One-Shot Learning for Semantic Segmentation
About
Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). We use this FCN to perform dense pixel-level prediction on a test image for the new semantic class. Our architecture shows a 25% relative meanIoU improvement compared to the best baseline methods for one-shot segmentation on unseen classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots• 2017
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Few-shot Segmentation | PASCAL-5i | mIoU (Fold 0)35.9 | 325 | |
| Semantic segmentation | COCO 2014 (val) | mIoU46.2 | 251 | |
| Few-shot Semantic Segmentation | PASCAL-5^i (test) | FB-IoU61.5 | 177 | |
| Semantic segmentation | PASCAL-5i | Mean mIoU43.9 | 111 | |
| Semantic segmentation | PASCAL-5^i (test) | Mean Score44 | 107 | |
| Semantic segmentation | PASCAL 5-shot 5i | Mean mIoU43.95 | 100 | |
| Few-shot Segmentation | PASCAL 5i (val) | mIoU (Mean)40.8 | 83 | |
| Semantic segmentation | PASCAL-5^i Fold-1 | mIoU58.1 | 75 | |
| Semantic segmentation | PASCAL-5^i Fold-3 | mIoU39.1 | 75 | |
| Semantic segmentation | PASCAL-5^i Fold-2 | mIoU42.7 | 75 |
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