Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach - Paolicelli, Valerio; Berton, Gabriele; Montagna, Francesco; Masone, Carlo; Caputo, Barbara (Frontiers in Computer Science, 2022)
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.