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.
@article{arxiv.2112.10982,
title = {Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning},
author = {Josh Myers-Dean and Yinan Zhao and Brian Price and Scott Cohen and Danna Gurari},
journal= {arXiv preprint arXiv:2112.10982},
year = {2022}
}