Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation

Understand

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.