NLWSNet：基于弱监督学习的嘈杂标签Web图像情感分析

薛罗阳; 毛启容; 黄晓华; 陈婕

doi:10.1631/FITEE.1900618

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NLWSNet：基于弱监督学习的嘈杂标签Web图像情感分析

常规文章 | Updated：2022-05-19

- NLWSNet：基于弱监督学习的嘈杂标签Web图像情感分析
- NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images
- 信息与电子工程前沿（英文） 2020年21卷第9期页码：1321-1333
- Affiliations：
  
  Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
  Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Zhenjiang 212013, China
  School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China
  Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 8000, Finland
- Author bio：
  
  Qi-rong MAO, E-mail: mao_qr@ujs.edu.cn
- Funds：
  
  Project supported by the Key Project of the National Natural Science Foundation of China (No. U1836220), the National Natural Science Foundation of China (No. 61672267), the Qing Lan Talent Program of Jiangsu Province, China, the Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, China, the Finnish Cultural Foundation, the Jiangsu SpeciallyAppointed Professor Program, China (No. 3051107219003), the Jiangsu Joint Research Project of Sino-Foreign Cooperative Education Platform, China, and the Talent Startup Project of Nanjing Institute of Technology, China (No. YKJ201982)
- DOI：10.1631/FITEE.1900618
  中图分类号： TP391.4
- 收稿：2019-11-12，
  
  修回：2020-;7-20，
  
  纸质出版：2020-09
- Accepted：
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薛罗阳, 毛启容, 黄晓华, 等. NLWSNet：基于弱监督学习的嘈杂标签Web图像情感分析[J]. 信息与电子工程前沿（英文）, 2020,21(9):1321-1333.

Luo-yang XUE, Qi-rong MAO, Xiao-hua HUANG, et al. NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1321-1333.
薛罗阳, 毛启容, 黄晓华, 等. NLWSNet：基于弱监督学习的嘈杂标签Web图像情感分析[J]. 信息与电子工程前沿（英文）, 2020,21(9):1321-1333. DOI： 10.1631/FITEE.1900618.

Luo-yang XUE, Qi-rong MAO, Xiao-hua HUANG, et al. NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1321-1333. DOI： 10.1631/FITEE.1900618.

摘要

大规模数据集推动了基于深度卷积神经网络情感分析的快速发展。但是，注释大规模数据集既昂贵又耗时。相反，从网络中很容易获得弱标注的Web图像。当直接使用Web图像训练深度学习模型时，嘈杂标签会导致性能急剧下降。针对这种情况，提出一种端到端的弱监督学习结构，该结构对于弱标签的Web图像具有鲁棒性。具体地说，该注意力模块通过降低训练过程中注意力得分，自动抑制带有错误标签样本的负面影响。另外，在弱监督学习方法中，类激活图模块通过关注正确标签样本的情感区域促进网络学习。除特征学习过程外，将正则化应用于分类器，以最小化同一类别样本的距离，并最大化不同类别样本质心之间的距离。对标记正确和错误的网页图像数据集进行定量和定性评估，结果表明该算法优于现有方法。

Abstract

Large-scale datasets are driving the rapid developments of deep convolutional neural networks for visual sentiment analysis. However

the annotation of large-scale datasets is expensive and time consuming. Instead

it is easy to obtain weakly labeled web images from the Internet. However

noisy labels still lead to seriously degraded performance when we use images directly from the web for training networks. To address this drawback

we propose an end-to-end weakly supervised learning network

which is robust to mislabeled web images. Specifically

the proposed attention module automatically eliminates the distraction of those samples with incorrect labels by reducing their attention scores in the training process. On the other hand

the special-class activation map module is designed to stimulate the network by focusing on the significant regions from the samples with correct labels in a weakly supervised learning approach. Besides the process of feature learning

applying regularization to the classifier is considered to minimize the distance of those samples within the same class and maximize the distance between different class centroids. Quantitative and qualitative evaluations on well- and mislabeled web image datasets demonstrate that the proposed algorithm outperforms the related methods.

关键词

Keywords

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