[12] Martin Engilberge, Edo Collins, and Sabine Süssstrunk. Color representation in deep neural networks. In 2017 IEEE International Conference on Image Processing (ICIP), pages 2786-2790, 2017.
[13] G.D. Finlayson, M.S. Drew, and B.V. Funt. Diagonal transforms suffice for color constancy. In 1993 (4th) International Conference on Computer Vision, pages 164-171, 1993.
[14] G.D. Finlayson, S.D. Hordley, Cheng Lu, and M.S. Drew. On the removal of shadows from images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1):59-68, 2006.
[15] B.V. Funt and G.D. Finlayson. Color constant color indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(5):522-529, 1995.
[16] Chase J. Gaudet and A. Maida. Deep quaternion networks. 2018 International Joint Conference on Neural Networks (IJCNN), pages 1-8, 2018.
[17] J. M. Geusebroek, R. van den Boomgaard, A. W. M. Smeulders, and H. Geerts. Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(12):1338-1350, 2001.
[18] T. Gevers, A. Gijsenij, J. van de Weijer, and J. M. Geusebroek. Color in Computer Vision : Fundamentals and Applications. Series in Imaging Science and Technology. The Wiley-IS&T, 2012.
[19] T. Gevers, A. Gijsenij, J. van de Weijer, and J. M. Geusebroek. Color in Computer Vision : Fundamentals and Applications. Series in Imaging Science and Technology. The Wiley-IS&T, 2012.
[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778. IEEE, June 2016.
[21] Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan. AugMix: A simple data processing method to improve robustness and uncertainty. Proceedings of the International Conference on Learning Representations (ICLR), 2020.
[22] Geoffrey E Hinton, Sara Sabour, and Nicolas Frosst. Matrix capsules with EM routing. In International Conference on Learning Representations, 2018.
[23] Osman Semih Kayhan and Jan C van Gemert. On translation invariance in cnns: Convolutional layers can exploit absolute spatial location. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14274-14285, 2020.
[24] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. International Conference on Learning Representations, 12 2014.
[25] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
[26] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 2013 IEEE International Conference on Computer Vision Workshops, pages 554–561, 2013.
[27] Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical Report 0, University of Toronto, Toronto, Ontario, 2009.
[28] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, volume 86, pages 2278-2324, 1998.
[29] Attila Lengyel, Sourav Garg, Michael Milford, and Jan C. van Gemert. Zero-shot day-night domain adaptation with a physics prior. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4399-4409, October 2021.
[30] Attila Lengyel and Jan van Gemert. Exploiting learned symmetries in group equivariant convolutions. In 2021 IEEE International Conference on Image Processing (ICIP), pages 759-763, 2021.
[31] Fei-Fei Li, Marco Andreeto, Marc'Aurelio Ranzato, and Pietro Perona. Caltech 101, Apr 2022.
[32] Lachlan E. MacDonald, Sameera Ramasinghe, and Simon Lucey. Enabling equivariance for arbitrary lie groups. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8183-8192, June 2022.
[33] Bruce A. Maxwell, Casey A. Smith, Maan Qraitem, Ross Messing, Spencer Whitt, Nicolas Thien, and Richard M. Friedhoff. Real-time physics-based removal of shadows and shading from road surfaces. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1277-1285, 2019.
[34] Artem Moskalev, Anna Seplierskaia, Ivan Sosnovik, and Arnold Smeulders. Liegg: Studying learned lie group generators. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 25212-25223. Curran Associates, Inc., 2022.
[35] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008.
[36] Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. An overview of early vision in inceptionv1. Distill, 2020. https://distill.pub/2020/circuits/early-vision.
[37] Chris Olah, Nick Cammarata, Chelsea Voss, Ludwig Schubert, and Gabriel Goh. Naturally occurring equivariance in neural networks. Distill, 2020. https://distill.pub/2020/circuits/equivariance.
[38] Daniel Osorio and Misha Vorobyev. Colour vision as an adaptation to frugivory in primates. Proceedings. Biological sciences / The Royal Society, 263:593-9, 06 1996.
[39] Gokhan Ozbulak. Image colorization by capsule networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019.
[40] Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar. Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition, 2012.