Aerospace information acquisition and image generation based on supervised contrastive learning

QI Yi-chen; ZHAO Wei-chao

doi:10.37188/CJLCD.2023-0056

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Aerospace information acquisition and image generation based on supervised contrastive learning

Object Detection and Recognition | 更新时间：2023-11-03

- Aerospace information acquisition and image generation based on supervised contrastive learning
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 11, Pages: 1531-1541(2023)
- 作者机构：
  
  1.东北大学计算机科学与工程学院，辽宁沈阳 110167
  2.中国科学院长春光学精密机械与物理研究所网络与信息化技术中心，吉林长春 130033
- 作者简介：
- 基金信息：
- DOI：10.37188/CJLCD.2023-0056
  CLC：
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QI Yi-chen, ZHAO Wei-chao. Aerospace information acquisition and image generation based on supervised contrastive learning. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1531-1541(2023)
DOI：

QI Yi-chen, ZHAO Wei-chao. Aerospace information acquisition and image generation based on supervised contrastive learning. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1531-1541(2023) DOI： 10.37188/CJLCD.2023-0056.

摘要

为了提高获取开源航天信息的效率并解决开源航天信息内容较长、数量较为有限、应用常用文本分类模型鲁棒性较差以及文本信息不够直观等问题，本文提出一种基于有监督对比学习的航天信息分类方法。该方法基于带有注意力机制（Attention）的双向长短期记忆网络（Bidirectional Long Short-Term Memory， BiLSTM），融合对比学习技术，对开源的信息进行处理并分析，进而高效地筛选出航天类的信息，利用unCLIP（un-Contrastive Language-Image Pre-Training）模型生成信息对应的图像。实验结果表明，对比CNN（Convolutional Neural Networks）、BiLSTM、Transformer和BiLSTM-Attention等常用的文本分类方法，该方法在准确率、召回率和F1-Score上均表现良好，其中F1-Score达到0.97，同时以图像的形式呈现信息，使信息更加清晰直观。本文方法可以充分使用网络公开的数据资源，有效地提取开源航天信息并生成对应图像，对航天信息的分析和研究具有重要价值。

Abstract

In order to improve the efficiency of obtaining open source aerospace information， and solve the problems of long open source aerospace information content， relatively limited quantity， poor robustness of commonly used text classification models， and unintuitive text information， this paper proposes a method for aerospace information text classification based on supervised contrastive learning. The method is based on the bidirectional long short-term memory （BiLSTM） network with the attention mechanism， integrates comparative learning technology， processes and analyzes open source information， efficiently screenes out aerospace information， and uses the unCLIP （un-Contrastive Language-Image Pre-Training） model to generate an image corresponding to the information. The experimental results show that compared with commonly used text classification methods such as CNN （Convolutional Neural Networks）， BiLSTM， Transformer and BiLSTM-Attention， this method performes well in accuracy， recall and F1-Score， among them， F1-Score reaches 0.97. At the same time， information is presented in the form of images to make information clearer and more intuitive. It can make full use of open data resources on the network， effectively extract open-source space information and generate corresponding images， which is of great value to the analysis and research of aerospace information.

关键词

有监督文本分类对比学习文本生成图像航天信息

Keywords

supervised text classificationcontrastive learningtext-to-image synthesisaerospace information

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