Synthetic Convolutional Features for Improved Semantic Segmentation

Abstract

Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss. However, it remains challenging to successfully leverage synthetic data for improving semantic segmentation with additional synthetic images. Therefore, we suggest to generate intermediate convolutional features and propose the first synthesis approach that is catered to such intermediate convolutional features. This allows us to generate new features from label masks and include them successfully into the training procedure in order to improve the performance of semantic segmentation. Experimental results and analysis on two challenging datasets Cityscapes and ADE20K show that our generated feature improves performance on segmentation tasks.

arXiv commentECCV 2020 Workshop on Assistive Computer Vision and Robotics

References in corpus (8)

• Conditional Generative Adversarial Nets
• Generative Adversarial Text to Image Synthesis
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Data Augmentation Generative Adversarial Networks
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
• Semantic Segmentation using Adversarial Networks
• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
• Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing