  For human pose estimation in still images, this paper proposes three semi-
and weakly-supervised learning schemes. While recent advances of convolutional
neural networks improve human pose estimation using supervised training data,
our focus is to explore the semi- and weakly-supervised schemes. Our proposed
schemes initially learn conventional model(s) for pose estimation from a small
amount of standard training images with human pose annotations. For the first
semi-supervised learning scheme, this conventional pose model detects candidate
poses in training images with no human annotation. From these candidate poses,
only true-positives are selected by a classifier using a pose feature
representing the configuration of all body parts. The accuracies of these
candidate pose estimation and true-positive pose selection are improved by
action labels provided to these images in our second and third learning
schemes, which are semi- and weakly-supervised learning. While the first and
second learning schemes select only poses that are similar to those in the
supervised training data, the third scheme selects more true-positive poses
that are significantly different from any supervised poses. This pose selection
is achieved by pose clustering using outlier pose detection with Dirichlet
process mixtures and the Bayes factor. The proposed schemes are validated with
large-scale human pose datasets.
