# Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics

arXiv: https://arxiv.org/abs/2008.01177 · HTML: https://mlsanity.com/paper/2008.01177/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [Relation-Aware Diffusion Model for Controllable Poster Layout Generation](https://mlsanity.com/paper/2306.09086/) (2023)
- [Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2311.13602/) (2023)
- [Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model](https://mlsanity.com/paper/2304.14630/) (2023)
- [DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer](https://mlsanity.com/paper/2303.03755/) (2023)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [ProCrop: Learning Aesthetic Image Cropping from Professional Compositions](https://mlsanity.com/paper/2505.22490/) (2025)
- [Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling](https://mlsanity.com/paper/2304.08366/) (2023)
- [SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior](https://mlsanity.com/paper/2510.15749/) (2025)
- [Smaller But Better: Unifying Layout Generation with Smaller Large Language Models](https://mlsanity.com/paper/2502.14005/) (2025)
- [ReSpark: Leveraging Previous Data Reports as References to Generate New Reports with LLMs](https://mlsanity.com/paper/2502.02329/) (2025)
- [Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective](https://mlsanity.com/paper/2503.02631/) (2025)
- [Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support](https://mlsanity.com/paper/2604.07989/) (2026)

## Abstract (arXiv)

Infographic is a data visualization technique which combines graphic and textual descriptions in an aesthetic and effective manner. Creating infographics is a difficult and time-consuming process which often requires significant attempts and adjustments even for experienced designers, not to mention novice users with limited design expertise. Recently, a few approaches have been proposed to automate the creation process by applying predefined blueprints to user information. However, predefined blueprints are often hard to create, hence limited in volume and diversity. In contrast, good infogrpahics have been created by professionals and accumulated on the Internet rapidly. These online examples often represent a wide variety of design styles, and serve as exemplars or inspiration to people who like to create their own infographics. Based on these observations, we propose to generate infographics by automatically imitating examples. We present a two-stage approach, namely retrieve-then-adapt. In the retrieval stage, we index online examples by their visual elements. For a given user information, we transform it to a concrete query by sampling from a learned distribution about visual elements, and then find appropriate examples in our example library based on the similarity between example indexes and the query. For a retrieved example, we generate an initial drafts by replacing its content with user information. However, in many cases, user information cannot be perfectly fitted to retrieved examples. Therefore, we further introduce an adaption stage. Specifically, we propose a MCMC-like approach and leverage recursive neural networks to help adjust the initial draft and improve its visual appearance iteratively, until a satisfactory result is obtained. We implement our approach on proportion-related infographics, and demonstrate its effectiveness by sample results and expert reviews.