TY - GEN
T1 - Learned contextual feature reweighting for image geo-localization
AU - Kim, Hyo Jin
AU - Dunn, Enrique
AU - Frahm, Jan Michael
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85041900931&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041900931&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.346
DO - 10.1109/CVPR.2017.346
M3 - Conference contribution
AN - SCOPUS:85041900931
T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
SP - 3251
EP - 3260
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -