A. Krizhevsky and G. Hinton, Learning multiple layers of features from tiny images, tech. rep, 2009.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet : A large-scale hierarchical image database, 2009 IEEE conference on computer vision and pattern recognition, pp.248-255, 2009.

, Alegoria : Advanced linking and exploitation of digitized ge0graphic iconographic heritage

F. Radenovi?, G. Tolias, and O. Chum, Fine-tuning cnn image retrieval with no human annotation, IEEE transactions on pattern analysis and machine intelligence, vol.41, pp.1655-1668, 2018.

H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han, Largescale image retrieval with attentive deep local features, Proceedings of the IEEE International Conference on Computer Vision, pp.3456-3465, 2017.

F. Radenovi?, A. Iscen, G. Tolias, Y. Avrithis, and O. Chum, Revisiting oxford and paris : Large-scale image retrieval benchmarking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5706-5715, 2018.

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, Lost in quantization : Improving particular object retrieval in large scale image databases, 2008 IEEE conference on computer vision and pattern recognition, pp.1-8, 2008.

, OpenStreetMap contributors, 2017.

P. Kaiser, J. D. Wegner, A. Lucchi, M. Jaggi, T. Hofmann et al., Learning aerial image segmentation from online maps, IEEE Transactions on Geoscience and Remote Sensing, vol.55, issue.11, pp.6054-6068, 2017.

K. Regmi and A. Borji, Cross-view image synthesis using conditional gans, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3501-3510, 2018.

K. Chen, K. Fu, X. Gao, M. Yan, W. Zhang et al., Effective fusion of multi-modal data with group convolutions for semantic segmentation of aerial imagery, IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, pp.3911-3914, 2019.

N. Audebert, B. L. Saux, and S. Lefèvre, Joint learning from earth observation and openstreetmap data to get faster better semantic maps, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.67-75, 2017.

J. L. Schönberger, M. Pollefeys, A. Geiger, and T. Sattler, Semantic visual localization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.6896-6906, 2018.

Y. Shi, X. Yu, L. Liu, T. Zhang, and H. Li, Optimal feature transport for cross-view image geo-localization, 2019.

T. Naseer, G. L. Oliveira, T. Brox, and W. Burgard, Semantics-aware visual localization under challenging perceptual conditions, 2017 IEEE International Conference on Robotics and Automation (ICRA), pp.2614-2620, 2017.

L. Liu and H. Li, Lending orientation to neural networks for cross-view geo-localization, 2019.

F. Walch, C. Hazirbas, L. Leal-taixe, T. Sattler, S. Hilsenbeck et al., Image-based localization using lstms for structured feature correlation, Proceedings of the IEEE International Conference on Computer Vision, pp.627-637, 2017.

S. Hu, M. Feng, R. M. Nguyen, and G. Lee, Cvmnet : Cross-view matching network for image-based groundto-aerial geo-localization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7258-7267, 2018.

E. Mohedano, K. Mcguinness, N. E. O'connor, A. Salvador, F. Marques et al., Bags of local convolutional features for scalable instance search, Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp.327-331, 2016.

T. Lin, Y. Cui, S. Belongie, and J. Hays, Learning deep representations for ground-to-aerial geolocalization, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.5007-5015, 2015.

T. Lin and S. Maji, Visualizing and understanding deep texture representations, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2791-2799, 2016.

R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, Netvlad : Cnn architecture for weakly supervised place recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.5297-5307, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01242052

H. Jégou, M. Douze, C. Schmid, and P. Pérez, Aggregating local descriptors into a compact image representation, CVPR 2010-23rd IEEE Conference on Computer Vision & Pattern Recognition, pp.3304-3311, 2010.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

S. Bianco, D. Mazzini, D. P. Pau, and R. Schettini, Local detectors and compact descriptors for visual search : a quantitative comparison, Digital Signal Processing, vol.44, pp.1-13, 2015.

D. G. Lowe, Object recognition from local scaleinvariant features, in iccv, vol.99, pp.1150-1157, 1999.

N. Garcia, B. Renoust, and Y. Nakashima, Understanding art through multi-modal retrieval in paintings, 2019.

A. Grover and J. Leskovec, node2vec : Scalable feature learning for networks, Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.855-864, 2016.

K. Li, C. Zou, S. Bu, Y. Liang, J. Zhang et al., Multi-modal feature fusion for geographic image annotation, Pattern Recognition, vol.73, pp.1-14, 2018.

J. Bromley, I. Guyon, Y. Lecun, E. Säckinger, and R. Shah, Signature verification using a" siamese" time delay neural network, Advances in neural information processing systems, pp.737-744, 1994.

G. Koch, R. Zemel, and R. Salakhutdinov, Siamese neural networks for one-shot image recognition, ICML deep learning workshop, vol.2, 2015.

D. Chung, K. Tahboub, and E. J. Delp, A two stream siamese convolutional neural network for person reidentification, Proceedings of the IEEE International Conference on Computer Vision, 1983.

R. Hadsell, S. Chopra, and Y. Lecun, Dimensionality reduction by learning an invariant mapping, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol.2, pp.1735-1742, 2006.

T. Trzcinski, J. Komorowski, L. Dabala, K. Czarnota, G. Kurzejamski et al., Scone : Siamese constellation embedding descriptor for image matching, Proceedings of the European Conference on Computer Vision (ECCV), pp.0-0, 2018.

L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr, Fully-convolutional siamese networks for object tracking, European conference on computer vision, pp.850-865, 2016.

L. Leal-taixé, C. Canton-ferrer, and K. Schindler, Learning by tracking : Siamese cnn for robust target association, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.33-40, 2016.

R. R. Varior, M. Haloi, and G. Wang, Gated siamese convolutional neural network architecture for human reidentification, European conference on computer vision, pp.791-808, 2016.

P. Mukherjee, B. Lall, and S. Lattupally, Object cosegmentation using deep siamese network, 2018.

S. Maheshwary and H. Misra, Matching resumes to jobs via deep siamese network, Companion Proceedings of the The Web Conference, pp.87-88, 2018.

R. Daudt, B. L. Saux, A. Boulch, and Y. Gousseau, Détection dense de changements par réseaux de neurones siamois, 2018.

B. Harwood, B. Kumar, G. Carneiro, I. Reid, and T. Drummond, Smart mining for deep metric learning, Proceedings of the IEEE International Conference on Computer Vision, pp.2821-2829, 2017.

D. P. Kingma and J. Ba, Adam : A method for stochastic optimization, 2014.

T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, A simple framework for contrastive learning of visual representations, 2020.