Fetching the paper…
Reading the bibliography…
Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data.
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, “Analysis of representations for domain adaptation,” NeurIPS , pp. 137–144, 2006
2006
Earlier work this paper cites.
G. Griffin, A. Holub, and P. Perona, “Caltech-256 object category dataset,” 2007
2007
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in ICVGIP , 2008, pp. 722–729
2008
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” TKDE , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
Y. Mansour, M. Mohri, and A. Rostamizadeh, “Domain adaptation: Learning bounds and algorithms,” in COLT , 2009
2009
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 CVPR , 2009, pp. 248–255
2009
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 L. Fei-Fei, “Novel dataset for fine-grained image categorization,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops , June 2011
2011
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar, “Cats and dogs,” in CVPR , 2012, pp. 3498–3505
2012
Earlier work this paper cites.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in CVPR , 2014, pp. 1717–1724
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “Fitnets: Hints for thin deep nets,” in ICLR , 2015
2015
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in ICML , 2015, pp. 97–105
2015
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” JMLR , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
J. Yim, D. Joo, J. Bae, and J. Kim, “A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,” in CVPR , 2017, pp. 4133–4141
2017
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer,” in ICLR , 2017
2017
Earlier work this paper cites.
M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan, “Transferable representation learning with deep adaptation networks,” TPAMI , vol. 41, no. 12, pp. 3071–3085, 2018
2018
Earlier work this paper cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in CVPR , 2018, pp. 4510–4520
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in ACL , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in CVPR , 2019, pp. 3967–3976
2019
Earlier work this paper cites.
W. M. Kouw and M. Loog, “A review of domain adaptation without target labels,” TPAMI , vol. 43, no. 3, pp. 766–785, 2019
2019
Earlier work this paper cites.
G. F. Elsayed, I. Goodfellow, and J. Sohl-Dickstein, “Adversarial reprogramming of neural networks,” in ICLR , 2019
2019
Earlier work this paper cites.
P. Neekhara, S. Hussain, S. Dubnov, and F. Koushanfar, “Adversarial reprogramming of text classification neural networks,” in EMNLP , 2019, pp. 5215–5224
2019
Earlier work this paper cites.
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,” NeurIPS , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
G. Kang, L. Jiang, Y. Wei, Y. Yang, and A. Hauptmann, “Contrastive adaptation network for single-and multi-source domain adaptation,” TPAMI , vol. 44, no. 4, pp. 1793–1804, 2020
2020
Earlier work this paper cites.
Y. Tian, D. Krishnan, and P. Isola, “Contrastive representation distillation,” in ICLR , 2020
2020
Earlier work this paper cites.
N. Passalis, M. Tzelepi, and A. Tefas, “Heterogeneous knowledge distillation using information flow modeling,” in CVPR , 2020, pp. 2339–2348
2020
Cited alongside, same era.
H.-J. Ye, S. Lu, and D.-C. Zhan, “Distilling cross-task knowledge via relationship matching,” in CVPR , 2020, pp. 12 396–12 405
2020
Cited alongside, same era.
J. Liang, D. Hu, and J. Feng, “Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,” in ICML , 2020, pp. 6028–6039
2020
Cited alongside, same era.
R. Li, Q. Jiao et al. , “Model adaptation: Unsupervised domain adaptation without source data,” in CVPR , 2020, pp. 9638–9647
2020
Cited alongside, same era.
Y.-Y. Tsai, P.-Y. Chen, and T.-Y. Ho, “Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources,” in ICML , 2020, pp. 9614–9624
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: BERT pre-training of image transformers,” in ICLR , 2022
2022
Later among the works it cites.
C. Li, G. Wang, B. Wang, X. Liang, Z. Li, and X. Chang, “Ds-net++: Dynamic weight slicing for efficient inference in cnns and vision transformers,” TPAMI , 2022
2022
Later among the works it cites.
C. Mou and J. Zhang, “Transcl: Transformer makes strong and flexible compressive learning,” TPAMI , 2022
2022
Later among the works it cites.
H. Lin, Y. Zhang, Z. Qiu, S. Niu, C. Gan, Y. Liu, and M. Tan, “Prototype-guided continual adaptation for class-incremental unsupervised domain adaptation,” in ECCV , 2022, pp. 351–368
2022
Later among the works it cites.
