Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies
October 11, 2024 ยท The Cartographer ยท ๐ North American Chapter of the Association for Computational Linguistics
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"Title-pattern auto-detect: Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Tec"
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Authors
Yingqiang Gao, Lukas Fischer, Alexa Lintner, Sarah Ebling
arXiv ID
2410.08860
Category
cs.CL: Computation & Language
Cross-listed
cs.CV
Citations
6
Venue
North American Chapter of the Association for Computational Linguistics
Last Checked
23 hours ago
Abstract
Audio descriptions (ADs) function as acoustic commentaries designed to assist blind persons and persons with visual impairments in accessing digital media content on television and in movies, among other settings. As an accessibility service typically provided by trained AD professionals, the generation of ADs demands significant human effort, making the process both time-consuming and costly. Recent advancements in natural language processing (NLP) and computer vision (CV), particularly in large language models (LLMs) and vision-language models (VLMs), have allowed for getting a step closer to automatic AD generation. This paper reviews the technologies pertinent to AD generation in the era of LLMs and VLMs: we discuss how state-of-the-art NLP and CV technologies can be applied to generate ADs and identify essential research directions for the future.
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