Human pose estimation is a fundamental and challenging task in computer
vision. Larger-scale and more accurate keypoint annotations, while helpful for
improving the accuracy of supervised pose estimation, are often expensive and
difficult to obtain. Semi-supervised pose estimation tries to leverage a large
amount of unlabeled data to improve model performance, which can alleviate the
problem of insufficient labeled samples. The latest semi-supervised learning
usually adopts a strong and weak data augmented teacher-student learning
framework to deal with the challenge of "Human postural diversity and its
long-tailed distribution". Appropriate data augmentation method is one of the
key factors affecting the accuracy and generalization of semi-supervised
models. Aiming at the problem that the difference of sample learning is not
considered in the fixed keypoint masking augmentation method, this paper
proposes an adaptive keypoint masking method, which can fully mine the
information in the samples and obtain better estimation performance. In order
to further improve the generalization and robustness of the model, this paper
proposes a dual-branch data augmentation scheme, which can perform Mixup on
samples and features on the basis of adaptive keypoint masking. The
effectiveness of the proposed method is verified on COCO and MPII,
outperforming the state-of-the-art semi-supervised pose estimation by 5.2% and
0.3%, respectively.

人体姿势估计是计算机视觉中的基本且具有挑战性的任务。本文提出了一种自适应关键点遮罩方法和双分支数据增广方案，用于改进半监督姿势估计的准确性和泛化能力，超过了现有的半监督姿势估计方法。

自适应关键点屏蔽的半监督 2D 人体姿态估计

Semi-supervised 2D Human Pose Estimation via Adaptive Keypoint Masking

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.

本文介绍了一种简单而高效的框架，从建模伪标签的不确定性角度来估计半监督姿势估计任务中的伪标签质量，并在两个教师生成不同决策边界的基础上构建了两个最大差异学生（MDSs）以有效推动。实验结果表明，我们的方法改善了三个数据集上的半监督姿势估计性能。