TCP-RBA:利用随机笔触增强对中国传统绘画分类的半监督学习

Yahui Ding, Hongjuan Wang, Nan Liu, Tong Li
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引用次数: 0

摘要

中国传统绘画(TCP)具有重要的文化意义,反映了中国丰富的历史和美学。近年来,中国传统绘画分类取得了令人瞩目的成绩,但为这些任务获取准确的注释既耗时又昂贵,需要专业艺术专家的参与。为了应对这一挑战,我们提出了一种用于传统绘画分类的半监督学习(SSL)方法,即使在标签数量有限的情况下也能取得优异的成绩。为了改进全局表示学习,我们采用了基于自我注意力的 MobileVit 模型作为骨干网络。此外,我们还提出了一种数据增强策略--随机画笔增强(RBA),它整合了画笔以提高性能。对比实验证实了 TCP-RBA 在中国画分类中的有效性,即使在只有 10 个标签(每个标签代表一个类别)的测试数据集上,其准确率也高达 88.27%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TCP-RBA: Semi-supervised learning for traditional chinese painting classification with random brushwork augment
Traditional Chinese painting (TCP), culturally significant, reflects China’s rich history and aesthetics. In recent years, TCP classification has shown impressive performance, but obtaining accurate annotations for these tasks is time-consuming and expensive, involving professional art experts. To address this challenge, we present a semi-supervised learning (SSL) method for traditional painting classification, achieving exceptional results even with a limited number of labels. To improve global representation learning, we employ the self-attention-based MobileVit model as the backbone network. Furthermore, We present a data augmentation strategy, Random Brushwork Augment (RBA), which integrates brushwork to enhance the performance. Comparative experiments confirm the effectiveness of TCP-RBA in Chinese painting classification, demonstrating outstanding accuracy of 88.27% on the test dataset, even with only 10 labels, each representing a single class.
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