Debias Fine-Grained Radical Modeling via Global-Local Attention Mutual Learning for Zero-Shot Chinese Character Recognition

Song-Liang Pan, Kunchi Li, Da-Han Wang, Xu-Yao Zhang, Guo-Sen Xie, Jiantao Liu, Shunzhi Zhu

Abstract: In zero-shot Chinese character recognition (ZSCCR), radicals play a critical bridge for transferring semantic knowledge from seen to unseen characters. However, existing radical-based approaches tend to coarsely map Chinese character images to radical embeddings, disregarding the imperative for fine-grained radical learning and the prevalent imbalanced issue among radicals. This leads to underemphasizing less frequent radicals or introducing bias towards more prevalent ones. To address this, we propose a novel framework of Debiasing fine-grained Radical Modeling via Global-Local radical attention mutual learning for ZSCCR, termed DeRMGL. DeRMGL features two branches for global and local radical modeling. The multi-local branch partitions learnable radical prototypes into non-overlapping blocks by frequency, each with a dedicated attention network. Since radicals in each block have similar occurrence frequencies, attention scores are less skewed by frequency disparities, effectively alleviating radical imbalance. The global branch maintains full radical coverage to coordinate holistic semantic scoring. During training, mutual learning loss optimizes both branches collaboratively, and during inference, their predictions are combined. Furthermore, an enhanced Radical Prototype-based Attention (RPA) network serves as the backbone to leverage visual-semantic knowledge. Extensive experiments on four ZSCCR benchmark datasets demonstrate that DeRMGL achieves state-of-the-art performance.

External IDs:doi:10.1109/tmm.2026.3654386