Mining Latent Classes for Few-shot Segmentation

Understand

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples.

• Existing methods suffer the problem of feature undermining, i.e.
• potential novel classes are treated as background during training phase.
• Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.