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English(EN) Studies on Sign Language Retrieval in Low-Resource Dictionary Settings

AI模型在低资源环境下改进手语检索

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种用于手语检索的姿态编码器，即使在低资源词汇设置下也能有效运行。当在美式手语 (ASL-Citizen) 上训练并应用于其他六种数据量很少的手语时，该编码器实现了 36.4% 至 80.7% 的检索准确率。其性能的关键在于 InfoNCE 训练目标以及编码器中不同身体部位之间的注意力阻塞。与现有方法相比，这种方法显著提高了准确率，在某些配置下达到了高达 68.0% 的单次准确率。 AI

影响 通过改进手语识别技术，提高聋哑人群的可及性，尤其是在数据有限的地区。

排序理由 详细介绍手语检索新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AI模型在低资源环境下改进手语检索

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详细介绍手语检索新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ushnish Sarkar, Vineet Kumar Rakesh, Bhaswar Chattopadhyay, Tapas Samanta ·

    低资源词典设置下的手语检索研究

    arXiv:2611.16621v1 Announce Type: new Abstract: Most sign languages have no annotated corpus, only small dictionaries with one or a few example videos per sign. This study examines how well a pose encoder trained on a single sign language serves such languages without any target-…

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