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.

翻译：受微弱监管的语义分解通常受到阶级激活地图的启发,这些地图是作为阶级偏差区域所突出的假面罩而形成的。尽管我们为回顾每个阶级的确切和完整位置做出了巨大努力,但现有方法通常仍受到不属于标签对象的未经请求的校外误判预测的影响,这种预测是可以避免的,因为与图像等级类标签的矛盾很容易被检测到。在本文中,我们以插接和播放方式开发了一个基于等级排序的CR1.3校正机制(OCR)。首先,我们根据先前的说明相关性和海后预测相关性,将每一类的语义分类分为CSandidate(IC)和OCl组。然后,我们得出了不同的校正损失,因为与图像级级标签相冲突很容易被检测到IC组。我们用半线基线(例如,AclinityNet,SEAMAM, MCTrefren),我们可以在Pasal + MS% + MIC+ 数据中取得显著的绩效收益(例如,% + MOC+ + mOC+ 额外的数据)。