Commit 3eb18c4e by 曹润柘

update 17

parent 12cfc171
...@@ -10,7 +10,7 @@ ...@@ -10,7 +10,7 @@
\node(process_2)[process,fill=blue!20,right of = process_1,xshift=7.0cm,text width=4cm,align=center]{\baselineskip=4pt\LARGE{[[0.2,...,0.3], \qquad ..., \qquad 0.3,...,0.5]]}\par}; \node(process_2)[process,fill=blue!20,right of = process_1,xshift=7.0cm,text width=4cm,align=center]{\baselineskip=4pt\LARGE{[[0.2,...,0.3], \qquad ..., \qquad 0.3,...,0.5]]}\par};
\node(text_2)[below of = process_2,yshift=-2cm,scale=1.5]{语音特征}; \node(text_2)[below of = process_2,yshift=-2cm,scale=1.5]{语音特征};
\node(process_3)[process,fill=orange!20,minimum width=6cm,minimum height=5cm,right of = process_2,xshift=8.2cm,text width=4cm,align=center]{}; \node(process_3)[process,fill=orange!20,minimum width=6cm,minimum height=5cm,right of = process_2,xshift=8.2cm,text width=4cm,align=center]{};
\node(text_3)[below of = process_3,yshift=-3cm,scale=1.5]{源语文本及其词格}; \node(text_3)[below of = process_3,yshift=-3cm,scale=1.5]{源语文本及其词格};
\node(cir_s)[cir,very thick, below of = process_3,xshift=-2.2cm,yshift=1.1cm]{\LARGE S}; \node(cir_s)[cir,very thick, below of = process_3,xshift=-2.2cm,yshift=1.1cm]{\LARGE S};
\node(cir_a)[cir,right of = cir_s,xshift=1cm,yshift=0.8cm]{\LARGE a}; \node(cir_a)[cir,right of = cir_s,xshift=1cm,yshift=0.8cm]{\LARGE a};
\node(cir_c)[cir,right of = cir_a,xshift=1.2cm,yshift=0cm]{\LARGE c}; \node(cir_c)[cir,right of = cir_a,xshift=1.2cm,yshift=0cm]{\LARGE c};
......
...@@ -25,7 +25,7 @@ ...@@ -25,7 +25,7 @@
\parinterval 基于上下文的翻译是机器翻译的一个重要分支。传统方法中,机器翻译通常被定义为对一个句子进行翻译的问题。但是,现实中每句话往往不是独立出现的。比如,人们会使用语音进行表达,或者通过图片来传递信息,这些语音和图片内容都可以伴随着文字一起出现在翻译场景中。此外,句子往往存在于段落或者篇章之中,如果要理解这个句子,也需要整个段落或者篇章的信息。而这些上下文信息都是机器翻译可以利用的。 \parinterval 基于上下文的翻译是机器翻译的一个重要分支。传统方法中,机器翻译通常被定义为对一个句子进行翻译的问题。但是,现实中每句话往往不是独立出现的。比如,人们会使用语音进行表达,或者通过图片来传递信息,这些语音和图片内容都可以伴随着文字一起出现在翻译场景中。此外,句子往往存在于段落或者篇章之中,如果要理解这个句子,也需要整个段落或者篇章的信息。而这些上下文信息都是机器翻译可以利用的。
\parinterval 本章在句子级翻译的基础上将问题扩展为更大上下文中的翻译,具体包括语音翻译、图像翻译、篇章翻译三个主题。这些问题均为机器翻译应用中的真实需求。同时,使用多模态等信息也是当下自然语言处理的热点方向之一。 \parinterval 本章在句子级翻译的基础上将问题扩展为更大上下文中的翻译,具体包括语音翻译、图像翻译、篇章翻译三个主题。这些问题均为机器翻译应用中的真实需求。同时,使用多模态等信息也是当下自然语言处理的热点方向之一。
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...@@ -33,9 +33,9 @@ ...@@ -33,9 +33,9 @@
\section{机器翻译需要更多的上下文} \section{机器翻译需要更多的上下文}
