MSRF-Net: a meta-learning-based U-Net architecture with multiscale fusion and adaptive reweighting for aesthetic evaluation of hard pen calligraphy

Evaluating the aesthetics of hard pen calligraphy requires capturing both fine details and overall composition. To address this, we introduce MSRF-Net, a model that integrates a MultiScale Fusion Block (MSFB) and an Adaptive Reweighting Block (ARB) within a U-Net architecture, enhanced by meta-learning. MSRF-Net effectively captures and reweights multiscale features, improving classification accuracy. Experiments on a dataset of 19,867 calligraphy samples demonstrate that MSRF-Net surpasses state-of-the-art models, offering a robust solution for aesthetic evaluation. Future work will explore its application to other perceptual evaluation tasks.