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    <abstract>The existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.In our work, we provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions.The hint generator assists the navigation agent in developing a global understanding of the visual environment. It directs the agent’s attention toward related navigation details, including the relevant sub-instruction, potential challenges in recognition and ambiguities in grounding, and the targeted viewpoint description. To train the hint generator, we construct a synthetic dataset based on landmarks in the instructions and visible and distinctive objects in the visual environment.We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics. The experimental results demonstrate that generating hints not only enhances the navigation performance but also helps improve the agent’s interpretability.</abstract>
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%0 Conference Proceedings
%T NavHint: Vision and Language Navigation Agent with a Hint Generator
%A Zhang, Yue
%A Guo, Quan
%A Kordjamshidi, Parisa
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F zhang-etal-2024-navhint
%X The existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.In our work, we provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions.The hint generator assists the navigation agent in developing a global understanding of the visual environment. It directs the agent’s attention toward related navigation details, including the relevant sub-instruction, potential challenges in recognition and ambiguities in grounding, and the targeted viewpoint description. To train the hint generator, we construct a synthetic dataset based on landmarks in the instructions and visible and distinctive objects in the visual environment.We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics. The experimental results demonstrate that generating hints not only enhances the navigation performance but also helps improve the agent’s interpretability.
%U https://aclanthology.org/2024.findings-eacl.7
%P 92-103

Markdown (Informal)

[NavHint: Vision and Language Navigation Agent with a Hint Generator](https://aclanthology.org/2024.findings-eacl.7) (Zhang et al., Findings 2024)

• NavHint: Vision and Language Navigation Agent with a Hint Generator (Zhang et al., Findings 2024)

ACL

• Yue Zhang, Quan Guo, and Parisa Kordjamshidi. 2024. NavHint: Vision and Language Navigation Agent with a Hint Generator. In Findings of the Association for Computational Linguistics: EACL 2024, pages 92–103, St. Julian’s, Malta. Association for Computational Linguistics.