A Practical Hybrid Active Learning Approach for Human Pose Estimation

Abstract

Active learning (AL) has not received much attention in deep learning (DL) for human pose estimation. In this paper, a practical hybrid active learning strategy is proposed for training a human pose estimation model, and it is tested in an industrial online environment. The conducted experiments show that the active learning strategy to select diverse samples to be annotated outperforms the baseline method with random sampling. As a result, the strategy enables a significant improvement in the performance of pose estimation.