  CNNs are often assumed to be capable of using contextual information about
distinct objects (such as their directional relations) inside their receptive
field. However, the nature and limits of this capacity has never been explored
in full. We explore a specific type of relationship~-- directional~-- using a
standard U-Net trained to optimize a cross-entropy loss function for
segmentation. We train this network on a pretext segmentation task requiring
directional relation reasoning for success and state that, with enough data and
a sufficiently large receptive field, it succeeds to learn the proposed task.
We further explore what the network has learned by analysing scenarios where
the directional relationships are perturbed, and show that the network has
learned to reason using these relationships.
