论文标题
SimPropnet：改进的相似性传播，用于几个图像分割
SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation
论文作者
论文摘要
使用一组（支持）图像掩码对的目标（查询）图像中的特定对象类（QUERY）图像中的特定对象类的图像分割（FSS）方法对特定对象类执行图像分割。最新的基于神经网络的FSS方法利用了支持图像的前景特征与查询图像特征之间的高维特征相似性。在这项工作中，我们证明了现有方法中这种相似性信息的利用差距，并提出了一个框架-SimPropnet，以弥合这些差距。我们建议共同预测支持和查询口罩，以迫使支持功能与查询功能共享特征。我们还建议在查询的背景区域中使用相似性，并使用一种新型的前景式融合机制来支持图像。我们的方法可在Pascal-5i数据集中获得一杆和五弹性分割的最新结果。本文包括针对拟议的改进和与当代方法进行定量比较的详细分析和消融研究。
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.
