The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.
VLN↻BERT: A Recurrent Vision-and-Language BERT for Navigation
Abstract
Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision-and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Markov decision process present in VLN, requiring history-dependent attention and decision making. In this paper we propose a recurrent BERT model that is time-aware for use in VLN. Specifically, we equip the BERT model with a recurrent function that maintains cross-modal state information for the agent. Through extensive experiments on R2R and REVERIE we demonstrate that our model can replace more complex encoder-decoder models to achieve state-of-the-art results. Moreover, our approach can be generalised to other transformer-based architectures, supports pre-training, and is capable of solving navigation and referring expression tasks simultaneously.
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In the citing paper
“While VLMbench [290] is well-suited for evaluating visionlanguage models (VLMs) such as CLIP and GPT-4V, its task set is relatively small (only 8 tasks), potentially limiting its comprehensiveness in benchmarking generalizable policies.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [290]
In the citing paper
“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 [290]
In the citing paper
“FSN passes on the messages through a small number of bottleneck latents, thus requiring the model to purify the most necessary information from each modality for crossmodal sharing.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [175]
In the citing paper
“This strategy uses the fusion bottleneck as a bridge, and not only improves fusion performance, but also reduces computational cost.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [175]
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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
SpatialGPT: Zero-Shot Vision-and-Language Navigation via Spatial CoT over Structured Spatial Memory
Jiang, Z., & Wang, X. (2025) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3748636.3762753 · Cited by 1
Indoor Scene Recognition in Vision-and-Language Navigation
Zhang, H., Qi, Y., Zhao, M., & Liu, Y. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3645249 · Cited by 2
Self-citationFlexVLN: 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
Fine-Grained Alignment Supervision Matters in Vision-and-Language Navigation
He, K., Huang, Y., Jing, Y., Wu, Q., & Wang, L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3658949 · Cited by 1
Self-citationFGPT: Fine-Grained Prompt Tuning for Zero-Shot Anomaly Detection
Yuexin, W., Wang, X., Li, H., Liu, J., Tillo, T., & Xiao, J. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3661382 · Cited by 1
MSNav: Zero-Shot Vision-And-Language Navigation with Dynamic Memory and LLM Spatial Reasoning
Liu, C., Zhou, Z., Zhang, J., Zhang, M., Huang, S., & Duan, H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463005 · Cited by 3
Li, Y., Qin, K., Chen, W., & Wu, H. (2026) · Electronics · doi:10.3390/electronics15102179 · Cited by 1
Ping, Y., Ding, H., Liang, T., Zhou, L., Lei, G., Chen, X., Wu, J., Zhou, J., & Zhang, T. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3717937 · Cited by 2
A Cross-Modal Object-Aware Transformer for Vision-and-Language Navigation
Ni, H., Chen, J., Zhu, D., & Shi, D. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00149 · Cited by 1
Auxiliary Fine-grained Alignment Constraints for Vision-and-Language Navigation
Cui, Y., Huang, R., Zhang, Y., Cen, Y., Xie, L., Yan, Y., & Yin, E. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00446 · Cited by 1
“The wallpaper is ugly”: Indoor Localization using Vision and Language
Pate, S., & Wong, L. L. S. (2023) · IEEE RO-MAN · doi:10.1109/ro-man57019.2023.10309343 · Cited by 2
Hallucination Detection for Grounded Instruction Generation
Zhao, L., Nguyen, K., & Daumé, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.266 · Cited by 2
Yuan, Z., Tang, P., Sang, X., Zhang, F., & Zhang, Z. (2024) · The Visual Computer · doi:10.1007/s00371-024-03469-1 · Cited by 1
Metadata
- Authors
- Hong, Y., Wu, Q., Qi, Y., Rodriguez-Opazo, C., & Gould, S.
- Affiliations
- Australian National University (AU)
- Australian Centre for Robotic Vision (AU)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 1643-1653
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3172675210
- Cited by
- 264
- References
- 69
- Citations by year
- 2026: 46 · 2025: 60 · 2024: 63 · 2023: 62 · 2022: 27 · 2021: 6
- FWCI
- 12.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- 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, partially observable Markov decision processes, pre-training, transformer models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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