  Implicit representation mapping (IRM) can translate image features to any
continuous resolution, showcasing its potent capability for
ultra-high-resolution image segmentation refinement. Current IRM-based methods
for refining ultra-high-resolution image segmentation often rely on CNN-based
encoders to extract image features and apply a Shared Implicit Representation
Mapping Function (SIRMF) to convert pixel-wise features into segmented results.
Hence, these methods exhibit two crucial limitations. Firstly, the CNN-based
encoder may not effectively capture long-distance information, resulting in a
lack of global semantic information in the pixel-wise features. Secondly, SIRMF
is shared across all samples, which limits its ability to generalize and handle
diverse inputs. To address these limitations, we propose a novel approach that
leverages the newly proposed Adaptive Implicit Representation Mapping (AIRM)
for ultra-high-resolution Image Segmentation. Specifically, the proposed method
comprises two components: (1) the Affinity Empowered Encoder (AEE), a robust
feature extractor that leverages the benefits of the transformer architecture
and semantic affinity to model long-distance features effectively, and (2) the
Adaptive Implicit Representation Mapping Function (AIRMF), which adaptively
translates pixel-wise features without neglecting the global semantic
information, allowing for flexible and precise feature translation. We
evaluated our method on the commonly used ultra-high-resolution segmentation
refinement datasets, i.e., BIG and PASCAL VOC 2012. The extensive experiments
demonstrate that our method outperforms competitors by a large margin. The code
is provided in supplementary material.
