Affiliation:
1. School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China
2. School of New Media, Beijing Institute of Graphic Communication, Beijing, China
Abstract
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.
Reference19 articles.
1. David Berthelot , Nicholas Carlini , Ian Goodfellow , Nicolas Papernot , Avital Oliver , Colin Raffel A. , Mixmatch: A holistic approach to semi-supervised learning, Advances in Neural Information Processing Systems 32(2019).
2. Deep, big, simple neural nets for handwritten digit recognition;Dan Cireşan Claudiu;Neural Computation,2010
3. A comparative study of oil paintings and Chinese ink paintings on composition;Zhen-Bao Fan;The Visual Computer,2023
4. Wei Jiang , Xianglian Meng , Ji Xi , et al. Multilevel attention and multiscale feature fusion network for author classification of Chinese ink-wash paintings, Discrete Dynamics in Nature and Society 2022(2022).
5. Mtffnet: a multi-task feature fusion framework for Chinese painting classification;Wei Jiang;Cognitive Computation,2021