Content-aware graphic layout generation aims to automatically arrange visual
elements along with a given content, such as an e-commerce product image. In
this paper, we argue that the current layout generation approaches suffer from
the limited training data for the high-dimensional layout structure. We show
that a simple retrieval augmentation can significantly improve the generation
quality. Our model, which is named Retrieval-Augmented Layout Transformer
(RALF), retrieves nearest neighbor layout examples based on an input image and
feeds these results into an autoregressive generator. Our model can apply
retrieval augmentation to various controllable generation tasks and yield
high-quality layouts within a unified architecture. Our extensive experiments
show that RALF successfully generates content-aware layouts in both constrained
and unconstrained settings and significantly outperforms the baselines.

本文介绍了一种名为 RALF 的模型，它通过检索相似的布局示例来提高内容感知图形布局的生成质量，并成功地在不受限制和受限制的环境中生成高质量的布局。

检索增强式布局变换器用于内容感知布局生成

Retrieval-Augmented Layout Transformer for Content-Aware Layout  Generation

Current research in form understanding predominantly relies on large
pre-trained language models, necessitating extensive data for pre-training.
However, the importance of layout structure (i.e., the spatial relationship
between the entity blocks in the visually rich document) to relation extraction
has been overlooked. In this paper, we propose REgion-Aware Relation Extraction
(RE$^2$) that leverages region-level spatial structure among the entity blocks
to improve their relation prediction. We design an edge-aware graph attention
network to learn the interaction between entities while considering their
spatial relationship defined by their region-level representations. We also
introduce a constraint objective to regularize the model towards consistency
with the inherent constraints of the relation extraction task. Extensive
experiments across various datasets, languages and domains demonstrate the
superiority of our proposed approach.

本文提出了一种基于区域感知的关系抽取方法 (RE$^2$)，它利用边缘感知的图注意力网络学习实体之间的交互，并考虑实体之间由其区域级表示定义的空间关系，同时引入约束目标以使模型符合关系抽取任务的内在约束。实验结果表明，我们的方法在各种数据集、语言和领域中具有优越性。