This study is concerned with few-shot segmentation, i.e., segmenting the region of an unseen object class in a query image, given support image(s) of its instances. The current methods rely on the pretrained CNN features of the support and query images. The key to good performance depends on the proper fusion of their mid-level and high-level features; the former contains shape-oriented information, while the latter has class-oriented information. Current state-of-the-art methods follow the approach of Tian et al., which gives the mid-level features the primary role and the high-level features the secondary role. In this paper, we reinterpret this widely employed approach by redifining the roles of the multi-level features; we swap the primary and secondary roles. Specifically, we regard that the current methods improve the initial estimate generated from the high-level features using the mid-level features. This reinterpretation suggests a new application of the current methods: to apply the same network multiple times to iteratively update the estimate of the object's region, starting from its initial estimate. Our experiments show that this method is effective and has updated the previous state-of-the-art on COCO-20$^i$ in the 1-shot and 5-shot settings and on PASCAL-5$^i$ in the 1-shot setting.
翻译:本研究涉及几个片段,即将一个不可见的物体类别区域分割成一个查询图像区域,以其实例的辅助图像为背景。目前的方法依靠经过预先训练的CNN支持和查询图像的CNN功能。良好表现的关键取决于其中高层次特征的适当融合;前者包含面向形状的信息,而后者则有面向阶级的信息。目前最先进的方法采用天等(Tian et al.)的方法,使中层特征的主要作用和高层次特征成为次要特征。在本文件中,我们重新解释这一广泛采用的方法,对多层次特征的作用进行重新配置;我们交换主要和次要作用。具体地说,我们认为,目前的方法改进了利用中层特征从高层特征得出的初步估计数得出的初步估计数。重新解释表明,从最初估计开始,采用同样的网络多次更新对目标区域的估计。我们的实验显示,这一方法是有效的,并且更新了前一阶段的PA-25美元和前一阶段的PA-SA-SA-SA-25美元。