# Multimodal Color Recommendation in Vector Graphic Documents

arXiv: https://arxiv.org/abs/2308.04118 · HTML: https://mlsanity.com/paper/2308.04118/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Should You Mask 15% in Masked Language Modeling?](https://mlsanity.com/paper/2202.08005/) (2022)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://mlsanity.com/paper/1703.10186/) (2017)
- [Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation](https://mlsanity.com/paper/1804.04128/) (2018)
- [InfoColorizer: Interactive Recommendation of Color Palettes for Infographics](https://mlsanity.com/paper/2102.02041/) (2021)
- [Color Recommendation for Vector Graphic Documents based on Multi-Palette Representation](https://mlsanity.com/paper/2209.10820/) (2022)
- [Generative Colorization of Structured Mobile Web Pages](https://mlsanity.com/paper/2212.11541/) (2022)
- [Character Sequence Models for ColorfulWords](https://mlsanity.com/paper/1609.08777/) (2016)
- [Generating Compositional Color Representations from Text](https://mlsanity.com/paper/2109.10477/) (2021)

## Built on by (most cited first, 5 of 5)

- [OpenCOLE: Towards Reproducible Automatic Graphic Design Generation](https://mlsanity.com/paper/2406.08232/) (2024)
- [Multimodal Markup Document Models for Graphic Design Completion](https://mlsanity.com/paper/2409.19051/) (2024)
- [GraphicBench: A Planning Benchmark for Graphic Design with Language Agents](https://mlsanity.com/paper/2504.11571/) (2025)
- [Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations](https://mlsanity.com/paper/2410.07211/) (2024)
- [ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendation](https://mlsanity.com/paper/2508.08987/) (2025)

## Abstract (arXiv)

Color selection plays a critical role in graphic document design and requires sufficient consideration of various contexts. However, recommending appropriate colors which harmonize with the other colors and textual contexts in documents is a challenging task, even for experienced designers. In this study, we propose a multimodal masked color model that integrates both color and textual contexts to provide text-aware color recommendation for graphic documents. Our proposed model comprises self-attention networks to capture the relationships between colors in multiple palettes, and cross-attention networks that incorporate both color and CLIP-based text representations. Our proposed method primarily focuses on color palette completion, which recommends colors based on the given colors and text. Additionally, it is applicable for another color recommendation task, full palette generation, which generates a complete color palette corresponding to the given text. Experimental results demonstrate that our proposed approach surpasses previous color palette completion methods on accuracy, color distribution, and user experience, as well as full palette generation methods concerning color diversity and similarity to the ground truth palettes.