Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a promising training-free strategy to reduce this cost by reusing token computation results; however, existing token caching approaches rely on visual domain methods for cacheable token selection, leading to challenges when adapted to VLN models. 1) Visual domain methods become invalid when there is viewpoint migration. 2) Visual domain methods neglect critical edge information without the aid of additional algorithms. 3) Visual domain methods overlook the temporal variation of scenarios and lack adjustability in cache budgets. In this paper, we develop detailed analyses and find that the impacts of these challenges exhibit invariance and analyzability in the frequency domain. Based on these, we propose a frequency-guided token caching framework, called FreqCache. Utilizing the inherent properties of the frequency domain, FreqCache achieves optimal token cache establishment, refreshment, and adaptive adjustment. Experiments show that FreqCache achieves 1.59x speedup with ignorable overhead, showing the effect of integrating frequency domain methods in VLN token caching.

翻译：视觉-语言-导航（Vision-Language-Navigation, VLN）模型虽展现出优异的导航精度，但计算开销高昂。令牌缓存技术作为一种无需重新训练的计算开销降低策略，通过复用令牌计算结果展现出潜力；然而，现有令牌缓存方法依赖视觉域方法进行可缓存令牌选择，在适配VLN模型时面临挑战：1）视角迁移时视觉域方法失效；2）缺乏辅助算法时，视觉域方法忽略关键边缘信息；3）视觉域方法忽略场景时序变化且缺乏缓存预算可调节性。本文通过深入分析发现，上述挑战的影响在频域中具有不变性与可分析性。据此提出频域引导令牌缓存框架FreqCache，利用频域固有属性实现最优令牌缓存建立、刷新与自适应调节。实验表明，FreqCache以可忽略的额外开销实现1.59倍加速，验证了频域方法在VLN令牌缓存中的集成效果。