  Self-training via pseudo labeling is a conventional, simple, and popular
pipeline to leverage unlabeled data. In this work, we first construct a strong
baseline of self-training (namely ST) for semi-supervised semantic segmentation
via injecting strong data augmentations (SDA) on unlabeled images to alleviate
overfitting noisy labels as well as decouple similar predictions between the
teacher and student. With this simple mechanism, our ST outperforms all
existing methods without any bells and whistles, e.g., iterative re-training.
Inspired by the impressive results, we thoroughly investigate the SDA and
provide some empirical analysis. Nevertheless, incorrect pseudo labels are
still prone to accumulate and degrade the performance. To this end, we further
propose an advanced self-training framework (namely ST++), that performs
selective re-training via prioritizing reliable unlabeled images based on
holistic prediction-level stability. Concretely, several model checkpoints are
saved in the first stage supervised training, and the discrepancy of their
predictions on the unlabeled image serves as a measurement for reliability. Our
image-level selection offers holistic contextual information for learning. We
demonstrate that it is more suitable for segmentation than common pixel-wise
selection. As a result, ST++ further boosts the performance of our ST. Code is
available at https://github.com/LiheYoung/ST-PlusPlus.
