Visual relation extraction (VRE) aims to extract relations between entities
from visuallyrich documents. Existing methods usually predict relations for
each entity pair independently based on entity features but ignore the global
structure information, i.e., dependencies between entity pairs. The absence of
global structure information may make the model struggle to learn long-range
relations and easily predict conflicted results. To alleviate such limitations,
we propose a GlObal Structure knowledgeguided relation Extraction (GOSE)
framework, which captures dependencies between entity pairs in an iterative
manner. Given a scanned image of the document, GOSE firstly generates
preliminary relation predictions on entity pairs. Secondly, it mines global
structure knowledge based on prediction results of the previous iteration and
further incorporates global structure knowledge into entity representations.
This "generate-capture-incorporate" schema is performed multiple times so that
entity representations and global structure knowledge can mutually reinforce
each other. Extensive experiments show that GOSE not only outperforms previous
methods on the standard fine-tuning setting but also shows promising
superiority in cross-lingual learning; even yields stronger data-efficient
performance in the low-resource setting.

该论文提出了一种 GOSE 框架来进行视觉关系抽取，该框架通过迭代方式捕捉实体对之间的依赖关系以及全局结构知识，并将其融合到实体表示中，实现了实体表示和全局结构知识的相互增强，该方法在标准微调设置下，以及跨语言学习和低资源设置下均表现出有希望的优越性。

面向视觉化文档的全局结构知识引导关系抽取方法

Global Structure Knowledge-Guided Relation Extraction Method for  Visually-Rich Document

This paper presents a view-guided solution for the task of point cloud
completion. Unlike most existing methods directly inferring the missing points
using shape priors, we address this task by introducing ViPC (view-guided point
cloud completion) that takes the missing crucial global structure information
from an extra single-view image. By leveraging a framework that sequentially
performs effective cross-modality and cross-level fusions, our method achieves
significantly superior results over typical existing solutions on a new
large-scale dataset we collect for the view-guided point cloud completion task.

本文提出了一种基于视角引导的点云补全解决方案，通过从额外单视图图像中获取缺失的关键全局结构信息，利用依次执行有效跨模态和跨层次融合的框架，相比于现有解决方案在视角引导点云完形填空任务上取得了显著优越的结果。

视角引导下的点云补全

View-Guided Point Cloud Completion

Previous work approaches the SQL-to-text generation task using vanilla
Seq2Seq models, which may not fully capture the inherent graph-structured
information in SQL query. In this paper, we first introduce a strategy to
represent the SQL query as a directed graph and then employ a graph-to-sequence
model to encode the global structure information into node embeddings. This
model can effectively learn the correlation between the SQL query pattern and
its interpretation. Experimental results on the WikiSQL dataset and
Stackoverflow dataset show that our model significantly outperforms the Seq2Seq
and Tree2Seq baselines, achieving the state-of-the-art performance.

本文提出了一种将 SQL 查询表示为有向图的策略，并采用图到序列模型将全局结构信息编码成节点嵌入的方法，从而有效地学习 SQL 查询模式和其解释之间的相关性，实验结果表明，我们的模型在 WikiSQL 数据集和 Stackoverflow 数据集上表现显著优于 Seq2Seq 和 Tree2Seq 基线模型，达到了最先进的性能水平。