  This paper is about few-shot segmentation of foreground objects in images. We
train a CNN on small subsets of training images, each mimicking the few-shot
setting. In each subset, one image serves as the query and the other(s) as
support image(s) with ground-truth segmentation. The CNN first extracts feature
maps from the query and support images. Then, a class feature vector is
computed as an average of the support's feature maps over the known foreground.
Finally, the target object is segmented in the query image by using a cosine
similarity between the class feature vector and the query's feature map. We
make two contributions by: (1) Improving discriminativeness of features so
their activations are high on the foreground and low elsewhere; and (2)
Boosting inference with an ensemble of experts guided with the gradient of loss
incurred when segmenting the support images in testing. Our evaluations on the
PASCAL-$5^i$ and COCO-$20^i$ datasets demonstrate that we significantly
outperform existing approaches.
