@inproceedings{
sakai2024a,
title={A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation},
author={Tomoya Sakai and Haoxiang Qiu and Takayuki Katsuki and Daiki Kimura and Takayuki Osogami and Tadanobu Inoue},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=p3nPHMpx04}
}
The goal of *generalized* few-shot semantic segmentation (GFSS) is to recognize *novel-class* objects through training with a few annotated examples and the *base-class* model that learned the knowledge about the base classes.
Unlike the classic few-shot semantic segmentation, GFSS aims to classify pixels into both base and novel classes, meaning it is a more practical setting.
Current GFSS methods rely on several techniques such as using combinations of customized modules, carefully designed loss functions, meta-learning, and transductive learning.
However, we found that a simple rule and standard supervised learning substantially improve the GFSS performance.
In this paper, we propose a simple yet effective method for GFSS that does not use the techniques mentioned above.
Also, we theoretically show that our method perfectly maintains the segmentation performance of the base-class model over most of the base classes.
Through numerical experiments, we demonstrated the effectiveness of our method.
It improved in novel-class segmentation performance in the $1$-shot scenario by $6.1$% on the PASCAL-$5^i$ dataset, $4.7$% on the PASCAL-$10^i$ dataset, and $1.0$% on the COCO-$20^i$ dataset.
A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation
Tomoya Sakai Haoxiang Qiu Takayuki Katsuki Daiki Kimura Takayuki Osogami Tadanobu Inoue