@inproceedings{luo-etal-2022-g3,
title = "G3: Geolocation via Guidebook Grounding",
author = "Luo, Grace and
Biamby, Giscard and
Darrell, Trevor and
Fried, Daniel and
Rohrbach, Anna",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.430",
doi = "10.18653/v1/2022.findings-emnlp.430",
pages = "5841--5853",
abstract = "We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game. Our approach predicts a country for each image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo labels achieves the best performance. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5{\%} in Top-1 accuracy. Our dataset and code can be found at \url{https://github.com/g-luo/geolocation_via_guidebook_grounding}.",
}
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<abstract>We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game. Our approach predicts a country for each image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo labels achieves the best performance. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5% in Top-1 accuracy. Our dataset and code can be found at https://github.com/g-luo/geolocation_via_guidebook_grounding.</abstract>
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%0 Conference Proceedings
%T G3: Geolocation via Guidebook Grounding
%A Luo, Grace
%A Biamby, Giscard
%A Darrell, Trevor
%A Fried, Daniel
%A Rohrbach, Anna
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F luo-etal-2022-g3
%X We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game. Our approach predicts a country for each image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo labels achieves the best performance. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5% in Top-1 accuracy. Our dataset and code can be found at https://github.com/g-luo/geolocation_via_guidebook_grounding.
%R 10.18653/v1/2022.findings-emnlp.430
%U https://aclanthology.org/2022.findings-emnlp.430
%U https://doi.org/10.18653/v1/2022.findings-emnlp.430
%P 5841-5853
Markdown (Informal)
[G3: Geolocation via Guidebook Grounding](https://aclanthology.org/2022.findings-emnlp.430) (Luo et al., Findings 2022)
ACL
- Grace Luo, Giscard Biamby, Trevor Darrell, Daniel Fried, and Anna Rohrbach. 2022. G3: Geolocation via Guidebook Grounding. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5841–5853, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.