Zhu, H.; Wang, J.; and Jordan, M. I. 2017 · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H.; Eusebio, J.; Chakraborty, S.; and Panchanathan, S. 2017 · 2017
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Conditional adversarial domain adaptation
Long, M.; Cao, Z.; Wang, J.; and Jordan, M. I. 2018 · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K.; Watanabe, K.; Ushiku, Y.; and Harada, T. 2018 · 2018
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LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Tan, H.; and Bansal, M. 2019 · 2019
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Bridging theory and algorithm for domain adaptation
Zhang, Y.; Liu, T.; Long, M.; and Jordan, M. 2019 · 2019
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Minimum class confusion for versatile domain adaptation
Jin, Y.; Wang, X.; Long, M.; and Wang, J. 2020 · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Liang, J.; Hu, D.; and Feng, J. 2020 · 2020
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Unsupervised domain adaptation via structurally regularized deep clustering
Tang, H.; Chen, K.; and Jia, K. 2020 · 2020
Cited alongside, same era.
A survey of unsupervised deep domain adaptation
Wilson, G.; and Cook, D. J. 2020 · 2020
Cited alongside, same era.
Knowledge distillation for model-agnostic meta-learning
Zhang, M.; Wang, D.; and Gai, S. 2020 · 2020
Cited alongside, same era.
Pareto Self-Supervised Training for Few-Shot Learning
Chen, Z.; Ge, J.; Zhan, H.; Huang, S.; and Wang, D. 2021 · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2021 · 2021
Cited alongside, same era.
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Domain Adaptation via Prompt Learning
Ge, C.; Huang, R.; Xie, M.; Lai, Z.; Song, S.; Li, S.; and Huang, G. 2022 · 2022
Later among the works it cites.
Visual prompt tuning
Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022 · 2022
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A closer look at smoothness in domain adversarial training
Rangwani, H.; Aithal, S. K.; Mishra, M.; Jain, A.; and Radhakrishnan, V. B. 2022 · 2022
Later among the works it cites.
Safe self-refinement for transformer-based domain adaptation
Sun, T.; Lu, C.; Zhang, T.; and Ling, H. 2022 · 2022
Later among the works it cites.
Decoupled self-supervised learning for graphs
Xiao, T.; Chen, Z.; Guo, Z.; Zhuang, Z.; and Wang, S. 2022 · 2022
Later among the works it cites.
Cdtrans: Cross-domain transformer for unsupervised domain adaptation
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Gao, P.; Geng, S.; Zhang, R.; Ma, T.; Fang, R.; Zhang, Y.; Li, H.; and Qiao, Y. 2021 · 2021
Cited alongside, same era.
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
Huang, Z.; Zeng, Z.; Huang, Y.; Liu, B.; Fu, D.; and Fu, J. 2021 · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C.; Yang, Y.; Xia, Y.; Chen, Y.-T.; Parekh, Z.; Pham, H.; Le, Q.; Sung, Y.-H.; Li, Z.; and Duerig, T. 2021 · 2021
Cited alongside, same era.
Vilt: Vision-and-language transformer without convolution or region supervision
Kim, W.; Son, B.; and Kim, I. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; and Jégou, H. 2021 · 2021
Cited alongside, same era.
Exploring Visual Prompts for Adapting Large-Scale Models
Bahng, H.; Jahanian, A.; Sankaranarayanan, S.; and Isola, P. 2022 · 2022
Cited alongside, same era.
Xu, T.; Chen, W.; Wang, P.; Wang, F.; Li, H.; and Jin, R. 2022 · 2022
Later among the works it cites.
Domain generalized few-shot image classification via meta regularization network
Zhang, M.; Huang, S.; and Wang, D. 2022 · 2022
Later among the works it cites.
CALIP: Zero-Shot Enhancement of CLIP with Parameter-Free Attention
Guo, Z.; Zhang, R.; Qiu, L.; Ma, X.; Miao, X.; He, X.; and Cui, B. 2023 · 2023
Closest in time.
Maple: Multi-modal prompt learning
Khattak, M. U.; Rasheed, H.; Maaz, M.; Khan, S.; and Khan, F. S. 2023 · 2023
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Pfeiffer, J.; Ruder, S.; Vulić, I.; and Ponti, E. M. 2023 · 2023
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Tvt: Transferable vision transformer for unsupervised domain adaptation
Yang, J.; Liu, J.; Xu, N.; and Huang, J. 2023 · 2023
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Improving Cross-domain Few-shot Classification with Multilayer Perceptron
Bai, S.; Zhou, W.; Luan, Z.; Wang, D.; and Badong, C. 2024 · 2024
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Visda: A synthetic-to-real benchmark for visual domain adaptation
Peng, X.; Usman, B.; Kaushik, N.; Wang, D.; Hoffman, J.; and Saenko, K. 2018 · 2026
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