Few-shot segmentation (FSS) methods perform image segmentation for a
particular object class in a target (query) image, using a small set of
(support) image-mask pairs. Recent deep neural network based FSS methods
leverage high-dimensional feature similarity between the foreground features of
the support images and the query image features. In this work, we demonstrate
gaps in the utilization of this similarity information in existing methods, and
present a framework - SimPropNet, to bridge those gaps. We propose to jointly
predict the support and query masks to force the support features to share
characteristics with the query features. We also propose to utilize
similarities in the background regions of the query and support images using a
novel foreground-background attentive fusion mechanism. Our method achieves
state-of-the-art results for one-shot and five-shot segmentation on the
PASCAL-5i dataset. The paper includes detailed analysis and ablation studies
for the proposed improvements and quantitative comparisons with contemporary
methods.

本文提出了一种叫做 SimPropNet 的框架，利用前景背景关注融合机制，同时预测支持和查询掩模，以共享特征信息来填补现有方法在利用相似性信息方面的不足，其在 PASCAL-5i 数据集上实现了最先进的一次和五次分割结果。