Multi-label image recognition with two-stream dynamic graph convolution networks

doi:10.1016/j.imavis.2021.104238

Multi-label image recognition with two-stream dynamic graph convolution networks

2021-09-01

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DOI：10.1016/j.imavis.2021.104238

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Recent studies use Graph Convolution Networks (GCN) to model label correlation for multi-label images because of the outstanding performance of GCN in relational modeling tasks. However, the traditional GCN has low generalization, and the current state-of-the-arts' accuracy is poor. Therefore, we propose a Two-Stream Dynamic Graph Convolution Network (2S-DGCN) to improve the performance of multi-label image recognition. In 2SDGCN, we first obtain the Up Confidence Score of prediction categories (UCS), the content-aware category and the label discriminant vector by a Semantic Attention Module (SAM) and a Dynamic Graph Convolution Network (DGCN) in upstream. Then fed the new graph feature nodes reconstructed by lateral embedding the content aware category and the label discriminant vector into a DGCN to produce the Down Confidence Score of prediction categories (DCS) in downstream. Finally, the Final Confidence Score of prediction categories (FCS) for multi-label image recognition is synthesized by fusing the UCS and DCS. Extensive experiments on the public multi-label benchmarks achieve mAPs of 85.6% on MS-COCO and 95.4% on VOC 2007. The results of compared experiment and visualization demonstrate that our method has better performance than the current state-ofthe-art methods. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Multi-label image recognition

Two streams

Reconstructing graph feature nodes

Dynamic graph convolution networks

Multi-label image recognition with two-stream dynamic graph convolution networks

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