\parinterval 长期以来,机器翻译都是指句子级翻译。主要原因在于,句子级的翻译建模可以大大简化问题,使得机器翻译方法更容易被实践和验证。但是人类使用语言的过程并不是孤立地在一个个句子上进行的。这个问题可以类比于人类学习语言的过程:小孩成长过程中会接受视觉、听觉、触觉等多种信号,这些信号的共同作用使得他们产生对客观世界的“认识”,同时促使他们使用“语言”进行表达。从这个角度说,语言能力并不是由单一因素形成的,它往往伴随着其他信息的相互作用,比如,当们翻译一句话的时候,会用到看到的画面、听到的语调、甚至前面说过的句子中的信息。 \parinterval 长期以来,机器翻译都是指句子级翻译。主要原因在于,句子级的翻译建模可以大大简化问题,使得机器翻译方法更容易被实践和验证。但是人类使用语言的过程并不是孤立地在一个个句子上进行的。这个问题可以类比于人类学习语言的过程:小孩成长过程中会接受视觉、听觉、触觉等多种信号,这些信号的共同作用使得他们产生对客观世界的“认识”,同时促使他们使用“语言”进行表达。从这个角度说,语言能力并不是由单一因素形成的,它往往伴随着其他信息的相互作用,比如,当们翻译一句话的时候,会用到看到的画面、听到的语调、甚至前面说过的句子中的信息。
\parinterval 广义上,当前句子以外的信息都可以被看作一种上下文。比如,图\ref{fig:17-1}中,需要把英语句子“A girl jumps off a bank .”翻译为汉语。但是,其中的“bank”有多个含义,因此仅仅使用英语句子本身的信息可能会将其翻译为“银行”,而非正确的译文“河床”。但是,图\ref{fig:17-1}中也提供了这个英语句子所对应的图片,显然图片中直接展示了河床,这时“bank”是没有歧义的。通常也会把这种使用图片和文字一起进行机器翻译的任务称作{\small\bfnew{多模态机器翻译}}\index{多模态机器翻译}(Multi-Modal Machine Translation)\index{Multi-Modal Machine Translation} \parinterval 广义上,当前句子以外的信息都可以被看作一种上下文。比如,图\ref{fig:17-1}中,需要把英语句子“A girl jumps off a bank .”翻译为汉语。但是,其中的“bank”有多个含义,因此仅仅使用英语句子本身的信息可能会将其翻译为“银行”,而非正确的译文“河床”。但是,图\ref{fig:17-1}中也提供了这个英语句子所对应的图片,显然图片中直接展示了河床,这时“bank”是没有歧义的。通常也会把这种使用图片和文字一起进行机器翻译的任务称作{\small\bfnew{多模态机器翻译}}\index{多模态机器翻译}(Multi-Modal Machine Translation)\index{Multi-model Machine Translation}
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\begin{figure}[htp] \begin{figure}[htp]
...@@ -62,7 +62,7 @@ ...@@ -62,7 +62,7 @@
\subsection{音频处理} \subsection{音频处理}
\parinterval 为了保证对相关内容描述的完整性,这里对语音处理的基本知识作简要介绍。不同于文本,音频本质上是经过若干信号处理之后的{\small\bfnew{波形}}(Waveform)\index{Waveform}。具体来说,声音是一种空气的震动,因此可以被转换为模拟信号。模拟信号是一段连续的信号,经过采样变为离散的数字信号。采样是每隔固定的时间记录一下声音的振幅,采样率表示每秒的采样点数,单位是赫兹(Hz)。采样率越高,结果的损失则越小。通常来说,采样的标准是能够通过离散化的数字信号重现原始语音。我们日常生活中使用的手机和电脑设备的采样率一般为16kHz,表示每秒16000个采样点;而音频CD的采样率可以达到44.1kHz。 经过进一步的量化,将采样点的值转换为整型数值保存,从而减少占用的存储空间,通常采用的是16位量化。将采样率和量化位数相乘,就可以得到{\small\bfnew{比特率}}\index{比特率}(Bits Per Second,BPS)\index{Bits Per Second},表示音频每秒占用的位数。例如,16kHz采样率和16位量化的音频,比特率为256kb/s。音频处理的整体流程如图\ref{fig:17-2}所示\upcite{洪青阳2020语音识别原理与应用,陈果果2020语音识别实战} \parinterval 为了保证对相关内容描述的完整性,这里对语音处理的基本知识作简要介绍。不同于文本,音频本质上是经过若干信号处理之后的{\small\bfnew{波形}}(Waveform)\index{Waveform}。具体来说,声音是一种空气的震动,因此可以被转换为模拟信号。模拟信号是一段连续的信号,经过采样变为离散的数字信号。采样是每隔固定的时间记录一下声音的振幅,采样率表示每秒的采样点数,单位是赫兹(Hz)。采样率越高,结果的损失则越小。通常来说,采样的标准是能够通过离散化的数字信号重现原始语音。日常生活中使用的手机和电脑设备的采样率一般为16kHz,表示每秒16000个采样点;而音频CD的采样率可以达到44.1kHz。 经过进一步的量化,将采样点的值转换为整型数值保存,从而减少占用的存储空间,通常采用的是16位量化。将采样率和量化位数相乘,就可以得到{\small\bfnew{比特率}}\index{比特率}(Bits Per Second,BPS)\index{Bits Per Second},表示音频每秒占用的位数。例如,16kHz采样率和16位量化的音频,比特率为256kb/s。音频处理的整体流程如图\ref{fig:17-2}所示\upcite{洪青阳2020语音识别原理与应用,陈果果2020语音识别实战}
