One-Shot Learning for Semantic Segmentation
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2017
- DOI:
- 10.48550/arXiv.1709.03410
- arXiv:
- arXiv:1709.03410
- Bibcode:
- 2017arXiv170903410S
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- To appear in the proceedings of the British Machine Vision Conference (BMVC) 2017. The code is available at https://github.com/lzzcd001/OSLSM