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

We develop a robust multi-scale structure-aware neural network for human pose
estimation. This method improves the recent deep conv-deconv hourglass models
with four key improvements: (1) multi-scale supervision to strengthen
contextual feature learning in matching body keypoints by combining feature
heatmaps across scales, (2) multi-scale regression network at the end to
globally optimize the structural matching of the multi-scale features, (3)
structure-aware loss used in the intermediate supervision and at the regression
to improve the matching of keypoints and respective neighbors to infer a
higher-order matching configurations, and (4) a keypoint masking training
scheme that can effectively fine-tune our network to robustly localize occluded
keypoints via adjacent matches. Our method can effectively improve
state-of-the-art pose estimation methods that suffer from difficulties in scale
varieties, occlusions, and complex multi-person scenarios. This multi-scale
supervision tightly integrates with the regression network to effectively (i)
localize keypoints using the ensemble of multi-scale features, and (ii) infer
global pose configuration by maximizing structural consistencies across
multiple keypoints and scales. The keypoint masking training enhances these
advantages to focus learning on hard occlusion samples. Our method achieves the
leading position in the MPII challenge leaderboard among the state-of-the-art
methods.

本文提出了一种多尺度结构感知神经网络，通过多尺度监督、多尺度回归网络、中间监督和结构感知损失以及关键点掩蔽训练方案等四个方面对深度卷积 - 反卷积沙漏模型进行改进，以有效地提高人体姿势估计的性能。该网络不仅可以解决尺度差异、遮挡和复杂多人场景等问题，而且可以全局优化多尺度特征的结构匹配，并在 MPII 挑战排行榜上取得了领先地位。