In this paper, we study the graphic layout generation problem of producing
high-quality visual-textual presentation designs for given images. We note that
image compositions, which contain not only global semantics but also spatial
information, would largely affect layout results. Hence, we propose a deep
generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to
synthesize layouts based on the global and spatial visual contents of input
images. To obtain training images from images that already contain manually
designed graphic layout data, previous work suggests masking design elements
(e.g., texts and embellishments) as model inputs, which inevitably leaves hint
of the ground truth. We study the misalignment between the training inputs
(with hint masks) and test inputs (without masks), and design a novel domain
alignment module (DAM) to narrow this gap. For training, we built a large-scale
layout dataset which consists of 60,548 advertising posters with annotated
layout information. To evaluate the generated layouts, we propose three novel
metrics according to aesthetic intuitions. Through both quantitative and
qualitative evaluations, we demonstrate that the proposed model can synthesize
high-quality graphic layouts according to image compositions.

本文提出了一种基于图像元素的深度生成模型 CGL-GAN，并设计了域对齐模块 DAM 来缩小训练和测试输入之间的差距，进而生成符合美感直觉的高质量图形布局。

面向视觉文本呈现设计的组合感知图形布局生成对抗网络

Composition-aware Graphic Layout GAN for Visual-textual Presentation  Designs

Aesthetic image cropping is a practical but challenging task which aims at
finding the best crops with the highest aesthetic quality in an image.
Recently, many deep learning methods have been proposed to address this
problem, but they did not reveal the intrinsic mechanism of aesthetic
evaluation. In this paper, we propose an interpretable image cropping model to
unveil the mystery. For each image, we use a fully convolutional network to
produce an aesthetic score map, which is shared among all candidate crops
during crop-level aesthetic evaluation. Then, we require the aesthetic score
map to be both composition-aware and saliency-aware. In particular, the same
region is assigned with different aesthetic scores based on its relative
positions in different crops. Moreover, a visually salient region is supposed
to have more sensitive aesthetic scores so that our network can learn to place
salient objects at more proper positions. Such an aesthetic score map can be
used to localize aesthetically important regions in an image, which sheds light
on the composition rules learned by our model. We show the competitive
performance of our model in the image cropping task on several benchmark
datasets, and also demonstrate its generality in real-world applications.

该研究提出了一种可解释的图像裁剪模型，使用深度学习方法生成美学评分地图，在裁剪级别的美学评价中考虑图像的组合和显著性，从而实现高质量的美学图像裁剪。