Variance-insensitive and Target-preserving Mask Refinement for
  Interactive Image Segmentation
Chaowei Fang Ziyin Zhou Junye Chen Hanjing Su Qingyao Wu Guanbin Li
Point-based interactive image segmentation can ease the burden of mask
annotation in applications such as semantic segmentation and image editing.
However, fully extracting the target mask with limited user inputs remains
challenging. We introduce a novel method, Variance-Insensitive and
Target-Preserving Mask Refinement to enhance segmentation quality with fewer
user inputs. Regarding the last segmentation result as the initial mask, an
iterative refinement process is commonly employed to continually enhance the
initial mask. Nevertheless, conventional techniques suffer from sensitivity to
the variance in the initial mask. To circumvent this problem, our proposed
method incorporates a mask matching algorithm for ensuring consistent
inferences from different types of initial masks. We also introduce a
target-aware zooming algorithm to preserve object information during
downsampling, balancing efficiency and accuracy. Experiments on GrabCut,
Berkeley, SBD, and DAVIS datasets demonstrate our method's state-of-the-art
performance in interactive image segmentation.