main.texRead-only preview
%===============================================================
%Template for UPC-TALP based on CTU and modified to match UPC-TALP colors and style
%The original template from Czech Technical University % Author: Martin Malý.
% They are defined by new graphical manual - 2017.
% Share and modify as you like. Keep the name of the authors.
% It is forbidden to use the template commercially.
%===============================================================
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#3
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#1
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#1
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#3
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#1
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#3
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{\includegraphics[height=1ex]{Bert-pic-removebg-preview.png}}
}}
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{\includegraphics[height=1.5ex]{trad_off_acc.pdf}}
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{\includegraphics[height=1.5ex]{trad_simple.pdf}}
{\includegraphics[height=1ex]{trad_simple.pdf}}
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{\includegraphics[height=1.5ex]{dataset-trad.pdf}}
{\includegraphics[height=1ex]{dataset-trad.pdf}}
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{\includegraphics[height=1.5ex]{ALL-trad.pdf}}
{\includegraphics[height=1ex]{ALL-trad.pdf}}
}}
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{\includegraphics[height=2ex]{fast-accur.pdf}}
%{\includegraphics[height=1.5ex]{trad-off.pdf}}
{\includegraphics[height=1.5ex]{fast-accur.pdf}}
{\includegraphics[height=1ex]{fast-accur.pdf}}
}}
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{\includegraphics[height=2ex]{fast-simple.pdf}}
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{\includegraphics[height=1.5ex]{fast-simple.pdf}}
{\includegraphics[height=1ex]{fast-simple.pdf}}
}}
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%====================================================
%========== DEFINITION OF AUTHORS ETC...=============
%====================================================
\author[PhD Thesis Defense]{ Your Name}
\institute[]{Department of Computer Science \\ TALP Group
\vspace{2mm} \\
Advisors: \\ Prof. John \\
Dr. Rosa\\
\vspace{2mm}}
\title[XX ]{your title}
\date[Feb 10, 2021]{PhD Thesis Defense \\ \small{Feb 10, 2021}}
%====================================================
%========== BEGINNING OF DOCUMENT ===================
%====================================================
\begin{document}
\begin{frame}[plain]
%\transboxin
\titlepage
\begin{center}%
%\vspace{-2mm}
%\tableofcontents{current}
\includegraphics[height=1cm]{darkblue-logo.pdf}
%\includegraphics[height=1cm]{darkblue-talp-logo.pdf}
%\includegraphics[height=1.5cm]{upclogo.pdf}
%\includegraphics[height=1.1cm]{files/IRIS.png}
%\includegraphics[height=1.3cm]{files/logo_iri2.png}https://www.overleaf.com/project/5bed4779c7eea31291e7e8b2#Navigation27
%\includegraphics[height=1.3cm]{files/IBT.png}
%\includegraphics[height=1.3cm]{files/biocev-logo-CMYK-horizontal.pdf}
\end{center}
\note[item]{you note here}
\end{frame}
%\logo{\includegraphics[height=1cm]{files/TALP.png}}
%\logo{\includegraphics[height=0.6cm]{darkblue-logo.pdf}}
%\logo{\includegraphics[height=0.5cm]{darkblue-logo.pdf}}
%% outline index
% \begin{frame}
% \frametitle{Outline}
% \tableofcontents[currentsection]
% \end{frame}
%}
\begin{frame}[plain]{Table of Contents}
\tableofcontents[currentsection]
\end{frame}
\begin{frame}[plain]{Table of Contents}
\tableofcontents[]
\note[item]{note here }
\end{frame}
%\section{Introduction}
\section{Problem Identification}
\begin{frame}
\frametitle{Highlight with a block}
%\begin{block}{Text Recognition}
\begin{block}{\samll}<0>
\begin{itemize}
\item [A] Fixed lexicon [\textcolor{darkblue}{some text }].
%\item Lexicon free [\textcolor{darkblue}{shi et al. 2016, Ghosh et al. 2017, Gao et al. 2017, Fang et al. 2018, Xing et al., 2019, Hu et al. 2020}].
