NavHint: Vision and Language Navigation Agent with a Hint Generator
Yue Zhang Quan Guo Parisa Kordjamshidi
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 interpretability
of the agent's actions.