CRNet: Cross-Reference Networks for Few-Shot Segmentation
- URL: http://arxiv.org/abs/2003.10658v1
- Date: Tue, 24 Mar 2020 04:55:43 GMT
- Title: CRNet: Cross-Reference Networks for Few-Shot Segmentation
- Authors: Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu
- Abstract summary: Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images.
With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images.
Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
- Score: 59.85183776573642
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the past few years, state-of-the-art image segmentation algorithms are
based on deep convolutional neural networks. To render a deep network with the
ability to understand a concept, humans need to collect a large amount of
pixel-level annotated data to train the models, which is time-consuming and
tedious. Recently, few-shot segmentation is proposed to solve this problem.
Few-shot segmentation aims to learn a segmentation model that can be
generalized to novel classes with only a few training images. In this paper, we
propose a cross-reference network (CRNet) for few-shot segmentation. Unlike
previous works which only predict the mask in the query image, our proposed
model concurrently make predictions for both the support image and the query
image. With a cross-reference mechanism, our network can better find the
co-occurrent objects in the two images, thus helping the few-shot segmentation
task. We also develop a mask refinement module to recurrently refine the
prediction of the foreground regions. For the $k$-shot learning, we propose to
finetune parts of networks to take advantage of multiple labeled support
images. Experiments on the PASCAL VOC 2012 dataset show that our network
achieves state-of-the-art performance.
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