mlsanityData to 14 Oct 2026

Text to Image Generation: Leaving no Language Behind

arXiv 2208.09333 ↗First posted 19 Aug 2022cs.CL cs.AI cs.CVcited by 4

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Abstract (arXiv)

One of the latest applications of Artificial Intelligence (AI) is to generate images from natural language descriptions. These generators are now becoming available and achieve impressive results that have been used for example in the front cover of magazines. As the input to the generators is in the form of a natural language text, a question that arises immediately is how these models behave when the input is written in different languages. In this paper we perform an initial exploration of how the performance of three popular text-to-image generators depends on the language. The results show that there is a significant performance degradation when using languages other than English, especially for languages that are not widely used. This observation leads us to discuss different alternatives on how text-to-image generators can be improved so that performance is consistent across different languages. This is fundamental to ensure that this new technology can be used by non-native English speakers and to preserve linguistic diversity.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 19 Aug 2022.

Builds on

11 of 20 references.

References · 11

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  3. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  5. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  6. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  7. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
  8. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
  9. 2020Beyond English-Centric Multilingual Machine Translationcited by 561
  10. 2017When Will AI Exceed Human Performance? Evidence from AI Expertscited by 41
  11. 2020Learning to Scale Multilingual Representations for Vision-Language Taskscited by 22

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023Towards Equitable Representation in Text-to-Image Synthesis Models with the Cross-Cultural Understanding Benchmark (CCUB) Datasetcited by 12
  2. 2023Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humanscited by 8
  3. 2024A Comparative Investigation of Compositional Syntax and Semantics in DALL-E 2cited by 2
  4. 2024Open Conversational LLMs do not know most Spanish wordscited by 1