# NavHint: Vision and Language Navigation Agent with a Hint Generator

arXiv: https://arxiv.org/abs/2402.02559 · HTML: https://mlsanity.com/paper/2402.02559/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 40 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 30 of 36)

- [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)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [Speaker-Follower Models for Vision-and-Language Navigation](https://mlsanity.com/paper/1806.02724/) (2018)
- [A Recurrent Vision-and-Language BERT for Navigation](https://mlsanity.com/paper/2011.13922/) (2020)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training](https://mlsanity.com/paper/2002.10638/) (2020)
- [Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2202.11742/) (2022)
- [Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks](https://mlsanity.com/paper/1911.07883/) (2019)
- [Self-Monitoring Navigation Agent via Auxiliary Progress Estimation](https://mlsanity.com/paper/1901.03035/) (2019)
- [ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2304.03047/) (2023)
- [Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation](https://mlsanity.com/paper/1905.12255/) (2019)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Scaling Data Generation in Vision-and-Language Navigation](https://mlsanity.com/paper/2307.15644/) (2023)
- [General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping](https://mlsanity.com/paper/1907.05446/) (2019)
- [Language and Visual Entity Relationship Graph for Agent Navigation](https://mlsanity.com/paper/2010.09304/) (2020)
- [Structured Scene Memory for Vision-Language Navigation](https://mlsanity.com/paper/2103.03454/) (2021)
- [Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2210.07506/) (2022)
- [EnvEdit: Environment Editing for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.15685/) (2022)
- [Object-and-Action Aware Model for Visual Language Navigation](https://mlsanity.com/paper/2007.14626/) (2020)
- [HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.11591/) (2022)
- [Sub-Instruction Aware Vision-and-Language Navigation](https://mlsanity.com/paper/2004.02707/) (2020)
- [Less is More: Generating Grounded Navigation Instructions from Landmarks](https://mlsanity.com/paper/2111.12872/) (2021)
- [Neighbor-view Enhanced Model for Vision and Language Navigation](https://mlsanity.com/paper/2107.07201/) (2021)
- [Improving Vision-and-Language Navigation by Generating Future-View Image Semantics](https://mlsanity.com/paper/2304.04907/) (2023)
- [Learning Navigational Visual Representations with Semantic Map Supervision](https://mlsanity.com/paper/2307.12335/) (2023)

## Built on by (most cited first, 12 of 12)

- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector](https://mlsanity.com/paper/2503.20188/) (2025)
- [SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models](https://mlsanity.com/paper/2410.03878/) (2024)
- [Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel](https://mlsanity.com/paper/2412.08467/) (2024)
- [Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.23556/) (2024)
- [VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation](https://mlsanity.com/paper/2403.12415/) (2024)
- [Narrowing the Gap between Vision and Action in Navigation](https://mlsanity.com/paper/2408.10388/) (2024)
- [On the Holistic Approach for Detecting Human Image Forgery](https://mlsanity.com/paper/2601.04715/) (2026)
- [Vision-and-Language Navigation with Analogical Textual Descriptions in LLMs](https://mlsanity.com/paper/2509.25139/) (2025)
- [VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions](https://mlsanity.com/paper/2604.10533/) (2026)
- [To Ask or Not to Ask? Detecting Absence of Information in Vision and Language Navigation](https://mlsanity.com/paper/2411.05831/) (2024)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)

## Abstract (arXiv)

Existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment. In our work, we provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions. The hint generator assists the navigation agent in developing a global understanding of the visual environment. It directs the agent's attention toward related navigation details, including the relevant sub-instruction, potential challenges in recognition and ambiguities in grounding, and the targeted viewpoint description. To train the hint generator, we construct a synthetic dataset based on landmarks in the instructions and visible and distinctive objects in the visual environment. We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics. The experimental results demonstrate that generating hints not only enhances the navigation performance but also helps improve the interpretability of the agent's actions.