[1] Flickr: https://www.flickr.com.
[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020.
[3] Gordon A. Christie, Neil Fendley, James Wilson, and Ryan Mukherjee. Functional map of the world. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6172-6180, 2017.
[4] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university ofSingapore. In ACM International Conference on Image and Video Retrieval, 2009.
[5] Brandon Clark, Alec Kerrigan, Parth Parag Kulkarni, Vicente Vivanco Cepeda, and Mubarak Shah. Where we are and what we're looking at: Query based worldwide image geo-localization using hierarchies and scenes. arXiv preprint arXiv:2303.04249, 2023.
[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[7] James Hays and Alexei A. Efros. Im2gps: estimating geographic information from a single image. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, 2008.
[8] Mike Izbicki, Evangelos E Papalexakis, and Vassilis J Tsotras. Exploiting the earth's spherical geometry to geolocate images. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II, pages 3–19. Springer, 2020.
[9] Martha Larson, Mohammad Soleymani, Guillaume Gravier, Bogdan Ionescu, and Gareth J.F. Jones. The benchmarking initiative for multimedia evaluation: Mediaeval 2016. IEEE MultiMedia, 24(1):93–96, 2017.
[10] Shuai Liao, Xirong Li, Heng Tao Shen, Yang Yang, and Xiaoyong Du. Tag features for geo-aware image classification. IEEE Transactions on Multimedia, 17(7):1058-1067, 2015.
[11] Liu Liu and Hongdong Li. Lending orientation to neural networks for cross-view geolocation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5624-5633, 2019.
[12] Eric Muller-Budack, Kader Pustu-Iren, and Ralph Ewerth. Geolocation estimation of photos using a hierarchical model and scene classification. In Proceedings of the European conference on computer vision (ECCV), pages 563-579, 2018.
[13] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019.
[14] Shraman Pramanick, Ewa M Nowara, Joshua Gleason, Carlos D Castillo, and Rama Chellappa. Where in the world is this image? transformer-based geo-localization in the wild. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVIII, pages 196-215. Springer, 2022.
[15] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[16] Krishna Regmi and Mubarak Shah. Bridging the domain gap for ground-to-aerial image matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 470-479, 2019.