The goal of multi-label image classification is to predict a set of labels for a single image. Recent work has shown that explicitly modeling the co-occurrence relationship between classes is critical for achieving good performance on this task. State-of-theart approaches model this using graph convolutional networks, which are complex and computationally expensive. We propose a novel, efficient association module as an alternative. This is coupled with a transformer-based feature-extraction backbone. The proposed model was evaluated using two standard datasets: MS-COCO and PASCAL VOC. The results show that the proposed model outperforms several strong baseline models.