Papers › Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
Wanrong Zhu, Xin Eric Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
One of the most challenging topics in Natural Language Processing (NLP) is visually-grounded language understanding and reasoning. Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates a real-life urban environment. Due to the lack of human-annotated instructions that illustrate intricate urban scenes, outdoor VLN remains a challenging task to solve. This paper introduces a Multimodal Text Style Transfer (MTST) learning approach and leverages external multimodal resources to mitigate data scarcity in outdoor navigation tasks. We first enrich the navigation data by transferring the style of the instructions generated by Google Maps API, then pre-train the navigator with the augmented external outdoor navigation dataset. Experimental results show that our MTST learning approach is model-agnostic, and our MTST approach significantly outperforms the baseline models on the outdoor VLN task, improving task completion rate by 8.7% relatively on the test set.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Vision and Language Navigation | Touchdown Dataset | VLN Transformer +M-50 +style | Task Completion (TC) | 16.2 | #5 of 12 | Archive leaderboard | report |
| Vision and Language Navigation | Touchdown Dataset | VLN Transformer | Task Completion (TC) | 14.9 | #6 of 12 | Archive leaderboard | report |
| Vision and Language Navigation | Touchdown Dataset | Gated Attention (GA) | Task Completion (TC) | 11.9 | #9 of 12 | Archive leaderboard | report |
| Vision and Language Navigation | Touchdown Dataset | RConcat | Task Completion (TC) | 11.8 | #10 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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