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.

提出一种利用物体检测和新的评估指标 SOA 的文本图像生成模型， 并通过用户研究和定量评估表明，明确建模物体的模型优于仅建模全局图像特征，并且使用 SOA 指标可以更精确地衡量生成图像与图像描述的一致性。