  Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has
long been suffering from fragmentary object regions led by Class Activation Map
(CAM), which is incapable of generating fine-grained masks for semantic
segmentation. To guide CAM to find more non-discriminating object patterns,
this paper turns to an interesting working mechanism in agent learning named
Complementary Learning System (CLS). CLS holds that the neocortex builds a
sensation of general knowledge, while the hippocampus specially learns specific
details, completing the learned patterns. Motivated by this simple but
effective learning pattern, we propose a General-Specific Learning Mechanism
(GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask.
Specifically, GSLM develops a General Learning Module (GLM) and a Specific
Learning Module (SLM). The GLM is trained with image-level supervision to
extract coarse and general localization representations from CAM. Based on the
general knowledge in the GLM, the SLM progressively exploits the specific
spatial knowledge from the localization representations, expanding the CAM in
an explicit way. To this end, we propose the Seed Reactivation to help SLM
reactivate non-discriminating regions by setting a boundary for activation
values, which successively identifies more regions of CAM. Without extra
refinement processes, our method is able to achieve breakthrough improvements
for CAM of over 20.0% mIoU on PASCAL VOC 2012 and 10.0% mIoU on MS COCO 2014
datasets, representing a new state-of-the-art among existing WSSS methods.
