  In recent interactive segmentation algorithms, previous probability maps are
used as network input to help predictions in the current segmentation round.
However, despite the utilization of previous masks, useful information
contained in the probability maps is not well propagated to the current
predictions. In this paper, to overcome this limitation, we propose a novel and
effective algorithm for click-based interactive image segmentation, called MFP,
which attempts to make full use of probability maps. We first modulate previous
probability maps to enhance their representations of user-specified objects.
Then, we feed the modulated probability maps as additional input to the
segmentation network. We implement the proposed MFP algorithm based on the
ResNet-34, HRNet-18, and ViT-B backbones and assess the performance extensively
on various datasets. It is demonstrated that MFP meaningfully outperforms the
existing algorithms using identical backbones. The source codes are available
at https://github.com/cwlee00/MFP.
