Abstract
We address the problem of large scale image geolocalization where the location of an image is estimated by
identifying geo-tagged reference images depicting the same
place. We propose a novel model for learning image representations that integrates context-aware feature reweighting in order to effectively focus on regions that positively
contribute to geo-localization. In particular, we introduce
a Contextual Reweighting Network (CRN) that predicts the
importance of each region in the feature map based on the
image context. Our model is learned end-to-end for the image geo-localization task, and requires no annotation other
than image geo-tags for training. In experimental results,
the proposed approach significantly outperforms the previous state-of-the-art on the standard geo-localization benchmark datasets.We also demonstrate that our CRN discovers
task-relevant contexts without any additional supervision