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