  Existing studies in weakly-supervised semantic segmentation (WSSS) using
image-level weak supervision have several limitations: sparse object coverage,
inaccurate object boundaries, and co-occurring pixels from non-target objects.
To overcome these challenges, we propose a novel framework, namely Explicit
Pseudo-pixel Supervision (EPS), which learns from pixel-level feedback by
combining two weak supervisions; the image-level label provides the object
identity via the localization map and the saliency map from the off-the-shelf
saliency detection model offers rich boundaries. We devise a joint training
strategy to fully utilize the complementary relationship between both
information. Our method can obtain accurate object boundaries and discard
co-occurring pixels, thereby significantly improving the quality of
pseudo-masks. Experimental results show that the proposed method remarkably
outperforms existing methods by resolving key challenges of WSSS and achieves
the new state-of-the-art performance on both PASCAL VOC 2012 and MS COCO 2014
datasets.
