Images or videos always contain multiple objects or actions. Multi-label
recognition has been witnessed to achieve pretty performance attribute to the
rapid development of deep learning technologies. Recently, graph convolution
network (GCN) is leveraged to boost the performance of multi-label recognition.
However, what is the best way for label correlation modeling and how feature
learning can be improved with label system awareness are still unclear. In this
paper, we propose a label graph superimposing framework to improve the
conventional GCN+CNN framework developed for multi-label recognition in the
following two aspects. Firstly, we model the label correlations by
superimposing label graph built from statistical co-occurrence information into
the graph constructed from knowledge priors of labels, and then multi-layer
graph convolutions are applied on the final superimposed graph for label
embedding abstraction. Secondly, we propose to leverage embedding of the whole
label system for better representation learning. In detail, lateral connections
between GCN and CNN are added at shallow, middle and deep layers to inject
information of label system into backbone CNN for label-awareness in the
feature learning process. Extensive experiments are carried out on MS-COCO and
Charades datasets, showing that our proposed solution can greatly improve the
recognition performance and achieves new state-of-the-art recognition
performance.

本研究提出一种基于标签图叠加框架的多标签识别解决方案，以图卷积网络（GCN）为基础，通过超 impose 技术建立标签相互作用图，并将其与 GCN 及 CNN 的信息交流，以提高特征学习和数据表示。实验表明，该方法大大提高了多标签识别的性能且达到了新的最佳表现。