Descriptor Distillation: A Teacher-Student-Regularized Framework for Learning Local Descriptors-Reference-Cited by-同舟云学术

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Descriptor Distillation: A Teacher-Student-Regularized Framework for Learning Local Descriptors

Published:2024-04-24 Issue:9 Volume:132 Page:3787-3805
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Liu Yuzhen,Dong Qiulei^ORCID

Funder

National Natural Science Foundation of China

Strategic Priority Research Program of the Chinese Academy of Sciences

Beijing Municipal Science and Technology Commission, Adminitrative Commission of Zhongguancun Science Park

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11263-024-02039-1.pdf

Reference50 articles.

1. Balntas, V., Lenc, K., Vedaldi, A., Tuytelaars, T., Matas, J., & Mykolajczyk, K. (2020). Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(11).

2. Balntas, V., Riba, E., Ponsa, D., & Mikolajczyk, K. (2016). Learning local feature descriptors with triplets and shallow convolutional neural networks. In British machine vision conference, vol. 1, p. 3.

3. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3), 346–359.

4. Bökman, G., & Kahl, F. (2022). A case for using rotation invariant features in state of the art feature matchers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5110–5119.

5. Brown, M., Hua, G., & Winder, S. (2010). Discriminative learning of local image descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1), 43–57.

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