Learning from Unlabeled 3D Environments for Vision-and-Language Navigation

Author:

Chen ShizheORCID,Guhur Pierre-Louis,Tapaswi MakarandORCID,Schmid Cordelia,Laptev IvanORCID

Publisher

Springer Nature Switzerland

Reference57 articles.

1. Anderson, P., Shrivastava, A., Parikh, D., Batra, D., Lee, S.: Chasing ghosts: instruction following as bayesian state tracking. NeurIPS 32 (2019)

2. Anderson, P., et al.: Sim-to-real transfer for vision-and-language navigation. In: CoRL, pp. 671–681. PMLR (2021)

3. Anderson, P., et al.: Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments. In: CVPR, pp. 3674–3683 (2018)

4. Chang, A., et al.: Matterport3d: learning from rgb-d data in indoor environments. In: 3DV, pp. 667–676. IEEE (2017)

5. Chaplot, D.S., Gandhi, D.P., Gupta, A., Salakhutdinov, R.R.: Object goal navigation using goal-oriented semantic exploration. NeurIPS 33, 4247–4258 (2020)

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1. Mind the Gap: Improving Success Rate of Vision-and-Language Navigation by Revisiting Oracle Success Routes;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26

2. RREx-BoT: Remote Referring Expressions with a Bag of Tricks;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

3. Improving Vision-and-Language Navigation by Generating Future-View Image Semantics;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

4. LANA: A Language-Capable Navigator for Instruction Following and Generation;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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