CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence

By Guankai Li, Chi Zhang, Guosheng Lin. 2021

28 citations recorded by the source corpus.

Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images. Recently, researchers design various learning-based algorithms to undertake the co-segmentation task. The main difficulty in this task is how to effectively transfer information between images to make conditional predictions. 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 representations and exchange information in a cycle and iterative manner. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on four popular benchmark datasets — PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%, 2.2%, and 2.9%, respectively.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Deep ViT Features as Dense Visual Descriptors (2021, methodology): “Later, CNN-based methods applied supervised training [29] or fine-tuning [52, 27, 28] on intra-class co-segmentation datasets.”