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We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
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.
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.
X-Fork architecture has a single discriminator and a single generator.
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.
We use our network to address the task of estimating the geolocation and geoorientation of a ground image.
#6 best model for Cross-View Image-to-Image Translation on cvusa