CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence
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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.
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