  Recent attention has been devoted to the pursuit of learning semantic
segmentation models exclusively from image tags, a paradigm known as
image-level Weakly Supervised Semantic Segmentation (WSSS). Existing attempts
adopt the Class Activation Maps (CAMs) as priors to mine object regions yet
observe the imbalanced activation issue, where only the most discriminative
object parts are located. In this paper, we argue that the distribution
discrepancy between the discriminative and the non-discriminative parts of
objects prevents the model from producing complete and precise pseudo masks as
ground truths. For this purpose, we propose a Pixel-Level Domain Adaptation
(PLDA) method to encourage the model in learning pixel-wise domain-invariant
features. Specifically, a multi-head domain classifier trained adversarially
with the feature extraction is introduced to promote the emergence of pixel
features that are invariant with respect to the shift between the source (i.e.,
the discriminative object parts) and the target (\textit{i.e.}, the
non-discriminative object parts) domains. In addition, we come up with a
Confident Pseudo-Supervision strategy to guarantee the discriminative ability
of each pixel for the segmentation task, which serves as a complement to the
intra-image domain adversarial training. Our method is conceptually simple,
intuitive and can be easily integrated into existing WSSS methods. Taking
several strong baseline models as instances, we experimentally demonstrate the
effectiveness of our approach under a wide range of settings.
