Abstract
In this demonstration, we present Country Guesser, a live system that guesses the country that a photo is taken in. In particular, given a Google Street View image, our federated ranking model uses a combination of computer vision, machine learning and text retrieval methods to compute a ranking of likely countries of the location shown in a given image from Street View. Interestingly, using text-based features to probe large pre-trained language models can assist to provide cross-modal supervision. We are not aware of previous country guessing systems informed by visual and textual features.
The bulk of this work was undertaken by the first author during his Master’s thesis. We gratefully acknowledge the funding by the High Tech Agenda Bavaria (https://www.bayern.de/wp-content/uploads/2019/10/Regierungserklaerung_101019_engl.pdf) to the third author that supported part of this research.
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Notes
- 1.
Between acceptance of this paper and our preparation of the publication version, we found another work, [1] which attempts country guessing of photos that are not necessarily from Street View, which is very much in the spirit of this paper.
- 2.
https://gitlab.com/Timperator/which-country-is-this (cited 2022-01-10).
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The authors wish to thank the three anonymous reviewers, whose comments improved the quality of this paper.
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Menzner, T., Mittag, F., Leidner, J.L. (2023). Which Country Is This? Automatic Country Ranking of Street View Photos. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_26
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