One Shot Learning for Semantic Segmentation
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.
BMVC