  Semantic image parsing, which refers to the process of decomposing images
into semantic regions and constructing the structure representation of the
input, has recently aroused widespread interest in the field of computer
vision. The recent application of deep representation learning has driven this
field into a new stage of development. In this paper, we summarize three
aspects of the progress of research on semantic image parsing, i.e.,
category-level semantic segmentation, instance-level semantic segmentation, and
beyond segmentation. Specifically, we first review the general frameworks for
each task and introduce the relevant variants. The advantages and limitations
of each method are also discussed. Moreover, we present a comprehensive
comparison of different benchmark datasets and evaluation metrics. Finally, we
explore the future trends and challenges of semantic image parsing.

Computer Vision and Pattern Recognition