引入FPN可以改变目标函数和骨干网之间的反向传播路径。图2显示了不使用FPN和基于FPN的检测框架之间的差异。在无FPN检测管道中,只有骨干特征C5直接受目标函数的监督。 图2。无FPN检测框架和基于FPN检测框架的反向传播路径的比较 由于深度神经网络中存在梯度消失问题,骨干网的浅层(即{fs0, fs1, fs2, fs3})将难以通过...
1、论文总述本篇论文针对FPN的作用进行了深入分析,认为主要有两个功能:特征融合和对不同尺度目标进行分而治之优化,然后作者发现这两个作用中比较重要的是分而治之思想,并不是特征融合,… 贾小树发表于论文阅读 【YOLOF 解读】You Only Look One-level Feature(CVPR 2021) AI 菌发表于CV顶会研... 如何玩好Wh...
这篇文章主要目的是修正 FPN 带来的大目标性能损害,发现其原因主要来源于 FPN 中不合适的 back-propagation,因此作者提出了基于不确定性的辅助损失和构建新的 FPN 结构来解决上述问题。 论文标题:You Should Look at All Objects 收录会议: ECCV 2022 论文链接: https://arxiv.org/abs/2207.07889 代码链接: https...
然后,网络使用P来预测分配到每个金字塔层次l的目标的分类和回归信息如下: 目标分配规则是用低分辨率金字塔特征(如P5)来预测大尺度目标,用高分辨率金字塔特征(如P2)来预测小尺度目标。在网络优化过程中,金字塔每一级l的损失计算如下: FPN分析 引入FPN可以改变目标函数和骨干网之间的反向传播路径。图2显示了不使用FPN和...
It makes each level of the backbone network only has the ability to look at the objects within a certain scale range. Based on these analysis, two feasible strategies are proposed to enable each level of the backbone to look at all objects in the FPN-based detection frameworks. Specifically,...
proposed to enable each level of the backbone to look at all objects in the FPN-based detection frameworks. Specifically, one is to introduce auxiliary objective functions to make each backbone level directly receive the back-propagation signals of various-scale objects during training. The other ...
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