HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation

Understand

While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation powers.

• In this paper, we propose Hierarchically Supervised Semantic Segmentation (HS3), a training scheme that supervises intermediate layers in a segmentation network to learn meaningful representations by varying task complexity.
• To enforce a consistent performance vs.
• complexity trade-off throughout the network, we derive various sets of class clusters to supervise each transitional layer of the network.