  Human parsing, or human body part semantic segmentation, has been an active
research topic due to its wide potential applications. In this paper, we
propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the
cross-dataset human parsing problem, where the annotations are at different
granularities. Starting from the prior knowledge of the human body hierarchical
structure, we devise a graph pyramid module (GPM) by stacking three levels of
graph structures from coarse granularity to fine granularity subsequently. At
each level, GPM utilizes the self-attention mechanism to model the correlations
between context nodes. Then, it adopts a top-down mechanism to progressively
refine the hierarchical features through all the levels. GPM also enables
efficient mutual learning. Specifically, the network weights of the first two
levels are shared to exchange the learned coarse-granularity information across
different datasets. By making use of the multi-granularity labels, Grapy-ML
learns a more discriminative feature representation and achieves
state-of-the-art performance, which is demonstrated by extensive experiments on
the three popular benchmarks, e.g. CIHP dataset. The source code is publicly
available at https://github.com/Charleshhy/Grapy-ML.
