Character Sequence Models for ColorfulWords
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Abstract (arXiv)
We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Sep 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 2.
5 papers.
- 2023Multimodal Color Recommendation in Vector Graphic Documentscited by 5
- 2023Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Languagecited by 4
- 2021Generating Compositional Color Representations from Textcited by 3
- 2025Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?cited by 3
- 2025ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendationcited by 0
- 2025ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendation12 Aug 2025
- 2025Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?13 Jun 2025
- 2023Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language22 Nov 2023
- 2023Multimodal Color Recommendation in Vector Graphic Documents8 Aug 2023
- 2021Generating Compositional Color Representations from Text22 Sep 2021