# Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support

arXiv: https://arxiv.org/abs/2604.07989 · HTML: https://mlsanity.com/paper/2604.07989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval](https://mlsanity.com/paper/2412.14475/) (2024)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Data Formulator: AI-powered Concept-driven Visualization Authoring](https://mlsanity.com/paper/2309.10094/) (2023)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [Towards Natural Language-Based Visualization Authoring](https://mlsanity.com/paper/2208.10947/) (2022)
- [Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline](https://mlsanity.com/paper/1907.13550/) (2019)
- [ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation](https://mlsanity.com/paper/2505.18668/) (2025)
- [KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation](https://mlsanity.com/paper/2107.12548/) (2021)
- [InfoColorizer: Interactive Recommendation of Color Palettes for Infographics](https://mlsanity.com/paper/2102.02041/) (2021)
- [Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics](https://mlsanity.com/paper/2008.01177/) (2020)
- [GenQuery: Supporting Expressive Visual Search with Generative Models](https://mlsanity.com/paper/2310.01287/) (2023)
- [Structure-aware Visualization Retrieval](https://mlsanity.com/paper/2202.05960/) (2022)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [Learning Style Similarity for Searching Infographics](https://mlsanity.com/paper/1505.01214/) (2015)
- [VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations](https://mlsanity.com/paper/2008.07702/) (2020)
- [Chart2Vec: A Universal Embedding of Context-Aware Visualizations](https://mlsanity.com/paper/2306.08304/) (2023)
- [Epigraphics: Message-Driven Infographics Authoring](https://mlsanity.com/paper/2404.10152/) (2024)
- [Infographics Wizard: Flexible Infographics Authoring and Design Exploration](https://mlsanity.com/paper/2204.09904/) (2022)
- [The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance](https://mlsanity.com/paper/2212.10533/) (2022)
- [Safire: Similarity Framework for Visualization Retrieval](https://mlsanity.com/paper/2510.16662/) (2025)

## Abstract (arXiv)

While infographics have become a powerful medium for communicating data-driven stories, authoring them from scratch remains challenging, especially for novice users. Retrieving relevant exemplars from a large corpus can provide design inspiration and promote reuse, substantially lowering the barrier to infographic authoring. However, effective retrieval is difficult because users often express design intent in ambiguous natural language, while infographics embody rich and multi-faceted visual designs. As a result, keyword-based search often fails to capture design intent, and general-purpose vision-language retrieval models trained on natural images are ill-suited to the text-heavy, multi-component nature of infographics. To address these challenges, we develop an intent-aware infographic retrieval framework that better aligns user queries with infographic designs. We first conduct a formative study of how people describe infographics and derive an intent taxonomy spanning content and visual design facets. This taxonomy is then leveraged to enrich and refine free-form user queries, guiding the retrieval process with intent-specific cues. Building on the retrieved exemplars, users can adapt the designs to their own data with high-level edit intents, supported by an interactive agent that performs low-level adaptation. Both quantitative evaluations and user studies are conducted to demonstrate that our method improves retrieval quality over baseline methods while better supporting intent satisfaction and efficient infographic authoring.