  Generalized few-shot semantic segmentation was introduced to move beyond only
evaluating few-shot segmentation models on novel classes to include testing
their ability to remember base classes. While the current state-of-the-art
approach is based on meta-learning, it performs poorly and saturates in
learning after observing only a few shots. We propose the first fine-tuning
solution, and demonstrate that it addresses the saturation problem while
achieving state-of-the-art results on two datasets, PASCAL-5i and COCO-20i. We
also show that it outperforms existing methods, whether fine-tuning multiple
final layers or only the final layer. Finally, we present a triplet loss
regularization that shows how to redistribute the balance of performance
between novel and base categories so that there is a smaller gap between them.
