In　ultra-high　resolution　image　segmentation　task　for　robotic　platforms　like　UAVs　and　autonomous　vehicles,　existing　paradigms　process　a　downsampled　input　image　through　a　deep　network　and　the　original　high-resolution　image　through　a　shallow　network,　then　fusing　their　features　for　final　segmentation.　Although　these　features　are　designed　to　be　complementary,　they　often　contain　redundant　or　even　conflicting　semantic　information,　which　leads　to　blurred　edge　contours,　particularly　for　small　objects.　This　is　especially　detrimental　to　robotics　applications　requiring　precise　spatial　awareness.　To　address　this　challenge,　we　propose　a　novel　paradigm　that　disentangles　the　task　into　two　independent　subtasks　concerning　high-　and　low-resolution　inputs,　leveraging　high-resolution　features　exclusively　to　capture　low-level　structured　details　and　low-resolution　features　for　extracting　semantics.　Specifically,　for　the　high-resolution　input,　we　propose　a　region-pixel　association　experts　scheme　that　partitions　the　image　into　multiple　regions.　For　the　low-resolution　input,　we　assign　compact　semantic　tokens　to　the　partitioned　regions.　Additionally,　we　incorporate　a　high-resolution　local　perception　scheme　with　an　efficient　field-enriched　local　context　module　to　enhance　small　object　recognition　in　case　of　incorrect　semantic　assignment.　Extensive　experiments　demonstrate　the　state-of-theart　performance　of　our　method　and　validate　the　effectiveness　of　each　designed　component.　©　2016　IEEE.