In the vision-and-language navigation (VLN) task, an agent follows natural language instructions and navigate in visual environments. Compared to the indoor navigation task that has been broadly studied, navigation in real-life outdoor environments remains a significant challenge with its complicated visual inputs and an insufficient amount of instructions that illustrate the intricate urban scenes . In this paper , we introduce a Multimodal Text Style Transfer (MTST) learning approach to mitigate the problem of data scarcity in outdoor navigation tasks by effectively leveraging external multimodal resources . 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 22%DIFDELCMD < \\%%% %DIF < relatively on the test set and achieving new state-of-the-art performance.abstract 
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 %DIFDELCMD < \\%%% %DIF < relatively on the test set and achieving new state-of-the-art performance.abstract 8.7\% relatively on the test set.
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