一项关于神经网络结构对单眼深度估计的普遍性的研究
IEEE transactions on pattern analysis and machine intelligence
|November 13, 2023
概括
变压器显示形状偏差,改善单眼深度估计概括,与显示纹理偏差的CNN不同. 这项研究分析了骨干网络,以在深度估计任务中更好地实现分布之外的性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 单眼深度估计对于自主系统至关重要.
- 目前的模型显示性能增长,但缺乏概括分析.
- 现有的评估通常使用有限的基准数据集.
研究的目的:
- 在单眼深度估计中调查骨干网络泛化.
- 分析CNN和变形金刚的内部表现.
- 确定影响模型性能的偏差 (形状与纹理).
主要方法:
- 对分发和分发之外的数据集进行评估的最新模型.
- 分析了使用合成纹理转移数据的中间层表示.
- 在各种骨干网络上进行了密集废除研究.
主要成果:
- 变压器表现出强烈的形状偏差;CNN表现出强烈的纹理偏差.
- 与形状偏差模型相比,纹理偏差模型的概括性很差.
- 美国有线电视新闻网的局部诱导纹理偏见;变形金刚的自我注意力诱导形状偏见.
结论:
- 形状偏差模型对于单眼深度估计概括是优越的.
- 骨干网络架构显著影响偏差和性能.
- 结果在真实世界的驾驶数据集上得到验证.
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