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\begin{figure}[htp] \begin{figure}[htp]
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\end{figure} \end{figure}
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\parinterval 经过了上述的预处理操作,可以得到音频对应的帧序列,之后通过不同的操作来提取不同类型的声学特征。常用的声学特征包括{\small\bfnew{Mel频率倒谱系数}}\index{Mel频率倒谱系数}(Mel-Frequency Cepstral Coefficient,MFCC)\index{Mel-Frequency Cepstral Coefficient}{\small\bfnew{感知线性预测系数}}\index{感知线性预测系数}(Perceptual Lienar Predictive,PLP)\index{Perceptual Lienar Predictive}{\small\bfnew{滤波器组}}\index{滤波器组}(Filter-bank,Fbank)\index{Filter-bank}等。MFCC、PLP和Fbank特征都需要对预处理后的音频做{\small\bfnew{短时傅里叶变换}}\index{短时傅里叶变换}(Short-time Fourier Tranform,STFT)\index{Short-time Fourier Tranform},得到具有规律的线性分辨率。之后再经过特定的操作,得到各种声学特征。不同声学特征的特点是不同的,MFCC去相关性较好,PLP抗噪性强,FBank可以保留更多的语音原始特征。在语音翻译中,比较常用的声学特征为FBank或MFCC\upcite{洪青阳2020语音识别原理与应用} \parinterval 经过了上述的预处理操作,可以得到音频对应的帧序列,之后通过不同的操作来提取不同类型的声学特征。常用的声学特征包括{\small\bfnew{Mel频率倒谱系数}}\index{Mel频率倒谱系数}(Mel-frequency Cepstral Coefficient,MFCC)\index{Mel-Frequency Cepstral Coefficient}{\small\bfnew{感知线性预测系数}}\index{感知线性预测系数}(Perceptual Lienar Predictive,PLP)\index{Perceptual Lienar Predictive}{\small\bfnew{滤波器组}}\index{滤波器组}(Filter-bank,Fbank)\index{Filter-bank}等。MFCC、PLP和Fbank特征都需要对预处理后的音频做{\small\bfnew{短时傅里叶变换}}\index{短时傅里叶变换}(Short-time Fourier Tranform,STFT)\index{Short-time Fourier Tranform},得到具有规律的线性分辨率。之后再经过特定的操作,得到各种声学特征。不同声学特征的特点是不同的,MFCC去相关性较好,PLP抗噪性强,FBank可以保留更多的语音原始特征。在语音翻译中,比较常用的声学特征为FBank或MFCC\upcite{洪青阳2020语音识别原理与应用}
\parinterval 实际上,提取到的声学特征可以类比于计算机视觉中的像素特征,或者自然语言处理中的词嵌入表示。不同之处在于,声学特征更加复杂多变,可能存在着较多的噪声和冗余信息。此外,相比对应的文字序列,音频提取到的特征序列长度要大十倍以上。比如,人类正常交流中每秒钟一般可以说2-3个字,而每秒钟的语音可以提取得到100帧的特征序列。巨大的长度比差异也为声学特征建模带来了挑战。 \parinterval 实际上,提取到的声学特征可以类比于计算机视觉中的像素特征,或者自然语言处理中的词嵌入表示。不同之处在于,声学特征更加复杂多变,可能存在着较多的噪声和冗余信息。此外,相比对应的文字序列,音频提取到的特征序列长度要大十倍以上。比如,人类正常交流中每秒钟一般可以说2-3个字,而每秒钟的语音可以提取得到100帧的特征序列。巨大的长度比差异也为声学特征建模带来了挑战。
...@@ -147,7 +147,7 @@ ...@@ -147,7 +147,7 @@
\parinterval 可以看出,词格可以保存多条搜索路径,路径中保存了输入序列的时间信息以及解码过程。翻译模型基于词格进行翻译,可以降低语音识别模型带来的误差\upcite{DBLP:conf/acl/ZhangGCF19,DBLP:conf/acl/SperberNPW19}。但在端到端语音识别模型中,一般基于束搜索方法进行解码,且解码序列的长度与输入序列并不匹配,相比传统声学模型解码丢失了语音的时间信息,因此这种基于词格的方法主要集中在传统语音识别系统上。 \parinterval 可以看出,词格可以保存多条搜索路径,路径中保存了输入序列的时间信息以及解码过程。翻译模型基于词格进行翻译,可以降低语音识别模型带来的误差\upcite{DBLP:conf/acl/ZhangGCF19,DBLP:conf/acl/SperberNPW19}。但在端到端语音识别模型中,一般基于束搜索方法进行解码,且解码序列的长度与输入序列并不匹配,相比传统声学模型解码丢失了语音的时间信息,因此这种基于词格的方法主要集中在传统语音识别系统上。
