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English(EN) Intra- and Cross-Scale Feature Interaction Network for Referring Remote Sensing Image Segmentation

新数据集和模型推动遥感图像分割发展

研究人员推出了NWPU-Refer,这是一个旨在改进指代遥感图像分割(RRSIS)的新数据集。该数据集是迄今为止最大、最多样化的双语RRSIS数据集,包含15,003张高分辨率图像和超过49,000个标注目标。同时,研究团队提出了多尺度指代分割网络(MRSNet),该网络利用类内和分层特征交互模块来提高分割精度。实验表明,MRSNet在新的NWPU-Refer数据集和现有的RRSIS-D数据集上均表现出竞争力。 AI

影响 推动了遥感图像分割技术的发展,可能改进卫星和航空数据的分析。

排序理由 该集群包含一篇学术论文,详细介绍了用于特定计算机视觉任务的新数据集和模型。[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 →

新数据集和模型推动遥感图像分割发展

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该集群包含一篇学术论文,详细介绍了用于特定计算机视觉任务的新数据集和模型。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhigang Yang, Huiguang Yao, Linmao Tian, Xuezhi Zhao, Qiang Li, Qi Wang ·

    面向遥感图像分割的类内与跨尺度特征交互网络

    arXiv:2506.03583v2 Announce Type: replace Abstract: Referring remote sensing image segmentation (RRSIS) is a challenging multimodal task that requires accurate localization and pixel-level segmentation of targets specified by natural-language expressions. Existing RRSIS datasets …