F. Ye and A. G. Bors, “Dynamic self-supervised teacher-student network learning,” TPAMI , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
F. Liu, W. Xu, J. Lu, G. Zhang, A. Gretton, and D. J. Sutherland, “Learning deep kernels for non-parametric two-sample tests,” in ICML , 2020, pp. 6316–6326
2020
Cited alongside, same era.
2021
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 ICML . 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 ICCV , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
J. Liang, D. Hu, Y. Wang, R. He, and J. Feng, “Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer,” TPAMI , vol. 44, no. 11, pp. 8602–8617, 2021
2021
Cited alongside, same era.
J. Dong, Y. Cong, G. Sun, Z. Fang, and Z. Ding, “Where and how to transfer: knowledge aggregation-induced transferability perception for unsupervised domain adaptation,” TPAMI , 2021
2021
Cited alongside, same era.
L. Wang and K.-J. Yoon, “Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks,” TPAMI , 2021
2021
Cited alongside, same era.
J. Zhang, H. Peng, K. Wu, M. Liu, B. Xiao, J. Fu, and L. Yuan, “Minivit: Compressing vision transformers with weight multiplexing,” in CVPR , 2022, pp. 12 145–12 154
2022
Later among the works it cites.
2022
Later among the works it cites.
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 ECCV , 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 NeurIPS , 2022
2022
Later among the works it cites.
Y.-H. Wu, Y. Liu, X. Zhan, and M.-M. Cheng, “P2t: Pyramid pooling transformer for scene understanding,” TPAMI , 2022
2022
Later among the works it cites.
S. Qian, Y. Zhu, W. Li, M. Li, and J. Jia, “What makes for good tokenizers in vision transformer?” TPAMI , 2022
2022
Later among the works it cites.
S. Sun, X. Yue, H. Zhao, P. H. Torr, and S. Bai, “Patch-based separable transformer for visual recognition,” TPAMI , 2022
2022
Later among the works it cites.
R. J. Chen, C. Chen, Y. Li, T. Y. Chen, A. D. Trister, R. G. Krishnan, and F. Mahmood, “Scaling vision transformers to gigapixel images via hierarchical self-supervised learning,” in CVPR , 2022, pp. 16 144–16 155
2022
Later among the works it cites.
2022
Later among the works it cites.
B. Zhao, Q. Cui, R. Song, Y. Qiu, and J. Liang, “Decoupled knowledge distillation,” in CVPR , 2022, pp. 11 953–11 962
2022
Later among the works it cites.
D. Chen, J.-P. Mei, H. Zhang, C. Wang, Y. Feng, and C. Chen, “Knowledge distillation with the reused teacher classifier,” in CVPR , 2022, pp. 11 933–11 942
2022
Later among the works it cites.
Z. Yang, Z. Li, M. Shao, D. Shi, Z. Yuan, and C. Yuan, “Masked generative distillation,” in ECCV , 2022, pp. 53–69
2022
Later among the works it cites.
H.-J. Ye, S. Lu, and D.-C. Zhan, “Generalized knowledge distillation via relationship matching,” TPAMI , vol. 45, no. 2, pp. 1817–1834, 2022
2022
Later among the works it cites.
K. Wu, J. Zhang, H. Peng, M. Liu, B. Xiao, J. Fu, and L. Yuan, “Tinyvit: Fast pretraining distillation for small vision transformers,” in ECCV , 2022, pp. 68–85
2022
Later among the works it cites.
S. Abnar, M. Dehghani, B. Neyshabur, and H. Sedghi, “Exploring the limits of large scale pre-training,” in ICLR , 2022
2022
Later among the works it cites.
Y. Yamada and M. Otani, “Does robustness on imagenet transfer to downstream tasks?” in CVPR , 2022, pp. 9215–9224
2022
Later among the works it cites.
2022
Later among the works it cites.
P. Neekhara, S. Hussain, J. Du, S. Dubnov, F. Koushanfar, and J. McAuley, “Cross-modal adversarial reprogramming,” in WACV , 2022, pp. 2427–2435
2022
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 CVPR , 2022, pp. 16 000–16 009
2022
Later among the works it cites.
P. Z. Ramirez, A. Cardace, L. De Luigi, A. Tonioni, S. Salti, and L. Di Stefano, “Learning good features to transfer across tasks and domains,” TPAMI , 2023
2023
Closest in time.
Z. Peng, Z. Guo, W. Huang, Y. Wang, L. Xie, J. Jiao, Q. Tian, and Q. Ye, “Conformer: Local features coupling global representations for recognition and detection,” TPAMI , 2023
2023
Closest in time.