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TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
AbstractWe study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a Street View environment to a goal position, and then guess a location in its observed environment described in natural language to find a hidden object. The data contains 9326 examples of English instructions and spatial descriptions paired with demonstrations. We perform qualitative linguistic analysis, and show that the data displays a rich use of spatial reasoning. Empirical analysis shows the data presents an open challenge to existing methods.
Related Material
[pdf] [supp][bibtex]@InProceedings{Chen_2019_CVPR,
author = {Chen, Howard and Suhr, Alane and Misra, Dipendra and Snavely, Noah and Artzi, Yoav},
title = {TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
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