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Multi-label image recognition is a task that predicts a set of object labelsin an image. As the objects co-occur in the physical world, it is desirable tomodel label dependencies. Previous existing methods resort to either recurrentnetworks or pre-defined label correlation graphs for this purpose. In thispaper, instead of using a pre-defined graph which is inflexible and may besub-optimal for multi-label classification, we propose the A-GCN, whichleverages the popular Graph Convolutional Networks with an Adaptive labelcorrelation graph to model label dependencies. Specifically, we introduce aplug-and-play Label Graph (LG) module to learn label correlations with wordembeddings, and then utilize traditional GCN to map this graph intolabel-dependent object classifiers which are further applied to image features.The basic LG module incorporates two 1x1 convolutional layers and uses the dotproduct to generate label graphs. In addition, we propose a sparse correlationconstraint to enhance the LG module and also explore different LGarchitectures. We validate our method on two diverse multi-label datasets:MS-COCO and Fashion550K. Experimental results show that our A-GCN significantlyimproves baseline methods and achieves performance superior or comparable tothe state of the art.

Learning Category Correlations for Multi-label Image Recognition with Graph Networks