%\item \textbf{Text}
%\item \textcolor{cvut_navy}{Text}
%\item \textcolor{cvut_navy}{\textbf{Text}}
\end{itemize}
\end{block}
\begin{block}{\samll}<0>
\begin{itemize}
%\item Fixed lexicon [\textcolor{darkblue}{Wang et al. 2012, Jaderberg et al. 2016}].
\item [B] Lexicon free [\textcolor{darkblue}{some text }].
%\item \textbf{Text}
%\item \textcolor{cvut_navy}{Text}
%\item \textcolor{cvut_navy}{\textbf{Text}}
\end{itemize}
\end{block}
%\includegraphics[width=0.8\textwidth]{intro.pdf}
%\includegraphics[width=0.7\textwidth \textcolor{white}{right}]{accv-overview-2.pdf}
\uncover<1>{
}
\vspace{-0.1cm}
%\tiny Cnnectionist Temporal Classification CTC [\textcolor{darkblue}{Graves et al. 2006}]
\end{frame}
\begin{frame}
\frametitle{Highlight with a block}
%\begin{block}{Text Recognition}
\begin{block}{\samll}
\begin{itemize}
\item [A] Fixed lexicon [\textcolor{darkblue}{some text}].
%\item Lexicon free [\textcolor{darkblue}{shi et al. 2016, Ghosh et al. 2017, Gao et al. 2017, Fang et al. 2018, Xing et al., 2019, Hu et al. 2020}].
%\item \textbf{Text}
%\item \textcolor{cvut_navy}{Text}
%\item \textcolor{cvut_navy}{\textbf{Text}}
\end{itemize}
\end{block}
\begin{block}{\samll}<0>
\begin{itemize}
%\item Fixed lexicon [\textcolor{darkblue}{Wang et al. 2012, Jaderberg et al. 2016}].
\item [B] Lexicon free [\textcolor{darkblue}{some text}].
%\item \textbf{Text}
%\item \textcolor{cvut_navy}{Text}
%\item \textcolor{cvut_navy}{\textbf{Text}}
\end{itemize}
\end{block}
%\includegraphics[width=0.8\textwidth]{intro.pdf}
%\includegraphics[width=0.7\textwidth \textcolor{white}{right}]{accv-overview-2.pdf}
\vspace{0.1cm}
\end{frame}
\begin{frame}[noframenumbering]{Add Block in order A ...}
\begin{alertblock}{\small}
\begin{itemize}
\item [A] \textbf{Lack of public dataset:} Most state-of-art deep ......
%\item \textbf{Fast and easy to re-train:} Statistical Language Modelling (LM) can be trained on specific domain.
%\item The system can be used as a \textbf{drop-in replacement} for any text-spotting algorithm that ranks the output words.
%\item This \textbf{hybrid approach} between deep learning and classical statistical modelling opens the possibility to produce accurate results with very simple models.
\end{itemize}
\end{alertblock}
\visible<2->{
\begin{alertblock}{\small}
\begin{itemize}
% \item \textbf{Lack of public dataset:} Most state-of-art deep models trained on synthetic dataset.
\item [B] \textbf{Fast and easy to re-train:} Statistical Language ....
%\item The system can be used as a \textbf{drop-in replacement} for any text-spotting algorithm that ranks the output words.
%\item This \textbf{hybrid approach} between deep learning and classical statistical modelling opens the possibility to produce accurate results with very simple models.
\end{itemize}
\end{alertblock}
}
\visible<3->{
\begin{alertblock}{\small}
\begin{itemize}
% \item \textbf{Lack of public dataset:} Most state-of-art deep models trained on synthetic dataset.
%\item \textbf{Fast and easy to re-train:} Statistical Language Modelling (LM) can be trained on specific domain.
\item [C] The system can be used as a .....
%\item This \textbf{hybrid approach} between deep learning and classical statistical modelling opens the possibility to produce accurate results with very simple models.
\end{itemize}
\end{alertblock}
}
\visible<4->{
\begin{alertblock}{\small}
\begin{itemize}
% \item \textbf{Lack of public dataset:} Most state-of-art deep models trained on synthetic dataset.
