语义分割--Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network

Large Kernel Matters–Improve Semantic Segmentation by Global Convolutional Network
https://arxiv.org/abs/1703.02719

语义分割问题需要同时解决两个问题：classification 和 localization，将图像中的每个物体精确分割出来，同时对每个物体进行分类。分类和定位这两个问题对于CNN 设计要求有所区别。
For the classification task, the models are required to be invariant to various transformations like translation and rotation.

But for the localization task, models should be transformation-sensitive, i.e., precisely locate every pixel for each semantic category

当前的语义分割算法主要侧重于 localization , which may be suboptimal for classification

怎么解决这个contradictory 了？这里我们的策略是使用 Large Kernel

这里我们设计了一个 Global Convolutional Network 采用 Large Kernel
from the localization view, the structure must be fully-convolutional without any fully-connected layer or global pooling layer that used by many classification networks,since the latter will discard localization information

from the classification view, motivated by the densely-connected structure of classification models, the kernel size of the convolutional structure should be as large as possible.

对于 GCN 模块计算量问题： Instead of directly using larger kernel or global convolution, our GCN module employs a
combination of 1 × k + k × 1 and k × 1 + 1 × k convolutions, which enables densely connections within a large k×k region inthe feature map.

为了提升物体边缘分割精度，提出Boundary Refinement
we propose a Boundary Refinement (BR) block shown in Figure 2 C. Here, we models the boundary alignment as a residual structure.

Kernal 尺寸越大，效果越好

GCN 与其他两个convolution结构对比：

上面三个图主要证明 GCN 效果比其他卷积结构好

PASCAL VOC 2012 test set

Cityscapes test set

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