  Superpixel segmentation has recently seen important progress benefiting from
the advances in differentiable deep learning. However, the very high-resolution
superpixel segmentation still remains challenging due to the expensive memory
and computation cost, making the current advanced superpixel networks fail to
process. In this paper, we devise Patch Calibration Networks (PCNet), aiming to
efficiently and accurately implement high-resolution superpixel segmentation.
PCNet follows the principle of producing high-resolution output from
low-resolution input for saving GPU memory and relieving computation cost. To
recall the fine details destroyed by the down-sampling operation, we propose a
novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the
main superpixel generation branch. In particular, DPC takes a local patch from
the high-resolution images and dynamically generates a binary mask to impose
the network to focus on region boundaries. By sharing the parameters of DPC and
main branches, the fine-detailed knowledge learned from high-resolution patches
will be transferred to help calibrate the destroyed information. To the best of
our knowledge, we make the first attempt to consider the deep-learning-based
superpixel generation for high-resolution cases. To facilitate this research,
we build evaluation benchmarks from two public datasets and one new constructed
one, covering a wide range of diversities from fine-grained human parts to
cityscapes. Extensive experiments demonstrate that our PCNet can not only
perform favorably against the state-of-the-arts in the quantitative results but
also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs.
