Referring expression comprehension (REC) aims to localize a target object in
an image described by a referring expression phrased in natural language.
Different from the object detection task that queried object labels have been
pre-defined, the REC problem only can observe the queries during the test. It
thus more challenging than a conventional computer vision problem. This task
has attracted a lot of attention from both computer vision and natural language
processing community, and several lines of work have been proposed, from
CNN-RNN model, modular network to complex graph-based model. In this survey, we
first examine the state of the art by comparing modern approaches to the
problem. We classify methods by their mechanism to encode the visual and
textual modalities. In particular, we examine the common approach of joint
embedding images and expressions to a common feature space. We also discuss
modular architectures and graph-based models that interface with structured
graph representation. In the second part of this survey, we review the datasets
available for training and evaluating REC systems. We then group results
according to the datasets, backbone models, settings so that they can be fairly
compared. Finally, we discuss promising future directions for the field, in
particular the compositional referring expression comprehension that requires
longer reasoning chain to address.

本文调查了现代方法解决自然语言与计算机视觉领域中的指代表达理解问题，包括用于编码视觉和文本模态的机制，结构化图表示的模块化架构和基于图形的模型，以及针对小型数据集的结果，提出了复合指称表述理解方向的潜在未来研究方向。

指代表达理解：方法与数据集综述

Referring Expression Comprehension: A Survey of Methods and Datasets

Most syntactic dependency parsing models may fall into one of two categories:
transition- and graph-based models. The former models enjoy high inference
efficiency with linear time complexity, but they rely on the stacking or
re-ranking of partially-built parse trees to build a complete parse tree and
are stuck with slower training for the necessity of dynamic oracle training.
The latter, graph-based models, may boast better performance but are
unfortunately marred by polynomial time inference. In this paper, we propose a
novel parsing order objective, resulting in a novel dependency parsing model
capable of both global (in sentence scope) feature extraction as in graph
models and linear time inference as in transitional models. The proposed global
greedy parser only uses two arc-building actions, left and right arcs, for
projective parsing. When equipped with two extra non-projective arc-building
actions, the proposed parser may also smoothly support non-projective parsing.
Using multiple benchmark treebanks, including the Penn Treebank (PTB), the
CoNLL-X treebanks, and the Universal Dependency Treebanks, we evaluate our
parser and demonstrate that the proposed novel parser achieves good performance
with faster training and decoding.

本文提出了一种能够同时进行全局特征提取和线性时间推理的依存句法分析模型，支持投影和非投影句子的解析，经检验表现良好。