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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
Research works printed but not linked: 2
- loshchilov, 2018, Decoupled weight de-cay regularization, International Conference on Learning Representations
- aliakbarian, 2018, Viena2: A driving anticipation dataset, Asian Conference on Computer Vision, 449
Cited by
Sorted by citations
Items 201–266 of 266
Semantic Map Based Robot Navigation with Natural Language Input
Yang, G., Huang, X., & Guo, Y. P. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731181 · Cited by 1
Lin, C., & Liang, M. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625502305 · Cited by 1
Research on the Application of Large Language Models in Shore Power Management Decision
Fu, R., Jiao, Y., Chen, D., Lan, Z., Hu, W., & Qu, J. (2024) · International Conference on Intelligent Power and Systems (ICIPS) · doi:10.1109/icips64173.2024.10899932 · Cited by 1
Vision-and-Language Pretraining: Methods, Applications, and Future ChallengesPreprint
Nguyen, T., Nguyen, C.-D. T., Wu, X., Ng, S.-K., & Luu, A.-T. (2023) · Research Square · doi:10.21203/rs.3.rs-3120051/v1 · Cited by 1
g3D-LF: Generalizable 3D-Language Feature Fields for Embodied TasksPreprint
Wang, Z., & Lee, G. H. (2024) · Qeios · doi:10.32388/841d1t · Cited by 1
Unmute: Unifying Navigation and Multimodal Dialogue-Like Text GenerationPreprint
Rawal, N., Bigazzi, R., Baraldi, L., & Cucchiara, R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4777514 · Cited by 1
RewardVLN: AnImproved Agent Navigation Based On Visual-Instruction Alignment*
Liu, R., Kong, P., Wu, S., & Zhang, W. (2024) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm62033.2024.10715935
Learning Cross-modal Knowledge Reasoning and Heuristic-prompt for Visual-language Navigation
Zhou, D., Pang, Z., & Li, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679740
Integrating Reinforcement Learning Into Vision-Language Models for Object Localization
Gan, Z., Zheng, Y., Tan, J., & Wang, X. (2024) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio64047.2024.10907467
EKCA-Cap: Improving dense captioning via external knowledge and context awareness
Zhu, Z., Zhou, F., Huang, S., & Li, T. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129867
Exploring Graph-aware Reasoning and Bidirectional Selection for Vision-Language Navigation
Zhou, D., Deng, J., Pang, Z., & Li, W. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888565
Variational Interpretable Framework for Multimodal Instruction ExecutionPreprint
Morgan, K., Hsu, L., & Quinn, Z. (2025) · Preprints.org · doi:10.20944/preprints202504.0394.v1
Pandey, A., & Parihar, A. S. (2025) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici65870.2025.11069974
Learning Causality-Aware Exploration with Transformers for Goal-Oriented Navigation
Wang, R., Yu, T., Li, M., Cao, Y., Liu, Y., & Yao, L. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3748659
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion
Wang, H., Wang, L., Zhou, S., Tian, J., Qin, Z., Wang, Y., Hua, G., & Tang, W. (2025) · 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/cvpr52734.2025.01246 · Cited by 1
Computational Approaches to Cross-Cultural Multimodal Film and TV Integration Using AMTAs
Zhong, H. (2025) · International Conference on Data Intelligence and Cognitive Informatics (ICDICI) · doi:10.1109/icdici66477.2025.11135123
End-to-End Model for Vision-Language Navigation Based on Pre-Trained Model
Wu, M.-Y., Feng, B., Fan, W., & Feng, Y. (2025) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/fasta65681.2025.11138280
Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments
Simons, C., Liu, Z., Marcus, B., Roy–Chowdhury, A. K., & Karydis, K. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11164135
Language-Conditioned Waypoint Predictor for Continuous Vision-and-Language Navigation
Wang, Z., Qi, Y., An, D. U., Xu, Y., Li, H., & Zhang, Z. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209085
Self-citationBetween Monocular and Panoramic: Multi-View Vision-and-Language Navigation
Li, M. (2025) · International Conference on Image and Video Processing (ICIVP) · doi:10.1109/icivp66296.2025.00031
Zhou, B., Zhang, Z., Wang, J., & Lu, Z. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246428
Vision-Language Navigation with Continual Learning for Unseen Environments
Li, Z., Lu, Y., Shang, D., Tu, Z., & Qiao, H. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246709
L-SNI: A Language-Driven Semantic Navigation System for Inspection Tasks
Ma, J., Guo, W., Wu, X., Zhuang, Z., Zeng, R., Shi, Y., & Ma, G. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247032
HAVEN: From Human Guidance to Assistant by Evolution Network in Vision-and-Language Navigation
Lai, P., Xie, Z., & Yang, H. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227307
SPENav: Dynamic Object Filtering With Spatial Perception Enhancement for Vision-Language Navigation
Yuan, S., Zhang, H., Liu, L., Zhu, L., & Dong, X. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3651320 · Cited by 1
Memory-Driven Querying Transformer for Vision-and-Language Navigation
Jin, C., Zhang, Y., & Yang, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5740-0_21
View Invariant Learning for Vision-Language Navigation in Continuous Environments
Sun, J. Q., Weng, H., Xing, X., Yeum, C. M., & Crowley, M. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3669785 · Cited by 5
Xu, Y., Pan, Y., & Liu, Z. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3679426 · Cited by 5
Robots that can see : Learning visually guided behavior
Pashevich, A. (2021) · theses.fr (ABES) · doi:10.70675/03640d1fz838cz4bcbz8665z5f769976ea7b
Chen, P., Zhang, G., Ma, J., & Li, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37422-6_32 · Cited by 3
