基于层次嵌套对抗网络的图像文本合成
Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network
摘要：
本文提出了一种新的方法来处理具有挑战性的任务，生成摄影图像的语义图像描述的条件。我们的方法在网络层次结构中引入了伴随的层次嵌套对抗目标，这些目标规范了中间层表示，并帮助生成器训练捕获复杂的图像统计数据。我们提出了一个可扩展的单流生成器架构，以更好地适应联合鉴别器，并将生成的图像推到高分辨率。我们采用了多用途的对抗损失，以鼓励更有效的图像和文本信息的使用，以提高语义一致性和图像保真度的同时。此外，我们引入了一种新的视觉语义相似性度量来评估生成的图像的语义一致性。通过在三个公共数据集上进行广泛的实验验证，我们的方法在不同的评估指标上显着改善了所有数据集上的现有技术。
英文摘要：
This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions. Our method introduces accompanying hierarchical-nested adversarial objectives inside the network hierarchies, which regularize mid-level representations and assist generator training to capture the complex image statistics. We present an extensile single-stream generator architecture to better adapt the jointed discriminators and push generated images up to high resolutions. We adopt a multi-purpose adversarial loss to encourage more effective image and text information usage in order to improve the semantic consistency and image fidelity simultaneously. Furthermore, we introduce a new visual-semantic similarity measure to evaluate the semantic consistency of generated images. With extensive experimental validation on three public datasets, our method significantly improves previous state of the arts on all datasets over different evaluation metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
