Enhancing the Discriminative Ability for Multi-Label Classification by Handling Data Imbalance

In computer vision, long-tailed multi-label visual recognition is a challenging problem due to the imbalance between classes and the recognition of rare classes. Previous methods for resolving data imbalance mostly originate from single-label classification, which can be obstructed by the label co-occurrence issue, and they attempt to minimize or compensate for it. In this paper, we propose a novel tail class priority sampling method for long-tailed multi-label classification that untangles both issues. Our method samples tail class more often and earlier in order to make the model learn tail classes before it has bias toward common classes. Due to label co-occurrence, other classes will be spontaneously learned in same iteration, ensuring a balanced representation of the head and medium classes. To further enhance the recognition performance, we modify to a bilateral structure that samples both original and proposed sampling distribution to better represent the tail classes. We evaluate our proposed method on two widely used datasets in long-tailed version, COCO-LT and VOC-LT, and compare it with previous methods. The experimental results show that our method achieves a new state-of-the-art performance for tail classes on both datasets. Our method is applicable in various real-world scenarios, making rare class recognition achievable, and can be easily incorporated into conventional recognition frameworks.