  Generative adversarial networks conditioned on textual image descriptions are
capable of generating realistic-looking images. However, current methods still
struggle to generate images based on complex image captions from a
heterogeneous domain. Furthermore, quantitatively evaluating these
text-to-image models is challenging, as most evaluation metrics only judge
image quality but not the conformity between the image and its caption. To
address these challenges we introduce a new model that explicitly models
individual objects within an image and a new evaluation metric called Semantic
Object Accuracy (SOA) that specifically evaluates images given an image
caption. The SOA uses a pre-trained object detector to evaluate if a generated
image contains objects that are mentioned in the image caption, e.g. whether an
image generated from "a car driving down the street" contains a car. We perform
a user study comparing several text-to-image models and show that our SOA
metric ranks the models the same way as humans, whereas other metrics such as
the Inception Score do not. Our evaluation also shows that models which
explicitly model objects outperform models which only model global image
characteristics.
