# Improving Vision-and-Language Navigation by Generating Future-View Image Semantics

arXiv: https://arxiv.org/abs/2304.04907 · HTML: https://mlsanity.com/paper/2304.04907/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 48 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 30 of 45)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [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)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [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)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (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)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [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)
- [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)
- [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)
- [Airbert: In-domain Pretraining for Vision-and-Language Navigation](https://mlsanity.com/paper/2108.09105/) (2021)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Pathdreamer: A World Model for Indoor Navigation](https://mlsanity.com/paper/2105.08756/) (2021)
- [Topological Planning with Transformers for Vision-and-Language Navigation](https://mlsanity.com/paper/2012.05292/) (2020)
- [General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping](https://mlsanity.com/paper/1907.05446/) (2019)
- [Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction](https://mlsanity.com/paper/1809.00786/) (2018)
- [Language and Visual Entity Relationship Graph for Agent Navigation](https://mlsanity.com/paper/2010.09304/) (2020)

## Built on by (most cited first, 30 of 46)

- [Towards Learning a Generalist Model for Embodied Navigation](https://mlsanity.com/paper/2312.02010/) (2023)
- [Scaling Data Generation in Vision-and-Language Navigation](https://mlsanity.com/paper/2307.15644/) (2023)
- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation](https://mlsanity.com/paper/2305.19195/) (2023)
- [Lookahead Exploration with Neural Radiance Representation for Continuous Vision-Language Navigation](https://mlsanity.com/paper/2404.01943/) (2024)
- [Vision-and-Language Navigation via Causal Learning](https://mlsanity.com/paper/2404.10241/) (2024)
- [NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2506.23468/) (2025)
- [CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory](https://mlsanity.com/paper/2505.05622/) (2025)
- [Vision-Language Models for Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/2502.07855/) (2025)
- [Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation](https://mlsanity.com/paper/2311.13209/) (2023)
- [Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities](https://mlsanity.com/paper/2507.13019/) (2025)
- [RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation](https://mlsanity.com/paper/2412.08591/) (2024)
- [GenEx: Generating an Explorable World](https://mlsanity.com/paper/2412.09624/) (2024)
- [Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2502.11168/) (2025)
- [OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation](https://mlsanity.com/paper/2403.17334/) (2024)
- [Correctable Landmark Discovery via Large Models for Vision-Language Navigation](https://mlsanity.com/paper/2405.18721/) (2024)
- [Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.20897/) (2025)
- [SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts](https://mlsanity.com/paper/2412.05552/) (2024)
- [Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation](https://mlsanity.com/paper/2402.17587/) (2024)
- [COSMO: Combination of Selective Memorization for Low-cost Vision-and-Language Navigation](https://mlsanity.com/paper/2503.24065/) (2025)
- [VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation](https://mlsanity.com/paper/2403.12415/) (2024)
- [Do Visual Imaginations Improve Vision-and-Language Navigation Agents?](https://mlsanity.com/paper/2503.16394/) (2025)
- [NavHint: Vision and Language Navigation Agent with a Hint Generator](https://mlsanity.com/paper/2402.02559/) (2024)
- [Generative World Explorer](https://mlsanity.com/paper/2411.11844/) (2024)
- [REGNav: Room Expert Guided Image-Goal Navigation](https://mlsanity.com/paper/2502.10785/) (2025)
- [Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments](https://mlsanity.com/paper/2407.21452/) (2024)
- [NavQ: Learning a Q-Model for Foresighted Vision-and-Language Navigation](https://mlsanity.com/paper/2510.16457/) (2025)
- [CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation](https://mlsanity.com/paper/2505.16663/) (2025)
- [textscNaVIDA: Vision-Language Navigation with Inverse Dynamics Augmentation](https://mlsanity.com/paper/2601.18188/) (2026)
- [Masked Path Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2305.14268/) (2023)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent can benefit from generating the potential future view during navigation. Intuitively, humans will have an expectation of how the future environment will look like, based on the natural language instructions and surrounding views, which will aid correct navigation. Hence, to equip the agent with this ability to generate the semantics of future navigation views, we first propose three proxy tasks during the agent's in-domain pre-training: Masked Panorama Modeling (MPM), Masked Trajectory Modeling (MTM), and Action Prediction with Image Generation (APIG). These three objectives teach the model to predict missing views in a panorama (MPM), predict missing steps in the full trajectory (MTM), and generate the next view based on the full instruction and navigation history (APIG), respectively. We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step. Empirically, our VLN-SIG achieves the new state-of-the-art on both the Room-to-Room dataset and the CVDN dataset. We further show that our agent learns to fill in missing patches in future views qualitatively, which brings more interpretability over agents' predicted actions. Lastly, we demonstrate that learning to predict future view semantics also enables the agent to have better performance on longer paths.