Papers › Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language...

Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View

10 Jan 2020arXiv:2001.03671archive 2025-07-28

Harsh Mehta, Yoav Artzi, Jason Baldridge, Eugene Ie, Piotr Mirowski

The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both of the Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in Chen et al. (2019) and show that the panoramas we have added to StreetLearn fully support both Touchdown tasks and can be used effectively for further research and comparison.

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google-research/valan officialmentioned in papermentioned on GitHubtf report
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clones lil-lab/touchdown/sdr/model.py community (archive-listed) ran · our draft was wrong CC-BY-4.0 · pointer only · ba08314f96790e8e · report
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Tasks

Vision and Language Navigation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Vision and Language Navigation Touchdown Dataset Retouch-RConcat Task Completion (TC) 12.8 #8 of 12 Archive leaderboard report

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