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English(EN) SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting

SMART框架在MLLM的指导下统一了手语识别和识别

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SMART，一个利用多模态大语言模型（MLLM）统一手语识别和识别的新框架。该方法利用MLLM生成的运动描述作为语义线索，以改善视频-文本对齐，即使在小批量大小的情况下也是如此，这对于内存密集型手语视频训练至关重要。SMART还增强了时序表示学习和密集时序定位，并在四个基准数据集上证明了其有效性。 AI

影响 该框架可以通过改进自动手语翻译来促进聋哑和听障社区的可及性。

排序理由 该集群描述了一篇研究论文，其中详细介绍了一种用于手语识别和识别的新框架。

在 Hugging Face Daily Papers 阅读 →

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SMART框架在MLLM的指导下统一了手语识别和识别

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该集群描述了一篇研究论文，其中详细介绍了一种用于手语识别和识别的新框架。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    SMART：MLLM引导的时间对齐，用于统一手语识别和识别

    Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. However, existing methods rely on sentence-level gloss annotations, providing limited temporal and semantic guidance for fine-grained …

  2. arXiv cs.CV TIER_1 English(EN) · Eunjee Choi, JungHoon Sung, Seongwhan Cho, Chu Xin, Younggeun Choi ·

    SMART：MLLM引导的时间对齐，用于统一手语识别和识别

    arXiv:2608.25493v1 Announce Type: new Abstract: Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. However, existing methods rely on sentence-level gloss annotations, providing limited …

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