%\item \textbf{Fast and easy to re-train:} Statistical Language Modelling (LM) can be trained on specific domain.
%\item The system can be used as a \textbf{drop-in replacement} for any text-spotting algorithm that ranks the output words.
\item [D] This \textbf{hybrid approach} between deep learning .....
\end{itemize}
\end{alertblock}
}
\note[item]{\textbf{Block adding ...} \\ as I mentioned before \\
\textcolor{blue}{(A-)} all STOA models are trained on .... \\
\textcolor{blue}{(B-)} \textbf{Second}, Fast and easy t... \\
\\
\textcolor{blue}{(C-)} \textbf{Also}, the model can be used as a \textbf{drop-in replacement} for any text-spotting algorithm that ranks the output words. \\
\\
And \\
\\
\textcolor{blue}{(D-)} \textbf{finally} combining deep learning with a classical model can achieve a better result with low cost model (even training on CPU).I'm not generalizing here just in this case}
\end{frame}
\note[item]{\textbf{Why post-processing?} \\ as I mentioned before \\
\textcolor{blue}{(A-)} all STOA models are trained on this one synthetic dataset. \\
\textcolor{blue}{(B-)} \textbf{Second}, Fast and easy to re-train and can be adopted to any specific domain. \\
\\
\textcolor{blue}{(C-)} \textbf{Also}, the model can be used as a \textbf{drop-in replacement} for any text-spotting algorithm that ranks the output words. \\
\\
And \\
\\
\textcolor{blue}{(D-)} \textbf{finally} combining deep learning with a classical model can achieve a better result with low cost model (even training on CPU).I'm not generalizing here just in this case}
\end{frame}
\begin{frame}{Literature review block}
\frametitle{Literature review block}
%\begin{center}
Work addresses scene understanding, and benefit from combining text cue and visual context in image or text retrieval:
\\~~\\
\vspace{-1cm}
\begin{minipage}{4cm}
\begin{alertblock}{\small Lexicon Generation}
\textcolor{darkblue}{Patel et al. (2016)}
\end{alertblock}
\end{minipage}
\space \space \space generation of new lexicon with \textbf{topic modeling}
\begin{minipage}{4cm}
\begin{block}{\small Logo Retrieval}
\textcolor{darkblue}{Karaoglu et al. (2017)}
\end{block}
\end{minipage}
\space \space \space learn \textbf{textual information} from logos \\
\begin{minipage}{4cm}
\begin{alertblock}{\small Text detection}
\textcolor{darkblue}{Prasad et al. (2018)}
\end{alertblock}
\end{minipage} \space \space \space using object information for text detection
%\end{center}
\note[item]{show this slide 2 seconds }
\end{frame}
\begin{frame}[noframenumbering]{Literature review block }
%\section{literature Review}
\setbeamercovered{transparent}
%\begin{center}
%Work addresses scene understanding, text cue or visual context in text spotting:
%Work addresses scene understanding, and benefit from combining text cue and visual context in \textcolor{red}{text} retrieval:
Work addresses scene understanding, and benefit from combining text cue and visual context in image or text retrieval:
\\~~\\
\vspace{-1cm}
\begin{minipage}{4cm}
\begin{alertblock}{\small Lexicon Generation}
\textcolor{darkblue}{Patel et al. (2016)}
\end{alertblock}
\end{minipage}
\space \space \space generation of new lexicon with \textbf{topic modeling}
\begin{minipage}{4cm}
\begin{block}{\small Logo Retrieval}<0>
\textcolor{darkblue}{Karaoglu et al. (2017)}
\end{block}
\end{minipage}
\space \space \space \texttransparent{0.1}{learn \textbf{textual information} from logos} \\
\begin{minipage}{4cm}
\begin{alertblock}{\small Text detection}<0>
\textcolor{darkblue}{Prasad et al. (2018)}
\end{alertblock}
\end{minipage} \space \space \space \texttransparent{0.1}{using object information for text detection}
\note[item]{I will start with \textbf{topic modeling and lexicon generations} }
\end{frame}
\begin{frame}[noframenumbering]
\frametitle{Image}
\begin{itemize}
\item The task involves detecting the viewer’s interpretation of an Ad image captured as text.
