Indices and tables

References

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.

LYFC21

Shiqi Lin, Tao Yu, Ruoyu Feng, and Zhibo Chen. Patch autoaugment. 2021. arXiv:2103.11099.

LGG+18

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. arXiv ePrint 1708.02002, 2018.

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.

ROF+21

Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, and Marc Pollefeys. Defmo: deblurring and shape recovery of fast moving objects. In CVPR. 2021.

SEG17

Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, and Ali Gholipour. Tversky loss function for image segmentation using 3d fully convolutional deep networks. arXiv ePrint 1706.05721, 2017.

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.