Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel
  Benchmark
Deyi Ji Feng Zhao Hongtao Lu Mingyuan Tao Jieping Ye
With the increasing interest and rapid development of methods for Ultra-High
Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of
scenes with full fine-grained dense annotations is urgently needed to
facilitate the field. To this end, the URUR dataset is introduced, in the
meaning of Ultra-High Resolution dataset with Ultra-Rich Context. As the name
suggests, URUR contains amounts of images with high enough resolution (3,008
images of size 5,120x5,120), a wide range of complex scenes (from 63 cities),
rich-enough context (1 million instances with 8 categories) and fine-grained
annotations (about 80 billion manually annotated pixels), which is far superior
to all the existing UHR datasets including DeepGlobe, Inria Aerial, UDD, etc..
Moreover, we also propose WSDNet, a more efficient and effective framework for
UHR segmentation especially with ultra-rich context. Specifically, multi-level
Discrete Wavelet Transform (DWT) is naturally integrated to release computation
burden while preserve more spatial details, along with a Wavelet Smooth Loss
(WSL) to reconstruct original structured context and texture with a smooth
constrain. Experiments on several UHR datasets demonstrate its state-of-the-art
performance. The dataset is available at https://github.com/jankyee/URUR.