  Localizing keypoints of an object is a basic visual problem. However,
supervised learning of a keypoint localization network often requires a large
amount of data, which is expensive and time-consuming to obtain. To remedy
this, there is an ever-growing interest in semi-supervised learning (SSL),
which leverages a small set of labeled data along with a large set of unlabeled
data. Among these SSL approaches, pseudo-labeling (PL) is one of the most
popular. PL approaches apply pseudo-labels to unlabeled data, and then train
the model with a combination of the labeled and pseudo-labeled data
iteratively. The key to the success of PL is the selection of high-quality
pseudo-labeled samples. Previous works mostly select training samples by
manually setting a single confidence threshold. We propose to automatically
select reliable pseudo-labeled samples with a series of dynamic thresholds,
which constitutes a learning curriculum. Extensive experiments on six keypoint
localization benchmark datasets demonstrate that the proposed approach
significantly outperforms the previous state-of-the-art SSL approaches.
