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[bibtex]@InProceedings{Xiao_2025_WACV, author = {Xiao, Jiuhong and Zhu, Gao and Loianno, Giuseppe}, title = {VG-SSL: Benchmarking Self-Supervised Representation Learning Approaches for Visual Geo-Localization}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {6667-6677} }
VG-SSL: Benchmarking Self-Supervised Representation Learning Approaches for Visual Geo-Localization
Abstract
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.
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