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。