Papers › Part-aware Prototype Network for Few-shot Semantic Segmentation
Part-aware Prototype Network for Few-shot Semantic Segmentation
Yongfei Liu, Xiangyi Zhang, Songyang Zhang, Xuming He
Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing methods either focus on the restrictive setting of one-way few-shot segmentation or suffer from incomplete coverage of object regions. In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes, capable of capturing diverse and fine-grained object features. In addition, we propose to leverage unlabeled data to enrich our part-aware prototypes, resulting in better modeling of intra-class variations of semantic objects. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images. Extensive experimental evaluations on two benchmarks show that our method outperforms the prior art with a sizable margin.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | PPNet (ResNet-50) | Mean IoU | 29.0 | #82 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | PPNet (ResNet-50) | learnable parameters (million) | 31.5 | #82 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (2-way 1-shot) | PPNet (ResNet-50) | mIoU | 20.4 | #5 of 6 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | PPNet (ResNet-50) | Mean IoU | 38.5 | #74 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | PPNet (ResNet-50) | learnable parameters (million) | 31.5 | #74 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | PPNet (ResNet-50) | Mean IoU | 51.5 | #102 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | PPNet (ResNet-50) | learnable parameters (million) | 31.5 | #102 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | PPNet (ResNet-50) | Mean IoU | 62.0 | #82 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | PPNet (ResNet-50) | learnable parameters (million) | 31.5 | #82 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | Pascal5i | PPNet | meanIOU | 55.16 | #3 of 3 | 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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