  The objective of human parsing is to partition a human in an image into
constituent parts. This task involves labeling each pixel of the human image
according to the classes. Since the human body comprises hierarchically
structured parts, each body part of an image can have its sole position
distribution characteristic. Probably, a human head is less likely to be under
the feet, and arms are more likely to be near the torso. Inspired by this
observation, we make instance class distributions by accumulating the original
human parsing label in the horizontal and vertical directions, which can be
utilized as supervision signals. Using these horizontal and vertical class
distribution labels, the network is guided to exploit the intrinsic position
distribution of each class. We combine two guided features to form a spatial
guidance map, which is then superimposed onto the baseline network by
multiplication and concatenation to distinguish the human parts precisely. We
conducted extensive experiments to demonstrate the effectiveness and
superiority of our method on three well-known benchmarks: LIP, ATR, and CIHP
databases.
