  The performance of the Vision-and-Language Navigation~(VLN) tasks has
witnessed rapid progress recently thanks to the use of large pre-trained
vision-and-language models. However, full fine-tuning the pre-trained model for
every downstream VLN task is becoming costly due to the considerable model
size. Recent research hotspot of Parameter-Efficient Transfer Learning (PETL)
shows great potential in efficiently tuning large pre-trained models for the
common CV and NLP tasks, which exploits the most of the representation
knowledge implied in the pre-trained model while only tunes a minimal set of
parameters. However, simply utilizing existing PETL methods for the more
challenging VLN tasks may bring non-trivial degeneration to the performance.
Therefore, we present the first study to explore PETL methods for VLN tasks and
propose a VLN-specific PETL method named VLN-PETL. Specifically, we design two
PETL modules: Historical Interaction Booster (HIB) and Cross-modal Interaction
Booster (CIB). Then we combine these two modules with several existing PETL
methods as the integrated VLN-PETL. Extensive experimental results on four
mainstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the effectiveness of
our proposed VLN-PETL, where VLN-PETL achieves comparable or even better
performance to full fine-tuning and outperforms other PETL methods with
promising margins.
