  Mining precise class-aware attention maps, a.k.a, class activation maps, is
essential for weakly supervised semantic segmentation. In this paper, we
present L2G, a simple online local-to-global knowledge transfer framework for
high-quality object attention mining. We observe that classification models can
discover object regions with more details when replacing the input image with
its local patches. Taking this into account, we first leverage a local
classification network to extract attentions from multiple local patches
randomly cropped from the input image. Then, we utilize a global network to
learn complementary attention knowledge across multiple local attention maps
online. Our framework conducts the global network to learn the captured rich
object detail knowledge from a global view and thereby produces high-quality
attention maps that can be directly used as pseudo annotations for semantic
segmentation networks. Experiments show that our method attains 72.1% and 44.2%
mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014,
respectively, setting new state-of-the-art records. Code is available at
https://github.com/PengtaoJiang/L2G.
