LaTeX的列举环境
LaTeX中的列举项有 itemize, enumerate和description.
itemize就是无序列表的枚举条目。item后可以加选项表示一个条目之前的标记符号，默认是黑点。
\begin{itemize}
\item
Deep learning-based solution for table detection
\item
Deep learning-based solution for table structure recognition
\item[+]
Present another proof for the efficacy of fine-tuning
deep neural networks
\end{itemize}效果：
enumerate 则是有序列表的枚举条目。如果要指定格式，则要先引用 \usepackage{enumerate}（不要指定格式可以不导入，就按默认格式）
\begin{enumerate}
\item
Deep learning-based solution for table detection
\item
Deep learning-based solution for table structure recognition
\item
Present another proof for the efficacy of fine-tuning
deep neural networks
\end{enumerate}
\begin{enumerate}[(1)]
\item
Deep learning-based solution for table detection
\item
Deep learning-based solution for table structure recognition
\item
Present another proof for the efficacy of fine-tuning
deep neural networks
\end{enumerate}效果：
description 是描述类型，一般是对于较长文本的列举。显示的效果是首行向前突出。
\begin{description}
\item
We present a deep learning-based solution for table
detection, where the domain of general purpose object
detectors is adapted to the highly different realm of
document images. Transfer learning is performed by
carefully fine-tuning a pre-trained model of Faster RCNN
by Ren et al. [6] for the detection of tables in
documents.
\item
Furthermore, we present a deep learning-based solution
for table structure recognition (i.e. the identification of
rows, columns, and cells) where again the general purpose
domain is adapted and transfer leaning is performed
by augmenting and fine-tuning an FCN semantic segmentation
model by Shelhamer et al. [7] pre-trained on Pascal
VOC 2011 [8].
\item
We present another proof for the efficacy of fine-tuning
deep neural networks even when source and target domains
are highly dissimilar and the target training set is
rather small.
\end{description}效果：
[TABS_R id=1738]