\item Fine-tune BERT is used to learn textual and visual cues.
%\item Specific domain dataset is used: Ads Dataset
\item Google Vision API is used to extract scene text information.
\end{itemize}
\begin{figure}
\begin{center}
%%%\vspace{10pt}
%\vspace{-0.3cm}
\vspace{0.2cm}
\includegraphics[width=0.6\textwidth]{BERT-2020.pdf}
\label{ActionButton}
\end{center}
\end{figure}
\note[item]{}
\end{frame}
\begin{frame}
\frametitle{Table}
\begin{itemize}
%\item Unigram Language Model (ULM)
\item The ULM is based on a combined corpu ....
%\item The advantage of ULM is very simple to build, train and \textcolor{red}{adapt to new domains} opening the possibility to improve baseline performance for specific applications.
\end{itemize}
\begin{block}{}
\begin{table}
\centering
%\caption{Total count of unique words - Dictionary.}
\resizebox{\columnwidth}{!}{
\begin{tabular}{|l|c|c|c|c|}
%\begin{tabularx}{\columnwidth}{|lcccc|}
\hline
\multicolumn{5}{|c|}{Unique Count of Textual Data}\\
\hline
\rowcolor{Gray}
%INPUT & \cellcolor{applegreen}{token} & \cellcolor{red}{$\leq$ PAD $\geq$} & \cellcolor{amber}{300D} \\
Dictionary & words & nouns & verb & adjectives \\
\hline
%\rowcolor{Gray}
%\multicolumn{5}{|c|}{Dictionary} \\
% \hline
Dict-90K words level & 87,629 & 20,146 & 6,956 & 15,534 \\ %\hdashline
Language model & 8870,209 & 2695,906 & 139,385 & 824,581 \\ %\hdashline
\hline
\end{tabular}
}
%\end{tabularx}
%\label{se:Dic}
\end{table}
\end{block}
\note[item]{.}
\end{frame}
\begin{frame}[noframenumbering]{Fade out text}
%\begin{itemize}
% \item In this thesis, \hl{ we have demonstrated the benefit of leveraging these NLP techniques into computer vision problems that boost the performance of the text spotting system up to 3.3 points on a benchmark dataset without tuning or training}. Therefore, to answer to our main question:
%\end{itemize}
\begin{block}{\small}
%The work in the present dissertation has addressed this specific problem.
\begin{itemize}
\item The main limitation of this approach is that \textbf{depends on the baseline softmax} output to re-rank the most closely related word
%\item In particular, the \hl{semantic relatedness score suppresses unrelated words and boosts the most probably related word} by simple dot product multiplication.
\end{itemize}
%\end{itemize}
\end{block}
\begin{block}{\small}
%The work in the present dissertation has addressed this specific problem.
\begin{itemize}
% \item The main limitation of this approach is that \textbf{depends on the baseline softmax} output to re-rank the most closely related word
\item [] As in the example, the semantic relatedness score \textbf{\textcolor{gray}{suppresses unrelated words}} and \textbf{\textcolor{blue}{boosts the most probably related word}} by simple dot product multiplication. (Visual context: parking meters)
\end{itemize}
%\end{itemize}
\end{block}
\begin{table}[t]
\begin{center}
%\caption{Sample of the modified probabilities before and after the visual context, As shown in Figure \ref{fig:overview-of-all-model} these individual word scores are modified (re-ranked) based on the presence of others object classes, scene classes and image caption which are extracted through state-of-the-art frameworks from each problem domain.}
%Sample of the modified probabilities before and after the visual
