Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training. This paper presents a Fully Guided Network (FGN) for few-shot instance segmentation. FGN perceives FSIS as a guided model where a so-called support set is encoded and utilized to guide the predictions of a base instance segmentation network (i.e., Mask R-CNN), critical to which is the guidance mechanism. In this view, FGN introduces different guidance mechanisms into the various key components in Mask R-CNN, including Attention-Guided RPN, Relation-Guided Detector, and Attention-Guided FCN, in order to make full use of the guidance effect from the support set and adapt better to the inter-class generalization. Experiments on public datasets demonstrate that our proposed FGN can outperform the state-of-the-art methods.
@article{arxiv.2003.13954,
title = {FGN: Fully Guided Network for Few-Shot Instance Segmentation},
author = {Zhibo Fan and Jin-Gang Yu and Zhihao Liang and Jiarong Ou and Changxin Gao and Gui-Song Xia and Yuanqing Li},
journal= {arXiv preprint arXiv:2003.13954},
year = {2020}
}