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 \href{https://github.com/cwlee00/MFP}{https://github.com/cwlee00/MFP}.

翻译：在最近的交互式分割算法中，先前的概率图被用作网络输入以帮助当前分割轮的预测。然而，尽管利用了先前的掩码，概率图中包含的有用信息并未被很好地传播到当前预测中。本文针对这一局限性，提出了一种新颖且有效的基于点击的交互式图像分割算法，称为MFP，旨在充分利用概率图。我们首先对先前的概率图进行调制，以增强其对用户指定对象的表征。然后，将调制后的概率图作为额外输入送入分割网络。我们基于ResNet-34、HRNet-18和ViT-B骨干网络实现了所提出的MFP算法，并在多个数据集上进行了广泛性能评估。结果表明，MFP在使用相同骨干网络的情况下显著优于现有算法。源代码可在\href{https://github.com/cwlee00/MFP}{https://github.com/cwlee00/MFP}获取。