Convolutional Random Walk Networks for Semantic Image Segmentation
Convolutional Random Walk Networks for Semantic Image Segmentation
Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resolution inside the deep layers. This leads to predictions with poor localization around the boundaries. Prior work has attempted to address this issue by post-processing …