Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition

• URL: http://arxiv.org/abs/2012.02994v1
• Date: Sat, 5 Dec 2020 10:10:12 GMT
• Title: Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition
• Authors: Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, and Yu Qiao
• Abstract summary: We propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. Experiments on public multi-label benchmarks demonstrate the effectiveness of our method.
• Score: 53.17837649440601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies often exploit Graph Convolutional Network (GCN) to model label dependencies to improve recognition accuracy for multi-label image recognition. However, constructing a graph by counting the label co-occurrence possibilities of the training data may degrade model generalizability, especially when there exist occasional co-occurrence objects in test images. Our goal is to eliminate such bias and enhance the robustness of the learnt features. To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. ADD-GCN adopts a Dynamic Graph Convolutional Network (D-GCN) to model the relation of content-aware category representations that are generated by a Semantic Attention Module (SAM). Extensive experiments on public multi-label benchmarks demonstrate the effectiveness of our method, which achieves mAPs of 85.2%, 96.0%, and 95.5% on MS-COCO, VOC2007, and VOC2012, respectively, and outperforms current state-of-the-art methods with a clear margin. All codes can be found at https://github.com/Yejin0111/ADD-GCN.

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