图片版教程:
文字版教程:
ref.bib里面写参考的文献,ref.bib和document.tex要挨着放,同一个目录里面.
解析一下bib文件格式:aboyeji2023effect是引用文献的关键字,需要在正文document.tex里面使用\cite指令引用该关键字
@article{aboyeji2023effect,
title={Effect of Number of Lanes on Traffic Characteristics of Reinforcement Learning based Autonomous Driving},
author={Aboyeji, Esther and Ajani, Oladayo S and Mallipeddi, Rammohan},
journal={IEEE Access},
year={2023},
publisher={IEEE}
}
ref.bib的全部内容如下
@phdthesis{de2023convolutional,
title={Convolutional neural networks for the segmentation of small rodent brain MRI},
author={De Feo, Riccardo},
year={2023},
school={It{\"a}-Suomen yliopisto}
}
@article{aboyeji2023effect,
title={Effect of Number of Lanes on Traffic Characteristics of Reinforcement Learning based Autonomous Driving},
author={Aboyeji, Esther and Ajani, Oladayo S and Mallipeddi, Rammohan},
journal={IEEE Access},
year={2023},
publisher={IEEE}
}
@article{medsker2001recurrent,
title={Recurrent neural networks},
author={Medsker, Larry R and Jain, LC},
journal={Design and Applications},
volume={5},
number={64-67},
pages={2},
year={2001}
}
@article{creswell2018generative,
title={Generative adversarial networks: An overview},
author={Creswell, Antonia and White, Tom and Dumoulin, Vincent and Arulkumaran, Kai and Sengupta, Biswa and Bharath, Anil A},
journal={IEEE signal processing magazine},
volume={35},
number={1},
pages={53--65},
year={2018},
publisher={IEEE}
}
document.tex是正文,用的是IEEE模板,删掉了多余的,使用\cite{引用文献关键字}引用bib文件里面的文章,
\bibliography{ref.bib}
\bibliographystyle{plain}
指令指定了bib文件名字,因为默认是bib文件和document.tex挨着放,直接写ref.bib指定引用文件的名字即可!注意,一定要把bib文件的名字写全了!,不能只写ref,要写ref.bib否则加载不出来!
\bibliography{ref.bib}
\bibliographystyle{plain}这两个命令你可以到我下面document.tex里面去找,位置在倒数第八和倒数第七行。这两指令的位置也是会自动定格了Reference的位置,也就是说,这两指令在哪儿,Reference就生成在哪儿!千万别写错位置了!
如下是document.tex的全部内容
\documentclass[conference]{IEEEtran}
\IEEEoverridecommandlockouts
% The preceding line is only needed to identify funding in the first footnote. If that is unneeded, please comment it out.
\usepackage{cite}
\usepackage{verbatim}
\usepackage{amsmath,amssymb,amsfonts}
\usepackage{algorithmic}
\usepackage{graphicx}
\usepackage{textcomp}
\usepackage{xcolor}
\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
\begin{document}
\title{Conference Paper Title*\\
{\footnotesize \textsuperscript{*}Note: Sub-titles are not captured in Xplore and
should not be used}
\thanks{Identify applicable funding agency here. If none, delete this.}
}
\author{\IEEEauthorblockN{1\textsuperscript{st} XXX XXX}
\IEEEauthorblockA{\textit{Department of XXX XXX and XXX} \\
\textit{XXX}\\
XXXg, China \\
[email protected]}
}
\maketitle
\begin{abstract}
This document is a model and instructions for \LaTeX.
This and the IEEEtran.cls file define the components of your paper [title, text, heads, etc.]. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes,
or Math in Paper Title or Abstract.
\end{abstract}
\begin{IEEEkeywords}
component, formatting, style, styling, insert
\end{IEEEkeywords}
\section{Introduction}
In the current era of digitalization, the explosive growth of image and video data poses significant challenges to our ability to comprehend visual information.With the continuous evolution of social media, medical imaging, autonomous driving \cite{aboyeji2023effect}and understanding realistic scenes , a profound understanding of images and videos has become a central issue in many fields. Traditional image processing techniques, however, face challenges in handling complex scenes and abstract concepts. This has prompted the demand for higher-level visual understanding and reasoning methods to interpret information within images more accurately and comprehensively.Over the past few years, the development of deep learning technologies has ushered in significant breakthroughs in the field of image processing. Technologies such as Convolutional Neural Networks\cite{de2023convolutional}(CNN), Recurrent Neural Networks\cite{medsker2001recurrent} (RNN), Generative Adversarial Networks\cite{creswell2018generative} (GAN), Attention Mechanism, and Reinforcement Learning have notably advanced tasks like image classification, object detection, and semantic segmentation. Despite their success in localized tasks, challenges persist in achieving higher-level visual understanding and reasoning.This research aims to address key challenges in visual understanding and reasoning, including but not limited to: understanding abstract concepts in images, reasoning across different scenes, and handling ambiguous or uncertain information. These issues not only pose challenges for the visual understanding of ordinary university students but also represent critical problems awaiting solutions in the current field of image processing.This paper proposes a comprehensive approach based on deep learning and knowledge graphs to enhance high-level understanding and reasoning capabilities for images and videos. By combining image feature extraction and semantic knowledge modeling, we aim to build a more holistic and intelligent visual understanding system. Furthermore, the paper explores the incorporation of reinforcement learning techniques during the training process to enhance the system's adaptability to uncertain information. The innovation of this comprehensive approach lies in its consideration of the entire visual reasoning process, pushing image processing towards a more intelligent and comprehensive direction.
\bibliography{ref.bib}
\bibliographystyle{plain}
\vspace{12pt}
\color{red}
\end{document}
视频版教程:
PS:这里网页端会看不见这个链接,因此下面还有一个点击直接访问的连接
视频链接:
:【【LaTeX】LaTeX参考文献管理:用Bib TeX 管理参考文献】
最后
\bibliographystyle{plain}指令解析: