  Fully convolutional networks (FCN) have achieved great success in human
parsing in recent years. In conventional human parsing tasks, pixel-level
labeling is required for guiding the training, which usually involves enormous
human labeling efforts. To ease the labeling efforts, we propose a novel weakly
supervised human parsing method which only requires simple object keypoint
annotations for learning. We develop an iterative learning method to generate
pseudo part segmentation masks from keypoint labels. With these pseudo masks,
we train an FCN network to output pixel-level human parsing predictions.
Furthermore, we develop a correlation network to perform joint prediction of
part and object segmentation masks and improve the segmentation performance.
The experiment results show that our weakly supervised method is able to
achieve very competitive human parsing results. Despite our method only uses
simple keypoint annotations for learning, we are able to achieve comparable
performance with fully supervised methods which use the expensive pixel-level
annotations.
