  Weakly supervised semantic segmentation (WSSS) with image-level labels
intends to achieve dense tasks without laborious annotations. However, due to
the ambiguous contexts and fuzzy regions, the performance of WSSS, especially
the stages of generating Class Activation Maps (CAMs) and refining pseudo
masks, widely suffers from ambiguity while being barely noticed by previous
literature. In this work, we propose UniA, a unified single-staged WSSS
framework, to efficiently tackle this issue from the perspective of uncertainty
inference and affinity diversification, respectively. When activating class
objects, we argue that the false activation stems from the bias to the
ambiguous regions during the feature extraction. Therefore, we design a more
robust feature representation with a probabilistic Gaussian distribution and
introduce the uncertainty estimation to avoid the bias. A distribution loss is
particularly proposed to supervise the process, which effectively captures the
ambiguity and models the complex dependencies among features. When refining
pseudo labels, we observe that the affinity from the prevailing refinement
methods intends to be similar among ambiguities. To this end, an affinity
diversification module is proposed to promote diversity among semantics. A
mutual complementing refinement is proposed to initially rectify the ambiguous
affinity with multiple inferred pseudo labels. More importantly, a contrastive
affinity loss is further designed to diversify the relations among unrelated
semantics, which reliably propagates the diversity into the whole feature
representations and helps generate better pseudo masks. Extensive experiments
are conducted on PASCAL VOC, MS COCO, and medical ACDC datasets, which validate
the efficiency of UniA tackling ambiguity and the superiority over recent
single-staged or even most multi-staged competitors.
