43,538 research outputs found
Cross Modal Distillation for Supervision Transfer
In this work we propose a technique that transfers supervision between images
from different modalities. We use learned representations from a large labeled
modality as a supervisory signal for training representations for a new
unlabeled paired modality. Our method enables learning of rich representations
for unlabeled modalities and can be used as a pre-training procedure for new
modalities with limited labeled data. We show experimental results where we
transfer supervision from labeled RGB images to unlabeled depth and optical
flow images and demonstrate large improvements for both these cross modal
supervision transfers. Code, data and pre-trained models are available at
https://github.com/s-gupta/fast-rcnn/tree/distillationComment: Updated version (v2) contains additional experiments and result
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.Comment: To appear in the proceedings of the British Machine Vision Conference
(BMVC) 2017. The code is available at https://github.com/lzzcd001/OSLS
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