  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.

Computer Vision and Pattern Recognition;Artificial Intelligence