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English(EN) Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment

新方法利用时间对齐改进手语识别

研究人员开发了一种新颖的手语识别方法,采用了迁移学习和一种称为TA3N的域自适应方法。该方法利用时间关系网络(TRN)来对齐多尺度时间特征,与标准的基于神经网络的迁移学习相比,表现出更优越的性能,尤其是在美国手语(ASL)方面。实验还表明,对齐短期时间特征是有效的,并且对于手语样本,RGB数据通常优于光流。 AI

影响 这项研究通过提高自动化识别系统的准确性,有可能显著改善使用手语的个人的沟通可及性。

排序理由 该集群包含一篇详细介绍手语识别新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新方法利用时间对齐改进手语识别

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该集群包含一篇详细介绍手语识别新方法的学术论文。[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) · Keren Artiaga (Victor), Yang Li (Victor), Ercan Engin Kuruoglu (Victor), Wai Kin (Victor), Chan ·

    使用多尺度时间对齐的域自适应进行跨手语迁移学习

    arXiv:2608.16804v1 Announce Type: new Abstract: Sign language serves as a vital means of communication for individuals with hearing impairments, yet recognition resources for the over 100 distinct sign languages are severely lacking. In response, we present our work on sign langu…