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English(EN) RAIDAL: Redundancy-Aware Information Density Active Learning for CTC-Based Continuous Sign Language Recognition

新的主动学习方法提高了手语识别训练效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RAIDAL，一种新颖的主动学习方法，旨在提高连续手语识别（CSLR）模型训练的效率。该方法通过利用通常用于推理的CTC解码器来识别视频中的相关片段，从而解决了视频数据高昂的标注成本挑战。通过关注这些已识别的词条（gloss）区域，RAIDAL避免了冗余帧和停顿带来的噪声，从而实现了更有效的样本选择。跨多个数据集和架构的实验表明，RAIDAL显著提高了数据效率，尤其是在大规模词汇、预算受限的情况下。 AI

影响 提高了手语识别模型训练的效率，可能降低了辅助工具的门槛。

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

在 arXiv cs.CV 阅读 →

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新的主动学习方法提高了手语识别训练效率

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详细介绍CSLR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.CV TIER_1 English(EN) · Rafael A. Diniz Augusto, Gabriel L. Oliveira, Erickson R. Nascimento ·

    RAIDAL：用于基于CTC的连续手语识别的冗余感知信息密度主动学习

    arXiv:2609.06843v2 Announce Type: replace Abstract: Continuous sign language recognition (CSLR) is a key technology for accessibility, yet its development remains limited by the high cost of annotating continuous video streams. Active learning offers a path toward mitigating this…

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