The DOI matches its Crossref record; 54 of the 55 references it cites were found in a registry or an index.
Airbert: In-domain Pretraining for Vision-and-Language Navigation
Open accessAbstract
Vision-and-language navigation (VLN) aims to enable embodied agents to navigate in realistic environments using natural language instructions. Given the scarcity of domain-specific training data and the high diversity of image and language inputs, the generalization of VLN agents to unseen environments remains challenging. Recent methods explore pretraining to improve generalization, however, the use of generic image-caption datasets or existing small-scale VLN environments is suboptimal and results in limited improvements. In this work, we introduce BnB1, a large-scale and diverse in-domain VLN dataset. We first collect image-caption (IC) pairs from hundreds of thousands of listings from online rental marketplaces. Using IC pairs we next propose automatic strategies to generate millions of VLN path-instruction (PI) pairs. We further propose a shuffling loss that improves the learning of temporal order inside PI pairs. We use BnB to pretrain our Airbert2model that can be adapted to discriminative and generative settings and show that it outperforms state of the art for Room-to-Room (R2R) navigation and Remote Referring Expression (REVERIE) benchmarks. Moreover, our in-domain pretraining significantly increases performance on a challenging few-shot VLN evaluation, where we train the model only on VLN instructions from a few houses.
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“In contrast, GemBench [289], built upon RLBench, focuses on task generalization, incorporating zero-shot task setups and vision-language extensions to assess robotic performance in previously unseen tasks and objects.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [289]
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“While GemBench [289] presents unique advantages in multimodal fusion and generalization, it also remains constrained to simulation and lacks real-world validation.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [289]
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“Unlike VLMbench [290] and Gem-Bench [289], which primarily evaluate vision-language models (VLMs) in robotic control, VIMA [291] further integrates multi-modal task execution and precise instruction following, making it distinct in its benchmarking approach.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [289]
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Citations
Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
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Places: A 10 Million Image Database for Scene Recognition
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Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation
Huang, B., Zheng, Y., Sui, D., Lan, C., Zhao, X., Zhang, X., Meng, J., Xiao, M., Zou, Y., & Yu, D. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3599649 · Cited by 3
VLN-ChEnv: Vision-language Navigation in Changeable Environments
Liu, S., Zhang, H., Qiao, Q., Wu, Q., & Wang, P. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755202 · Cited by 5
A comprehensive review of recent advancements in vision-and-language navigation
Khan, J. T., Aafaq, N., Ali, Q., & Mohsin, M. (2026) · Discover Computing · doi:10.1007/s10791-026-09977-z · Cited by 4
MiniVLN: Efficient Vision-and-Language Navigation by Progressive Knowledge Distillation
Zhu, J., Qiao, Y., Zhang, S., He, X., Wu, Q., & Liu, J. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127306 · Cited by 6
Ameri, K., Hempel, M., Sharif, H., Lopez, J., & Perumalla, K. S. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2020022 · Cited by 2
Tree-Structured Trajectory Encoding for Vision-and-Language Navigation
Zhou, X., & Mu, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25494 · Cited by 1
He, Z., Wang, N., Wang, L., Liu, C., & Chen, Q. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01339-z · Cited by 3
Towards Coarse-grained Visual Language Navigation Task Planning Enhanced by Event Knowledge Graph
Zhao, K., Song, Y., Zhao, H., Liu, H., Li, T., & Li, Z. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679711 · Cited by 1
Fine-Grained Alignment in Vision-and-Language Navigation Through Bayesian Optimization
Song, Y., Gianni, M., Yang, C., Lin, K., Chiu, T.-C., Nguyen, T. A., & Lee, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31663-9_21 · Cited by 2
Ambiguity Resolution in Vision-and-Language Navigation with Large Language Models
Wei, S., Wang, C., & Qi, J. (2024) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba62489.2024.10868019 · Cited by 1
Dual-Branch Dynamic Perception and Interaction Framework for Aerial Vision-and-Language Navigation
Wang, Z. (2025) · International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID) · doi:10.1109/icaid65275.2025.11034446 · Cited by 1
Action-Aware Visual-Textual Alignment for Long-Instruction Vision-and-Language Navigation
Huang, B., Zheng, Y., Lan, C., Sui, D., Zhao, X., Zhang, X., Xiao, M., & Yu, D. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3748656 · Cited by 1
History-Guided Prompt Generation for Vision-and-Language Navigation
Guo, W. Q., Wang, Z., Hu, Y., & Gao, J. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3613147 · Cited by 1
Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation
Wang, R., Yu, T., Wu, J., Liu, Y., McAuley, J., & Yao, L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247068 · Cited by 6
OpenNav: Open-World Navigation with Multimodal Large Language Models
Yuan, M., Wang, L., & Waslander, S. L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247593 · Cited by 4
Yuan, C., Zhou, Y., Guo, C., Han, D., Shi, G., & Wang, W. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3642913 · Cited by 1
FlexVLN: Flexible Adaptation for Diverse Vision-and-Language Navigation Tasks
Zhang, S., Qiao, Y., Wang, Q., Guo, L., Wei, Z., & Liu, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3581809 · Cited by 1
Bridging the Visual Semantic Gap in VLN via Semantically Richer Instructions
Stanke, J. O., Earle, B., & Soto, Á. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_4 · Cited by 1
Auxiliary Fine-grained Alignment Constraints for Vision-and-Language Navigation
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Unmute: Unifying Navigation and Multimodal Dialogue-Like Text GenerationPreprint
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RewardVLN: AnImproved Agent Navigation Based On Visual-Instruction Alignment*
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Learning Cross-modal Knowledge Reasoning and Heuristic-prompt for Visual-language Navigation
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Brain-Inspired Visual Language Navigation Robot Position Deviation Correction
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Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion
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Harnessing Input-Adaptive Inference for Efficient VLN
Kang, D., Perincherry, A., Coalson, Z., Gabriel, A., Lee, S., & Hong, S. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00770 · Cited by 1
Emergent Capabilities of Large Foundation Models for Intelligent Navigation
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Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models
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Vision-and-Language Navigation: A Component-Centric Survey of Interactions, Coupling, and Deployment
Wu, X., Wu, Y., & Peng, X. (2026) · Applied Sciences · doi:10.3390/app16168050
Speaker-Mediated Route Knowledge Transfer for Goal-Oriented Vision-and-Language Navigation
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FedNASP: Federated Vision-Language Navigation with Adaptive Step-wise Personalization
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Metadata
- Authors
- Guhur, P.-L., Tapaswi, M., Chen, S., Laptev, I., & Schmid, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- International Institute of Information Technology, Hyderabad (IN)
- PSL Research University,Inria, École normale supérieure, CNRS,Paris,France
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 1614-1623
- OpenAlex
- W3195026654
- Cited by
- 139
- References
- 55
- Citations by year
- 2026: 11 · 2025: 39 · 2024: 38 · 2023: 29 · 2022: 22
- FWCI
- 7.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-19-P3IA-0001)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- vision-and-language navigation, few-shot generalization, embodied agents
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