基于单阶段GANs的文本生成图像模型

Text to image generation based on single-stage GANs

针对目前生成以文本为条件的图像通常会遇到生成质量差、训练不稳定的问题,提出了通过单阶段生成对抗网络(GANs)生成高质量图像的模型。具体而言,在GANs的生成器中引入注意力机制生成细粒度的图像,同时通过在判别器中添加局部-全局语言表示,来精准地鉴别生成图像和真实图像;通过生成器和判别器之间的相互博弈,最终生成高质量图像。在基准数据集上的实验结果表明,与具有多阶段框架的最新模型相比,该模型生成的图像更加真实且取得了当前最高的IS值,能够较好地应用于通过文本描述生成图像的场景。

For the current generation of images conditioned on text usually encounters the problems of poor quality and unstable training,a model for generating high-quality images through single-stage generative adversarial networks(GANs)is proposed.Specifically,the attention mechanism is introduced into the generator to generate fine-grained images,also,local language is added to the discriminator to indicate accurate discrimination between the generated image and the real image.Finally,a high-quality image is generated through the mutual game of the generator and the discriminator.The experimental results on the benchmark dataset show that,compared with the latest model with a multi-stage framework,the image generated by the model is more realistic and achieves the highest IS value,which can be better applied to scenes that generate images through text descriptions.