%\caption{Sample of the modified probabilities before and after the visual context. As shown these individual word scores are modified (re-ranked) based on the presence of other object classes and scene classes which are extracted through state-of-the-art frameworks from each problem domain.}
\begin{threeparttable}
%\begin{tabular}{l|rl|rl|rl|rl}
%\begin{tabular}{l|rl|rl|rl}
%\hline \bf \small word & \ \small w1 & \small p1 & \small w3 & \small p3 \\ \hline
\begin{tabular}{|c|cl|cl|}
%\hline \bf \small word & \ \small w1 & \small p1 & \small w3 & \small p3 \\
\hline
\bf $w$ & \multicolumn{2}{c|} {\small Text Spotting Model} & \multicolumn{2}{c|}{ \small Visual Re-ranker Model} \\
\hline \hline
%$w1$ & \fadingtext{top color=gray,bottom color=gray}{quotas} & $0.5$ & \bf quarters & $5.4\text{e-}7$ \\
$w_{1}$ & quotas & $0.5$ & \fadingtext{top color=blue,bottom color=blue}{\textbf{quarters}} & $5.4\text{e-}7$ \\
%$w_{1}$ & quotas & $0.5$ & \fadingtext{top color=black,bottom color=back}{\textbf{quarters}} & $5.4\text{e-}7$ \\
%$w_{1}$ & quotas & $0.5$ & \textcolor{blue}{\textbf{quarters}} & $5.4\text{e-}7$ \\
$w_{2}$ & quartos & $0.1$ & \fadingtext{top color=gray,bottom color=gray}{quartos} & $5.2\text{e-}8$ \\
% $w2$ & quartos & $0.1$ & quartos & $5.2\text{e-}8$ \\
$w_{3}$ & \bf quarters & $0.05$ & \hbox{ \fadingtext{top color=gray,bottom color=gray}{\textcolor{red}{quotas}}} & $9.0\text{e-}9$ \\
\hline\end{tabular}
%\label{table_1}
%\begin{tablenotes}
% \small
%\item \textbf{Bold font} word indicate ground truth.
%\item Visual information
%*VCI (visual context information), result of best three word, see Figure \ref{figure1} for more details. %However, we overcome the limitation of false recognition of short words of the baseline.
%\end{tablenotes}
\end{threeparttable}
\label{tb: modified prob-ch4}
\end{center}
%\caption{fddddddddddddddd }
\end{table}
\end{frame}
\begin{frame}[plain]{Math block}
\begin{block}{Attention 1}
\begin{equation*}
c_{t}=\sum_{j=1}^{T} \alpha_{t j} h_{j}, \alpha_{t j}=\frac{\exp \left(e_{t j}\right)}{\sum_{k=1}^{T} \exp \left(e_{t k}\right)}, e_{t j}=a\left(s_{t-1}, h_{j}\right)
\end{equation*}
\end{block}
%The attention mechanisms provide the model with direct access between state at a different point in time. \cite{bahdanau2014neural} introduces an attention model that computes the context vector $c_{t}$ as the weighted mean of $h$ state sequence, given the model the hidden state $h_{t}$ at each time step:
%\noindent where $\alpha_{ti}$ is the weight computed at each time $t$ step for hidden state $h_{j}$, %T is the number of time steps for the input sequence. The $c$ context vector is used to compute the new state sequence $s$, where $s_{t}$ depends on previous state $s_{t-1}$. The $\alpha_{t j}$ weight are then computed as:
\begin{block}{Attention 2}
\begin{equation*}
c_{t}=\sum_{j=1}^{T} \alpha_{t j} h_{j}, \alpha_{t j}=\frac{\exp \left(e_{t j}\right)}{\sum_{k=1}^{T} \exp \left(e_{t k}\right)}, e_{t j}=a\left(\cancel{\textcolor{red}{s_{t-1}}}, h_{j}\right)
\end{equation*}
\end{block}
\end{frame}
\begin{frame}{Figure}
\centering
\begin{tikzpicture}
\begin{axis}[
%legend pos=south east,
legend pos=north east,
%legend pos=outer north east
grid=major,
% legend
%yticklabel=\empty,
%legend pos=outer north east,
legend cell align={left}, % all
grid,
%legend style={fill=none},
