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Ultra-high resolution image segmentation has raised increasing interests inrecent years due to its realistic applications. In this paper, we innovate thewidely used high-resolution image segmentation pipeline, in which an ultra-highresolution image is partitioned into regular patches for local segmentation andthen the local results are merged into a high-resolution semantic mask. Inparticular, we introduce a novel locality-aware context fusion basedsegmentation model to process local patches, where the relevance between localpatch and its various contexts are jointly and complementarily utilized tohandle the semantic regions with large variations. Additionally, we present thealternating local enhancement module that restricts the negative impact ofredundant information introduced from the contexts, and thus is endowed withthe ability of fixing the locality-aware features to produce refined results.Furthermore, in comprehensive experiments, we demonstrate that our modeloutperforms other state-of-the-art methods in public benchmarks. Our releasedcodes are available at: https://github.com/liqiokkk/FCtL.

Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement