Conditional Independence (CI) graph is a special type of a Probabilistic
Graphical Model (PGM) where the feature connections are modeled using an
undirected graph and the edge weights show the partial correlation strength
between the features. Since the CI graphs capture direct dependence between
features, they have been garnering increasing interest within the research
community for gaining insights into the systems from various domains, in
particular discovering the domain topology. In this work, we propose algorithms
for performing knowledge propagation over the CI graphs. Our experiments
demonstrate that our techniques improve upon the state-of-the-art on the
publicly available Cora and PubMed datasets.

本研究提出了在条件独立图上进行知识传播的算法，实验证明该技术在公开可用的 Cora 和 PubMed 数据集上优于现有技术。

条件独立图上的知识传播

Knowledge Propagation over Conditional Independence Graphs

Semantic concept hierarchy is still under-explored for semantic segmentation
due to the inefficiency and complicated optimization of incorporating
structural inference into dense prediction. This lack of modeling semantic
correlations also makes prior works must tune highly-specified models for each
task due to the label discrepancy across datasets. It severely limits the
generalization capability of segmentation models for open set concept
vocabulary and annotation utilization. In this paper, we propose a
Dynamic-Structured Semantic Propagation Network (DSSPN) that builds a semantic
neuron graph by explicitly incorporating the semantic concept hierarchy into
network construction. Each neuron represents the instantiated module for
recognizing a specific type of entity such as a super-class (e.g. food) or a
specific concept (e.g. pizza). During training, DSSPN performs the
dynamic-structured neuron computation graph by only activating a sub-graph of
neurons for each image in a principled way. A dense semantic-enhanced neural
block is proposed to propagate the learned knowledge of all ancestor neurons
into each fine-grained child neuron for feature evolving. Another merit of such
semantic explainable structure is the ability of learning a unified model
concurrently on diverse datasets by selectively activating different neuron
sub-graphs for each annotation at each step. Extensive experiments on four
public semantic segmentation datasets (i.e. ADE20K, COCO-Stuff, Cityscape and
Mapillary) demonstrate the superiority of our DSSPN over state-of-the-art
segmentation models. Moreoever, we demonstrate a universal segmentation model
that is jointly trained on diverse datasets can surpass the performance of the
common fine-tuning scheme for exploiting multiple domain knowledge.

本文提出了一种动态结构语义传播网络 (DSSPN)，利用语义概念层次结构来构建语义神经元图，并通过神经元图的子图激活方式以及密集的语义增强神经块来优化语义分割性能。