来自Apple和Gallaudet University的研究人员开发了一种伪标注管道，以显著降低标注手语数据所需的成本和时间。这种新方法使用手语模型的稀疏预测和K-Shot LLM方法来估计手语词汇、拼写词和手语分类器的标注。该管道旨在克服数据稀缺性这一限制AI驱动的手语解释的难题，一位专业翻译员在近500个视频上验证了该方法。 AI
影响 加速了标注手语数据集的创建，可能改进面向聋哑和听障人士的AI辅助工具。
排序理由 该集群包含详细介绍手语分析和生成新方法和数据集的学术论文。
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AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to t…
arXiv:2604.27232v1 Announce Type: new Abstract: Models of sign language have historically lagged behind those for spoken language (text and speech). Recent work has greatly improved their performance on tasks like sign language translation and isolated sign recognition. However, …
Models of sign language have historically lagged behind those for spoken language (text and speech). Recent work has greatly improved their performance on tasks like sign language translation and isolated sign recognition. However, it remains unclear to what extent existing model…
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