Boundary Perception Guidance: A Scribble-SupervisedSemantic Segmentation Approach
Abstract
Semantic segmentation suffers from the fact that
densely annotated masks are expensive to obtain.
To tackle this problem, we aim at learning to segment by only leveraging scribbles that are much
easier to collect for supervision. To fully explore
the limited pixel-level annotations from scribbles,
we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic
components, i.e. prediction refinement and boundary regression. Specifically, the prediction refinement progressively makes a better segmentation by
adopting an iterative upsampling and a semantic
feature enhancement strategy. In the boundary regression, we employ class-agnostic edge maps for
supervision to effectively guide the segmentation
network in localizing the boundaries between different semantic regions, leading to producing finegrained representation of feature maps for semantic segmentation. Experimental results on the PASCAL VOC 2012 demonstrate the proposed BPG
achieves mIoU of 73.2% without fully connected
Conditional Random Field (CRF) and 76.0% with
CRF, setting up the new state-of-the-art in literature.