  Weakly supervised semantic segmentation is typically inspired by class
activation maps, which serve as pseudo masks with class-discriminative regions
highlighted. Although tremendous efforts have been made to recall precise and
complete locations for each class, existing methods still commonly suffer from
the unsolicited Out-of-Candidate (OC) error predictions that not belongs to the
label candidates, which could be avoidable since the contradiction with
image-level class tags is easy to be detected. In this paper, we develop a
group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a
plug-and-play fashion. Firstly, we adaptively split the semantic categories
into In-Candidate (IC) and OC groups for each OC pixel according to their prior
annotation correlation and posterior prediction correlation. Then, we derive a
differentiable rectification loss to force OC pixels to shift to the IC group.
Incorporating our OCR with seminal baselines (e.g., AffinityNet, SEAM,
MCTformer), we can achieve remarkable performance gains on both Pascal VOC
(+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with
negligible extra training overhead, which justifies the effectiveness and
generality of our OCR.
