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
this https URL.

该篇论文研究了如何通过使用人类编写的指南书中的知识来改善地理定位任务，并提出了一种通过使用 StreetView 图像数据集和 GeoGuessr 的文本指南进行地理定位的方法，该方法可以通过从指南书中自动提取的线索来预测每张图像的国家，并使用伪标签来监督地学习，明显优于现有的只使用图像的地理定位方法。