  Visual Geo-localization (VG) has emerged as a significant research area,
aiming to identify geolocation based on visual features. Most VG approaches use
learnable feature extractors for representation learning. Recently,
Self-Supervised Learning (SSL) methods have also demonstrated comparable
performance to supervised methods by using numerous unlabeled images for
representation learning. In this work, we present a novel unified VG-SSL
framework with the goal to enhance performance and training efficiency on a
large VG dataset by SSL methods. Our work incorporates multiple SSL methods
tailored for VG: SimCLR, MoCov2, BYOL, SimSiam, Barlow Twins, and VICReg. We
systematically analyze the performance of different training strategies and
study the optimal parameter settings for the adaptation of SSL methods for the
VG task. The results demonstrate that our method, without the significant
computation and memory usage associated with Hard Negative Mining (HNM), can
match or even surpass the VG performance of the baseline that employs HNM. The
code is available at https://github.com/arplaboratory/VG_SSL.
