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English(EN) Text-to-seed generation: Training-free open-vocabulary seeded semantic segmentation via re-purposing diffusion as text-guided seed generator

Text-to-Seed框架使用扩散模型进行开放词汇量分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的、无需训练的开放词汇量语义分割框架，名为Text-to-Seed (T2S)。该方法重新利用扩散模型（特别是Stable Diffusion）来生成文本引导的种子点。然后，这些种子点被用作Segment Anything Model (SAM)的提示，以生成精确的对象掩码。T2S在标准基准测试中表现出色，无需进行特定任务的训练或额外的标注，突显了语义基础与种子驱动的空间分割之间的协同作用。 AI

影响 通过利用扩散模型和现有的分割工具，引入了一种新颖的无需训练的语义分割方法。

排序理由 详细介绍一种新的语义分割方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Text-to-Seed框架使用扩散模型进行开放词汇量分割

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详细介绍一种新的语义分割方法的论文。[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) · Kumju Jo, Heesun Jung, Sungyong Baik ·

    文本到种子生成：通过重新利用扩散模型作为文本引导种子生成器实现无训练、开放词汇的种子语义分割

    arXiv:2608.26624v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment image regions corresponding to arbitrary text queries. Although the Segment Anything Model (SAM) is a powerful foundation model for segmentation, its standalone performanc…

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