Journal of East China Normal University(Natural Science) >

2022 , Vol. 2022 >Issue 5: 136 - 146

DOI: https://doi.org/10.3969/j.issn.1000-5641.2022.05.012

Construction and Analysis of Supply Chain Knowledge Graph

Survey of few-shot instance segmentation methods

  • Xueming ZHOU ,
  • Dingjiang HUANG
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  • School of Data Science and Engineering, East China Normal University, Shanghai　200062, China

Received date: 2022-07-16

  Accepted date: 2022-07-16

  Online published: 2022-09-26

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Abstract

Instance segmentation is an important task in computer vision. In recent years, the development of meta- and few-shot learning has promoted the combination of computer vision learning tasks, which has overcome the bottleneck of detection and classification with regard to objects that are difficult to manually label and those with high labeling costs. Although great progress has been made with few-shot semantic segmentation and object detection, instance segmentation based on few-shot learning has not become a research hotspot until very recently. Beginning with an overview of few-shot instance segmentation, existing approaches are divided into categories of anchor-based and anchor-free algorithms. The architectures and primary technologies behind those approaches are respectively discussed, and common datasets and evaluation indices are described. Additionally, advantages and disadvantages of algorithm performance are analyzed, and future development directions and challenges are presented.

Key words： few-shot learning; instance segmentation; computer vision

Cite this article

Xueming ZHOU , Dingjiang HUANG . Survey of few-shot instance segmentation methods[J]. Journal of East China Normal University(Natural Science), 2022 , 2022(5) : 136 -146 . DOI: 10.3969/j.issn.1000-5641.2022.05.012

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