Papers › A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation
A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation
Tomoya Sakai, Haoxiang Qiu, Takayuki Katsuki, Daiki Kimura, Takayuki Osogami, Tadanobu Inoue
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-5ⁱ dataset, 4.7% on the PASCAL-10ⁱ dataset, and 1.0% on the COCO-20ⁱ dataset.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Generalized Few-Shot Semantic Segmentation | COCO-20i (1-shot) | BCM (ResNet-50) | Mean Base and Novel | 33.85 | #3 of 6 | Archive leaderboard | report |
| Generalized Few-Shot Semantic Segmentation | COCO-20i (5-shot) | BCM (ResNet-50) | Mean Base and Novel | 40.24 | #3 of 5 | Archive leaderboard | report |
| Generalized Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | BCM (ResNet-50) | Mean Base and Novel | 56.20 | #2 of 5 | Archive leaderboard | report |
| Generalized Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | BCM (ResNet-50) | Mean Base and Novel | 63.29 | #3 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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