# DA-Font: Few-Shot Font Generation via Dual-Attention Hybrid Integration

arXiv: https://arxiv.org/abs/2509.16632 · HTML: https://mlsanity.com/paper/2509.16632/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Is Cosine-Similarity of Embeddings Really About Similarity?](https://mlsanity.com/paper/2403.05440/) (2024)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning](https://mlsanity.com/paper/1910.04987/) (2019)
- [FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning](https://mlsanity.com/paper/2312.12142/) (2023)
- [Few-shot Font Generation with Localized Style Representations and Factorization](https://mlsanity.com/paper/2009.11042/) (2020)
- [Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts](https://mlsanity.com/paper/2104.00887/) (2021)
- [Diff-Font: Diffusion Model for Robust One-Shot Font Generation](https://mlsanity.com/paper/2212.05895/) (2022)
- [Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator](https://mlsanity.com/paper/2205.00146/) (2022)
- [LIP: Local Importance-based Pooling](https://mlsanity.com/paper/1908.04156/) (2019)
- [Learning to Write Stylized Chinese Characters by Reading a Handful of Examples](https://mlsanity.com/paper/1712.06424/) (2017)
- [Refining activation downsampling with SoftPool](https://mlsanity.com/paper/2101.00440/) (2021)
- [CF-Font: Content Fusion for Few-shot Font Generation](https://mlsanity.com/paper/2303.14017/) (2023)
- [Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis](https://mlsanity.com/paper/2402.16506/) (2024)
- [Few shot font generation via transferring similarity guided global style and quantization local style](https://mlsanity.com/paper/2309.00827/) (2023)
- [PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution](https://mlsanity.com/paper/2409.13435/) (2024)

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

- [DRG-Font: Dynamic Reference-Guided Few-shot Font Generation via Contrastive Style-Content Disentanglement](https://mlsanity.com/paper/2604.13797/) (2026)

## Abstract (arXiv)

Few-shot font generation aims to create new fonts with a limited number of glyph references. It can be used to significantly reduce the labor cost of manual font design. However, due to the variety and complexity of font styles, the results generated by existing methods often suffer from visible defects, such as stroke errors, artifacts and blurriness. To address these issues, we propose DA-Font, a novel framework which integrates a Dual-Attention Hybrid Module (DAHM). Specifically, we introduce two synergistic attention blocks: the component attention block that leverages component information from content images to guide the style transfer process, and the relation attention block that further refines spatial relationships through interacting the content feature with both original and stylized component-wise representations. These two blocks collaborate to preserve accurate character shapes and stylistic textures. Moreover, we also design a corner consistency loss and an elastic mesh feature loss to better improve geometric alignment. Extensive experiments show that our DA-Font outperforms the state-of-the-art methods across diverse font styles and characters, demonstrating its effectiveness in enhancing structural integrity and local fidelity. The source code can be found at hrefhttps://github.com/wrchen2001/DA-Fonttextithttps://github.com/wrchen2001/DA-Font.