mlsanityData to 7 Oct 2026

Images Speak Volumes: User-Centric Assessment of Image Generation for Accessible Communication

arXiv 2410.03430 ↗First posted 4 Oct 2024cs.CV cs.CLcited by 2

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Abstract (arXiv)

Explanatory images play a pivotal role in accessible and easy-to-read (E2R) texts. However, the images available in online databases are not tailored toward the respective texts, and the creation of customized images is expensive. In this large-scale study, we investigated whether text-to-image generation models can close this gap by providing customizable images quickly and easily. We benchmarked seven, four open- and three closed-source, image generation models and provide an extensive evaluation of the resulting images. In addition, we performed a user study with people from the E2R target group to examine whether the images met their requirements. We find that some of the models show remarkable performance, but none of the models are ready to be used at a larger scale without human supervision. Our research is an important step toward facilitating the creation of accessible information for E2R creators and tailoring accessible images to the target group's needs.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

20 of 34 references.

References · 20

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8 more
  1. 2022mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionscited by 185
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in Germancited by 2
  2. 2025Template-Based Text-to-Image Alignment for Language Accessibility: A Study on Visualizing Text Simplificationscited by 0