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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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
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
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00636 · Cited by 5,416
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Self-citationVQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Places: A 10 Million Image Database for Scene Recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042
Pointer NetworksPreprint
Vinyals, O., Fortunato, M., & Jaitly, N. (2015) · Neural Information Processing Systems · doi:10.48550/arxiv.1506.03134 · Cited by 3,824
Cited by
Sorted by citations
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
Visual Language Maps for Robot Navigation
Huang, C., Mees, O., Zeng, A., & Burgard, W. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160969 · Cited by 376
Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation
Chen, S., Guhur, P.-L., Tapaswi, M., Schmid, C., & Laptev, I. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01604 · Cited by 228
Self-citationVision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions
Gu, J., Stefani, E., Wu, Q., Thomason, J., & Wang, X. E. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.524 · Cited by 169
ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments
An, D., Wang, H., Wang, W., Wang, Z., Huang, Y., He, K., & Wang, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3386695 · Cited by 103
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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