  Compared with common image segmentation tasks targeted at low-resolution
images, higher resolution detailed image segmentation receives much less
attention. In this paper, we propose and study a task named Meticulous Object
Segmentation (MOS), which is focused on segmenting well-defined foreground
objects with elaborate shapes in high resolution images (e.g. 2k - 4k). To this
end, we propose the MeticulousNet which leverages a dedicated decoder to
capture the object boundary details. Specifically, we design a Hierarchical
Point-wise Refining (HierPR) block to better delineate object boundaries, and
reformulate the decoding process as a recursive coarse to fine refinement of
the object mask. To evaluate segmentation quality near object boundaries, we
propose the Meticulosity Quality (MQ) score considering both the mask coverage
and boundary precision. In addition, we collect a MOS benchmark dataset
including 600 high quality images with complex objects. We provide
comprehensive empirical evidence showing that MeticulousNet can reveal
pixel-accurate segmentation boundaries and is superior to state-of-the-art
methods for high resolution object segmentation tasks.
