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English(EN) Edge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception

新的SALD框架使用边缘-云扩散技术提升遥感感知能力

研究人员开发了一个结构感知隐扩散(SALD)框架,以应对高分辨率遥感数据的传输挑战。该系统通过在边缘端将影像解耦为压缩的低频分量和结构先验,从而最大限度地减少带宽使用。在云端,SALD利用结构门控大核模块和语义引导引擎来重建细节并防止生成性幻觉。实验表明,即使在严重的带宽限制下,SALD也能提高感知质量和下游任务性能,例如目标检测。 AI

影响 在带宽限制下,改善了遥感数据传输和下游感知任务。

排序理由 这是一篇描述新数据重建框架的研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的SALD框架使用边缘-云扩散技术提升遥感感知能力

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这是一篇描述新数据重建框架的研究论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yun Li, Xianju Li ·

    面向下游遥感感知的边缘-云协同结构感知隐扩散重建

    arXiv:2604.25319v1 Announce Type: new Abstract: The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth forces the use of extreme high-ratio compression, which irreversibly destroys hig…

  2. arXiv cs.CV TIER_1 English(EN) · Xianju Li ·

    面向下游遥感感知的边缘-云协同结构感知潜在扩散重建

    The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth forces the use of extreme high-ratio compression, which irreversibly destroys high-frequency structural details essential for dow…