%xlabel=\textsc{Re-ranked caption},
xlabel=Caption,
ylabel= BLEU,
% legend style={
% at={(0.5,0.96)},
% anchor=west,
% mark size=1pt,
% legend columns=-1,
% /tikz/every even column/.append style={column sep=0.cm}
% every node near coord/.append style={font=\tiny}Sim
%},
]
% \axispath\draw
% (7.49165,-10.02171)
% |- (8.31801,-11.32467)
% node[near start,left] {$\frac{dy}{dx} = -1.58$};
%\addplot [blue!30, smooth,mark=*, line width=0.5mm] coordinates {
\addplot [only marks,
scatter,
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mark size=2.9pt] coordinates {
%\addplot [scatter, mark=*, mark size=3pt, line width=2pt, mesh] coordinates {
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% B1 beam
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%(14, 0.88)
%(15, 0.93)
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%(18. 0.88)
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%darksalmon!50
%\addplot [darksalmon!90, smooth, mark=square*, line width=0.5mm] coordinates {
\addplot [only marks,
scatter,
mark=halfcircle*,
mark size=2.9pt] coordinates {
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% \addplot plot coordinates {
% SBERT-sts
%(1, 0.27)
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%(3, 0.03)
%(4, 0.76)
%(5, 0.73)
%(6, 0.57)
%(7, 0.98)
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%(13, 0.69)
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%(15, 0.75)
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%(17, 0.81)
%%(18, 0.90)
%%(19, 0.20)
};
\addplot [black!30, smooth, line width=0.8mm] coordinates {
% %\addplot [blue(ncs)!20, line width=1mm] coordinates {
% (1,0.40)
%(2, 0.18)
%(3, 0)
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%(8, 0)
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%(15, 0.44)
%(16, 0)
%(1, 0.53)
%(2, 0.49)
%(3, 0.37)
%(4, 0.66)
%(5, 0.44)
%(6, 0.54)
%(7,0.22)
%(8, 0.17)
%(9, 0.33) %
%(10,0.44)
%(11, 0.19)
%(12, 0.54)
%(13,0.30)
%(14, 0.90)
%(15, 0.49)
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%(1,0.26)
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% BLEU
\legend{ \small Baseline \\ Similarity\\}
\end{axis}
\end{tikzpicture}
\end{frame}
\centering
\begin{frame}{Figure 2}
\centerning
\begin{tikzpicture}
\centering
\begin{axis}[
ybar, axis on top,
title={},
legend cell align={left},
%grid=both,
grid=minor,
%xmajorgrids=true,
%ymajorgrids=true,
%grid=major,
%minor tick num=5,
% width=\textwidth,
%height=8cm, width=10.5cm,
height=6cm, width=8.5cm,
%axis x line*=bottom,
%axis y line*=bottom,
ymin=0,
%legend cell align={left},
%enlarge x limits={abs=0.5},
% ybar,
% enlargelimits=-0.15,
%enlarge x limits=0.25,
% enlarge y limits={upper,value=0.2},
%legend cell align=left,
%enlarge x limits=0.5,
%major tick length=0cm,
%height=8cm, width=6.5cm,
bar width=0.14cm,
%ymajorgrids, tick align=inside,
%grid=major,
ymajorgrids, tick align=inside,
major grid style={draw=white},
%enlarge y limits={value=.001,upper},
ymin=1000, ymax=13000,
% ybar, enlarge x limits={abs=1}
enlarge x limits={abs=0.1cm}
axis x line*=bottom,
max space between ticks=14pt,
%max space between ticks=40pt,
%axis y line*=right,
%enlargelimits=0.15,
%ymajorgrids, tick align=inside, % put more numbers
%axis y line*=left,
%axis x line*=left,
%xmin=0
% axis y line*=left,
%enlarge x limits={abs=0.5cm}
