  Semi-supervised learning (SSL) essentially pursues class boundary exploration
with less dependence on human annotations. Although typical attempts focus on
ameliorating the inevitable error-prone pseudo-labeling, we think differently
and resort to exhausting informative semantics from multiple probably correct
candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for
accurate SSL semantic segmentation in a plug-and-play fashion, targeting
adaptively encouraging fuzzy positive predictions and suppressing
highly-probable negatives. Being conceptually simple yet practically effective,
FPL can remarkably alleviate interference from wrong pseudo labels and
progressively achieve clear pixel-level semantic discrimination. Concretely,
our FPL approach consists of two main components, including fuzzy positive
assignment (FPA) to provide an adaptive number of labels for each pixel and
fuzzy positive regularization (FPR) to restrict the predictions of fuzzy
positive categories to be larger than the rest under different perturbations.
Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with
consistent performance gain justify the superiority of our approach.
