  Semantic segmentation aims to classify every pixel of an input image.
Considering the difficulty of acquiring dense labels, researchers have recently
been resorting to weak labels to alleviate the annotation burden of
segmentation. However, existing works mainly concentrate on expanding the seed
of pseudo labels within the image's salient region. In this work, we propose a
non-salient region object mining approach for weakly supervised semantic
segmentation. We introduce a graph-based global reasoning unit to strengthen
the classification network's ability to capture global relations among disjoint
and distant regions. This helps the network activate the object features
outside the salient area. To further mine the non-salient region objects, we
propose to exert the segmentation network's self-correction ability.
Specifically, a potential object mining module is proposed to reduce the
false-negative rate in pseudo labels. Moreover, we propose a non-salient region
masking module for complex images to generate masked pseudo labels. Our
non-salient region masking module helps further discover the objects in the
non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate
state-of-the-art results compared to current methods.
