  Training a computer vision system to segment a novel class typically requires
collecting and painstakingly annotating lots of images with objects from that
class. Few-shot segmentation techniques reduce the required number of images to
learn to segment a new class, but careful annotations of object boundaries are
still required. On the other hand, interactive segmentation techniques only
focus on incrementally improving the segmentation of one object at a time
(typically, using clicks given by an expert) in a class-agnostic manner. We
combine the two concepts to drastically reduce the effort required to train
segmentation models for novel classes. Instead of trivially feeding interactive
segmentation masks as ground truth to a few-shot segmentation model, we propose
IFSENet, which can accept sparse supervision on a single or few support images
in the form of clicks to generate masks on support (training, at least clicked
upon once) as well as query (test, never clicked upon) images. To trade-off
effort for accuracy flexibly, the number of images and clicks can be
incrementally added to the support set to further improve the segmentation of
support as well as query images. The proposed model approaches the accuracy of
previous state-of-the-art few-shot segmentation models with considerably lower
annotation effort (clicks instead of maps), when tested on Pascal and SBD
datasets on query images. It also works well as an interactive segmentation
method on support images.
