Kavli Affiliate: Feng Wang
| First 5 Authors: Zhengyang Lu, Tianhao Guo, Feng Wang, ,
| Summary:
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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