  Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g.
human parsing, remains a challenging task. The ambiguous boundary between
different semantic parts and those categories with similar appearance usually
are confusing, leading to unexpected noises in ground truth masks. To tackle
the problem of learning with label noises, this work introduces a purification
strategy, called Self-Correction for Human Parsing (SCHP), to progressively
promote the reliability of the supervised labels as well as the learned models.
In particular, starting from a model trained with inaccurate annotations as
initialization, we design a cyclically learning scheduler to infer more
reliable pseudo-masks by iteratively aggregating the current learned model with
the former optimal one in an online manner. Besides, those correspondingly
corrected labels can in turn to further boost the model performance. In this
way, the models and the labels will reciprocally become more robust and
accurate during the self-correction learning cycles. Benefiting from the
superiority of SCHP, we achieve the best performance on two popular
single-person human parsing benchmarks, including LIP and Pascal-Person-Part
datasets. Our overall system ranks 1st in CVPR2019 LIP Challenge. Code is
available at https://github.com/PeikeLi/Self-Correction-Human-Parsing.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing