Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies

October 11, 2024 ยท The Cartographer ยท ๐Ÿ› North American Chapter of the Association for Computational Linguistics

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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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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