The 2D human pose estimation is a basic visual problem. However, supervised
learning of a model requires massive labeled images, which is expensive and
labor-intensive. In this paper, we aim at boosting the accuracy of a pose
estimator by excavating extra unlabeled images in a semi-supervised learning
(SSL) way. Most previous consistency-based SSL methods strive to constraint the
model to predict consistent results for differently augmented images. Following
this consensus, we revisit two core aspects including advanced data
augmentation methods and concise consistency training frameworks. Specifically,
we heuristically dig various collaborative combinations of existing data
augmentations, and discover novel superior data augmentation schemes to more
effectively add noise on unlabeled samples. They can compose easy-hard
augmentation pairs with larger transformation difficulty gaps, which play a
crucial role in consistency-based SSL. Moreover, we propose to strongly augment
unlabeled images repeatedly with diverse augmentations, generate multi-path
predictions sequentially, and optimize corresponding unsupervised consistency
losses using one single network. This simple and compact design is on a par
with previous methods consisting of dual or triple networks. Furthermore, it
can also be integrated with multiple networks to produce better performance.
Comparing to state-of-the-art SSL approaches, our method brings substantial
improvements on public datasets. Code is released for academic use in
https://github.com/hnuzhy/MultiAugs.

挖掘未标记图像的额外使用能够有效地提高 2D 人体姿态估计器的准确性，通过数据增强和一致性训练方法，我们提出了一种简洁紧凑的半监督学习方法，与先前的方法相媲美，并在公共数据集中带来了显著的改进。