HLNet: Modeling High and Low Frequencies for Scene Parsing

In this paper we propose to model high and low frequencies of segmentation map, based on the observation that the map can be seen as a mixture of different frequencies. Based on the sparsity of high frequencies and local similarity of low frequencies, we design special building blocks and further a novel High and Low frequency Network (HLNet) with two branches based on FCN to predict high and low frequencies of the segmentation map, respectively. Specifically, we design a high frequency branch with a small kernel size and high-resolution features to predict a sparse high frequency component. Mean-while, a low frequency branch with similarity computing and low-resolution features is employed to predict a low frequency component. On top of two branches, we combine two different frequency components to generate final result for scene parsing. We empirically demonstrate that the designed model achieves superior performance 44.07% on ADE20K, and 80.14% mIoU on Cityscapes datasets.