  Significant attention is being paid to multi-person pose estimation methods
recently, as there has been rapid progress in the field owing to convolutional
neural networks. Especially, recent method which exploits part confidence maps
and Part Affinity Fields (PAFs) has achieved accurate real-time prediction of
multi-person keypoints. However, human annotated labels are sometimes
inappropriate for learning models. For example, if there is a limb that extends
outside an image, a keypoint for the limb may not have annotations because it
exists outside of the image, and thus the labels for the limb can not be
generated. If a model is trained with data including such missing labels, the
output of the model for the location, even though it is correct, is penalized
as a false positive, which is likely to cause negative effects on the
performance of the model. In this paper, we point out the existence of some
patterns of inappropriate labels, and propose a novel method for correcting
such labels with a teacher model trained on such incomplete data. Experiments
on the COCO dataset show that training with the corrected labels improves the
performance of the model and also speeds up training.
