  Few-shot segmentation aims to devise a generalizing model that segments query
images from unseen classes during training with the guidance of a few support
images whose class tally with the class of the query. There exist two
domain-specific problems mentioned in the previous works, namely spatial
inconsistency and bias towards seen classes. Taking the former problem into
account, our method compares the support feature map with the query feature map
at multi scales to become scale-agnostic. As a solution to the latter problem,
a supervised model, called as base learner, is trained on available classes to
accurately identify pixels belonging to seen classes. Hence, subsequent meta
learner has a chance to discard areas belonging to seen classes with the help
of an ensemble learning model that coordinates meta learner with the base
learner. We simultaneously address these two vital problems for the first time
and achieve state-of-the-art performances on both PASCAL-5i and COCO-20i
datasets.
