  Semantic segmentation has been widely investigated in the community, in which
the state of the art techniques are based on supervised models. Those models
have reported unprecedented performance at the cost of requiring a large set of
high quality segmentation masks. To obtain such annotations is highly expensive
and time consuming, in particular, in semantic segmentation where pixel-level
annotations are required. In this work, we address this problem by proposing a
holistic solution framed as a three-stage self-training framework for
semi-supervised semantic segmentation. The key idea of our technique is the
extraction of the pseudo-masks statistical information to decrease uncertainty
in the predicted probability whilst enforcing segmentation consistency in a
multi-task fashion. We achieve this through a three-stage solution. Firstly, we
train a segmentation network to produce rough pseudo-masks which predicted
probability is highly uncertain. Secondly, we then decrease the uncertainty of
the pseudo-masks using a multi-task model that enforces consistency whilst
exploiting the rich statistical information of the data. We compare our
approach with existing methods for semi-supervised semantic segmentation and
demonstrate its state-of-the-art performance with extensive experiments.
