# Beyond Textual Knowledge-Leveraging Multimodal Knowledge Bases for Enhancing Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2603.26859 · HTML: https://mlsanity.com/paper/2603.26859/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2202.11742/) (2022)
- [Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation](https://mlsanity.com/paper/1905.12255/) (2019)
- [Airbert: In-domain Pretraining for Vision-and-Language Navigation](https://mlsanity.com/paper/2108.09105/) (2021)
- [BEVBert: Multimodal Map Pre-training for Language-guided Navigation](https://mlsanity.com/paper/2212.04385/) (2022)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation](https://mlsanity.com/paper/1803.07729/) (2018)
- [Object-and-Action Aware Model for Visual Language Navigation](https://mlsanity.com/paper/2007.14626/) (2020)
- [The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation](https://mlsanity.com/paper/2104.04167/) (2021)
- [Target-Driven Structured Transformer Planner for Vision-Language Navigation](https://mlsanity.com/paper/2207.11201/) (2022)
- [SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.14143/) (2021)
- [Vision-and-Language Navigation via Causal Learning](https://mlsanity.com/paper/2404.10241/) (2024)
- [Scene-Intuitive Agent for Remote Embodied Visual Grounding](https://mlsanity.com/paper/2103.12944/) (2021)
- [Learning Vision-and-Language Navigation from YouTube Videos](https://mlsanity.com/paper/2307.11984/) (2023)
- [ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts](https://mlsanity.com/paper/2205.15509/) (2022)
- [Lana: A Language-Capable Navigator for Instruction Following and Generation](https://mlsanity.com/paper/2303.08409/) (2023)
- [RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation](https://mlsanity.com/paper/2412.08591/) (2024)
- [Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.11383/) (2025)
- [g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks](https://mlsanity.com/paper/2411.17030/) (2024)
- [ESceme: Vision-and-Language Navigation with Episodic Scene Memory](https://mlsanity.com/paper/2303.01032/) (2023)
- [Augmented Commonsense Knowledge for Remote Object Grounding](https://mlsanity.com/paper/2406.01256/) (2024)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) requires an agent to navigate through complex unseen environments based on natural language instructions. However, existing methods often struggle to effectively capture key semantic cues and accurately align them with visual observations. To address this limitation, we propose Beyond Textual Knowledge (BTK), a VLN framework that synergistically integrates environment-specific textual knowledge with generative image knowledge bases. BTK employs Qwen3-4B to extract goal-related phrases and utilizes Flux-Schnell to construct two large-scale image knowledge bases: R2R-GP and REVERIE-GP. Additionally, we leverage BLIP-2 to construct a large-scale textual knowledge base derived from panoramic views, providing environment-specific semantic cues. These multimodal knowledge bases are effectively integrated via the Goal-Aware Augmentor and Knowledge Augmentor, significantly enhancing semantic grounding and cross-modal alignment. Extensive experiments on the R2R dataset with 7,189 trajectories and the REVERIE dataset with 21,702 instructions demonstrate that BTK significantly outperforms existing baselines. On the test unseen splits of R2R and REVERIE, SR increased by 5% and 2.07% respectively, and SPL increased by 4% and 3.69% respectively. The source code is available at https://github.com/yds3/IPM-BTK/.