Balanced masking strategy for multi-label image classification

Data imbalance is an essential issue in multi-label image classification that may reduce the model’s generalization ability. Unlike oversampling or undersampling in single-label image classification, simply removing or repeating some images in multi-label classification will influence labels in other categories due to complex label dependencies. Current methods usually tackle this issue from either data resampling or loss function design. This paper proposes a balanced masking strategy to solve the imbalance problem in feature space for graph-based methods. By selectively manipulating the label node embedding of the graph, the model can gain the priors of the majority of samples to maintain intra-class and inter-class balance simultaneously, thus performing better and more stable. Extensive experiments verify the adaptability and effectiveness of our method, which can be easily applied to any graph-based multi-label model to achieve highly competitive performance.