Fourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models

FreqNav: Stage-Wise Frequency Routing for Object-Oriented Aerial Vision-Language Navigation

“…In contrast, object-oriented navigation exhibits stage-disparate perceptual requirements: the initial exploration prioritizes global scene layouts and spatial geometry, whereas the final reach relies on localized target appearance and fine-grained semantic alignment. As established in prior studies (Xu et al 2020;Qi et al 2026;Wang et al 2026), global structural scene layouts are primarily encoded in low-frequency signal components, while fine-grained object details predominantly reside in high-frequency components. We argue that stagewise adaptive token selection can yield information-denser visual representations by prioritizing frequency components most relevant to the current phase.…”

“…To obtain hierarchical feature representations, recent methods have explicitly decompose multimodal features into different frequency components, associating low-frequency signals with global scene structure and higher frequencies with fine-grained local details (Xu et al 2020;Qi et al 2026). Meanwhile, visual token optimization methods improve efficiency through token merging, pruning, or query-based compression to remove redundant visual information (Bolya et al 2023;Li et al 2023;Wang et al 2026;Mnih et al 2014). However, these approaches assume a static frequency representation or token allocation, overlooking the evolving perceptual demands of embodied navigation.…”

FreqNav: Stage-Wise Frequency Routing for Object-Oriented Aerial Vision-Language Navigation

“…In contrast, object-oriented navigation exhibits stage-disparate perceptual requirements: the initial exploration prioritizes global scene layouts and spatial geometry, whereas the final reach relies on localized target appearance and fine-grained semantic alignment. As established in prior studies (Xu et al 2020;Qi et al 2026;Wang et al 2026), global structural scene layouts are primarily encoded in low-frequency signal components, while fine-grained object details predominantly reside in high-frequency components. We argue that stagewise adaptive token selection can yield information-denser visual representations by prioritizing frequency components most relevant to the current phase.…”

“…To obtain hierarchical feature representations, recent methods have explicitly decompose multimodal features into different frequency components, associating low-frequency signals with global scene structure and higher frequencies with fine-grained local details (Xu et al 2020;Qi et al 2026). Meanwhile, visual token optimization methods improve efficiency through token merging, pruning, or query-based compression to remove redundant visual information (Bolya et al 2023;Li et al 2023;Wang et al 2026;Mnih et al 2014). However, these approaches assume a static frequency representation or token allocation, overlooking the evolving perceptual demands of embodied navigation.…”

FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching

“…Some studies have incorporated frequency domain methods into model inference optimization. Fourier-VLM [25] compresses visual tokens via DCT. FlashCache [30] identifies performance-critical spectral outliers.…”

FreqNav: Stage-Wise Frequency Routing for Object-Oriented Aerial Vision-Language Navigation

“…In contrast, object-oriented navigation exhibits stage-disparate perceptual requirements: the initial exploration prioritizes global scene layouts and spatial geometry, whereas the final reach relies on localized target appearance and fine-grained semantic alignment. As established in prior studies (Xu et al 2020;Qi et al 2026;Wang et al 2026), global structural scene layouts are primarily encoded in low-frequency signal components, while fine-grained object details predominantly reside in high-frequency components. We argue that stagewise adaptive token selection can yield information-denser visual representations by prioritizing frequency components most relevant to the current phase.…”

“…To obtain hierarchical feature representations, recent methods have explicitly decompose multimodal features into different frequency components, associating low-frequency signals with global scene structure and higher frequencies with fine-grained local details (Xu et al 2020;Qi et al 2026). Meanwhile, visual token optimization methods improve efficiency through token merging, pruning, or query-based compression to remove redundant visual information (Bolya et al 2023;Li et al 2023;Wang et al 2026;Mnih et al 2014). However, these approaches assume a static frequency representation or token allocation, overlooking the evolving perceptual demands of embodied navigation.…”

FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching

“…Some studies have incorporated frequency domain methods into model inference optimization. Fourier-VLM [25] compresses visual tokens via DCT. FlashCache [30] identifies performance-critical spectral outliers.…”

FreqNav: Stage-Wise Frequency Routing for Object-Oriented Aerial Vision-Language Navigation

“…In contrast, object-oriented navigation exhibits stage-disparate perceptual requirements: the initial exploration prioritizes global scene layouts and spatial geometry, whereas the final reach relies on localized target appearance and fine-grained semantic alignment. As established in prior studies (Xu et al 2020;Qi et al 2026;Wang et al 2026), global structural scene layouts are primarily encoded in low-frequency signal components, while fine-grained object details predominantly reside in high-frequency components. We argue that stagewise adaptive token selection can yield information-denser visual representations by prioritizing frequency components most relevant to the current phase.…”

“…To obtain hierarchical feature representations, recent methods have explicitly decompose multimodal features into different frequency components, associating low-frequency signals with global scene structure and higher frequencies with fine-grained local details (Xu et al 2020;Qi et al 2026). Meanwhile, visual token optimization methods improve efficiency through token merging, pruning, or query-based compression to remove redundant visual information (Bolya et al 2023;Li et al 2023;Wang et al 2026;Mnih et al 2014). However, these approaches assume a static frequency representation or token allocation, overlooking the evolving perceptual demands of embodied navigation.…”

FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching

“…Some studies have incorporated frequency domain methods into model inference optimization. Fourier-VLM [25] compresses visual tokens via DCT. FlashCache [30] identifies performance-critical spectral outliers.…”