  Weakly supervised semantic segmentation has attracted much research interest
in recent years considering its advantage of low labeling cost. Most of the
advanced algorithms follow the design principle that expands and constrains the
seed regions from class activation maps (CAM). As well-known, conventional CAM
tends to be incomplete or over-activated due to weak supervision. Fortunately,
we find that semantic segmentation has a characteristic of spatial
transformation equivariance, which can form a few self-supervisions to help
weakly supervised learning. This work mainly explores the advantages of scale
equivariant constrains for CAM generation, formulated as a self-supervised
scale equivariant network (SSENet). Specifically, a novel scale equivariant
regularization is elaborately designed to ensure consistency of CAMs from the
same input image with different resolutions. This novel scale equivariant
regularization can guide the whole network to learn more accurate class
activation. This regularized CAM can be embedded in most recent advanced weakly
supervised semantic segmentation framework. Extensive experiments on PASCAL VOC
2012 datasets demonstrate that our method achieves the state-of-the-art
performance both quantitatively and qualitatively for weakly supervised
semantic segmentation. Code has been made available.
