  In this work we introduce a biologically inspired long-range skip connection
for the UNet architecture that relies on the perceptual illusion of hybrid
images, being images that simultaneously encode two images. The fusion of early
encoder features with deeper decoder ones allows UNet models to produce
finer-grained dense predictions. While proven in segmentation tasks, the
network's benefits are down-weighted for dense regression tasks as these
long-range skip connections additionally result in texture transfer artifacts.
Specifically for depth estimation, this hurts smoothness and introduces false
positive edges which are detrimental to the task due to the depth maps'
piece-wise smooth nature. The proposed HybridSkip connections show improved
performance in balancing the trade-off between edge preservation, and the
minimization of texture transfer artifacts that hurt smoothness. This is
achieved by the proper and balanced exchange of information that Hybrid-Skip
connections offer between the high and low frequency, encoder and decoder
features, respectively.

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing;Image and Video Processing