Generative adversarial networks conditioned on simple 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 synthesis models is still challenging, as most evaluation metrics only judge image quality but not the conformity between the image and its caption. To address the aforementioned challenges we introduce both 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. Our model adds an object pathway to both the generator and the discriminator to explicitly learn features of individual objects. The SOA uses a pre-trained object detector to evaluate if a generated image contains objects that are specifically mentioned in the image caption, e.g. whether an image generated from "a car driving down the street" contains a car. Our evaluation shows that models which explicitly model individual objects outperform models which only model global image characteristics. However, the SOA also shows that despite this increased performance current models still struggle to generate images that contain realistic objects of multiple different domains.

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。

基于语义的生成式文本到图像合成的准确性