Related papers: Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning

Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning

URL: http://arxiv.org/abs/2112.10982v1
Date: Tue, 21 Dec 2021 04:44:57 GMT
Title: Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning
Authors: Josh Myers-Dean, Yinan Zhao, Brian Price, Scott Cohen, and Danna Gurari
Abstract summary: Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes. While all approaches currently are based on meta-learning, they perform poorly and saturate 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-art results on two datasets.
Score: 35.51193811629467
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 all approaches currently are based on meta-learning, they perform poorly and saturate 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-art results on two datasets, PASCAL-$5^i$ and COCO-$20^i$. We also show 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.

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