Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary

Understand

Weakly-supervised semantic segmentation (WSSS) with image-level labels has been widely studied to relieve the annotation burden of the traditional segmentation task.

• In this paper, we show that existing fully-annotated base categories can help segment objects of novel categories with only image-level labels, even if base categories and novel categories have no overlap.
• We refer to this task as weak-shot semantic segmentation, which could also be treated as WSSS with auxiliary fully-annotated categories.
• Recent advanced WSSS methods usually obtain class activation maps (CAMs) and refine them by affinity propagation.