  In this paper, we solve the sample shortage problem in the human parsing
task. We begin with the self-learning strategy, which generates pseudo-labels
for unlabeled data to retrain the model. However, directly using noisy
pseudo-labels will cause error amplification and accumulation. Considering the
topology structure of human body, we propose a trainable graph reasoning method
that establishes internal structural connections between graph nodes to correct
two typical errors in the pseudo-labels, i.e., the global structural error and
the local consistency error. For the global error, we first transform
category-wise features into a high-level graph model with coarse-grained
structural information, and then decouple the high-level graph to reconstruct
the category features. The reconstructed features have a stronger ability to
represent the topology structure of the human body. Enlarging the receptive
field of features can effectively reducing the local error. We first project
feature pixels into a local graph model to capture pixel-wise relations in a
hierarchical graph manner, then reverse the relation information back to the
pixels. With the global structural and local consistency modules, these errors
are rectified and confident pseudo-labels are generated for retraining.
Extensive experiments on the LIP and the ATR datasets demonstrate the
effectiveness of our global and local rectification modules. Our method
outperforms other state-of-the-art methods in supervised human parsing tasks.
