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Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world.
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Wang, X., Xiong, W., Wang, H., Wang, W.Y.: Look before you leap: Bridging model-free and model-based reinforcement learning for planned-ahead vision-and-language navigation. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)
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Anderson, P., Shrivastava, A., Parikh, D., Batra, D., Lee, S.: Chasing ghosts: Instruction following as bayesian state tracking. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019)
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Jain, V., Magalhaes, G., Ku, A., Vaswani, A., Ie, E., Baldridge, J.: Stay on the path: Instruction fidelity in vision-and-language navigation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1862–1872. Association for Computational Linguistics (Jul 2019)
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Jain, V., Magalhaes, G., Ku, A., Vaswani, A., Ie, E., Baldridge, J.: Stay on the path: Instruction fidelity in vision-and-language navigation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1862–1872. Association for Computational Linguistics, Florence, Italy (Jul 2019)
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Tan, H., Yu, L., Bansal, M.: Learning to navigate unseen environments: Back translation with environmental dropout. In: 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). pp. 2610–2621 (Jun 2019)
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Wang, X., Huang, Q., Celikyilmaz, A., Gao, J., Shen, D., Wang, Y.F., Wang, W.Y., Zhang, L.: Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Fu, T.J., Wang, X.E., Peterson, M.F., Grafton, S.T., Eckstein, M.P., Wang, W.Y.: Counterfactual vision-and-language navigation via adversarial path sampler. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 71–86 (2020)
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Hao, W., Li, C., Li, X., Carin, L., Gao, J.: Towards learning a generic agent for vision-and-language navigation via pre-training. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
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Hisamoto, S., Post, M., Duh, K.: Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? Transactions of the Association for Computational Linguistics 8
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Hsu, T.M.H., Qi, H., Brown, M.: Federated visual classification with real-world data distribution. In: Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X. p. 76–92 (2020)
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Guo, P., Wang, P., Zhou, J., Jiang, S., Patel, V.M.: Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2423–2432 (June 2021)
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Huang, Y., Song, Z., Chen, D., Li, K., Arora, S.: TextHide: Tackling data privacy in language understanding tasks. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 1368–1382 (Nov 2020)
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Huang, Z., Liu, F., Zou, Y.: Federated learning for spoken language understanding. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 3467–3478. International Committee on Computational Linguistics, Barcelona, Spain (Online) (Dec 2020)
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Ku, A., Anderson, P., Patel, R., Ie, E., Baldridge, J.: Room-across-room: Multilingual vision-and-language navigation with dense spatiotemporal grounding. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 4392–4412 (Nov 2020)
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Li, X., Yin, X., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Dong, L., Wei, F., Choi, Y., Gao, J.: Oscar: Object-semantics aligned pre-training for vision-language tasks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J. (eds.) Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXX. Lecture Notes in Computer Science, vol. 12375, pp. 121–137. Springer (2020)
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Liu, F., Wu, Xian Ge, S., Fan, W., Zou, Y.: Federated learning for vision-and-language grounding problems. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 11572–11579 (2020)
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Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W.Y., Shen, C., van den Hengel, A.: REVERIE: remote embodied visual referring expression in real indoor environments. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. pp. 9979–9988. Computer Vision Foundation / IEEE (2020)
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Xiang, J., Wang, X., Wang, W.Y.: Learning to stop: A simple yet effective approach to urban vision-language navigation. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 699–707. Association for Computational Linguistics, Online (Nov 2020), https://aclanthology.org/2020.findings-emnlp.62
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Zhang, Y., Jia, R., Pei, H., Wang, W., Li, B., Song, D.: The secret revealer: Generative model-inversion attacks against deep neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
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2021
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Krantz, J., Gokaslan, A., Batra, D., Lee, S., Maksymets, O.: Waypoint models for instruction-guided navigation in continuous environments. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 15162–15171 (October 2021)
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Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10713–10722 (June 2021)
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Qi, Y., Pan, Z., Hong, Y., Yang, M.H., van den Hengel, A., Wu, Q.: The road to know-where: An object-and-room informed sequential bert for indoor vision-language navigation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1655–1664 (October 2021)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR (2021)
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Reddi, S.J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., McMahan, H.B.: Adaptive federated optimization. In: International Conference on Learning Representations (2021)
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Zhang, Y., Tan, H., Bansal, M.: Diagnosing the environment bias in vision-and-language navigation. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI’20 (2021)
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Gu, J., Stefani, E., Wu, Q., Thomason, J., Wang, X.: Vision-and-language navigation: A survey of tasks, methods, and future directions. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 7606–7623. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl-long.524, https://aclanthology.org/2022.acl-long.524
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Zhao, Y., Barnaghi, P., Haddadi, H.: Multimodal federated learning on iot data (2022)
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Zhu, W., Qi, Y., Narayana, P., Sone, K., Basu, S., Wang, X., Wu, Q., Eckstein, M., Wang, W.Y.: Diagnosing vision-and-language navigation: What really matters. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 5981–5993. Association for Computational Linguistics, Seattle, United States (Jul 2022), https://aclanthology.org/2022.naacl-main.438
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