  Current weakly supervised semantic segmentation (WSSS) frameworks usually
contain the separated mask-refinement model and the main semantic region mining
model. These approaches would contain redundant feature extraction backbones
and biased learning objectives, making them computational complex yet
sub-optimal to addressing the WSSS task. To solve this problem, this paper
establishes a compact learning framework that embeds the classification and
mask-refinement components into a unified deep model. With the shared feature
extraction backbone, our model is able to facilitate knowledge sharing between
the two components while preserving a low computational complexity. To
encourage high-quality knowledge interaction, we propose a novel alternative
self-dual teaching (ASDT) mechanism. Unlike the conventional distillation
strategy, the knowledge of the two teacher branches in our model is
alternatively distilled to the student branch by a Pulse Width Modulation
(PWM), which generates PW wave-like selection signal to guide the knowledge
distillation process. In this way, the student branch can help prevent the
model from falling into local minimum solutions caused by the imperfect
knowledge provided of either teacher branch. Comprehensive experiments on the
PASCAL VOC 2012 and COCO-Stuff 10K demonstrate the effectiveness of the
proposed alternative self-dual teaching mechanism as well as the new
state-of-the-art performance of our approach.
