We propose a new semi-supervised learning design for human pose estimation
that revisits the popular dual-student framework and enhances it two ways.
First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as
targets for learning from unlabeled data. This uses multi-view augmentations
and a threshold-and-refine procedure to produce a pool of pseudo-heatmaps.
Second, we select the learning targets from these pseudo-heatmaps guided by the
estimated cross-student uncertainty. We evaluate our proposed method on
multiple evaluation setups on the COCO benchmark. Our results show that our
model outperforms previous state-of-the-art semi-supervised pose estimators,
especially in extreme low-data regime. For example with only 0.5K labeled
images our method is capable of surpassing the best competitor by 7.22 mAP
(+25% absolute improvement). We also demonstrate that our model can learn
effectively from unlabeled data in the wild to further boost its generalization
and performance.

我们提出了一种新的半监督学习设计，用于人体姿势估计，通过增强双学生框架，引入去噪方案生成可靠的伪热图作为无标签数据学习的目标，并通过估计的交叉学生不确定性从伪热图中选择学习目标。我们在 COCO 基准测试上进行了多个评估设置，结果显示我们的模型在极低数据情况下优于以前最先进的半监督姿势估计器，例如，只有 0.5K 标记图像时，我们的方法能够超越最好的竞争者 7.22 mAP （绝对改进量为 + 25%），我们还证明了我们的模型可以从非标签数据中有效学习，以进一步提升其泛化和性能。