研究人员开发了RACANet，一种新颖的RGB-Thermal人群计数框架，通过显式建模局部空间差异和模态可靠性来提高准确性。该方法采用两阶段方法，首先进行跨模态对齐预训练，然后是局部锚点融合模块。该模块利用可靠区域生成语义锚点，并使用注意力机制自适应地重新分配特征。在基准数据集上的实验表明，RACANet优于现有方法。 AI
影响 通过整合可见光和热数据，引入了一种提高人群计数准确性的新方法。
排序理由 介绍RGB-T人群计数新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
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RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve counting accuracy through cross-modal feature fusion, most curre…
arXiv:2604.24543v1 Announce Type: new Abstract: RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve counting accurac…
RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve counting accuracy through cross-modal feature fusion, most curre…