Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi
| Challenge: | Existing methods to learn visual representations and action decoding schemes are limited to previously unseen instructions and environments. |
| Approach: | They propose a stochastic sampling scheme to reduce the gap between the expert actions in training and sampled actions in test to correct its own mistakes. |
| Outcome: | The proposed methods achieve 6% absolute gain over the previous best results on the Room-to-Room benchmark. |
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| Challenge: | A major challenge in vision-and-language navigation is the limited available training data, which hinders the models’ ability to generalize effectively. |
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| Challenge: | Current vision-language models owe their success to large-scale pretraining on web-collected data. |
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CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations (2022.findings-naacl)
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| Challenge: | Using multilingual instructions to learn a better cross-lingual representation is challenging for multilingual agents. |
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| Challenge: | Existing Vision-and-Language Navigation benchmarks assume instructions are feasible and the referenced target exists. |
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| Challenge: | Existing state-of-the-art VLN agents do not generalize well for long navigation tasks. |
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| Challenge: | Despite significant advances, few previous works are able to fully utilize the strong correspondence between visual and textual sequences. |
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| Challenge: | Curriculum learning has improved efficiency across machine learning domains, but remains underexplored for language model pretraining. |
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| Challenge: | Vision and language navigation (VLN) is a challenging task towards the creation of embodied agents. |
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Do What? Teaching Vision-Language-Action Models to Reject the Impossible (2025.findings-emnlp)
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| Challenge: | Recent studies show that VLAs can recognize, interpret, and respond to false-premise instructions. |
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