\parinterval 为了降低错误传播问题带来的影响,一种思路是通过一个后处理模型修正识别结果中的错误,再送给文本翻译模型进行翻译。也可以进一步对文本做{\small\bfnew{顺滑}}\index{顺滑}(Disfluency Detection\index{Disfluency Detection}),使得送给翻译系统的文本更加干净、流畅,比如除去一些导致停顿的语气词。这一做法在工业界得到了广泛应用,但由于每个模型只能串行地计算,也会带来额外的计算代价以及运算时间。另外一种思路是训练更加健壮的文本翻译模型,使其可以处理输入中存在的噪声或误差\upcite{DBLP:conf/acl/LiuTMCZ18} \parinterval 为了降低错误传播问题带来的影响,一种思路是通过一个后处理模型修正识别结果中的错误,再送给文本翻译模型进行翻译。也可以进一步对文本做{\small\bfnew{顺滑}}\index{顺滑}(Disfluency Detection\index{Disfluency Detection}处理,使得送给翻译系统的文本更加干净、流畅,比如除去一些导致停顿的语气词。这一做法在工业界得到了广泛应用,但由于每个模型只能串行地计算,也会带来额外的计算代价以及运算时间。另外一种思路是训练更加健壮的文本翻译模型,使其可以处理输入中存在的噪声或误差\upcite{DBLP:conf/acl/LiuTMCZ18}
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\end{figure} \end{figure}
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\parinterval 另外一种多任务学习的思想是通过两个解码器,分别预测语音对应的源语言句子和目标语言句子,具体有图\ref{fig:17-10}展示的三种方式\upcite{DBLP:conf/naacl/AnastasopoulosC18,DBLP:conf/asru/BaharBN19}。图\ref{fig:17-10}(a)中采用单编码器-双解码器的方式,两个解码器根据编码器的表示,分别预测源语言句子和目标语言句子,从而使编码器训练地更加充分。这种做法的好处在于源语言文的本生任务成可以辅助翻译过程,相当于为源语言语音提供了额外的“模态”信息。图\ref{fig:17-10}(b)则通过使用两个级联的解码器,先利用第一个解码器生成源语言句子,然后再利用第一个解码器的表示,通过第二个解码器生成目标语言句子。这种方法通过增加一个中间输出,降低了模型的训练难度,但同时也会带来额外的解码耗时,因为两个解码器需要串行地进行生成。图\ref{fig:17-10}(c) 中模型更进一步,第二个编码器联合编码器和第一个解码器的表示进行生成,更充分地利用了已有信息。 \parinterval 另外一种多任务学习的思想是通过两个解码器,分别预测语音对应的源语言句子和目标语言句子,具体有图\ref{fig:17-10}展示的三种方式\upcite{DBLP:conf/naacl/AnastasopoulosC18,DBLP:conf/asru/BaharBN19}。图\ref{fig:17-10}(a)中采用单编码器-双解码器的方式,两个解码器根据编码器的表示,分别预测源语言句子和目标语言句子,从而使编码器训练地更加充分。这种做法的好处在于源语言文的文本生成任务成可以辅助翻译过程,相当于为源语言语音提供了额外的“模态”信息。图\ref{fig:17-10}(b)则通过使用两个级联的解码器,先利用第一个解码器生成源语言句子,然后再利用第一个解码器的表示,通过第二个解码器生成目标语言句子。这种方法通过增加一个中间输出,降低了模型的训练难度,但同时也会带来额外的解码耗时,因为两个解码器需要串行地进行生成。图\ref{fig:17-10}(c) 中模型更进一步,第二个编码器联合编码器和第一个解码器的表示进行生成,更充分地利用了已有信息。
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\begin{figure}[htp] \begin{figure}[htp]
\centering \centering
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\end{figure} \end{figure}
%---------------------------------------------------------------------------------------------------- %----------------------------------------------------------------------------------------------------
