Leveraging Adaptive Implicit Presentation Mapping for Ultra High-Resolution Image Segmentation

This paper proposes a novel Adaptive Implicit Representation Mapping (AIRM) approach for ultra-high-resolution image segmentation, addressing limitations in current CNN-based IRM methods. Our method includes an Affinity Empowered Encoder (AEE) with transformer architecture to capture long-distance semantic information and an Adaptive Implicit Representation Mapping Function (AIRMF) that dynamically translates pixel-wise features while preserving global context.