# Multi-modal Discriminative Model for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/1905.13358 · HTML: https://mlsanity.com/paper/1905.13358/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 17 of 17)

- [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2203.12667/) (2022)
- [Cross-modal Map Learning for Vision and Language Navigation](https://mlsanity.com/paper/2203.05137/) (2022)
- [SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2108.11945/) (2021)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information](https://mlsanity.com/paper/2104.09580/) (2021)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2106.13948/) (2021)
- [Curriculum Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2111.07228/) (2021)
- [CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations](https://mlsanity.com/paper/2207.02185/) (2022)
- [Hallucination Detection for Grounded Instruction Generation](https://mlsanity.com/paper/2310.15319/) (2023)
- [NavQ: Learning a Q-Model for Foresighted Vision-and-Language Navigation](https://mlsanity.com/paper/2510.16457/) (2025)
- [Trajectory-Diversity-Driven Robust Vision-and-Language Navigation](https://mlsanity.com/paper/2603.15370/) (2026)
- [Find a Way Forward: a Language-Guided Semantic Map Navigator](https://mlsanity.com/paper/2203.03183/) (2022)
- [Which way is `right'?: Uncovering limitations of Vision-and-Language Navigation model](https://mlsanity.com/paper/2312.00151/) (2023)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following](https://mlsanity.com/paper/2301.00676/) (2022)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2304.02738/) (2023)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) is a natural language grounding task where agents have to interpret natural language instructions in the context of visual scenes in a dynamic environment to achieve prescribed navigation goals. Successful agents must have the ability to parse natural language of varying linguistic styles, ground them in potentially unfamiliar scenes, plan and react with ambiguous environmental feedback. Generalization ability is limited by the amount of human annotated data. In particular, paired vision-language sequence data is expensive to collect. We develop a discriminator that evaluates how well an instruction explains a given path in VLN task using multi-modal alignment. Our study reveals that only a small fraction of the high-quality augmented data from citetFried:2018:Speaker, as scored by our discriminator, is useful for training VLN agents with similar performance on previously unseen environments. We also show that a VLN agent warm-started with pre-trained components from the discriminator outperforms the benchmark success rates of 35.5 by 10% relative measure on previously unseen environments.