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English(EN) VPRef: A Cross-Domain Benchmark for Referring Remote Sensing Image Segmentation

新的基准和调查推动遥感图像分割发展

研究人员推出了VPRef,这是一个用于指代遥感图像分割的新基准,旨在解决由视觉和文本域漂移引起的性能下降问题。该基准包含超过46,000个语言-图像-标注三元组,并配备了一个基于Segment Anything Model (SAM3) 和低秩适配 (LoRA) 的参数高效适配框架。该框架采用伪标签驱动的自训练和随机多粒度文本提示混合,在仅修改模型一小部分参数的情况下提高了分割精度。另一项调查回顾了遥感图像语义分割中的深度学习范式,按分割粒度对方法进行分类,并分析了从像素级到图像级的各种策略,强调了向基础模型和多模态集成的演变。 AI

影响 遥感图像分割的进步可以改善地球观测和分析,用于环境监测和城市规划。

排序理由 该集群包含两篇与计算机视觉和遥感相关的学术论文,一篇介绍了新的基准和方法,另一篇对现有技术进行了调查。

在 arXiv cs.CV 阅读 →

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新的基准和调查推动遥感图像分割发展

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该集群包含两篇与计算机视觉和遥感相关的学术论文,一篇介绍了新的基准和方法,另一篇对现有技术进行了调查。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang ·

    VPRef:用于指代遥感图像分割的跨域基准

    arXiv:2609.16486v1 Announce Type: new Abstract: Rapid advancements in vision-language models have propelled Referring Remote Sensing Image Segmentation (RRSIS) to the forefront of Earth observation. However, practical deployments suffer severe performance degradation under a coup…

  2. arXiv cs.CV TIER_1 English(EN) · Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang ·

    从像素到图像:深度学习范式在遥感图像语义分割中的结构化调查

    arXiv:2505.15147v3 Announce Type: replace Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the fine-grained interpretation of surface features, making it a critical task in RS …