  Semi-Supervised Semantic Segmentation aims at training the segmentation model
with limited labeled data and a large amount of unlabeled data. To effectively
leverage the unlabeled data, pseudo labeling, along with the teacher-student
framework, is widely adopted in semi-supervised semantic segmentation. Though
proved to be effective, this paradigm suffers from incorrect pseudo labels
which inevitably exist and are taken as auxiliary training data. To alleviate
the negative impact of incorrect pseudo labels, we delve into the current
Semi-Supervised Semantic Segmentation frameworks. We argue that the unlabeled
data with pseudo labels can facilitate the learning of representative features
in the feature extractor, but it is unreliable to supervise the mask predictor.
Motivated by this consideration, we propose a novel framework, Gentle Teaching
Assistant (GTA-Seg) to disentangle the effects of pseudo labels on feature
extractor and mask predictor of the student model. Specifically, in addition to
the original teacher-student framework, our method introduces a teaching
assistant network which directly learns from pseudo labels generated by the
teacher network. The gentle teaching assistant (GTA) is coined gentle since it
only transfers the beneficial feature representation knowledge in the feature
extractor to the student model in an Exponential Moving Average (EMA) manner,
protecting the student model from the negative influences caused by unreliable
pseudo labels in the mask predictor. The student model is also supervised by
reliable labeled data to train an accurate mask predictor, further facilitating
feature representation. Extensive experiment results on benchmark datasets
validate that our method shows competitive performance against previous
methods. Code is available at https://github.com/Jin-Ying/GTA-Seg.
