One-Shot Learning for Semantic Segmentation

Amirreza Shaban; Shray Bansal; Zhen Liu; Irfan Essa; Byron Boots

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arXiv · 1709.03410

One-Shot Learning for Semantic Segmentation

Amirreza Shaban·Shray Bansal·Zhen Liu·Irfan Essa·Byron Boots

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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.

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BibTeX RIS

Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots. 2017-09-11. One-Shot Learning for Semantic Segmentation. https://arxiv.org/abs/1709.03410

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