  Learning semantic segmentation from weakly-labeled (e.g., image tags only)
data is challenging since it is hard to infer dense object regions from sparse
semantic tags. Despite being broadly studied, most current efforts directly
learn from limited semantic annotations carried by individual image or image
pairs, and struggle to obtain integral localization maps. Our work alleviates
this from a novel perspective, by exploring rich semantic contexts
synergistically among abundant weakly-labeled training data for network
learning and inference. In particular, we propose regional semantic contrast
and aggregation (RCA) . RCA is equipped with a regional memory bank to store
massive, diverse object patterns appearing in training data, which acts as
strong support for exploration of dataset-level semantic structure.
Particularly, we propose i) semantic contrast to drive network learning by
contrasting massive categorical object regions, leading to a more holistic
object pattern understanding, and ii) semantic aggregation to gather diverse
relational contexts in the memory to enrich semantic representations. In this
manner, RCA earns a strong capability of fine-grained semantic understanding,
and eventually establishes new state-of-the-art results on two popular
benchmarks, i.e., PASCAL VOC 2012 and COCO 2014.
