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Indices and tables

BRPM16

Vassileios Balntas, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk. Learning local feature descriptors with triplets and shallow convolutional neural networks. In British Machine Vision Conference (BMVC). 2016.

MMRM17

Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor’s margins: local descriptor learning loss. In Proceedings of NeurIPS. 2017.

MRM18

D. Mishkin, F. Radenovic, and J. Matas. Repeatability is Not Enough: Learning Affine Regions via Discriminability. In ECCV. 2018.

MTB+19

Arun Mukundan, Giorgos Tolias, Andrei Bursuc, Hervé Jégou, and Ondřej Chum. Understanding and improving kernel local descriptors. International Journal of Computer Vision, 2019.

Pul20

Milan Pultar. Improving the hardnet descriptor. arXiv ePrint 2007.09699, 2020.

SSP03

P. Simard, David Steinkraus, and John C. Platt. Best practices for convolutional neural networks applied to visual document analysis. Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pages 958–963, 2003.

YHO+19

Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: regularization strategy to train strong classifiers with localizable features. In International Conference on Computer Vision (ICCV). 2019.

ZnYNDLP18

Hongyi Zhang, Moustapha Cisse nad Yann N. Dauphin, and David Lopez-Paz. Mixup: beyond empirical risk minimization. International Conference on Learning Representations, 2018. URL: https://openreview.net/forum?id=r1Ddp1-Rb.

Zha19

Richard Zhang. Making convolutional networks shift-invariant again. In ICML. 2019.

Baumberg00

A. Baumberg. Reliable feature matching across widely separated views. In CVPR. 2000.