VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization
This provides a benchmarking framework for self-supervised learning in visual geo-localization, which could enhance geo-specific representations for robotics and autonomous vehicles, though it is incremental as it adapts existing SSL methods.
The authors tackled the problem of visual geo-localization by benchmarking self-supervised learning methods, showing that contrastive learning and information maximization techniques achieve performance matching or surpassing state-of-the-art supervised approaches.
Visual Geo-localization (VG) is a critical research area for identifying geo-locations from visual inputs, particularly in autonomous navigation for robotics and vehicles. Current VG methods often learn feature extractors from geo-labeled images to create dense, geographically relevant representations. Recent advances in Self-Supervised Learning (SSL) have demonstrated its capability to achieve performance on par with supervised techniques with unlabeled images. This study presents a novel VG-SSL framework, designed for versatile integration and benchmarking of diverse SSL methods for representation learning in VG, featuring a unique geo-related pair strategy, GeoPair. Through extensive performance analysis, we adapt SSL techniques to improve VG on datasets from hand-held and car-mounted cameras used in robotics and autonomous vehicles. Our results show that contrastive learning and information maximization methods yield superior geo-specific representation quality, matching or surpassing the performance of state-of-the-art VG techniques. To our knowledge, This is the first benchmarking study of SSL in VG, highlighting its potential in enhancing geo-specific visual representations for robotics and autonomous vehicles. The code is publicly available at https://github.com/arplaboratory/VG-SSL.