Multi-Label Image Recognition With Graph Convolutional Networks

Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5177-5186

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Chen_2019_CVPR,
author = {Chen, Zhao-Min and Wei, Xiu-Shen and Wang, Peng and Guo, Yanwen},
title = {Multi-Label Image Recognition With Graph Convolutional Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}