# Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas

arXiv: https://arxiv.org/abs/2203.13838 · HTML: https://mlsanity.com/paper/2203.13838/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 34 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2004.02857/) (2020)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments](https://mlsanity.com/paper/1811.12354/) (2018)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [Improving Vision-and-Language Navigation with Image-Text Pairs from the Web](https://mlsanity.com/paper/2004.14973/) (2020)
- [Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation](https://mlsanity.com/paper/1905.12255/) (2019)
- [Learning to Navigate in Cities Without a Map](https://mlsanity.com/paper/1804.00168/) (2018)
- [Gated-Attention Architectures for Task-Oriented Language Grounding](https://mlsanity.com/paper/1706.07230/) (2017)
- [Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation](https://mlsanity.com/paper/1906.00347/) (2019)
- [On the Evaluation of Vision-and-Language Navigation Instructions](https://mlsanity.com/paper/2101.10504/) (2021)
- [Diagnosing the Environment Bias in Vision-and-Language Navigation](https://mlsanity.com/paper/2005.03086/) (2020)
- [Diagnosing Vision-and-Language Navigation: What Really Matters](https://mlsanity.com/paper/2103.16561/) (2021)
- [Shifting the Baseline: Single Modality Performance on Visual Navigation & QA](https://mlsanity.com/paper/1811.00613/) (2018)
- [Learning To Follow Directions in Street View](https://mlsanity.com/paper/1903.00401/) (2019)
- [Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation](https://mlsanity.com/paper/2007.00229/) (2020)
- [Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem](https://mlsanity.com/paper/2012.15329/) (2020)
- [Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation](https://mlsanity.com/paper/2009.13112/) (2020)

## Built on by (most cited first, 24 of 24)

- [Evaluating the World Model Implicit in a Generative Model](https://mlsanity.com/paper/2406.03689/) (2024)
- [VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View](https://mlsanity.com/paper/2307.06082/) (2023)
- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Learning Vision-and-Language Navigation from YouTube Videos](https://mlsanity.com/paper/2307.11984/) (2023)
- [Ground then Navigate: Language-guided Navigation in Dynamic Scenes](https://mlsanity.com/paper/2209.11972/) (2022)
- [Policy Improvement using Language Feedback Models](https://mlsanity.com/paper/2402.07876/) (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)
- [Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty](https://mlsanity.com/paper/2312.01097/) (2023)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following](https://mlsanity.com/paper/2310.12344/) (2023)
- [How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?](https://mlsanity.com/paper/2402.09546/) (2024)
- [UrbanNav: Learning Language-Guided Urban Navigation from Web-Scale Human Trajectories](https://mlsanity.com/paper/2512.09607/) (2025)
- [A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues](https://mlsanity.com/paper/2207.11717/) (2022)
- [Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments with a Hierarchical Spatial-Cognition Long-Short Memory System](https://mlsanity.com/paper/2506.19433/) (2025)
- [Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM](https://mlsanity.com/paper/2404.17922/) (2024)
- [Multi-Task Domain Adaptation for Language Grounding with 3D Objects](https://mlsanity.com/paper/2407.02846/) (2024)
- [IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation](https://mlsanity.com/paper/2403.19336/) (2024)
- [GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions](https://mlsanity.com/paper/2508.21102/) (2025)
- [City Navigation in the Wild: Exploring Emergent Navigation from Web-Scale Knowledge in MLLMs](https://mlsanity.com/paper/2512.15933/) (2025)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.15691/) (2024)
- [PM-Nav: Priori-Map Guided Embodied Navigation in Functional Buildings](https://mlsanity.com/paper/2603.09113/) (2026)
- [Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification](https://mlsanity.com/paper/2604.16993/) (2026)

## Abstract (arXiv)

Vision and language navigation (VLN) is a challenging visually-grounded language understanding task. Given a natural language navigation instruction, a visual agent interacts with a graph-based environment equipped with panorama images and tries to follow the described route. Most prior work has been conducted in indoor scenarios where best results were obtained for navigation on routes that are similar to the training routes, with sharp drops in performance when testing on unseen environments. We focus on VLN in outdoor scenarios and find that in contrast to indoor VLN, most of the gain in outdoor VLN on unseen data is due to features like junction type embedding or heading delta that are specific to the respective environment graph, while image information plays a very minor role in generalizing VLN to unseen outdoor areas. These findings show a bias to specifics of graph representations of urban environments, demanding that VLN tasks grow in scale and diversity of geographical environments.