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Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years.
S. Thrun, “Lifelong learning algorithms,” in Learning to Learn , 1998, pp. 181–209
1998
Earlier work this paper cites.
S. Thrun and L. Pratt, “Learning to learn: Introduction and overview,” in Learning to Learn , 1998, pp. 3–17
1998
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,” Artificial Intelligence Review , pp. 77–95, 2002
2002
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “One-shot learning of object categories,” IEEE transactions on pattern analysis and machine intelligence , vol. 28, no. 4, pp. 594–611, 2006
2006
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering , pp. 1345–1359, 2009
2009
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop , 2011, p. 1
2011
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.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshop , 2013, pp. 554–561
2013
Earlier work this paper cites.
A. Dosovitskiy, J. T. Springenberg, M. A. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with convolutional neural networks,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2014, pp. 766–774
2014
Earlier work this paper cites.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in Proceedings of the International Conference on Machine Learning (ICML) Deep Learning Workshop , 2015
2015
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) Deep Learning and Representation Learning Workshop , 2015
2015
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2016, pp. 3630–3638
2016
Earlier work this paper cites.
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta-learning with memory-augmented neural networks,” in Proceedings of the International Conference on Machine Learning (ICML) , 2016, pp. 1842–1850
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
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 Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the International Conference on Machine Learning (ICML) , 2017, pp. 1126–1135
2017
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2017
2017
Earlier work this paper cites.
J. Snell, K. Swersky, R. Zemel, and R. Zemel, “Prototypical networks for few-shot learning,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2017, pp. 4077–4087
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1199–1208
2018
Earlier work this paper cites.
S. Gidaris and N. Komodakis, “Dynamic few-shot visual learning without forgetting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 4367–4375
2018
Earlier work this paper cites.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2018
2018
Earlier work this paper cites.
J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. E. Turner, “Versa: Versatile and efficient few-shot learning,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2018, pp. 1–9
2018
Earlier work this paper cites.
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel, “Meta-learning for semi-supervised few-shot classification,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2018
2018
Earlier work this paper cites.
G. Ghiasi, T. Lin, and Q. V. Le, “Dropblock: A regularization method for convolutional networks,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2018, pp. 10 750–10 760
2018
Earlier work this paper cites.
H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
K. Lee, S. Maji, A. Ravichandran, and S. Soatto, “Meta-learning with differentiable convex optimization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 10 657–10 665
2019
Cited alongside, same era.
Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele, “Meta-transfer learning for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 403–412
2019
Cited alongside, same era.
P. Mangla, M. Singh, A. Sinha, N. Kumari, V. N. Balasubramanian, and B. Krishnamurthy, “Charting the right manifold: Manifold mixup for few-shot learning,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) , 2020, pp. 2207–2216
2020
Later among the works it cites.
B. Liu, Y. Cao, Y. Lin, Q. Li, Z. Zhang, M. Long, and H. Hu, “Negative margin matters: Understanding margin in few-shot classification,” in Proceedings of the European Conference on Computer Vision (ECCV) , vol. 12349, 2020, pp. 438–455
2020
Later among the works it cites.
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals, “Rapid learning or feature reuse? towards understanding the effectiveness of maml,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
W. Xu, H. Wang, Z. Tu et al. , “Attentional constellation nets for few-shot learning,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2020
2020
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W. Li, J. Xu, J. Huo, L. Wang, Y. Gao, and J. Luo, “Distribution consistency based covariance metric networks for few-shot learning,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2019, pp. 8642–8649
2019
Cited alongside, same era.
W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, and J. Luo, “Revisiting local descriptor based image-to-class measure for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 7260–7268
2019
Cited alongside, same era.
L. Bertinetto, J. F. Henriques, P. H. S. Torr, and A. Vedaldi, “Meta-learning with differentiable closed-form solvers,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2019
2019
Cited alongside, same era.
W. Chen, Y. Liu, Z. Kira, Y. F. Wang, and J. Huang, “A closer look at few-shot classification,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2019
2019
Cited alongside, same era.
V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, and Y. Bengio, “Manifold mixup: Better representations by interpolating hidden states,” in Proceedings of the International Conference on Machine Learning (ICML) , vol. 97, 2019, pp. 6438–6447
2019
Cited alongside, same era.
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell, “Meta-learning with latent embedding optimization,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2019
2019
Cited alongside, same era.
R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen, “Cross attention network for few-shot classification,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2019
2019
Cited alongside, same era.
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, and M. Li, “Bag of tricks for image classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 558–567
2019
Cited alongside, same era.
Later among the works it cites.
C. Doersch, A. Gupta, and A. Zisserman, “Crosstransformers: Spatially-aware few-shot transfer,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2020
2020
Later among the works it cites.
C. Zhang, Y. Cai, G. Lin, and C. Shen, “Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 12 203–12 213
2020
Later among the works it cites.
J. Su, S. Maji, and B. Hariharan, “When does self-supervision improve few-shot learning?” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020, pp. 645–666
2020
Later among the works it cites.
E. D. Cubuk, B. Zoph, J. Shlens, and Q. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2020
2020
Later among the works it cites.
S. Yang, L. Liu, and M. Xu, “Free lunch for few-shot learning: Distribution calibration,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Closest in time.
J. Rajasegaran, S. Khan, M. Hayat, F. S. Khan, and M. Shah, “Self-supervised knowledge distillation for few-shot learning,” in Proceedings of the British Machine Vision Conference (BMVC) , 2021
2021
Closest in time.
J. Oh, H. Yoo, C. Kim, and S. Yun, “BOIL: towards representation change for few-shot learning,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Closest in time.
D. Wertheimer, L. Tang, and B. Hariharan, “Few-shot classification with feature map reconstruction networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 8012–8021
2021
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D. Kang, H. Kwon, J. Min, and M. Cho, “Relational embedding for few-shot classification,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2021
2021
Closest in time.
D. Kang, H. Kwon, J. Min, and M. Cho, “Relational embedding for few-shot classification,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2021
2021
Closest in time.
T. Pang, X. Yang, Y. Dong, H. Su, and J. Zhu, “Bag of tricks for adversarial training,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Closest in time.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Closest in time.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in Proceedings of the International Conference on Machine Learning (ICML) , vol. 139, 2021, pp. 10 347–10 357
2021
Closest in time.
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable DETR: deformable transformers for end-to-end object detection,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Closest in time.
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. S. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 6881–6890
2021
Closest in time.
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 (ICCV) , 2021, pp. 9992–10 002
2021
Closest in time.
H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang, “Cvt: Introducing convolutions to vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 22–31
2021
Closest in time.
M. Abbas, Q. Xiao, L. Chen, P.-Y. Chen, and T. Chen, “Sharp-maml: Sharpness-aware model-agnostic meta learning,” in Proceedings of the International Conference on Machine Learning (ICML) , 2022
2022
Closest in time.
J. Xie, F. Long, J. Lv, Q. Wang, and P. Li, “Joint distribution matters: Deep brownian distance covariance for few-shot classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 7972–7981
2022
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A. Afrasiyabi, H. Larochelle, J.-F. Lalonde, and C. Gagné, “Matching feature sets for few-shot image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 9014–9024
2022
Closest in time.