  Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has been greatly advanced by exploiting the outputs of Class Activation Map
(CAM) to generate the pseudo labels for semantic segmentation. However, CAM
merely discovers seeds from a small number of regions, which may be
insufficient to serve as pseudo masks for semantic segmentation. In this paper,
we formulate the expansion of object regions in CAM as an increase in
information. From the perspective of information theory, we propose a novel
Complementary Patch (CP) Representation and prove that the information of the
sum of the CAMs by a pair of input images with complementary hidden (patched)
parts, namely CP Pair, is greater than or equal to the information of the
baseline CAM. Therefore, a CAM with more information related to object seeds
can be obtained by narrowing down the gap between the sum of CAMs generated by
the CP Pair and the original CAM. We propose a CP Network (CPN) implemented by
a triplet network and three regularization functions. To further improve the
quality of the CAMs, we propose a Pixel-Region Correlation Module (PRCM) to
augment the contextual information by using object-region relations between the
feature maps and the CAMs. Experimental results on the PASCAL VOC 2012 datasets
show that our proposed method achieves a new state-of-the-art in WSSS,
validating the effectiveness of our CP Representation and CPN.
