  Despite the remarkable success of existing methods for few-shot segmentation,
there remain two crucial challenges. First, the feature learning for novel
classes is suppressed during the training on base classes in that the novel
classes are always treated as background. Thus, the semantics of novel classes
are not well learned. Second, most of existing methods fail to consider the
underlying semantic gap between the support and the query resulting from the
representative bias by the scarce support samples. To circumvent these two
challenges, we propose to activate the discriminability of novel classes
explicitly in both the feature encoding stage and the prediction stage for
segmentation. In the feature encoding stage, we design the Semantic-Preserving
Feature Learning module (SPFL) to first exploit and then retain the latent
semantics contained in the whole input image, especially those in the
background that belong to novel classes. In the prediction stage for
segmentation, we learn an Self-Refined Online Foreground-Background classifier
(SROFB), which is able to refine itself using the high-confidence pixels of
query image to facilitate its adaptation to the query image and bridge the
support-query semantic gap. Extensive experiments on PASCAL-5$^i$ and
COCO-20$^i$ datasets demonstrates the advantages of these two novel designs
both quantitatively and qualitatively.
