Current methods for few-shot segmentation (FSSeg) have mainly focused on
improving the performance of novel classes while neglecting the performance of
base classes. To overcome this limitation, the task of generalized few-shot
semantic segmentation (GFSSeg) has been introduced, aiming to predict
segmentation masks for both base and novel classes. However, the current
prototype-based methods do not explicitly consider the relationship between
base and novel classes when updating prototypes, leading to a limited
performance in identifying true categories. To address this challenge, we
propose a class contrastive loss and a class relationship loss to regulate
prototype updates and encourage a large distance between prototypes from
different classes, thus distinguishing the classes from each other while
maintaining the performance of the base classes. Our proposed approach achieves
new state-of-the-art performance for the generalized few-shot segmentation task
on PASCAL VOC and MS COCO datasets