# Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2007.00229 · HTML: https://mlsanity.com/paper/2007.00229/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [Pathdreamer: A World Model for Indoor Navigation](https://mlsanity.com/paper/2105.08756/) (2021)
- [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2203.12667/) (2022)
- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [EnvEdit: Environment Editing for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.15685/) (2022)
- [VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View](https://mlsanity.com/paper/2307.06082/) (2023)
- [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://mlsanity.com/paper/2306.07799/) (2023)
- [Diagnosing Vision-and-Language Navigation: What Really Matters](https://mlsanity.com/paper/2103.16561/) (2021)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas](https://mlsanity.com/paper/2203.13838/) (2022)
- [Ground then Navigate: Language-guided Navigation in Dynamic Scenes](https://mlsanity.com/paper/2209.11972/) (2022)
- [NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2411.08579/) (2024)
- [VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation](https://mlsanity.com/paper/2402.03561/) (2024)
- [Loc4Plan: Locating Before Planning for Outdoor Vision and Language Navigation](https://mlsanity.com/paper/2408.05090/) (2024)
- [Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations](https://mlsanity.com/paper/2305.13668/) (2023)
- [InstruGen: Automatic Instruction Generation for Vision-and-Language Navigation Via Large Multimodal Models](https://mlsanity.com/paper/2411.11394/) (2024)
- [A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues](https://mlsanity.com/paper/2207.11717/) (2022)
- [ULN: Towards Underspecified Vision-and-Language Navigation](https://mlsanity.com/paper/2210.10020/) (2022)
- [DialNav: Multi-turn Dialog Navigation with a Remote Guide](https://mlsanity.com/paper/2509.12894/) (2025)
- [What Is Near?: Room Locality Learning for Enhanced Robot Vision-Language-Navigation in Indoor Living Environments](https://mlsanity.com/paper/2309.05036/) (2023)
- [AIGeN: An Adversarial Approach for Instruction Generation in VLN](https://mlsanity.com/paper/2404.10054/) (2024)
- [Into the Unknown: Generating Geospatial Descriptions for New Environments](https://mlsanity.com/paper/2406.19967/) (2024)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)

## Abstract (arXiv)

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.