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Vision-and-Language Navigation (VLN) requires an agent to find a path to aremote location on the basis of natural-language instructions and a set ofphoto-realistic panoramas. Most existing methods take the words in theinstructions and the discrete views of each panorama as the minimal unit ofencoding. However, this requires a model to match different nouns (e.g., TV,table) against the same input view feature. In this work, we propose anobject-informed sequential BERT to encode visual perceptions and linguisticinstructions at the same fine-grained level, namely objects and words. Oursequential BERT also enables the visual-textual clues to be interpreted inlight of the temporal context, which is crucial to multi-round VLN tasks.Additionally, we enable the model to identify the relative direction (e.g.,left/right/front/back) of each navigable location and the room type (e.g.,bedroom, kitchen) of its current and final navigation goal, as such informationis widely mentioned in instructions implying the desired next and finallocations. We thus enable the model to know-where the objects lie in theimages, and to know-where they stand in the scene. Extensive experimentsdemonstrate the effectiveness compared against several state-of-the-art methodson three indoor VLN tasks: REVERIE, NDH, and R2R. Project repository:https://github.com/YuankaiQi/ORIST

The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation