Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks

Understand

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.