  Recent studies on semi-supervised semantic segmentation (SSS) have seen fast
progress. Despite their promising performance, current state-of-the-art methods
tend to increasingly complex designs at the cost of introducing more network
components and additional training procedures. Differently, in this work, we
follow a standard teacher-student framework and propose AugSeg, a simple and
clean approach that focuses mainly on data perturbations to boost the SSS
performance. We argue that various data augmentations should be adjusted to
better adapt to the semi-supervised scenarios instead of directly applying
these techniques from supervised learning. Specifically, we adopt a simplified
intensity-based augmentation that selects a random number of data
transformations with uniformly sampling distortion strengths from a continuous
space. Based on the estimated confidence of the model on different unlabeled
samples, we also randomly inject labelled information to augment the unlabeled
samples in an adaptive manner. Without bells and whistles, our simple AugSeg
can readily achieve new state-of-the-art performance on SSS benchmarks under
different partition protocols.
