Object-driven text-to-image synthesis via adversarial training

In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow attention-driven, multi-stage refinement for synthesizing complex images from text descriptions. With a novel object-driven attentive generative network, the Obj-GAN can synthesize salient objects by paying attention to their most relevant words in the text descriptions and their pre-generated class label. In addition, a novel object-wise discriminator based on the Fast R-CNN model is proposed to provide rich object-wise discrimination signals on whether the synthesized object matches the text description and the pre-generated class label. The proposed Obj-GAN significantly outperforms the previous state of the art in various metrics on the large-scale MS-COCO benchmark, increasing the inception score by 27% and decreasing the FID score by 11%. A thorough comparison between the classic grid attention and the new object-driven attention is provided through analyzing their mechanisms and visualizing their attention layers, showing insights of how the proposed model generates complex scenes in high quality.