\parinterval 那么,多模态机器翻译是如何计算上下文向量的呢?这里仿照第十章的内容给出描述。假设编码器输出的状态序列为$\{\mathbi{h}_1,...\mathbi{h}_m\}$,需要注意的是,这里的状态序列不是源语言句子的状态序列,而是通过基于卷积等操作提取到的图像的状态序列。假设图像的特征维度是$16 \times 16 \times 512$,其中前两个维度分别表示图像的高和宽,这里会将图像映射为$256 \times 512$ 的状态序列,其中$512$为每个状态的维度。对于目标语位置$j$,上下文向量$\mathbi{C}_{j}$被定义为对序列的编码器输出进行加权求和,如下: \parinterval 那么,多模态机器翻译是如何计算上下文向量的呢?这里仿照第十章的内容给出描述。假设编码器输出的状态序列为$\{\mathbi{h}_1,...\mathbi{h}_m\}$,需要注意的是,这里的状态序列不是源语言句子的状态序列,而是通过基于卷积等操作提取到的图像的状态序列。假设图像的特征维度是$16 \times 16 \times 512$,其中前两个维度分别表示图像的高和宽,这里会将图像映射为$256 \times 512$ 的状态序列,其中$512$为每个状态的维度。对于目标语位置$j$,上下文向量$\mathbi{C}_{j}$被定义为对序列的编码器输出进行加权求和,如下:
\begin{eqnarray} \begin{eqnarray}
\mathbi{C}_{j}&=& \sum_{i}{{\alpha}_{i,j}{\mathbi{h}}_{i}} \mathbi{C}_{j}&=& \sum_{i}{{\alpha}_{i,j}{\mathbi{h}}_{i}}
\end{eqnarray} \end{eqnarray}
...@@ -412,7 +412,7 @@ ...@@ -412,7 +412,7 @@
\parinterval 要想使编码器-解码器框架在图像描述生成中充分发挥作用,编码器也要更好的表示图像信息。对于编码器的改进,通常体现在向编码器中添加图像的语义信息\upcite{DBLP:conf/cvpr/YouJWFL16,DBLP:conf/cvpr/ChenZXNSLC17,DBLP:journals/pami/FuJCSZ17}和位置信息\upcite{DBLP:conf/cvpr/ChenZXNSLC17,DBLP:conf/ijcai/LiuSWWY17} \parinterval 要想使编码器-解码器框架在图像描述生成中充分发挥作用,编码器也要更好的表示图像信息。对于编码器的改进,通常体现在向编码器中添加图像的语义信息\upcite{DBLP:conf/cvpr/YouJWFL16,DBLP:conf/cvpr/ChenZXNSLC17,DBLP:journals/pami/FuJCSZ17}和位置信息\upcite{DBLP:conf/cvpr/ChenZXNSLC17,DBLP:conf/ijcai/LiuSWWY17}
\parinterval 图像的语义信息一般是指图像中存在的实体、属性、场景等等。如图\ref{fig:17-17}所示,从图像中利用属性或实体检测器提取出“girl”、“river”、“bank”等属性词和实体词,将他们作为图像的语义信息编码的一部分,再利用注意力机制计算目标语言单词与这些属性词或实体词之间的注意力权重\upcite{DBLP:conf/cvpr/YouJWFL16}。当然,除了图像中的实体和属性作为语义信息外,也可以将图片的场景信息加入到编码器当中\upcite{DBLP:journals/pami/FuJCSZ17}。有关如何做属性、实体和场景的检测,涉及到目标检测任务的工作,例如Faster-RCNN\upcite{DBLP:journals/pami/RenHG017}、YOLO\upcite{DBLP:journals/corr/abs-1804-02767,DBLP:journals/corr/abs-2004-10934}等等,这里不过多赘述。 \parinterval 图像的语义信息一般是指图像中存在的实体、属性、场景等等。如图\ref{fig:17-17}所示,从图像中利用属性或实体检测器提取出“girl”、“river”、“bank”等属性词和实体词,将他们作为图像的语义信息编码的一部分,再利用注意力机制计算目标语言单词与这些属性词或实体词之间的注意力权重\upcite{DBLP:conf/cvpr/YouJWFL16}。当然,除了图像中的实体和属性作为语义信息外,也可以将图片的场景信息加入到编码器当中\upcite{DBLP:journals/pami/FuJCSZ17}。有关如何做属性、实体和场景的检测,涉及到目标检测任务的工作,例如Faster-RCNN\upcite{DBLP:journals/pami/RenHG017}、YOLO\upcite{DBLP:journals/corr/abs-1804-02767,DBLP:journals/corr/abs-2004-10934}等等,这里不赘述。
%---------------------------------------------------------------------------------------------------- %----------------------------------------------------------------------------------------------------
\begin{figure}[htp] \begin{figure}[htp]
......
