Recently, One-stage Weakly Supervised Semantic Segmentation (WSSS) with
image-level labels has gained increasing interest due to simplification over
its cumbersome multi-stage counterpart. Limited by the inherent ambiguity of
Class Activation Map (CAM), we observe that one-stage pipelines often encounter
confirmation bias caused by incorrect CAM pseudo-labels, impairing their final
segmentation performance. Although recent works discard many unreliable
pseudo-labels to implicitly alleviate this issue, they fail to exploit
sufficient supervision for their models. To this end, we propose a dual student
framework with trustworthy progressive learning (DuPL). Specifically, we
propose a dual student network with a discrepancy loss to yield diverse CAMs
for each sub-net. The two sub-nets generate supervision for each other,
mitigating the confirmation bias caused by learning their own incorrect
pseudo-labels. In this process, we progressively introduce more trustworthy
pseudo-labels to be involved in the supervision through dynamic threshold
adjustment with an adaptive noise filtering strategy. Moreover, we believe that
every pixel, even discarded from supervision due to its unreliability, is
important for WSSS. Thus, we develop consistency regularization on these
discarded regions, providing supervision of every pixel. Experiment results
demonstrate the superiority of the proposed DuPL over the recent
state-of-the-art alternatives on PASCAL VOC 2012 and MS COCO datasets. Code is
available at this https URL

提出了一种名为 DuPL 的可信逐步学习的双学生框架，用于解决单阶段弱监督语义分割中由 CAM 伪标签引起的认证偏差问题，通过动态阈值调整和自适应噪声过滤策略在学习过程中逐渐引入更多可靠的伪标签，并针对被丢弃的区域开发了一致性正则化以提供每个像素的监督。实验结果表明，该方法在 PASCAL VOC 2012 和 MS COCO 数据集上优于最新的状态 - of-the-art 替代方案。