Papers › Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, Zeynep Akata
Semantic segmentation is one of the most fundamental problems in computer vision and pixel-level labelling in this context is particularly expensive. Hence, there have been several attempts to reduce the annotation effort such as learning from image level labels and bounding box annotations. In this paper we take this one step further and focus on the challenging task of zero- and few-shot learning of semantic segmentation. We define this task as image segmentation by assigning a label to every pixel even though either no labeled sample of that class was present during training, i.e. zero-label semantic segmentation, or only a few labeled samples were present, i.e. few-label semantic segmentation.Our goal is to transfer the knowledge from previously seen classes to novel classes. Our proposed semantic projection network (SPNet) achieves this goal by incorporating a class-level semantic information into any network designed for semantic segmentation, in an end-to-end manner. We also propose a benchmark for this task on the challenging COCO-Stuff and PASCAL VOC12 datasets. Our model is effective in segmenting novel classes, i.e. alleviating expensive dense annotations, but also in adapting to novel classes without forgetting its prior knowledge, i.e. generalized zero- and few-label semantic segmentation.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Semantic Segmentation | COCO-Stuff | SPNet | Inductive Setting hIoU | 14.0 | #10 of 15 | Archive leaderboard | report |
| Zero-Shot Semantic Segmentation | COCO-Stuff | SPNet | Transductive Setting hIoU | 30.3 | #10 of 15 | Archive leaderboard | report |
| Zero-Shot Semantic Segmentation | PASCAL VOC | SPNet | Transductive Setting hIoU | 38.8 | #10 of 13 | 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.
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