...@@ -5090,7 +5090,7 @@ author = {Yoshua Bengio and ...@@ -5090,7 +5090,7 @@ author = {Yoshua Bengio and
Tobias Weyand and Tobias Weyand and
Marco Andreetto and Marco Andreetto and
Hartwig Adam}, Hartwig Adam},
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...@@ -11622,28 +11622,28 @@ author = {Zhuang Liu and ...@@ -11622,28 +11622,28 @@ author = {Zhuang Liu and
publisher = {International Conference on Machine Learning}, publisher = {International Conference on Machine Learning},
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} }
%%%%% chapter 16------------------------------------------------------ %%%%% chapter 16------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% chapter 17------------------------------------------------------ %%%%% chapter 17------------------------------------------------------
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Gholamreza Anbarjafari}, Gholamreza Anbarjafari},
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...@@ -12721,7 +12705,6 @@ author = {Zhuang Liu and ...@@ -12721,7 +12705,6 @@ author = {Zhuang Liu and
Yoshua Bengio}, Yoshua Bengio},
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...@@ -12751,7 +12734,7 @@ author = {Zhuang Liu and ...@@ -12751,7 +12734,7 @@ author = {Zhuang Liu and
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Junqi Jin and Junqi Jin and
Runpeng Cui and Runpeng Cui and
...@@ -12759,9 +12742,7 @@ author = {Zhuang Liu and ...@@ -12759,9 +12742,7 @@ author = {Zhuang Liu and
Changshui Zhang}, Changshui Zhang},
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...@@ -12772,8 +12753,6 @@ author = {Zhuang Liu and ...@@ -12772,8 +12753,6 @@ author = {Zhuang Liu and
Tao Mei}, Tao Mei},
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...@@ -12788,21 +12767,19 @@ author = {Zhuang Liu and ...@@ -12788,21 +12767,19 @@ author = {Zhuang Liu and
publisher = {International Joint Conference on Artificial Intelligence}, publisher = {International Joint Conference on Artificial Intelligence},
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} }
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Chien-Yao Wang and Chien-Yao Wang and
Hong-Yuan Mark Liao}, Hong-Yuan Mark Liao},
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@inproceedings{DBLP:conf/cvpr/LuXPS17, @inproceedings{DBLP:conf/cvpr/LuXPS17,
...@@ -12840,15 +12817,13 @@ author = {Zhuang Liu and ...@@ -12840,15 +12817,13 @@ author = {Zhuang Liu and
publisher = {ACM Multimedia}, publisher = {ACM Multimedia},
year = {2017} year = {2017}
} }
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Hanli Wang and Hanli Wang and
Yihao Chen and Yihao Chen and
Pengjie Tang}, Pengjie Tang},
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...@@ -12861,12 +12836,11 @@ author = {Zhuang Liu and ...@@ -12861,12 +12836,11 @@ author = {Zhuang Liu and
publisher = {IEEE Conference on Computer Vision and Pattern Recognition}, publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2018} year = {2018}
} }
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author = {Qingzhong Wang and author = {Qingzhong Wang and
Antoni B. Chan}, Antoni B. Chan},
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...@@ -12874,8 +12848,6 @@ author = {Zhuang Liu and ...@@ -12874,8 +12848,6 @@ author = {Zhuang Liu and
Deming Ye and Deming Ye and
Dahua Lin}, Dahua Lin},
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...@@ -12900,28 +12872,24 @@ author = {Zhuang Liu and ...@@ -12900,28 +12872,24 @@ author = {Zhuang Liu and
Alexander Kirillov and Alexander Kirillov and
Sergey Zagoruyko}, Sergey Zagoruyko},
title = {End-to-End Object Detection with Transformers}, title = {End-to-End Object Detection with Transformers},
