FGN: Fully Guided Network for Few-Shot Instance Segmentation

2020/03/31 by Zhibo Fan, Fan, Zhibo, Jin-Gang Yu +11 · 3 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.2003.13954

Accepted by CVPR 2020, 10 pages, 6 figures,

arxiv created 2020/03/31 · openalex publication_date 2020/03/31 · arxiv updated 2020/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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.

Citations

• The Pascal Visual Object Classes (VOC) Challenge
• Prototypical Networks for Few-shot Learning
• Few-Shot Learning with Graph Neural Networks
• SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
• One-Shot Instance Segmentation

Cited by

• Weak Novel Categories without Tears: A Survey on Weak-Shot Learning
• Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
• DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency

Related

• MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation
• ISDA: Position-Aware Instance Segmentation with Deformable Attention
• Few-Shot Segmentation Propagation with Guided Networks
• Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
• Faster Training of Mask R-CNN by Focusing on Instance Boundaries