Spatial-Aware and Viewpoint-Robust Vision-Language Navigation
Zhong, Z., Lu, J., Liu, X., Yu, R., Zheng, X., Liu, Z., Wang, H., & Li, H. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3682894
Dual-Anchoring: Addressing State Drift in Vision-Language Navigation
Wu, K., Li, P., Lyu, K., Zhao, L., Zhao, L., He, Q., Wang, J., & Liu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36984-0_24 · Cited by 7
Enhancing Memory Ability of LLM Agent for Vision-and-Language Navigation
Bao, Y., Wang, J., & Wang, T. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11486783
Embodied Navigation with Auxiliary Task of Action Description Prediction
Kondoh, H., & Kanezaki, A. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00660 · Cited by 1
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
DSSNav: dual-stream slot-based fusion via Soft Actor-Critic for audio–visual navigation
Li, L., Duan, L., Zhao, K., & Si, B. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02363-1
Emergent Capabilities of Large Foundation Models for Intelligent Navigation
Sheng, Q. Z., Liu, Y., & Yao, L. (2026) · Springer eBooks · doi:10.1007/978-3-032-16502-2_14
LaViCoN: Language and Vision Cooperative Navigation for Assistive Mobile Robots
Yang, T. Y., Zhong, R., & Shi, W. (2026) · IEEE International Conference on Mobility, Operations, Services and Technologies (MOST) · doi:10.1109/most69733.2026.00016
Gao, P., Wang, P., Wang, F., Fujita, H., & Aljuaid, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00549-1
Xu, H., Liu, Z., Luomei, Y., & Xu, F. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3701286 · Cited by 11
Xi, Y., Liu, S., Pang, B. J., Zhou, T., Song, Y. H., Yuan, X., & Xu, Q. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3703976
IRAZON: Iterative ReAct With LLMs for Adaptive Zero-Shot Object Goal Navigation
Li, Y. N., Mei, L., Qin, M., Zhang, Y., & Hua, C. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3706949
DQ-VLN: Instruction-Conditioned Dynamic Querying for Vision-and-Language Navigation
Chen, X., & Lai, H. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567201
LangLoc: “Tell Me What You See”
Panwar, S. K., Nobari, R. Z., Lau, S. F. Y., Shaik, A. B. R., Günther, M., Pollefeys, M., & Baráth, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37167-6_34
Wang, L., Sheng, K., He, Z., Li, J., Qin, Y., Dai, H., Wang, X. F., Yang, J., Yan, Q., Liu, C., et al. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3711284
HIVE: Understanding Post-hallucination Reasoning in Vision Language Models
He, F., Wang, Z., Wang, Q., Guan, Q., Liu, D., Tang, R., & Li, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37271-0_34
DCSEN: Dual Channel Semantic Enhancement for Vision-and-Language Navigation
Li, H., Liu, L., Dong, X., Zhu, L., Chang, X., & Zhang, H. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3714818
Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models
Song, Y., Lin, B.-J., Liu, J., Chiu, T.-C., Nguyen, A., & Lee, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31663-9_8
Structured Decision-and-Recovery for Large Language Model-Based Vision-and-Language Navigation
Lian, T., Wang, Y., & Liu, J. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11615734 · Cited by 1
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
The Role of Variability in Human Navigational Instructions in Visual Language Robot Navigation
Sayour, M., Carreno‐Medrano, P., Burke, M., & Kulić, D. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3723312
Speaker-Mediated Route Knowledge Transfer for Goal-Oriented Vision-and-Language Navigation
Han, J., Li, X., Wang, B., Hu, Y., & Yin, B. (2026) · Electronics · doi:10.3390/electronics15173937
Vision-and-Language Navigation Based on Cosine Similarity and Bidirectional Alignment
Zhu, T. (2026) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat71030.2026.11682316
Sparse Latent Action World Model for Vision-Language Navigation
Yang, Z., Li, Y., Li, M., & Sun, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38404-1_13
LiveVLN: Reducing Stop-and-Go Loops in Vision-Language Navigation
Wang, X., Zhu, W., Wang, T., Geng, T., Zhang, Z., Qi, Z., Yang, J., & Zheng, F. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3734886 · Cited by 4
Zhang, J., Zhang, Y., Guan, R., Xu, Z., Tian, K., & Bo, Z. (2026) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia71496.2026.11684673
Yan, Y., Li, P., & Yin, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.117088
Li, M., Zhang, S., Gao, C., Li, J., Wang, L., Liu, H., & Li, Y. (2026) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57385.2026.11695775
Trajectory-Diversity-Driven Robust Vision-And-Language Navigation
Li, J., Wan, C., Dong, S., Ding, C., Wang, Q., Ma, Z., & Gong, Y. (2026) · doi:10.1109/cvprf71909.2026.00877
Rajkumar, K. V., Anurag, D. W., Venkat, A. S., Tirumanadham, N. S. K. M. K., Kumar, P. R., & Shariff, V. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11716806
HAVEN: A unified monocular RGB vision-and-language navigation framework for wheeled-legged robots
Dai, M., Huang, C., Zheng, B., Cheng, Z., Dong, W., & Wu, L. (2026) · Displays · doi:10.1016/j.displa.2026.103724
GELATO: Multi-Instruction Trajectory Reshaping via Geometric-Aware Multiagent-Based Orchestration
Huang, J., Gong, Y., Li, C., Duan, X., & Figueredo, L. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3743850 · Cited by 1
EdgeCurator: Efficient Cross-Modal Grounding for Real-Time Language-Guided UAV Navigation
Peng, S., & Wang, R. (2026) · doi:10.1145/3840749.3841574
Situated proactive task generation for embodied agents
Xu, H., Li, C., Yuan, X., & Liu, H. (2026) · C2 Science and Technology · doi:10.26599/c2st.2026.9700006
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
