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]
Citations
Cites
Deep Residual Learning for Image Recognition
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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
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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
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
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
Song, C. H., Sadler, B. M., Wu, J., Chao, W.-L., Washington, C., & Su, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00280 · Cited by 435
“Recent work [11,16,21,23] employs BERT and its variants [7,20] to get better language understanding.”
MethodA Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)
Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., & Mansoor, W. (2023) · Systems · doi:10.3390/systems11080400 · Cited by 390
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W., & Zhu, S.-C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01607 · Cited by 192
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
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
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