用于语义分割的自监督对比式表征学习

刘博翀; 蔡怀宇; 汪毅; 陈晓冬

doi:10.19665/j.issn1001-2400.20230304

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用于语义分割的自监督对比式表征学习

计算机科学与技术 | 更新时间：2024-04-03

- 用于语义分割的自监督对比式表征学习
- Self-supervised contrastive representation learning for semantic segmentation
- 西安电子科技大学学报 2024年51卷第1期页码：125-134
- 作者机构：
  
  天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室,天津 300072
- 作者简介：
  
  [ "刘博翀(1998—),男,天津大学硕士研究生,E-mail:[email protected]；" ]
  蔡怀宇(1965—),女,教授,E-mail:[email protected]
  [ "汪毅(1981—),女,副教授,E-mail:[email protected]；" ]
  [ "陈晓冬(1975—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  天津市科技计划项目(17ZXGGX00140)
- DOI：10.19665/j.issn1001-2400.20230304
  中图分类号： TP391.4
- 纸质出版日期：2024-1-20，
  
  网络出版日期：2023-8-22，
  
  收稿日期：2022-10-25，
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刘博翀, 蔡怀宇, 汪毅, 等. 用于语义分割的自监督对比式表征学习[J]. 西安电子科技大学学报, 2024,51(1):125-134.

Bochong LIU, Huaiyu CAI, Yi WANG, et al. Self-supervised contrastive representation learning for semantic segmentation[J]. Journal of Xidian University, 2024,51(1):125-134.
刘博翀, 蔡怀宇, 汪毅, 等. 用于语义分割的自监督对比式表征学习[J]. 西安电子科技大学学报, 2024,51(1):125-134. DOI： 10.19665/j.issn1001-2400.20230304.

Bochong LIU, Huaiyu CAI, Yi WANG, et al. Self-supervised contrastive representation learning for semantic segmentation[J]. Journal of Xidian University, 2024,51(1):125-134. DOI： 10.19665/j.issn1001-2400.20230304.

摘要

为了提升语义分割模型的精度

并减少逐像素标注大规模语义分割数据集的人力和时间成本

研究了自监督对比式表征学习的预训练方法

并结合语义分割任务的特点

设计了全局-局部交叉对比学习(GLCCL)方法。该方法将全局图像和局部分块后的一系列图像块输入到网络中分别编码全局和局部视觉表征

并通过构建包含全局对比、局部对比和全局-局部交叉对比的损失函数来指导模型训练

使得模型能够同时学习全局和局部区域的视觉表征以及跨区域语义相关性。使用该方法预训练BiSeNet再迁移到语义分割任务时

对比现有的自监督对比式表征学习和有监督预训练方法分别具有0.24%和0.9%平均交并比(MIoU)的性能提升。实验结果表明

该方法能够采用无标注的数据训练语义分割模型而实现分割效果的提升

具有一定的实用价值。

Abstract

To improve the accuracy of the semantic segmentation models and avoid the labor and time costs of pixel-wise image annotation for large-scale semantic segmentation datasets

this paper studies the pre-training methods of self-supervised contrastive representation learning

and designs the Global-Local Cross Contrastive Learning(GLCCL) method based on the characteristics of the semantic segmentation task.This method feeds global images and a series of image patches after local chunking into the network to extract global and local visual representations respectively

and guides the network training by constructing loss function that includes global contrast

local contrast

and global-local cross contrast

enabling the network to learn both global and local visual representations as well as cross-regional semantic correlations.When using this method to pre-train BiSeNet and transfer to the semantic segmentation task

compared with the existing self-supervised contrastive representational learning and supervised pre-training methods

the performance improvement of 0.24% and 0.9% mean intersection over union(MIoU) is achieved.Experimental results show that this method can improve the segmentation results by pre-training the semantic segmentation model with unlabeled data

which has a certain practical value.

关键词

语义分割自监督表征学习对比学习深度学习

Keywords

semantic segmentationself-supervised representation learningcontrastive learningdeep learning

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