论文标题
FGN：完全引导网络，用于几个弹片实例分段
FGN: Fully Guided Network for Few-Shot Instance Segmentation
论文作者
论文摘要
很少有shot实例细分（FSIS）与一般实例细分相连，这提供了一种可能在缺乏具有丰富标记的数据培训数据的实例细分的方法中。本文提供了一个完全引导的网络（FGN），以进行几个弹片实例细分。 FGN将FSI视为指导模型，其中所谓的支持集被编码并用来指导基本实例分割网络（即蒙版R-CNN）的预测，这是指导机制至关重要的。在这种观点中，FGN将不同的指导机制引入了蒙版R-CNN的各种关键组件，包括注意引导的RPN，关系引导的检测器和注意力引导的FCN，以充分利用支持集中的指导效应，并更好地适应校期间的一般普遍化。公共数据集上的实验表明，我们提出的FGN可以胜过最先进的方法。
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.
