Synthetic Convolutional Features for Improved Semantic Segmentation

He, Yang and Schiele, Bernt and Fritz, Mario
(2020) Synthetic Convolutional Features for Improved Semantic Segmentation.
In: Workshop on Assistive Computer Vision and Robotics at European Conference on Computer Vision (ECCV-Workshop).
Conference: ACVR International Workshop on Assistive Computer Vision and Robotics

Full text not available from this repository.
Official URL: https://arxiv.org/abs/2009.08849 https://arxiv.org...

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.

Actions

Actions (login required)

View Item View Item