Dual-Pyramid Attention Collaborative Network for Oracle Bone Inscription Classification

Abstract

Recent advances in oracle bone inscriptions (OBI) classification have explored various strategies such as zero-shot learning, augmentation, and complex convolution architectures. These strategies ultimately represent samples as global feature vectors in various forms but often fail to effectively capture the inherent multi-scale and region-specific features of OBI. The reliance on global feature vectors ignores the subtle differences between different scales and the uneven importance of different regions in the inscriptions. To address this issue, we propose a dual-pyramid attention collaborative network that enables classification models to learn OBI multi-scale attention. The dual-pyramid structure covers the convolutional and skeletonized features of pyramids. The spatial collaborative attention between level pairs corrects the bias produced by the pre-trained convolutional feature extractor. Experiments show that our model reduces training parameters by an average of 38% and improves accuracy by an average of 3.89% compared to state-of-the-art models.