Causality-based Cross-Modal Representation Learning for
  Vision-and-Language Navigation
Liuyi Wang Zongtao He Ronghao Dang Huiyi Chen Chengju Liu Qijun Chen
Vision-and-Language Navigation (VLN) has gained significant research interest
in recent years due to its potential applications in real-world scenarios.
However, existing VLN methods struggle with the issue of spurious associations,
resulting in poor generalization with a significant performance gap between
seen and unseen environments. In this paper, we tackle this challenge by
proposing a unified framework CausalVLN based on the causal learning paradigm
to train a robust navigator capable of learning unbiased feature
representations. Specifically, we establish reasonable assumptions about
confounders for vision and language in VLN using the structured causal model
(SCM). Building upon this, we propose an iterative backdoor-based
representation learning (IBRL) method that allows for the adaptive and
effective intervention on confounders. Furthermore, we introduce the visual and
linguistic backdoor causal encoders to enable unbiased feature expression for
multi-modalities during training and validation, enhancing the agent's
capability to generalize across different environments. Experiments on three
VLN datasets (R2R, RxR, and REVERIE) showcase the superiority of our proposed
method over previous state-of-the-art approaches. Moreover, detailed
visualization analysis demonstrates the effectiveness of CausalVLN in
significantly narrowing down the performance gap between seen and unseen
environments, underscoring its strong generalization capability.