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author = {Jun Yu and author = {Jun Yu and
Jing Li and Jing Li and
Zhou Yu and Zhou Yu and
Qingming Huang}, Qingming Huang},
title = {Multimodal Transformer With Multi-View Visual Representation for Image title = {Multimodal Transformer With Multi-View Visual Representation for Image
Captioning}, Captioning},
journal = {IEEE Transactions on Circuits and Systems for Video Technology}, publisher = {IEEE Transactions on Circuits and Systems for Video Technology},
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author={Zhong Huasong and Jingyuan Chen and Chen Shen and Hanwang Zhang and Jianqiang Huang and Xian-Sheng Hua}, author={Zhong Huasong and Jingyuan Chen and Chen Shen and Hanwang Zhang and Jianqiang Huang and Xian-Sheng Hua},
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...@@ -12944,7 +12912,6 @@ author = {Zhuang Liu and ...@@ -12944,7 +12912,6 @@ author = {Zhuang Liu and
Xiaokang Yang}, Xiaokang Yang},
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Answering}, Answering},
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...@@ -12963,7 +12930,6 @@ author = {Zhuang Liu and ...@@ -12963,7 +12930,6 @@ author = {Zhuang Liu and
Yejin Choi and Yejin Choi and
Jianfeng Gao}, Jianfeng Gao},
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...@@ -13005,23 +12971,21 @@ author = {Zhuang Liu and ...@@ -13005,23 +12971,21 @@ author = {Zhuang Liu and
pages = {465--476}, pages = {465--476},
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Majid Moradi Aliabadi and Majid Moradi Aliabadi and
Ming Dong and Ming Dong and
Ratna Babu Chinnam}, Ratna Babu Chinnam},
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Qianqian Wang and Qianqian Wang and
Quanxue Gao}, Quanxue Gao},
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...@@ -13063,12 +13027,11 @@ author = {Zhuang Liu and ...@@ -13063,12 +13027,11 @@ author = {Zhuang Liu and
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Honglak Lee}, Honglak Lee},
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...@@ -13076,8 +13039,7 @@ author = {Zhuang Liu and ...@@ -13076,8 +13039,7 @@ author = {Zhuang Liu and
Muhammad Zeshan Afzal}, Muhammad Zeshan Afzal},
title = {{TAC-GAN} - Text Conditioned Auxiliary Classifier Generative Adversarial title = {{TAC-GAN} - Text Conditioned Auxiliary Classifier Generative Adversarial
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...@@ -13142,12 +13104,11 @@ author = {Zhuang Liu and ...@@ -13142,12 +13104,11 @@ author = {Zhuang Liu and
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...@@ -13293,7 +13254,6 @@ author = {Zhuang Liu and ...@@ -13293,7 +13254,6 @@ author = {Zhuang Liu and
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...@@ -13302,8 +13262,6 @@ author = {Zhuang Liu and ...@@ -13302,8 +13262,6 @@ author = {Zhuang Liu and
Francisco Casacuberta}, Francisco Casacuberta},
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...@@ -13321,8 +13279,6 @@ author = {Zhuang Liu and ...@@ -13321,8 +13279,6 @@ author = {Zhuang Liu and
Juan Miguel Vilar}, Juan Miguel Vilar},
title = {Statistical Approaches to Computer-Assisted Translation}, title = {Statistical Approaches to Computer-Assisted Translation},
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...@@ -13351,7 +13307,6 @@ author = {Zhuang Liu and ...@@ -13351,7 +13307,6 @@ author = {Zhuang Liu and
title = {TurboTransformers: An Efficient {GPU} Serving System For Transformer title = {TurboTransformers: An Efficient {GPU} Serving System For Transformer
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@inproceedings{DBLP:conf/iclr/HuangCLWMW18, @inproceedings{DBLP:conf/iclr/HuangCLWMW18,
......
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