  Segmentation of ultra-high resolution images is increasingly demanded, yet
poses significant challenges for algorithm efficiency, in particular
considering the (GPU) memory limits. Current approaches either downsample an
ultra-high resolution image or crop it into small patches for separate
processing. In either way, the loss of local fine details or global contextual
information results in limited segmentation accuracy. We propose collaborative
Global-Local Networks (GLNet) to effectively preserve both global and local
information in a highly memory-efficient manner. GLNet is composed of a global
branch and a local branch, taking the downsampled entire image and its cropped
local patches as respective inputs. For segmentation, GLNet deeply fuses
feature maps from two branches, capturing both the high-resolution fine
structures from zoomed-in local patches and the contextual dependency from the
downsampled input. To further resolve the potential class imbalance problem
between background and foreground regions, we present a coarse-to-fine variant
of GLNet, also being memory-efficient. Extensive experiments and analyses have
been performed on three real-world ultra-high aerial and medical image datasets
(resolution up to 30 million pixels). With only one single 1080Ti GPU and less
than 2GB memory used, our GLNet yields high-quality segmentation results and
achieves much more competitive accuracy-memory usage trade-offs compared to
state-of-the-arts.
