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一种结合全局和局部相似性的小样本分割方法

刘宇轩 孟凡满 李宏亮 杨嘉莹 吴庆波 许林峰

刘宇轩, 孟凡满, 李宏亮, 等 . 一种结合全局和局部相似性的小样本分割方法[J]. 北京航空航天大学学报, 2021, 47(3): 665-674. doi: 10.13700/j.bh.1001-5965.2020.0450
引用本文: 刘宇轩, 孟凡满, 李宏亮, 等 . 一种结合全局和局部相似性的小样本分割方法[J]. 北京航空航天大学学报, 2021, 47(3): 665-674. doi: 10.13700/j.bh.1001-5965.2020.0450
LIU Yuxuan, MENG Fanman, LI Hongliang, et al. A few shot segmentation method combining global and local similarity[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 665-674. doi: 10.13700/j.bh.1001-5965.2020.0450(in Chinese)
Citation: LIU Yuxuan, MENG Fanman, LI Hongliang, et al. A few shot segmentation method combining global and local similarity[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 665-674. doi: 10.13700/j.bh.1001-5965.2020.0450(in Chinese)

一种结合全局和局部相似性的小样本分割方法

doi: 10.13700/j.bh.1001-5965.2020.0450
基金项目: 

国家自然科学基金 61871087

四川省科技厅自然科学基金 2018JY0141

详细信息
    作者简介:

    刘宇轩   男,硕士研究生。主要研究方向:计算机视觉与图像分割

    孟凡满   男,博士, 副教授,博士生导师。主要研究方向:智能图像分析、深度学习

    通讯作者:

    孟凡满, E-mail: fmmeng@uestc.edu.cn

  • 中图分类号: G202;U285.49;G623.58

A few shot segmentation method combining global and local similarity

Funds: 

National Natural Science Foundation of China 61871087

Natural Science Foundation of Sichuan Science and Technology Department 2018JY0141

More Information
  • 摘要:

    针对小样本分割中如何提取支持图像和查询图像共性信息的问题,提出一种新的小样本分割模型,同时结合了全局相似性和局部相似性,实现了更具泛化能力的小样本分割。具体地,根据支持图像和查询图像全局特征和局部特征之间的相似性,提出了一种新型注意力谱生成器,进而实现查询图像的注意力谱生成和区域分割。所提注意力谱生成器包含2个级联模块:全局引导器和局部引导器。在全局引导器中,提出了一种新的基于指数函数的全局相似性度量,对查询图像特征和支持图像的全局特征进行关系建模,输出前景增强的查询图像特征。在局部引导器中,通过引入局部关系矩阵对支持图像特征和查询图像特征之间的局部相似性进行建模,得到与类别无关的注意力谱。在Pascal-5i数据集上做了大量的实验,在1-shot设定下mIoU达到了59.9%,5-shot设定下mIoU达到了61.9%,均优于现有方法。

     

  • 图 1  本文方法总体框架

    Figure 1.  General framework of proposed method

    图 2  注意力谱生成器结构

    Figure 2.  Structure of attention generator

    图 3  全局引导器的细节结构

    Figure 3.  Detailed structure of global guider

    图 4  局部引导器的细节结构

    Figure 4.  Detailed structure of local guider

    图 5  上采样网络架构

    Figure 5.  Framework of up-sample network

    图 6  部分分割效果较好的可视化结果

    Figure 6.  Some visualized high-quality segmentation results

    图 7  部分分割效果较差的可视化结果

    Figure 7.  Some visualized low-quality segmentation results

    表  1  Pascal-5i四个子集的划分

    Table  1.   Four subsets setting of Pascal-5i

    子集 类别
    Fold0 飞机、自行车、鸟、船、瓶子
    Fold1 公交车、轿车、猫、椅子、牛
    Fold2 餐桌、狗、马、摩托车、人
    Fold3 盆栽、山羊、沙发、火车、显示器
    下载: 导出CSV

    表  2  不同主干网络下,本文与现有方法的1-shot对比实验mIoU结果

    Table  2.   Comparative experimental results (mIoU) of proposed method and existing methods under 1-shot setting using different backbone networks   %

