Title: Understanding Convolution for Semantic Segmentation,
Abstract: Recent advances in deep learning, especially deep convolutional neural
networks (CNNs), have led to significant improvement over previous semantic
segmentation systems. Here we show how to improve pixel-wise semantic
segmentation by manipulating convolution-related operations that are of both
theoretical and practical value. First, we design dense upsampling convolution
(DUC) to generate pixel-level prediction, which is able to capture and decode
more detailed information that is generally missing in bilinear upsampling.
Second, we propose a hybrid dilated convolution (HDC) framework in the encoding
phase. This framework 1) effectively enlarges the receptive fields (RF) of the
network to aggregate global information; 2) alleviates what we call the
"gridding issue" caused by the standard dilated convolution operation. We
evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a
state-of-art result of 80.1% mIOU in the test set at the time of submission. We
also have achieved state-of-the-art overall on the KITTI road estimation
benchmark and the PASCAL VOC2012 segmentation task. Our source code can be
found at this https URL .