One of the primary challenges faced by deep learning is the degree to which
current methods exploit superficial statistics and dataset bias, rather than
learning to generalise over the specific representations they have experienced.
This is a critical concern because generalisation enables robust reasoning over
unseen data, whereas leveraging superficial statistics is fragile to even small
changes in data distribution. To illuminate the issue and drive progress
towards a solution, we propose a test that explicitly evaluates abstract
reasoning over visual data. We introduce a large-scale benchmark of visual
questions that involve operations fundamental to many high-level vision tasks,
such as comparisons of counts and logical operations on complex visual
properties. The benchmark directly measures a method's ability to infer
high-level relationships and to generalise them over image-based concepts. It
includes multiple training/test splits that require controlled levels of
generalization. We evaluate a range of deep learning architectures, and find
that existing models, including those popular for vision-and-language tasks,
are unable to solve seemingly-simple instances. Models using relational
networks fare better but leave substantial room for improvement.

我们提出了一个可衡量深度学习模型在视觉问题中推理抽象关系和概括概念能力的测试基准，并评估了多种深度学习架构的测试结果，其中相对网络表现较好。

V-PROM：使用视觉渐进矩阵进行视觉推理的基准测试

V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive  Matrices

In this paper, we propose a novel 3D human pose estimation algorithm from a
single image based on neural networks. We adopted the structure of the
relational networks in order to capture the relations among different body
parts. In our method, each pair of different body parts generates features, and
the average of the features from all the pairs are used for 3D pose estimation.
In addition, we propose a dropout method that can be used in relational
modules, which inherently imposes robustness to the occlusions. The proposed
network achieves state-of-the-art performance for 3D pose estimation in Human
3.6M dataset, and it effectively produces plausible results even in the
existence of missing joints.

本文提出了一种基于神经网络的新型三维人体姿态估计算法，采用关系网络的结构来捕捉不同身体部位之间的关系，并使用每对不同身体部位生成的特征的平均值进行 3D 姿态估计，提出的网络在 Human 3.6M 数据集中实现了最先进的性能，并能在缺失关节的情况下有效地产生可信的结果。

基于关系网络的 3D 人体姿态估计

3D Human Pose Estimation with Relational Networks

We address the problem of semi-supervised learning in relational networks,
networks in which nodes are entities and links are the relationships or
interactions between them. Typically this problem is confounded with the
problem of graph-based semi-supervised learning (GSSL), because both problems
represent the data as a graph and predict the missing class labels of nodes.
However, not all graphs are created equally. In GSSL a graph is constructed,
often from independent data, based on similarity. As such, edges tend to
connect instances with the same class label. Relational networks, however, can
be more heterogeneous and edges do not always indicate similarity. For
instance, instead of links being more likely to connect nodes with the same
class label, they may occur more frequently between nodes with different class
labels (link-heterogeneity). Or nodes with the same class label do not
necessarily have the same type of connectivity across the whole network
(class-heterogeneity), e.g. in a network of sexual interactions we may observe
links between opposite genders in some parts of the graph and links between the
same genders in others. Performing classification in networks with different
types of heterogeneity is a hard problem that is made harder still when we do
not know a-priori the type or level of heterogeneity. Here we present two
scalable approaches for graph-based semi-supervised learning for the more
general case of relational networks. We demonstrate these approaches on
synthetic and real-world networks that display different link patterns within
and between classes. Compared to state-of-the-art approaches, ours give better
classification performance without prior knowledge of how classes interact. In
particular, our two-step label propagation algorithm gives consistently good
accuracy and runs on networks of over 1.6 million nodes and 30 million edges in
around 12 seconds.

本文针对关系网络中的半监督学习问题提出了两种可扩展的方法，可以更好地处理链接多样性和分类异质性等图结构异质性，相比于现有算法，我们的方法在不需要先验知识的情况下提供更好的分类性能。