The task of multi-label image recognition is to predict a set of object
labels that present in an image. As objects normally co-occur in an image, it
is desirable to model the label dependencies to improve the recognition
performance. To capture and explore such important dependencies, we propose a
multi-label classification model based on Graph Convolutional Network (GCN).
The model builds a directed graph over the object labels, where each node
(label) is represented by word embeddings of a label, and GCN is learned to map
this label graph into a set of inter-dependent object classifiers. These
classifiers are applied to the image descriptors extracted by another sub-net,
enabling the whole network to be end-to-end trainable. Furthermore, we propose
a novel re-weighted scheme to create an effective label correlation matrix to
guide information propagation among the nodes in GCN. Experiments on two
multi-label image recognition datasets show that our approach obviously
outperforms other existing state-of-the-art methods. In addition, visualization
analyses reveal that the classifiers learned by our model maintain meaningful
semantic topology.

提出了一种基于图卷积网络的多标签分类模型，通过建立对象标签之间的图，学习一个对相互依赖的物体分类器的表示，同时使用重新加权的方法来创建有效的标签相关矩阵以指导 GCN 中节点之间的信息传播，在两个多标签图像识别数据集上实验证明了方法的有效性，并且可视化分析表明该模型学习到的分类器保持有意义的语义拓扑结构。