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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.

BLRPM19

Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk. Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters. In ICCV. 2019.

DBK+21

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.

FYP+21

Yuantao Feng, Shiqi Yu, Hanyang Peng, Yan-ran Li, and Jianguo Zhang. Detect faces efficiently: a survey and evaluations. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2021.

KS19

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.

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.

MarquezNLopezABB16

Pablo Márquez-Neila, Javier López-Alberca, José M. Buenaposada, and Luis Baumela. Speeding-up homography estimation in mobile devices. J. Real-Time Image Process., 11(1):141–154, January 2016. URL: https://doi.org/10.1007/s11554-012-0314-1, doi:10.1007/s11554-012-0314-1.

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.

NCR+22

Anguelos Nicolaou, Vincent Christlein, Edgar Riba, Jian Shi, Georg Vogeler, and Mathias Seuret. Tormentor: deterministic dynamic-path, data augmentations with fractals. 2022. URL: https://arxiv.org/abs/2204.03776, doi:10.48550/ARXIV.2204.03776.

PDP20

Duc Duy Pham, Gurbandurdy Dovletov, and Josef Pauli. A differentiable convolutional distance transform layer for improved image segmentation. Pattern Recognition, 12544:432 – 444, 2020.

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.

SK20

Saurabh Singh and Shankar Krishnan. Filter response normalization layer: eliminating batch dependence in the training of deep neural networks. In CVPR. 2020.

SSW+21

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

TBLN+20

Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: learning local descriptor with hybrid similarity measure and triplet loss. In NeurIPS. 2020.

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.

ZBTvdW22

Simone Zini, Marco Buzzelli, Bartłomiej Twardowski, and Joost van de Weijer. Planckian jitter: enhancing the color quality of self-supervised visual representations. arXiv preprint arXiv:2202.07993, 2022.

Baumberg00

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