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

Yi Lin; Zixuan Wu; Jiatong Hu; Jiaming Yang; Chiwen Feng

doi:10.1117/12.3051070

27 September 2024 MSRF-Net: a meta-learning-based U-Net architecture with multiscale fusion and adaptive reweighting for aesthetic evaluation of hard pen calligraphy

Yi Lin, Zixuan Wu, Jiatong Hu, Jiaming Yang, Chiwen Feng

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132811B (2024) https://doi.org/10.1117/12.3051070
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Lin, Zixuan Wu, Jiatong Hu, Jiaming Yang, and Chiwen Feng "MSRF-Net: a meta-learning-based U-Net architecture with multiscale fusion and adaptive reweighting for aesthetic evaluation of hard pen calligraphy", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132811B (27 September 2024); https://doi.org/10.1117/12.3051070

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KEYWORDS

Education and training

Data modeling

Visual process modeling

Feature fusion

Performance modeling

Ablation

Deep learning

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