  Semantic segmentation is one of the basic topics in computer vision, it aims
to assign semantic labels to every pixel of an image. Unbalanced semantic label
distribution could have a negative influence on segmentation accuracy. In this
paper, we investigate using data augmentation approach to balance the semantic
label distribution in order to improve segmentation performance. We propose
using generative adversarial networks (GANs) to generate realistic images for
improving the performance of semantic segmentation networks. Experimental
results show that the proposed method can not only improve segmentation
performance on those classes with low accuracy, but also obtain 1.3% to 2.1%
increase in average segmentation accuracy. It shows that this augmentation
method can boost accuracy and be easily applicable to any other segmentation
models.
