Ms. Beibei Liu Profile

Beibei Liu

at Univ of Science and Technology of China

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Proceedings Article | 14 August 2019 Paper

Semi-supervised semantic image segmentation using dual discriminator adversarial networks

Beibei Liu, Bei Hua

Proceedings Volume 11179, 1117907 (2019) https://doi.org/10.1117/12.2539666

KEYWORDS: Image segmentation, Image fusion, Image processing, Network architectures, Data modeling, Convolutional neural networks, Image processing algorithms and systems

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Semantic image segmentation approaches based on convolutional neural networks require a large amount of pixel-level training data, but the labeling process is time consuming and laborious. In this paper, we propose a semi-supervised semantic segmentation method that can leverage unlabeled data in model training to alleviate the task of labeling. A novel GAN framework comprised of a generator network and a dual discriminator network is proposed, and the entire network is trained by coupling the standard multi-class cross entropy loss with the adversarial loss. To further improve the localization of object boundaries, a self-attention layer is added to the generator network to model long-range dependencies in images, and a skip layer is also added to combine deep layer with highly abstract information and shallow layer with detailed appearance information. The dual discriminator network includes a fully convolutional discriminator and a typical GAN discriminator, so that the input image can be discriminated on both pixel level and image level. For semi-supervised semantic segmentation, the predicted segmentation results of unlabeled images are selected by image-level discriminator, and then their trustworthy regions are generated by pixel-level discriminator to provide additional supervisory signals. Extensive experiments on PASCAL VOC 2012 dataset demonstrate that our approach outperforms existing semi-supervised semantic image segmentation methods on accuracy.

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