# Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning Source: [Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:02:49.336Z Authors: Josh Myers-Dean, Yinan Zhao, Brian Price, Scott Cohen, Danna Gurari Published: 2021-12-21T04:44:57.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()