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English(EN) TEMPL: A Resource-Efficient and Transparent Model-Free Framework for Pseudo-Label Generation in Material CT Scans

新框架在无需GPU的情况下为材料CT扫描生成伪标签

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TEMPL，一种新颖的框架，用于在材料CT扫描中生成伪标签，而无需预训练模型或GPU。这种无模型方法利用阈值处理、形态学膨胀和区域生长算法来高效创建注释。与Segment Anything Model (SAM)和传统方法相比，TEMPL在准确性和速度方面表现出优越性能，为资源受限环境中的注释引导和微观结构量化提供了一个有价值的工具。 AI

影响 为分割任务的训练数据生成提供了一种资源高效的方法，有可能加速材料科学领域的研究。

排序理由 该集群描述了一篇关于用于材料CT扫描伪标签生成的新颖框架的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架在无需GPU的情况下为材料CT扫描生成伪标签

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该集群描述了一篇关于用于材料CT扫描伪标签生成的新颖框架的新研究论文。[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) · Ashiqur Rahman, Venkata Devesh Reddy Seethi, Austin Yunker, Rajkumar Kettimuthu, Hamed Alhoori ·

    TEMPL: A Resource-Efficient and Transparent Model-Free Framework for Pseudo-Label Generation in Material CT Scans

    arXiv:2610.28974v1 Announce Type: new Abstract: The scarcity of large annotated datasets limits deep learning (DL) segmentation in material CT analysis. DL-based pseudo-labeling can help, but it is often computationally expensive and hard to interpret. We introduce a multi-stage,…

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