Learning from Unlabeled 3D Environments for Vision-and-Language Navigation
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AutoVLN builds graphs with very sparse nodes and edges that go across obstacles, limiting the quantity of sampled data and leading to impractical trajectories.
“Previous work AutoVLN [17] builds graphs with very sparse nodes and with edges that go across obstacles, limiting the quantity of sampled data and leading to impractical trajectories”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [17]
Section 3.1 (p. 5–6) says AutoVLN connects nodes only when they are visible and navigable from each other, contrary to the claim that edges cross obstacles. The paper also describes its dataset as large-scale and says it creates over 200,000 realistic training samples (Introduction, p. 2); it does not report that its graph limits sampling or produces impractical trajectories. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredThe created dataset is 3.81 times larger than the augmented dataset in AutoVLN.
“and ˆ 3.81 larger than the augmented dataset in AutoVLN [17].”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [17]
Section 3.3 (Table 1, p. 8) reports that the created HM3D-AutoVLN dataset has 217,703 instructions and is larger than the augmented REVERIE-Speaker dataset, which has 19,636. The paper does not identify an augmented dataset in AutoVLN for which the comparison is 3.81 times, so it supports the general size comparison but not the stated multiplier and comparator. (LLM, conf=0.84)
Experimental — accuracy not yet measuredScaling up the diversity of environments and the quantity of demonstration for training VLN agents is promising for improving generalization to unseen scenes.
“Recent research shows that scaling up the diversity of environments and the quantity of demonstration for training VLN agents are promising in improving generalization to unseen scenes [17, 37].”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [17]
ScaleVLN achieves state-of-the-art performance on object grounding task on the test leaderboard, comparable to the previous best method AutoVLN.
“ScaleVLN achieves state-of-the-art performance on object grounding task on the test leaderboard, comparable to the previous best method AutoVLN [17].”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [17]
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Cites
Matterport3D: Learning from RGB-D Data in Indoor Environments
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858 · see citing works
Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout
Tan, H., Yu, L., & Bansal, M. (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/n19-1268 · Cited by 293 · see citing works
Speaker-Follower Models for Vision-and-Language Navigation
Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., & Darrell, T. (2018) · arXiv · doi:10.48550/arxiv.1806.02724 · Cited by 291 · see citing works
VLN↻BERT: A Recurrent Vision-and-Language BERT for Navigation
Hong, Y., Wu, Q., Qi, Y., Rodriguez-Opazo, C., & Gould, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00169 · Cited by 264 · see citing works
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Ma, C.-Y., Lu, J., Wu, Z., AlRegib, G., Kira, Z., Socher, R., & Xiong, C. (2019) · arXiv · doi:10.48550/arxiv.1901.03035 · Cited by 200 · see citing works
Airbert: In-domain Pretraining for Vision-and-Language Navigation
Guhur, P.-L., Tapaswi, M., Chen, S., Laptev, I., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00166 · Cited by 139 · see citing works
Self-citationEnvedit: Environment Editing for Vision-and-Language Navigation
Li, J., Tan, H., & Bansal, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01497 · Cited by 87 · see citing works
Vision-Language Navigation with Random Environmental Mixup
Liu, C., Zhu, F., Chang, X., Liang, X., Ge, Z., & Shen, Y.-D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00167 · Cited by 80 · see citing works
Active visual information gathering for vision-language navigation
Wang, H., Wang, W., Shu, T., Liang, W., & Shen, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_19 · Cited by 74 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [1]1. Anderson, P., Shrivastava, A., Parikh, D., Batra, D., Lee, S.: Chasing ghosts: In- struction following as bayesian state tracking. NeurIPS 32 (2019) 3
- [2]2. Anderson, P., Shrivastava, A., Truong, J., Majumdar, A., Parikh, D., Batra, D., Lee, S.: Sim-to-real transfer for vision-and-language navigation. In: CoRL. pp. 671–
- [681]681. PMLR (2021) 4
- [3]3. Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., Van Den Hengel, A.: Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In: CVPR. pp. 3674–3683 (2018) 1, 3, 4, 5, 6, 9, 14
Cited by
Sorted by citations
GridMM: Grid Memory Map for Vision-and-Language Navigation
Wang, Z., Li, X., Yang, J., Liu, Y., & Jiang, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01432 · Cited by 93 · see citing works
Scaling Data Generation in Vision-and-Language Navigation
Wang, Z., Li, J., Hong, Y., Wang, Y., Wu, Q., Bansal, M., Gould, S., Tan, H., & Qiao, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01103 · Cited by 64 · see citing works
Wang, S., Zhou, D., Xie, L., Xu, C., Yan, Y., & Yin, E. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107320 · Cited by 11 · see citing works
- Authors
- Chen, S., Guhur, P.-L., Tapaswi, M., Schmid, C., & Laptev, I.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- International Institute of Information Technology, Hyderabad (IN)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 638-655
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4293329554
- Cited by
- 35
- References
- 9
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 9 · 2023: 12
- FWCI
- 2.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR-19-P3IA-0001)
- Grand Équipement National De Calcul Intensif
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- vision-and-language navigation, HM3D, pseudo-labeling, cross-view consistency, navigation graph, instruction generation, language model fine-tuning, dataset construction, generalization to unseen environments
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