  This work presents the first convolutional neural network that learns an
image-to-graph translation task without needing external supervision. Obtaining
graph representations of image content, where objects are represented as nodes
and their relationships as edges, is an important task in scene understanding.
Current approaches follow a fully-supervised approach thereby requiring
meticulous annotations. To overcome this, we are the first to present a
self-supervised approach based on a fully-differentiable auto-encoder in which
the bottleneck encodes the graph's nodes and edges. This self-supervised
approach can currently encode simple line drawings into graphs and obtains
comparable results to a fully-supervised baseline in terms of F1 score on
triplet matching. Besides these promising results, we provide several
directions for future research on how our approach can be extended to cover
more complex imagery.
