Few-shot segmentation (FSS) aims to segment unseen classes given only a few
annotated samples. Existing methods suffer the problem of feature undermining,
i.e. potential novel classes are treated as background during training phase.
Our method aims to alleviate this problem and enhance the feature embedding on
latent novel classes. In our work, we propose a novel joint-training framework.
Based on conventional episodic training on support-query pairs, we add an
additional mining branch that exploits latent novel classes via transferable
sub-clusters, and a new rectification technique on both background and
foreground categories to enforce more stable prototypes. Over and above that,
our transferable sub-cluster has the ability to leverage extra unlabeled data
for further feature enhancement. Extensive experiments on two FSS benchmarks
demonstrate that our method outperforms previous state-of-the-art by a large
margin of 3.7% mIOU on PASCAL-5i and 7.0% mIOU on COCO-20i at the cost of 74%
fewer parameters and 2.5x faster inference speed. The source code is available
at this https URL

本研究提出一种新的联合训练框架来缓解现有方法在处理潜在新类别时出现的特征削弱问题，并使用可转移子簇来增强特征嵌入，实验结果表明，该方法在 PASCAL-5i 和 COCO-20i 数据集上超过之前的最先进方法，并具有更少的参数和更快的推理速度。