Vision-and-Language Navigation via Causal Learning
Authors: Liuyi Wang, Zongtao He, Ronghao Dang, Mengjiao Shen, Chengju Liu, Qijun Chen

In the pursuit of robust and generalizable environment perception and language understanding the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents hindering their performance in unseen environments. This paper introduces the generalized cross-modal causal transformer (GOAT) a pioneering solution rooted in the paradigm of causal inference. By delving into both observable and unobservable confounders within vision language and history we propose the back-door and front-door adjustment causal learning (BACL and FACL) modules to promote unbiased learning by comprehensively mitigating potential spurious correlations. Additionally to capture global confounder features we propose a cross-modal feature pooling (CFP) module supervised by contrastive learning which is also shown to be effective in improving cross-modal representations during pre-training. Extensive experiments across multiple VLN datasets (R2R REVERIE RxR and SOON) underscore the superiority of our proposed method over previous state-of-the-art approaches. Code is available at https://github.com/CrystalSixone/VLN-GOAT.