An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation

Most semi-supervised learning models are consistencybased, which leverage unlabeled images by maximizing the similarity between different augmentations of an image. But when we apply them to human pose estimation that has extremely imbalanced class distribution, they often collapse and predict every pixel in unlabeled images as background. We find this is because the decision boundary passes the high-density areas of the minor class so more and more pixels are gradually mis-classified as background. In this work, we present a surprisingly simple approach to drive the model to learn in the correct direction. For each image, it composes a pair of easy-hard augmentations and uses the more accurate predictions on the easy image to teach the network to learn pose information of the hard one. The accuracy superiority of teaching signals allows the network to be "monotonically" improved which effectively avoids collapsing. We apply our method to the state-of-the-art pose estimators and it further improves their performance on three public datasets. The source code and pretrained models have been released at https://github.com/xierc/Semi Human Pose .