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English(EN) Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts

新框架将分割一切模型应用于地震解释

研究人员开发了一种新框架,无需进行广泛的再训练即可将分割一切模型(SAM)应用于地震解释。该方法利用与地质目标一致的地震属性和可视化选择,并结合混合提示策略。该方法同时采用用户定义的点提示和SAM的内部特征激活来提高分割准确性和特征可分离性,从而实现零样本性能。 AI

排序理由 该集群包含一篇学术论文,详细介绍了将现有AI模型应用于特定领域的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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  1. arXiv cs.AI TIER_1 English(EN) · Aniq Ahmad, Heather Bedle, Ahmad Mustafa ·

    Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts

    arXiv:2606.15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation. The Segment Anything Model (SAM), in particular, offers powerful zero shot segmentation …