  Human parsing is an essential branch of semantic segmentation, which is a
fine-grained semantic segmentation task to identify the constituent parts of
human. The challenge of human parsing is to extract effective semantic features
to resolve deformation and multi-scale variations. In this work, we proposed an
end-to-end model called C-DLinkNet based on LinkNet, which contains a new
module named Smooth Module to combine the multi-level features in Decoder part.
C-DLinkNet is capable of producing competitive parsing performance compared
with the state-of-the-art methods with smaller input sizes and no additional
information, i.e., achiving mIoU=53.05 on the validation set of LIP dataset.
