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.

为了减轻先前方法中语义歧义和类间相似性对像素级前景 - 背景分类准确性的限制，我们提出了 Relevant Intrinsic Feature Enhancement Network (RiFeNet)。通过引入一个无标签分支作为高效的数据利用方法，我们提高了前景实例的语义一致性，在测试时，无需额外的无标签数据和计算。此外，我们通过提出新颖的多层原型生成和交互模块扩展了前景和背景之间的类间变化，全局和局部原型之间的不同粒度补充性使得类似类别之间的区分更加明确。RiFeNet 在 PASCAL-5i 和 COCO 基准上的定性和定量性能超过了最先进的方法。