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English(EN) MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation

新数据集MonoIR-RS推动红外遥感视觉语言理解

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了MonoIR-RS，这是一个新的数据集和基准，旨在通过视觉语言模型促进对红外遥感图像的理解。该资源包括600,000张合成红外图像和超过59,000条红外感知字幕，专门调整为侧重于红外线索而非RGB外观。实验表明，将CLIP和VLM等模型适配到这种红外特定数据上，可以显著提高它们在图像字幕和检索等任务上的性能，减少对残余RGB信息的依赖。 AI

影响 该数据集有望实现更准确的红外图像解释，应用于环境监测和国防等领域。

排序理由 该集群描述了一篇介绍特定人工智能研究领域数据集和基准的新学术论文。

在 arXiv cs.CV 阅读 →

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新数据集MonoIR-RS推动红外遥感视觉语言理解

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jiaju Han, Ma Yaqi, Yahui Chai, Xuemeng Sun, Xin Li, Qike Zhang, Yingying Zhao, Xiang Chen, Luwei Yang, Chengyin Hu, Jiahuan Long ·

    MonoIR-RS：使用CLIP和VLM适配的红外遥感视觉语言学习

    arXiv:2607.06552v1 Announce Type: new Abstract: Infrared remote-sensing imagery captures intensity structure, object-background contrast, and illumination-invariant cues often invisible in RGB imagery. Yet, most remote-sensing vision-language resources and models focus on visible…

  2. arXiv cs.CV TIER_1 English(EN) · Jiahuan Long ·

    MonoIR-RS：使用CLIP和VLM适配的红外遥感视觉语言学习

    Infrared remote-sensing imagery captures intensity structure, object-background contrast, and illumination-invariant cues often invisible in RGB imagery. Yet, most remote-sensing vision-language resources and models focus on visible-band semantics, leaving infrared vision-languag…

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