# Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks

arXiv: https://arxiv.org/abs/2410.19307 · HTML: https://mlsanity.com/paper/2410.19307/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis](https://mlsanity.com/paper/1904.01310/) (2019)
- [StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1710.10916/) (2017)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [A Hierarchical Approach for Generating Descriptive Image Paragraphs](https://mlsanity.com/paper/1611.06607/) (2016)
- [Unpaired Image Captioning via Scene Graph Alignments](https://mlsanity.com/paper/1903.10658/) (2019)
- [Unsupervised Image Captioning](https://mlsanity.com/paper/1811.10787/) (2018)
- [Towards Diverse and Natural Image Descriptions via a Conditional GAN](https://mlsanity.com/paper/1703.06029/) (2017)
- [Expressing Visual Relationships via Language](https://mlsanity.com/paper/1906.07689/) (2019)
- [SemStyle: Learning to Generate Stylised Image Captions using Unaligned Text](https://mlsanity.com/paper/1805.07030/) (2018)
- ["Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention](https://mlsanity.com/paper/1807.03871/) (2018)
- [End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks](https://mlsanity.com/paper/2011.05552/) (2020)
- [Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training](https://mlsanity.com/paper/1804.08473/) (2018)
- [Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss](https://mlsanity.com/paper/1911.09428/) (2019)
- [Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior](https://mlsanity.com/paper/2404.07176/) (2024)
- [Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation](https://mlsanity.com/paper/2204.02386/) (2022)

## Built on by (most cited first, 1 of 1)

- [Depth-Aware Super-Resolution via Distance-Adaptive Variational Formulation](https://mlsanity.com/paper/2509.05746/) (2025)

## Abstract (arXiv)

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 urlhttps://github.com/Mnster00/poemtopainting.