  Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has attracted much attention due to low annotation costs. Existing methods
often rely on Class Activation Mapping (CAM) that measures the correlation
between image pixels and classifier weight. However, the classifier focuses
only on the discriminative regions while ignoring other useful information in
each image, resulting in incomplete localization maps. To address this issue,
we propose a Self-supervised Image-specific Prototype Exploration (SIPE) that
consists of an Image-specific Prototype Exploration (IPE) and a
General-Specific Consistency (GSC) loss. Specifically, IPE tailors prototypes
for every image to capture complete regions, formed our Image-Specific CAM
(IS-CAM), which is realized by two sequential steps. In addition, GSC is
proposed to construct the consistency of general CAM and our specific IS-CAM,
which further optimizes the feature representation and empowers a
self-correction ability of prototype exploration. Extensive experiments are
conducted on PASCAL VOC 2012 and MS COCO 2014 segmentation benchmark and
results show our SIPE achieves new state-of-the-art performance using only
image-level labels. The code is available at
https://github.com/chenqi1126/SIPE.
