DuPL: Dual Student with Trustworthy Progressive Learning for Robust
  Weakly Supervised Semantic Segmentation
Yuanchen Wu Xichen Ye Kequan Yang Jide Li Xiaoqiang Li
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 https://github.com/Wu0409/DuPL.