Transferable Representation Learning in Vision-and-Language Navigation

Haoshuo Huang, Vihan Jain, Harsh Mehta, Alexander Ku, Gabriel Magalhaes, Jason Baldridge, Eugene Ie; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 7404-7413

Abstract


Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric.

Related Material


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[bibtex]
@InProceedings{Huang_2019_ICCV,
author = {Huang, Haoshuo and Jain, Vihan and Mehta, Harsh and Ku, Alexander and Magalhaes, Gabriel and Baldridge, Jason and Ie, Eugene},
title = {Transferable Representation Learning in Vision-and-Language Navigation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}