  State-of-the-art semantic segmentation methods were almost exclusively
trained on images within a fixed resolution range. These segmentations are
inaccurate for very high-resolution images since using bicubic upsampling of
low-resolution segmentation does not adequately capture high-resolution details
along object boundaries. In this paper, we propose a novel approach to address
the high-resolution segmentation problem without using any high-resolution
training data. The key insight is our CascadePSP network which refines and
corrects local boundaries whenever possible. Although our network is trained
with low-resolution segmentation data, our method is applicable to any
resolution even for very high-resolution images larger than 4K. We present
quantitative and qualitative studies on different datasets to show that
CascadePSP can reveal pixel-accurate segmentation boundaries using our novel
refinement module without any finetuning. Thus, our method can be regarded as
class-agnostic. Finally, we demonstrate the application of our model to scene
parsing in multi-class segmentation.
