AI-Enhanced Art Appreciation: Generating Text from Artwork to Promote Inclusivity

Authors

  • Tanisha Shende Oberlin College

DOI:

https://doi.org/10.1609/aaai.v38i21.30556

Keywords:

Generative AI, Accessibility, Art, Human-computer Interaction, Visual processing

Abstract

Visual art facilitates expression, communication, and connection, yet it remains inaccessible to those who are visually-impaired and those who lack the resources to understand the techniques and history of art. In this work, I propose the development of a generative AI model that generates a description and interpretation of a given artwork. Such research can make art more accessible, support art education, and improve the ability of AI to understand and translate between creative media. Development will begin with a formative study to assess the needs and preferences of blind and low vision people and art experts. Following the formative study, the basic approach is to train the model on a database of artworks and their accompanying descriptions, predict sentiments from extracted visual data, and generate a paragraph closely resembling training textual data and incorporating sentiment analysis. The model will then be evaluated quantitatively through metrics like METEOR and qualitatively through Turing tests in an iterative process.

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Published

2024-03-24

How to Cite

Shende, T. (2024). AI-Enhanced Art Appreciation: Generating Text from Artwork to Promote Inclusivity. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23760-23762. https://doi.org/10.1609/aaai.v38i21.30556