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In recent years, few-shot learning problems have received a lot of attention.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , 2012, pp. 1097–1105
2012
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F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2015
2015
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, k. kavukcuoglu, and D. Wierstra, “Matching networks for one shot learning,” in Advances in Neural Information Processing Systems 29 , D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds., 2016, pp. 3630–3638
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Advances in Neural Information Processing Systems 30 , 2017, pp. 4077–4087
2017
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 34th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 6738–6746
2017
Earlier work this paper cites.
F. Sung, Y. Yang, L. Zhang, P. H. T. Tao Xiang, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 1199–1208
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, “Cosface: Large margin cosine loss for deep face recognition,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 5265–5274
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
Z. Cao, T. Zhang, W. Diao, Y. Zhang, X. Lyu, K. Fu, and X. Sun, “Meta-seg: A generalized meta-learning framework for multi-class few-shot semantic segmentation,” IEEE Access , vol. 7, pp. 166 109–166 121, 2019
2019
Later among the works it cites.
N. X. Jiankang Deng, Jia Guo and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4685–4694
2019
Later among the works it cites.
2019
Later among the works it cites.
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J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang, “Variational few-shot learning,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Cited alongside, same era.
Z. Wu, Y. Li, L. Guo, and K. Jia, “Parn: Position-aware relation networks for few-shot learning,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Cited alongside, same era.
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang, “A closer look at few-shot classification,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
Y. Zou, Y. Shi, D. Shi, Y. Wang, Y. Liang, and Y. Tian, “Adaptation-oriented feature projection for one-shot action recognition,” IEEE Transactions on Multimedia , pp. 1–1, 2020
2020
Closest in time.
H. Huang, J. Zhang, J. Zhang, J. Xu, and Q. Wu, “Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification,” IEEE Transactions on Multimedia , pp. 1–1, 2020
2020
Closest in time.
H.-Y. Tseng, H.-Y. Lee, J.-B. Huang, and M.-H. Yang, “Cross-domain few-shot classification via learned feature-wise transformation,” in International Conference on Learning Representations , 2020
2020
Closest in time.