7 papers with code • 7 benchmarks • 3 datasets
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
Ranked #1 on Image-to-Image Translation on Aerial-to-Map
In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map.
Ranked #1 on Cross-View Image-to-Image Translation on Ego2Top
Accurate environment perception is essential for automated driving.
Ranked #1 on Semantic Segmentation on Cam2BEV
For this, we propose to use homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image.
Next, for each building in the query image, we retrieve the $k$ nearest neighbors from the reference buildings using a Siamese network trained on both positive matching image pairs and negative pairs.