Semi-supervised learning on graphs is a widely applicable problem in network
science and machine learning. Two standard algorithms -- label propagation and
graph neural networks -- both operate by repeatedly passing information along
edges, the former by passing labels and the latter by passing node features,
modulated by neural networks. These two types of algorithms have largely
developed separately, and there is little understanding about the structure of
network data that would make one of these approaches work particularly well
compared to the other or when the approaches can be meaningfully combined.
Here, we develop a Markov random field model for the data generation process of
node attributes, based on correlations of attributes on and between vertices,
that motivates and unifies these algorithmic approaches. We show that label
propagation, a linearized graph convolutional network, and their combination
can all be derived as conditional expectations under our model, when
conditioning on different attributes. In addition, the data model highlights
deficiencies in existing graph neural networks (while producing new algorithmic
solutions), serves as a rigorous statistical framework for understanding graph
learning issues such as over-smoothing, creates a testbed for evaluating
inductive learning performance, and provides a way to sample graphs attributes
that resemble empirical data. We also find that a new algorithm derived from
our data generation model, which we call a Linear Graph Convolution, performs
extremely well in practice on empirical data, and provide theoretical
justification for why this is the case.

本文提出了一个马尔可夫随机场模型来统一解释半监督学习中的标签传播和图神经网络算法， 并给出了一个新的算法叫做线性图卷积， 在实际数据上表现出色。

一种统一的生成模型用于图学习算法：标签传播、图卷积和组合

A Unifying Generative Model for Graph Learning Algorithms: Label  Propagation, Graph Convolutions, and Combinations

Many applications such as autonomous navigation, urban planning and asset
monitoring, rely on the availability of accurate information about objects and
their geolocations. In this paper we propose to automatically detect and
compute the GPS coordinates of recurring stationary objects of interest using
street view imagery. Our processing pipeline relies on two fully convolutional
neural networks: the first segments objects in the images while the second
estimates their distance from the camera. To geolocate all the detected objects
coherently we propose a novel custom Markov Random Field model to perform
objects triangulation. The novelty of the resulting pipeline is the combined
use of monocular depth estimation and triangulation to enable automatic mapping
of complex scenes with multiple visually similar objects of interest. We
validate experimentally the effectiveness of our approach on two object
classes: traffic lights and telegraph poles. The experiments report high object
recall rates and GPS accuracy within 2 meters, which is comparable with the
precision of single-frequency GPS receivers.

本文提出了一种基于街景图像的自动检测并计算感兴趣的重复固定对象的 GPS 坐标的方法，使用两个全卷积神经网络进行处理并使用一种新颖的自定义马尔可夫随机场模型进行三角化，实验证明了本方法的有效性。

街景图像中物体的自动发现和地理标记

Automatic Discovery and Geotagging of Objects from Street View Imagery

We present a data-driven approach that colorizes 3D furniture models and
indoor scenes by leveraging indoor images on the internet. Our approach is able
to colorize the furniture automatically according to an example image. The core
is to learn image-guided mesh segmentation to segment the model into different
parts according to the image object. Given an indoor scene, the system supports
colorization-by-example, and has the ability to recommend the colorization
scheme that is consistent with a user-desired color theme. The latter is
realized by formulating the problem as a Markov random field model that imposes
user input as an additional constraint. We contribute to the community a
hierarchically organized image-model database with correspondences between each
image and the corresponding model at the part-level. Our experiments and a user
study show that our system produces perceptually convincing results comparable
to those generated by interior designers.

该文研究了基于数据驱动的方法对 3D 家具模型和室内场景进行着色，并使用互联网上的室内图像进行引导。使用图像引导网格分割将模型分成不同的部分，以提高着色效果，并以马尔可夫随机场模型实现一致的着色方案。实验结果表明，该系统可以产生与室内设计师生成的结果相媲美且具有说服力的结果。