  Generating realistic images from scene graphs asks neural networks to be able
to reason about object relationships and compositionality. As a relatively new
task, how to properly ensure the generated images comply with scene graphs or
how to measure task performance remains an open question. In this paper, we
propose to harness scene graph context to improve image generation from scene
graphs. We introduce a scene graph context network that pools features
generated by a graph convolutional neural network that are then provided to
both the image generation network and the adversarial loss. With the context
network, our model is trained to not only generate realistic looking images,
but also to better preserve non-spatial object relationships. We also define
two novel evaluation metrics, the relation score and the mean opinion relation
score, for this task that directly evaluate scene graph compliance. We use both
quantitative and qualitative studies to demonstrate that our pro-posed model
outperforms the state-of-the-art on this challenging task.
