Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach

Understand

Weakly supervised semantic segmentation is a challenging task as it only takes image-level information as supervision for training but produces pixel-level predictions for testing.

• To address such a challenging task, most recent state-of-the-art approaches propose to adopt two-step solutions, \emph{i.e.
• } 1) learn to generate pseudo pixel-level masks, and 2) engage FCNs to train the semantic segmentation networks with the pseudo masks.
• However, the two-step solutions usually employ many bells and whistles in producing high-quality pseudo masks, making this kind of methods complicated and inelegant.