A Mamba based vision transformer for fine grained image segmentation of mural figures
Qi Zhang, Guohua Geng, Pengbo Zhou, Longquan Yan, Mingquan Zhou, Zhaodi Li, Yangyang Liu · npj Heritage Science · 2025
Ancient murals, as valuable cultural heritage, preserve records of historical life and beliefs. To document and preserve these legacies, image segmentation algorithms are essential. This paper proposes a novel framework for fine-grained semantic segmentation of mythological figures and ornaments in murals, featuring an M-ViT Encoder and a Pyramid SCconv Decoder. The M-ViT Encoder incorporates a selective state-space model in the transformer units, mitigating correlations in sequential representations by building long-range dependencies, thus capturing local and global semantic information. The Pyramid SCconv Decoder reduces spatial and channel redundancy by applying separative transformations to encoder outputs, enhancing semantic feedback for fine-grained information separation. Experiments on fine-grained semantic segmentation of colored mural figures demonstrate that the proposed method outperforms other segmentation baselines, achieving 68.42% mIoU and 74.59% mAcc, and delivering state-of-the-art performance.