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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 由于RGB预训练模型和RGB-D数据之间的不匹配,RGB-D语义细分面临挑战.
    • 现有的方法往往无法有效编码深度地图中存在的3D几何关系.

    研究的目的:

    • 提出DFormer++,一个新的预训练和微调框架,用于学习RGB-D语义细分的可转移表示.
    • 为了解决RGB-D语义细分中的常见不匹配问题.

    主要方法:

    • 开发了DFormer++,这是一个使用ImageNet-1K图像深度对来预训练骨干的框架,可以直接编码RGB-D表示.
    • 引入了RGB-D注意力阻塞,具有针对编码RGB和深度信息的新型注意力机制.

    主要成果:

    • DFormer++有效地避免了RGB预训练的骨干对3D几何学的不匹配编码.
    • 量身定制的架构减少了多余的参数,实现了高效和准确的RGB-D感知.
    • 在三个流行的RGB-D语义细分基准上实现了最先进的性能.

    结论:

    • 提出的DFormer++框架成功地学习了强大的RGB-D表示.
    • 新的架构和预训练策略显著提高了RGB-D语义细分中的性能和效率.