# Vision-Dialog Navigation by Exploring Cross-modal Memory

arXiv: https://arxiv.org/abs/2003.06745 · HTML: https://mlsanity.com/paper/2003.06745/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2203.12667/) (2022)
- [HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.11591/) (2022)
- [The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation](https://mlsanity.com/paper/2104.04167/) (2021)
- [Diagnosing Vision-and-Language Navigation: What Really Matters](https://mlsanity.com/paper/2103.16561/) (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)
- [Grounded Entity-Landmark Adaptive Pre-training for Vision-and-Language Navigation](https://mlsanity.com/paper/2308.12587/) (2023)
- [ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation](https://mlsanity.com/paper/2103.10702/) (2021)
- [Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation](https://mlsanity.com/paper/2111.05759/) (2021)
- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation](https://mlsanity.com/paper/2107.11252/) (2021)
- [VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator](https://mlsanity.com/paper/2105.11589/) (2021)
- [ESceme: Vision-and-Language Navigation with Episodic Scene Memory](https://mlsanity.com/paper/2303.01032/) (2023)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment](https://mlsanity.com/paper/2101.07891/) (2021)
- [ELBA: Learning by Asking for Embodied Visual Navigation and Task Completion](https://mlsanity.com/paper/2302.04865/) (2023)
- [Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI](https://mlsanity.com/paper/2510.04978/) (2025)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control](https://mlsanity.com/paper/2607.03449/) (2026)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2304.02738/) (2023)

## Abstract (arXiv)

Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help with natural language and navigating according to human responses. Besides the common challenges faced in visual language navigation, vision-dialog navigation also requires to handle well with the language intentions of a series of questions about the temporal context from dialogue history and co-reasoning both dialogs and visual scenes. In this paper, we propose the Cross-modal Memory Network (CMN) for remembering and understanding the rich information relevant to historical navigation actions. Our CMN consists of two memory modules, the language memory module (L-mem) and the visual memory module (V-mem). Specifically, L-mem learns latent relationships between the current language interaction and a dialog history by employing a multi-head attention mechanism. V-mem learns to associate the current visual views and the cross-modal memory about the previous navigation actions. The cross-modal memory is generated via a vision-to-language attention and a language-to-vision attention. Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step. Experiments on the CVDN dataset show that our CMN outperforms the previous state-of-the-art model by a significant margin on both seen and unseen environments.