Vision-and-Language Navigation (VLN) has witnessed significant advancements
in recent years, largely attributed to meticulously curated datasets and
proficiently trained models. Nevertheless, when tested in diverse environments,
the trained models inevitably encounter significant shifts in data
distribution, highlighting that relying solely on pre-trained and fixed
navigation models is insufficient. To enhance models' generalization ability,
test-time adaptation (TTA) demonstrates significant potential in the computer
vision field by leveraging unlabeled test samples for model updates. However,
simply applying existing TTA methods to the VLN task cannot well handle the
adaptability-stability dilemma of VLN models, i.e., frequent updates can result
in drastic changes in model parameters, while occasional updates can make the
models ill-equipped to handle dynamically changing environments. Therefore, we
propose a Fast-Slow Test-Time Adaptation (FSTTA) approach for VLN by performing
decomposition-accumulation analysis for both gradients and parameters in a
unified framework. Specifically, in the fast update phase, gradients generated
during the recent multi-step navigation process are decomposed into components
with varying levels of consistency. Then, these components are adaptively
accumulated to pinpoint a concordant direction for fast model adaptation. In
the slow update phase, historically recorded parameters are gathered, and a
similar decomposition-accumulation analysis is conducted to revert the model to
a stable state. Extensive experiments show that our method obtains impressive
performance gains on four popular benchmarks.

通过快慢测试时适应（FSTTA）方法，解决视觉语言导航（VLN）模型的适应性与稳定性困境，实现模型的快速自适应和稳定回归，获得卓越的性能提升。