Deep convolutional neural networks (CNNs) are the backbone of state-of-art
semantic image segmentation systems. Recent work has shown that complementing
CNNs with fully-connected conditional random fields (CRFs) can significantly
enhance their object localization accuracy, yet dense CRF inference is
computationally expensive. We propose replacing the fully-connected CRF with
domain transform (DT), a modern edge-preserving filtering method in which the
amount of smoothing is controlled by a reference edge map. Domain transform
filtering is several times faster than dense CRF inference and we show that it
yields comparable semantic segmentation results, accurately capturing object
boundaries. Importantly, our formulation allows learning the reference edge map
from intermediate CNN features instead of using the image gradient magnitude as
in standard DT filtering. This produces task-specific edges in an end-to-end
trainable system optimizing the target semantic segmentation quality.

本研究提出一种基于深度卷积神经网络和领域变换滤波器的语义图像分割方法，该方法通过替代全连接条件随机场并控制去噪程度来有效提高对象定位精度和加速密集的条件随机场推理，而利用 CNN 特征来学习引用边缘地图可生成接近任务目标的边缘，进而优化了分割质量，从而在语义分割结果上取得了可比较的精度。