Rethinking Visual Geo-localization for Large-Scale Applications

• URL: http://arxiv.org/abs/2204.02287v2
• Date: Thu, 7 Apr 2022 12:57:38 GMT
• Title: Rethinking Visual Geo-localization for Large-Scale Applications
• Authors: Gabriele Berton, Carlo Masone, Barbara Caputo
• Abstract summary: We build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases. We design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem. We achieve state-of-the-art performance on a wide range of datasets and find that CosPlace is robust to heavy domain changes.
• Score: 18.09618985653891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perform on a real-world city-wide VG application, we build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases, with a size 30x bigger than the previous largest dataset for visual geo-localization. We find that current methods fail to scale to such large datasets, therefore we design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem avoiding the expensive mining needed by the commonly used contrastive learning. We achieve state-of-the-art performance on a wide range of datasets and find that CosPlace is robust to heavy domain changes. Moreover, we show that, compared to the previous state-of-the-art, CosPlace requires roughly 80% less GPU memory at train time, and it achieves better results with 8x smaller descriptors, paving the way for city-wide real-world visual geo-localization. Dataset, code and trained models are available for research purposes at https://github.com/gmberton/CosPlace.

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