用少量配对数据实现诗画生成，提升艺术表达的抽象匹配能力。
Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks
- 基于循环一致性对抗网络，建立诗与画的双向映射关系。
- 在新构建的CPDD数据集上超越已有方法，生成效果更一致。
- 适合研究跨模态生成、艺术计算与中文语义理解的学者。
古典中国诗词与绘画代表了艺术表达的巅峰，但其抽象与象征性特征给计算翻译带来挑战。现有方法多依赖大规模配对数据，而该领域数据稀缺。本文提出一种半监督方法，利用循环一致性对抗网络，融合有限的配对数据与大量未配对的诗画语料。核心思想是学习双向映射，强制视觉与文本模态间的语义对齐。引入新型评估指标，衡量生成作品的质量、多样性与一致性。在新构建的中文绘画描述数据集CPDD上进行充分实验，所提模型优于先前方法，展现出捕捉艺术表达象征本质的潜力。代码已公开于https://github.com/Mnster00/poemtopainting。
原文摘要 · Abstract (English)
Classical Chinese poetry and painting represent the epitome of artistic expression, but the abstract and symbolic nature of their relationship poses a significant challenge for computational translation. Most existing methods rely on large-scale paired datasets, which are scarce in this domain. In this work, we propose a semi-supervised approach using cycle-consistent adversarial networks to leverage the limited paired data and large unpaired corpus of poems and paintings. The key insight is to learn bidirectional mappings that enforce semantic alignment between the visual and textual modalities. We introduce novel evaluation metrics to assess the quality, diversity, and consistency of the generated poems and paintings. Extensive experiments are conducted on a new Chinese Painting Description Dataset (CPDD). The proposed model outperforms previous methods, showing promise in capturing the symbolic essence of artistic expression. Codes are available online \url{https://github.com/Mnster00/poemtopainting}.
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