Knowledge-Guided Graph Convolutional Network for Multi-Label Image Classification

Abstract

Multi-label image classification is a significant challenge in computer vision due to the presence of multiple interconnected objects in a single image. Traditional convolutional neural networks (CNN) often fail to capture semantic dependencies between labels, limiting performance in complex scenes. To address this issue, we propose a novel framework that combines Knowledge-Guided Graph Convolutional Network (KGGCN) with Darknet53 backbone to improve label dependency modeling. Our method fuses external semantic information from ConceptNet5, which allows the model to learn contextual relationships between labels. Our work evaluate this approach on two benchmark datasets, VOC 2007 and COCO, and obtain state-of-the-art results. KGGCN achieves an Average Precision (mAP) of 96.24% on VOC 2007 and 85.25% on COCO, outperforming existing methods in most categories. Moreover, ablation studies further highlight the benefits of external knowledge integration contributing to higher mAP scores. Finally, our proposed method KGGCN demonstrates the effectiveness of combining deep visual features with structured semantic knowledge for multi-label image classification.