  Image co-segmentation is an active computer vision task which aims to segment
the common objects in a set of images. Recently, researchers design various
learning-based algorithms to handle the co-segmentation task. The main
difficulty in this task is how to effectively transfer information between
images to infer the common object regions. In this paper, we present
CycleSegNet, a novel framework for the co-segmentation task. Our network design
has two key components: a region correspondence module which is the basic
operation for exchanging information between local image regions, and a cycle
refinement module which utilizes ConvLSTMs to progressively update image
embeddings and exchange information in a cycle manner. Experiment results on
four popular benchmark datasets -- PASCAL VOC dataset, MSRC dataset, Internet
dataset and iCoseg dataset demonstrate that our proposed method significantly
outperforms the existing networks and achieves new state-of-the-art
performance.
