Few-shot learning with representative global prototype
Author:
Funder
Engineering and Physical Sciences Research Council
National Natural Science Foundation of China
Publisher
Elsevier BV
Reference49 articles.
1. Bappy, J. H., Paul, S., Tuncel, E., & Roy-Chowdhury, A. K. (2017). The Impact of Typicality for Informative Representative Selection. In 2017 IEEE conference on computer vision and pattern recognition (pp. 771–780).
2. Chen, Y., Liu, Z., Xu, H., Darrell, T., & Wang, X. (2021). Meta-baseline: Exploring simple meta-learning for few-shot learning. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9062–9071).
3. Chopra, S., Hadsell, R., & LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE computer society conference on computer vision and pattern recognition (pp. 539–546).
4. Conditional variational image deraining;Du;IEEE Transactions on Image Processing,2020
5. Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with cooperation: Ensemble methods for few-shot classification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3723–3731).
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