We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task.

翻译：本文提出一种新型文本到图像生成网络——自适应布局精炼生成对抗网络（ALR-GAN），旨在无需任何辅助信息的情况下自适应地精炼合成图像的布局。ALR-GAN包含自适应布局精炼（ALR）模块和布局视觉精炼（LVR）损失函数。ALR模块将合成图像的布局结构（即物体与背景的位置信息）与对应真实图像的布局结构对齐。在该模块中，我们提出自适应布局精炼（ALR）损失函数，通过平衡困难特征与简单特征的匹配，实现更高效的布局结构对齐。基于精炼后的布局结构，LVR损失函数进一步对布局区域内的视觉表征进行细化。在两个广泛使用的数据集上的实验结果表明，ALR-GAN在文本到图像生成任务中达到了具有竞争力的性能。