MFP: Making Full Use of Probability Maps for Interactive Image Segmentation

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 framework for click-based interactive image segmentation, called MFP, 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 four datasets: GrabCut, Berkeley, DAVIS, and SBD. It is demonstrated that MFP meaningfully outperforms the existing algorithms using identical backbones.