%enlarge x limits={abs=1}
%enlarge y limits={abs=1}
%ymin=0
%xticklabel style={rotate=90,anchor=base,yshift=-0.4cm,xshift=-0.9cm,color=black},
% xticklabel style={rotate=90,yshift=-0.2cm,xshift=0,color=black},
xticklabel style={rotate=90,yshift=-0.05cm,xshift=0,color=black},
% texxt inside the bar
%xticklabel style={rotate=90,yshift=-0.4cm,xshift=2cm,color=black},
tickwidth=0pt,
enlarge x limits=true,
legend cell align={right}
%title=Left-align Legend with pgfplots,
%legend cell align={left}
%legend style={draw=none}
legend style={
at={(0.5,0.96)},
anchor=west,
mark size=1pt,
legend columns=-1,
% /tikz/every even column/.append style={column sep=0.cm}
every node near coord/.append style={font=\tiny}
},
ylabel={Frequency},
symbolic x coords={
%Baseline ,$\bert$ BERT,$\diamondsuit$ FDCLSTM, $\spadesuit$ TWE,
ski, clothing, ballplayer, tree, pizza, street, sign, car, washbasin, table, plate, human, light, seat, airliner, baseball
%Jan-12,Feb-12,
%Mar-12,
},
xtick=data,
%enlarge x limits,
%enlarge x limits=0.2,
%enlarge y limits={rel=-0.9,upper},
enlarge x limits={abs=0.01},
enlarge x limits=0.05,
enlarge y limits=0.05,
%restrict x to domain=1:2
nodes near coords={
% \pgfmathprintnumber[precision=0]{\pgfplotspointmeta}
}
]
%\addplot [draw=none, fill=black!30] coordinates {
\addplot [draw=none, fill=pistachio!40] coordinates {
%(Baseline , 26.2)
% (,6000 )
(ski, 12766)
(clothing, 9596)
(ballplayer, 10747)
(tree, 10460)
(pizza, 8972)
(street, 8945)
(sign, 8659)
(car, 8080)
(washbasin, 8046)
(table, 6792)
(plate, 6696)
(human, 6793)
(light, 6335)
(seat, 6031)
(airliner, 5461)
(baseball, 5075)
%(plate, 1085)
%(ballplayer, 14238)
%(car, 1069)
% (racket, 2599)
% (baseball, 0)
% (washbasin, 2283)
% (elephant, 1223 )
% (restaurant, 0)
% (elephant, 973)
% (parachute, 9399)
% (traffic, 0)
% (umbrella, 9196)
% (alp, 8868)
% (passenger, 8353)
% (pizza, 1209)
%(#,6000 )
% (seashore,8181)
%(wing,8034)
};
% };
%(Jan-12,67.5600)
%(Feb-12,88.2339)
%(Mar-12,78.6138)
% (Apr-12,58.9129) };
\addplot [draw=none,fill=darksalmon!50] coordinates {
(ski, 10901)
(clothing, 6682)
(ballplayer, 9468)
(tree, 8307)
(pizza, 8546)
(street, 7690)
(sign, 7468)
(car, 6358)
(washbasin, 7216)
(table, 5456)
(plate, 5213)
(human, 4917)
(light, 5357)
(seat, 5714)
(airliner, 5246)
(baseball, 4752)
%(ball, 1504)
%(plate, 4522)
%(pizza,8972)
% (x, 222)
%(baseball, 5153)
%(car, 3079)
%(baseball, 2437)
%(washbasin, 2486)
%(restaurant, 0)
%(elephant, 1223 )
%(parachute, 0)
};
% (Jan-12,77.5600)
% (Feb-12,78.2339)
% (Mar-12,88.6138)
% (Apr-12,78.9129) }
% \addplot [draw=none, fill=blue!30] coordinates {
\addplot [draw=none, fill=bananayellow!30] coordinates {
(ski, 8679)
(clothing, 3659)
(ballplayer, 7844)
(tree, 5503)
(pizza, 7744)
(street, 5673)
(sign, 5541)
(car, 4168)
(washbasin, 5808)
(table, 3728)
(plate, 3327)
(human, 2898)
(light, 4014)
(seat, 5002)
(airliner, 4501)
(baseball, 4186 )
% (x, 222)
% (LSTM-V (K3), 40)
%(LSTM-V (K3), 434)
% (CNN-90K (CNN-90K (K4)), 345)
};
\legend{Sim $th\geq 0.2$, Sim $th\geq 0.3$, Sim $th\geq 0.4$ }
\end{axis}
\end{tikzpicture}
\end{frame}
\end{document}
% =============================================================
% =========================== END =============================
% =============================================================
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