Two-Stream Non-Uniform Concentration Reasoning Network For Single Image Air Pollution Estimation

A difficulty of global-level translation is to preserve instance-level details in an image. Although some instance level translation methods can retain the details, most of them require either pre-trained object detection/segmentation network or annotation labels. in this work, we propose a novel method namely CyEDA to perform global level domain adaptation that can preserve image contents without any pre-trained networks integration or annotation labels. Specifically, we introduce blending masks and cycle-object edge consistency loss which exploit the preservation of image objects. We show that our approach can outperform other SOTAs in terms of image quality and FID score in both BDD100K and GTA datasets. The code and pre-trained models are publicly available at https://github.com/bjc1999/CyEDA.