Multi-Label Classification with Label Graph Superimposing


  • Ya Wang Peking University
  • Dongliang He Baidu Inc.
  • Fu Li Baidu Inc.
  • Xiang Long Baidu Inc.
  • Zhichao Zhou Baidu Inc.
  • Jinwen Ma Peking University
  • Shilei Wen Baidu Inc.



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.




How to Cite

Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., & Wen, S. (2020). Multi-Label Classification with Label Graph Superimposing. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12265-12272.



AAAI Technical Track: Vision