  Pseudo supervision is regarded as the core idea in semi-supervised learning
for semantic segmentation, and there is always a tradeoff between utilizing
only the high-quality pseudo labels and leveraging all the pseudo labels.
Addressing that, we propose a novel learning approach, called
Conservative-Progressive Collaborative Learning (CPCL), among which two
predictive networks are trained in parallel, and the pseudo supervision is
implemented based on both the agreement and disagreement of the two
predictions. One network seeks common ground via intersection supervision and
is supervised by the high-quality labels to ensure a more reliable supervision,
while the other network reserves differences via union supervision and is
supervised by all the pseudo labels to keep exploring with curiosity. Thus, the
collaboration of conservative evolution and progressive exploration can be
achieved. To reduce the influences of the suspicious pseudo labels, the loss is
dynamic re-weighted according to the prediction confidence. Extensive
experiments demonstrate that CPCL achieves state-of-the-art performance for
semi-supervised semantic segmentation.
