Enhancing Oracle Bone Character Category Discovery via Character Component Distillation and Self-Merged Pseudo-Label

Oraclebone characters (OBCs) are crucial for understanding ancient Chinese history, but existing recognition methods only recognize known categories in labeled data, neglecting novel categories in unlabeled data. This work introduces a novel approach to discovering new OBC categories in unlabeled data through generalized category discovery. We address the challenges posed by OBCs’ instinctive characteristics, such as misleading contrastive views from random cropping, sub-optimal learned representation, and insufficient supervision for unlabeled data. Our method features a symmetrical structure enhanced by character component distillation and self-merged pseudo-label. We utilize random geometric transforms to create symmetrical contrastive views to avoid misleading views. Then, the proposed character component distillation procedure optimizes symmetrical shared character components for better transferable representation. Finally, we construct a self-merged pseudo-label from the model and a symmetrical teacher model to provide stable and robust supervision for unlabeled data. Extensive experiments validate the superiority of our method in recognizing ’All’ and ’Novel’ OBC categories, providing an effective tool to aid OBC researchers.