In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. (ii) The negative impact of noise pseudo labels on training. Moreover, the labels used for 2D human pose estimation are relatively complex: keypoint category and keypoint position. To solve the problems mentioned above, we propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM). We introduce an additional auxiliary teacher and use the pseudo labels generated by the two teacher model in different periods to calculate the inconsistency score and remove outliers. Then, the two teacher models are updated through interactive training, and the student model is updated using the pseudo labels generated by two teachers. To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples. In addition, we also proposed a new indoor overhead fisheye human keypoint dataset WEPDTOF-Pose. Extensive experiments demonstrate that our method outperforms the previous best semi-supervised 2D human pose estimation method. We will release the code and dataset at https://github.com/hlz0606/SSPCM.

翻译：本文深入研究了半监督二维人体姿态估计问题。先前方法忽略了两个问题：（i）在大模型与轻量级模型进行交互训练时，轻量级模型产生的伪标签将被用于指导大模型；（ii）噪声伪标签对训练产生的负面影响。此外，用于二维人体姿态估计的标签相对复杂，包含关键点类别和关键点位置。为解决上述问题，我们提出了一种由位置不一致性伪标签修正模块驱动的半监督二维人体姿态估计框架（SSPCM）。该方法引入额外的辅助教师模型，利用两个教师模型在不同时期生成的伪标签计算不一致性评分并剔除异常值。随后通过交互训练更新两个教师模型，并利用二者生成的伪标签更新学生模型。为进一步提升学生模型性能，我们采用基于伪关键点感知的半监督Cut-Occlude方法生成更具难度和有效性的样本。此外，我们还提出了一个新的室内顶部鱼眼人体关键点数据集WEPDTOF-Pose。大量实验表明，我们的方法优于以往最优的半监督二维人体姿态估计方法。代码和数据集将在https://github.com/hlz0606/SSPCM 开源。