  The emerging vision-and-language navigation (VLN) problem aims at learning to
navigate an agent to the target location in unseen photo-realistic environments
according to the given language instruction. The main challenges of VLN arise
mainly from two aspects: first, the agent needs to attend to the meaningful
paragraphs of the language instruction corresponding to the dynamically-varying
visual environments; second, during the training process, the agent usually
imitate the shortest-path to the target location. Due to the discrepancy of
action selection between training and inference, the agent solely on the basis
of imitation learning does not perform well. Sampling the next action from its
predicted probability distribution during the training process allows the agent
to explore diverse routes from the environments, yielding higher success rates.
Nevertheless, without being presented with the shortest navigation paths during
the training process, the agent may arrive at the target location through an
unexpected longer route. To overcome these challenges, we design a cross-modal
grounding module, which is composed of two complementary attention mechanisms,
to equip the agent with a better ability to track the correspondence between
the textual and visual modalities. We then propose to recursively alternate the
learning schemes of imitation and exploration to narrow the discrepancy between
training and inference. We further exploit the advantages of both these two
learning schemes via adversarial learning. Extensive experimental results on
the Room-to-Room (R2R) benchmark dataset demonstrate that the proposed learning
scheme is generalized and complementary to prior arts. Our method performs well
against state-of-the-art approaches in terms of effectiveness and efficiency.

Computer Vision and Pattern Recognition;Artificial Intelligence