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.


Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: transformers for image recognition at scale. ICLR, 2021.


Davood Karimi and Septimiu E Salcudean. Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Transactions on medical imaging, 39(2):499–513, 2019.


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


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


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.


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


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


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


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


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.


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.


Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: detector-free local feature matching with transformers. In CVPR. 2021.


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.


Hongyi Zhang, Moustapha Cisse nad Yann N. Dauphin, and David Lopez-Paz. Mixup: beyond empirical risk minimization. International Conference on Learning Representations, 2018. URL:


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


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