Modeling the Uncertainty with Maximum Discrepant Students for
  Semi-supervised 2D Pose Estimation
Jiaqi Wu Junbiao Pang Qingming Huang
Semi-supervised pose estimation is a practically challenging task for
computer vision. Although numerous excellent semi-supervised classification
methods have emerged, these methods typically use confidence to evaluate the
quality of pseudo-labels, which is difficult to achieve in pose estimation
tasks. For example, in pose estimation, confidence represents only the
possibility that a position of the heatmap is a keypoint, not the quality of
that prediction. In this paper, we propose a simple yet efficient framework to
estimate the quality of pseudo-labels in semi-supervised pose estimation tasks
from the perspective of modeling the uncertainty of the pseudo-labels.
Concretely, under the dual mean-teacher framework, we construct the two maximum
discrepant students (MDSs) to effectively push two teachers to generate
different decision boundaries for the same sample. Moreover, we create multiple
uncertainties to assess the quality of the pseudo-labels. Experimental results
demonstrate that our method improves the performance of semi-supervised pose
estimation on three datasets.