# DOPE: Dual Object Perception-Enhancement Network for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2505.00743 · HTML: https://mlsanity.com/paper/2505.00743/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [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)
- [Speaker-Follower Models for Vision-and-Language Navigation](https://mlsanity.com/paper/1806.02724/) (2018)
- [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)
- [Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks](https://mlsanity.com/paper/1911.07883/) (2019)
- [ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2304.03047/) (2023)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Bird's-Eye-View Scene Graph for Vision-Language Navigation](https://mlsanity.com/paper/2308.04758/) (2023)
- [Learning Object Relation Graph and Tentative Policy for Visual Navigation](https://mlsanity.com/paper/2007.11018/) (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)
- [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)
- [Lana: A Language-Capable Navigator for Instruction Following and Generation](https://mlsanity.com/paper/2303.08409/) (2023)
- [Reinforced Structured State-Evolution for Vision-Language Navigation](https://mlsanity.com/paper/2204.09280/) (2022)
- [Correctable Landmark Discovery via Large Models for Vision-Language Navigation](https://mlsanity.com/paper/2405.18721/) (2024)

## Built on by (most cited first, 14 of 14)

- [Advancing Audio-Visual Navigation Through Multi-Agent Collaboration in 3D Environments](https://mlsanity.com/paper/2509.22698/) (2025)
- [Residual Cross-Modal Fusion Networks for Audio-Visual Navigation](https://mlsanity.com/paper/2601.08868/) (2026)
- [Audio Spatially-Guided Fusion for Audio-Visual Navigation](https://mlsanity.com/paper/2604.02389/) (2026)
- [Spatial-Aware Conditioned Fusion for Audio-Visual Navigation](https://mlsanity.com/paper/2604.02390/) (2026)
- [Reliability-Aware Geometric Fusion for Robust Audio-Visual Navigation](https://mlsanity.com/paper/2604.02391/) (2026)
- [Generalizable Audio-Visual Navigation via Binaural Difference Attention and Action Transition Prediction](https://mlsanity.com/paper/2604.05007/) (2026)
- [EAD-Net: Emotion-Aware Talking Head Generation with Spatial Refinement and Temporal Coherence](https://mlsanity.com/paper/2604.23325/) (2026)
- [MRUF: Multi-granularity Routing with Uncertainty-Aware Fusion for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2607.10599/) (2026)
- [HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models](https://mlsanity.com/paper/2607.13072/) (2026)
- [CTAN: Cycle-Temporal Attention Network for Embodied Audio-Visual Navigation](https://mlsanity.com/paper/2609.17420/) (2026)
- [Transformer-Based Token Fusion and Dynamic Graph Planning for Audio-Visual Navigation](https://mlsanity.com/paper/2609.17421/) (2026)
- [Talking Head Synthesis with Facial Landmark Guidance via 3D Gaussian Splatting](https://mlsanity.com/paper/2609.17422/) (2026)
- [WS-NeRF: A Mamba-Driven World-State-Aware Adaptive Deblurring Neural Radiance Field](https://mlsanity.com/paper/2609.21391/) (2026)
- [Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation](https://mlsanity.com/paper/2610.14077/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) is a challenging task where an agent must understand language instructions and navigate unfamiliar environments using visual cues. The agent must accurately locate the target based on visual information from the environment and complete tasks through interaction with the surroundings. Despite significant advancements in this field, two major limitations persist: (1) Many existing methods input complete language instructions directly into multi-layer Transformer networks without fully exploiting the detailed information within the instructions, thereby limiting the agent's language understanding capabilities during task execution; (2) Current approaches often overlook the modeling of object relationships across different modalities, failing to effectively utilize latent clues between objects, which affects the accuracy and robustness of navigation decisions. We propose a Dual Object Perception-Enhancement Network (DOPE) to address these issues to improve navigation performance. First, we design a Text Semantic Extraction (TSE) to extract relatively essential phrases from the text and input them into the Text Object Perception-Augmentation (TOPA) to fully leverage details such as objects and actions within the instructions. Second, we introduce an Image Object Perception-Augmentation (IOPA), which performs additional modeling of object information across different modalities, enabling the model to more effectively utilize latent clues between objects in images and text, enhancing decision-making accuracy. Extensive experiments on the R2R and REVERIE datasets validate the efficacy of the proposed approach.