  For few-shot semantic segmentation, the primary task is to extract
class-specific intrinsic information from limited labeled data. However, the
semantic ambiguity and inter-class similarity of previous methods limit the
accuracy of pixel-level foreground-background classification. To alleviate
these issues, we propose the Relevant Intrinsic Feature Enhancement Network
(RiFeNet). To improve the semantic consistency of foreground instances, we
propose an unlabeled branch as an efficient data utilization method, which
teaches the model how to extract intrinsic features robust to intra-class
differences. Notably, during testing, the proposed unlabeled branch is excluded
without extra unlabeled data and computation. Furthermore, we extend the
inter-class variability between foreground and background by proposing a novel
multi-level prototype generation and interaction module. The different-grained
complementarity between global and local prototypes allows for better
distinction between similar categories. The qualitative and quantitative
performance of RiFeNet surpasses the state-of-the-art methods on PASCAL-5i and
COCO benchmarks.
