AUTHOR=Paolicelli Valerio , Berton Gabriele , Montagna Francesco , Masone Carlo , Caputo Barbara TITLE=Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach JOURNAL=Frontiers in Computer Science VOLUME=4 YEAR=2022 URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2022.841817 DOI=10.3389/fcomp.2022.841817 ISSN=2624-9898 ABSTRACT=

We tackle the task of cross-domain visual geo-localization, where the goal is to geo-localize a given query image against a database of geo-tagged images, in the case where the query and the database belong to different visual domains. In particular, at training time, we consider having access to only few unlabeled queries from the target domain. To adapt our deep neural network to the database distribution, we rely on a 2-fold domain adaptation technique, based on a hybrid generative-discriminative approach. To further enhance the architecture, and to ensure robustness across domains, we employ a novel attention layer that can easily be plugged into existing architectures. Through a large number of experiments, we show that this adaptive-attentive approach makes the model robust to large domain shifts, such as unseen cities or weather conditions. Finally, we propose a new large-scale dataset for cross-domain visual geo-localization, called SVOX.