  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.
