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=Volume 4 - 2022

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 a few unlabeled queries from the target domain.
To adapt our deep neural network to the database distribution, we rely on a twofold 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 which 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.