HR-Net 论文解读（high-resolution

摘要

通过聚合多个平行特征层卷积的方法保证图像的高分辨率，并应用在现有的RCNN网络中，均取得了不错的优化，大幅度提升小目标检测性能。

Architecture:

The multi-resolution group convolution is a simple extension of the group convolution, which divides the input channels into several subsets of channels and performs a regular convolution over each subset over different spatial resolutions separately.

将输入通道划分为多个子集，并将这些子集在不同空间分辨率下进行正则卷积（regular convolution)

multi-resolution convolution

The input channels are divided into several subsets, and the output channels are also divided into several subsets. The input and output subsets are connected in a fully-connected fashion, and each connection is a regular convolution. Each subset of output channels is a summation of the outputs of the convolutions over each subset of input channels

卷积的输入和输出以全连接的方式连接

差异

• 每个subset都是在不同分辨率的情况下
• 输入与输出之间的全连接需要处理，下采样通过s=2,k=3的卷积实现，上采样通过线性插值的方法实现