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The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing . IEEE, 2008, pp. 722–729
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011
2011
Earlier work this paper cites.
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li, “Novel dataset for fine-grained image categorization: Stanford dogs,” in Proc. CVPR workshop on fine-grained visual categorization (FGVC) , vol. 2, no. 1. Citeseer, 2011
2011
Earlier work this paper cites.
G. Van Horn, S. Branson, R. Farrell, S. Haber, J. Barry, P. Ipeirotis, P. Perona, and S. Belongie, “Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 595–604
2015
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 843–852
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. Gebru, J. Krause, Y. Wang, D. Chen, J. Deng, and L. Fei-Fei, “Fine-grained car detection for visual census estimation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. Van Der Maaten, “Exploring the limits of weakly supervised pretraining,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 181–196
2018
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, pp. 8026–8037, 2019
2019
Cited alongside, same era.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 6023–6032
2019
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proceedings of the IEEE , vol. 109, no. 1, pp. 43–76, 2020
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 3045–3059
2021
Later among the works it cites.
2021
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 000–16 009
2022
Later among the works it cites.
X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer, “Scaling vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 104–12 113
2022
Later among the works it cites.
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2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
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 International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
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 Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
T. Ridnik, E. Ben-Baruch, A. Noy, and L. Zelnik-Manor, “Imagenet-21k pretraining for the masses,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021
2021
Cited alongside, same era.
K. Han, A. Xiao, E. Wu, J. Guo, C. Xu, and Y. Wang, “Transformer in transformer,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 34, pp. 15 908–15 919, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Conditional prompt learning for vision-language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 816–16 825
2022
Later among the works it cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in Proceedings of the European conference on computer vision (ECCV) . Springer, 2022, pp. 709–727
2022
Later among the works it cites.
S. Chen, C. Ge, Z. Tong, J. Wang, Y. Song, J. Wang, and P. Luo, “Adaptformer: Adapting vision transformers for scalable visual recognition,” in Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Later among the works it cites.
D. Lian, D. Zhou, J. Feng, and X. Wang, “Scaling & shifting your features: A new baseline for efficient model tuning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Later among the works it cites.
Y.-L. Sung, J. Cho, and M. Bansal, “Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5227–5237
2022
Later among the works it cites.
Y. Zhang, K. Zhou, and Z. Liu, “Neural prompt search,” arXiv preprint arXiv:2206.04673 , 2022
2022
Later among the works it cites.
E. B. Zaken, Y. Goldberg, and S. Ravfogel, “Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022, pp. 1–9
2022
Later among the works it cites.
2023
Closest in time.
M. Iman, H. R. Arabnia, and K. Rasheed, “A review of deep transfer learning and recent advancements,” Technologies , vol. 11, no. 2, p. 40, 2023
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
Closest in time.