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Most existing ultra-high resolution (UHR) segmentation methods alwaysstruggle in the dilemma of balancing memory cost and local characterizationaccuracy, which are both taken into account in our proposed GuidedPatch-Grouping Wavelet Transformer (GPWFormer) that achieves impressiveperformances. In this work, GPWFormer is a Transformer ($\mathcal{T}$)-CNN($\mathcal{C}$) mutual leaning framework, where $\mathcal{T}$ takes the wholeUHR image as input and harvests both local details and fine-grained long-rangecontextual dependencies, while $\mathcal{C}$ takes downsampled image as inputfor learning the category-wise deep context. For the sake of high inferencespeed and low computation complexity, $\mathcal{T}$ partitions the original UHRimage into patches and groups them dynamically, then learns the low-level localdetails with the lightweight multi-head Wavelet Transformer (WFormer) network.Meanwhile, the fine-grained long-range contextual dependencies are alsocaptured during this process, since patches that are far away in the spatialdomain can also be assigned to the same group. In addition, masks produced by$\mathcal{C}$ are utilized to guide the patch grouping process, providing aheuristics decision. Moreover, the congruence constraints between the twobranches are also exploited to maintain the spatial consistency among thepatches. Overall, we stack the multi-stage process in a pyramid way.Experiments show that GPWFormer outperforms the existing methods withsignificant improvements on five benchmark datasets.

Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation