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
this https URL

本文提出了一种基于动态图卷积神经网络 (D-GCN) 和语义注意力模块 (SAM) 的注意力驱动动态图卷积网络 (ADD-GCN) 来消除训练数据中标签共现对模型泛化性能的影响，实现图像特征的提取和标签识别任务，并且在公共多标签基准测试中的结果表明该方法的有效性。