华东师范大学学报（自然科学版） >

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

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

供应链知识图谱构建与分析

小样本实例分割综述

  • 周雪茗 ,
  • 黄定江
展开
  • 华东师范大学 数据科学与工程学院, 上海　200062

收稿日期: 2022-07-16

  录用日期: 2022-07-16

  网络出版日期: 2022-09-26

基金资助

国家自然科学基金(U1711262, 62072185, U1811264)

收起

Survey of few-shot instance segmentation methods

  • Xueming ZHOU ,
  • Dingjiang HUANG
Expand
  • 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

Fold

摘要

实例分割是计算机视觉领域中的一项重要任务, 近年来元学习和小样本学习的发展推动了小样本与计算机视觉任务的结合, 突破了对人工标注难、标注成本高的目标检测与分类瓶颈. 虽然在小样本图像分类、小样本语义分割和小样本目标检测上都取得了较大的发展, 但是基于小样本学习的实例分割近年来才成为研究热点. 从小样本实例分割的相关概念出发, 对现有小样本实例分割方法, 按照基于锚框和无锚框两类分别进行了系统性的概述, 并介绍了小样本实例分割常用的数据集及评价指标. 通过对算法性能和优缺点的分析对比, 以及研究现状的整理归纳, 对小样本实例分割未来发展方向和面临的挑战进行了展望.

关键词： 小样本学习; 实例分割; 计算机视觉

本文引用格式

周雪茗 , 黄定江 . 小样本实例分割综述[J]. 华东师范大学学报（自然科学版）, 2022 , 2022(5) : 136 -146 . DOI: 10.3969/j.issn.1000-5641.2022.05.012

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

参考文献

1 YAN X, CHEN Z, XU A, et al. Meta r-cnn: Towards general solver for instance-level low-shot learning [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 9577-9586.
2 SNELL J, SWERSKY K, ZEMEL R. Prototypical networks for few-shot learning [EB/OL]. (2017-06-19)[2022-07-10]. https://arxiv.org/abs/1703.05175.
3 KOCH G, ZEMEL R, SALAKHUTDINOV R. Siamese neural networks for one-shot image recognition [C]// ICML Deep Learning Workshop. 2015.
4 GARCIA V, BRUNA J. Few-shot learning with graph neural networks [EB/OL]. (2018-02-20)[2022-07-10]. https://arxiv.org/abs/1711.04043.
5 SHABAN A, BANSAL S, LIU Z, et al. One-shot learning for semantic segmentation [C]// British Machine Vision Conference. 2017.
6 RAKELLY K, SHELHAMER E, DARRELL T, et al. Conditional networks for few-shot semantic segmentation [C]// International Conference on Learning Representations. 2018.
7 ZHANG X, WEI Y, YANG Y, et al. Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Transactions on Cybernetics, 2020, 50 (9): 3855- 3865.
8 KARLINSKY L, SHTOK J, HARARY S, et al. Repmet: Representative-based metric learning for classification and one-shot object detection [EB/OL]. (2018-11-18)[2022-07-10]. https://arxiv.org/abs/1806.04728.
9 KANG B, LIU Z, WANG X, et al. Few-shot object detection via feature reweighting [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 8420-8429.
10 LIU S, JIA J, FIDLER S, et al. Sgn: Sequential grouping networks for instance segmentation [C]// Proceedings of the IEEE International Conference on Computer Vision. 2017: 3496-3504.
11 GAO N, SHAN Y, WANG Y, et al. Ssap: Single-shot instance segmentation with affinity pyramid [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 642-651.
12 PINHEIRO P O, COLLOBERT R, DOLLáR P. Learning to segment object candidates [EB/OL]. (2015-09-01)[2022-07-10]. https://arxiv.org/abs/1506.06204v2.
13 HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision. 2017: 2961-2969.
14 LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440.
15 TIAN Z, SHEN C, CHEN H, et al. Fcos: Fully convolutional one-stage object detection [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 9627-9636.
16 VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning [C]// Advances in Neural Information Processing Systems. 2016.
17 MICHAELIS C, USTYUZHANINOV I, BETHGE M, et al. One-shot instance segmentation [EB/OL]. (2019-05-28)[2022-07-10]. https://arxiv.org/abs/1811.11507.
18 FAN Z, YU J G, LIANG Z, et al. Fgn: Fully guided network for few-shot instance segmentation [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 9172-9181.
19 GANEA D A, BOOM B, POPPE R. Incremental few-shot instance segmentation [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1185-1194.
20 NGUYEN K, TODOROVIC S. Fapis: A few-shot anchor-free part-based instance segmenter [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 11099-11108.
21 CHOI D, YE-BIN M, KIM J, et al. FoxInst: A Frustratingly simple baseline for weakly few-shot instance segmentation [C]// International Conference on Learning Representations. 2021.
22 WANG X, HUANG T E, DARRELL T, et al. Frustratingly simple few-shot object detection [EB/OL]. (2019-05-28)[2022-07-10]. https://arxiv.org/abs/2003.06957.
23 REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 2015, 28.
24 TIAN Z, SHEN C, WANG X, et al. Boxinst: High-performance instance segmentation with box annotations [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 5443-5452.
25 LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco: Common objects in context [C]// European Conference on Computer Vision. Cham, Zug: Springer, 2014: 740-755.
26 EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 2010, 88 (2): 303- 338.
27 DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database [C]// IEEE Conference on Computer Vision and Pattern Recognition. 2009: 248-255.
Options
文章导航

/

〈 〉