    主干网络 方法 mIoU 平均值
    F0 F1 F2 F3
    VGG16 OSLSM[4] 33.6 55.3 40.9 33.5 40.8
    Co-FCN[29] 36.7 50.6 44.9 32.4 41.2
    SG-One[1] 40.2 58.4 48.4 38.4 46.4
    PANet[6] 42.3 58.0 51.1 41.2 48.2
    FWB[8] 47.0 59.6 52.6 48.3 51.9
    本文 50.3 65.3 53.0 50.8 54.9
    ResNet50 CANet[2] 52.5 65.9 51.3 51.9 55.4
    LTM[3] 54.6 65.6 56.6 51.3 57.0
    CRNet[30] 55.7
    本文 54.6 67.8 57.4 52.1 58.0
    ResNet101 FWB[8] 51.3 64.5 56.7 52.2 56.2
    本文 57.5 68.7 58.7 54.5 59.9
    下载: 导出CSV

    表  3  不同主干网络下,本文与现有方法的5-shot对比实验mIoU结果

    Table  3.   Comparative experimental results (mIoU) of proposed method and existing methods under 5-shot setting using different backbone networks   %

    主干网络 方法 mIoU 平均值
    F0 F1 F2 F3
    VGG16 OSLSM[4] 35.9 58.1 42.7 39.1 44.0
    Co-FCN[29] 37.5 50.0 44.1 33.9 41.4
    SG-One[1] 41.9 58.6 48.6 39.4 47.1
    PANet[6] 51.8 64.6 59.8 46.5 55.7
    FWB[8] 50.9 62.9 56.5 50.1 55.1
    本文 50.3 66.3 54.7 55.3 56.7
    ResNet50 CANet[2] 55.5 67.8 51.9 53.2 57.1
    LTM[3] 56.4 66.6 56.9 56.8 59.2
    CRNet[30] 58.8
    本文 54.8 68.1 59.9 56.2 59.8
    ResNet101 FWB[8] 54.9 67.4 62.2 55.3 60.0
    本文 58.1 69.8 60.8 58.9 61.9
    下载: 导出CSV

    表  4  不同主干网络下,本文与现有方法的1-shot对比实验FB-IoU结果

    Table  4.   Comparative experimental results (FB-IoU) of proposed method and existing methods under 1-shot setting using different backbone networks   %

    主干网络 方法 FB-IoU 平均值
    F0 F1 F2 F3
    VGG16 OSLSM[4] 61.3
    Co-FCN[29] 60.1
    SG-One[1] 63.1
    PANet[6] 66.5
    本文 68.6 77.3 65.3 68.4 69.9
    ResNet50 CANet[2] 71.0 76.7 54.0 67.2 67.2
    CRNet[30] 66.8
    本文 71.5 78.7 70.6 69.1 72.5
    ResNet101 本文 73.7 79.4 71.8 70.2 73.8
    下载: 导出CSV

    表  5  不同主干网络下,本文与现有方法的5-shot对比实验FB-IoU结果

    Table  5.   Comparative experimental results (FB-IoU) of proposed method and existing methods under 5-shot setting using different backbone networks   %

    主干网络 方法 FB-IoU 平均值
    F0 F1 F2 F3
    VGG16 OSLSM[4] 61.5
    Co-FCN[29] 60.2
    SG-One[1] 65.9
    PANet[6] 70.7
    本文 68.0 77.6 66.5 71.8 71.0
    ResNet50 CANet[2] 74.2 80.3 57.0 66.8 69.6
    CRNet[30] 71.5
    本文 71.1 78.8 72.7 69.7 73.1
    ResNet101 本文 73.5 80.0 72.6 72.9 74.8
    下载: 导出CSV

    表  6  全局相似性度量方式的对比实验mIoU结果

    Table  6.   Comparative experimental results (mIoU) of global similarity metric   %

    全局度量方式 mIoU
    1-shot 5-shot
    余弦距离 51.3 53.2
    通道维度拼接 46.5 47.2
    所提全局相似性度量 53.8 55.7
    下载: 导出CSV

    表  7  5-shot设定方案对比实验mIoU结果

    Table  7.   Comparative experimental results (mIoU) under 5-shot setting   %

    设定方式 mIoU
    平均化注意力谱 59.1
    所提k-shot方案 59.7
    下载: 导出CSV

    表  8  全局引导器和局部引导器的消去实验mIoU结果

    Table  8.   Ablation experimental results (mIoU) of global guider and local guider   %

    全局引导器 局部引导器 mIoU
    1-shot 5-shot
    53.8 55.7
    56.8 59.0
    58.0 59.7
    下载: 导出CSV

    表  9  损失函数的消去实验mIoU结果

    Table  9.   Ablation experimental result (mIoU) of loss function   %

    Lseg La Lseg0 mIoU
    1-shot 5-shot
    55.4 57.7
    56.6 58.6
    55.9 57.9
    58.0 59.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-24
  • 录用日期:  2020-09-11
  • 刊出日期:  2021-03-20

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