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eurosdr presentation
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56 changes: 0 additions & 56 deletions figures/results/transferability.tex
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56 changes: 0 additions & 56 deletions figures/results/vanilla.tex
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96 changes: 34 additions & 62 deletions presentation.tex
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\end{itemize}
\end{frame}

\begin{frame}{Our needs}
\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<1-> Global feature vector per graph.
\item<2-> Take structure into account.
\item<3-> Different building types \(\implies\) different graph sizes.
\item<4-> Handle small dataset.
\end{itemize}
\end{frame}

\begin{frame}{Proposed solution}
\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<1-> Use kernel trick:
\begin{itemize}[label=\(\rightarrow\)]
\item<2-> Compare graphs with kernels (scalar products);
\item<3-> Instead of computing feature vectors.
\end{itemize}
\end{itemize}
\end{frame}

\begin{frame}{Multiple attributes, multiple graphs}
\raggedleft
\includestandalone[mode=buildnew, height=.8\textheight]{figures/features/geometric/kernels_animated}
\end{frame}

\subsection{Image features}
\begin{frame}{Our needs}
\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<1-> Global feature vector per image.
\item<2-> Handles:
\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<2-> both depth and RGB images.
\item<3-> edges \& textures.
\item<4-> small dataset.
\end{itemize}
\end{itemize}
\vfill
\uncover<5->{
Proposed solution: \Acrfull{acr::scatnet}~\parencite{mallat2012group}.
}
\end{frame}

\begin{frame}{\texorpdfstring{\acrshort*{acr::scatnet}}{ScatNet} vs. \texorpdfstring{\acrshort*{acr::cnn}}{ConvNet}}
\begin{itemize}[label=\(\blacktriangleright\), font=\color{IGNGreen}]
\item<1-> Filters are hardcoded not learned:
\begin{itemize}
\item[\color{IGNDarkGreen} +]<2-> No need for a large dataset;
\item[\color{IGNDarkGreen} +]<3-> Interpretable;
\item[\color{IGNRed} ---]<4-> Features: not adaptable.
\end{itemize}
\item<5-> Limited to shallow layers:
\begin{itemize}
\item[\color{IGNRed} ---]<6-> Less powerful~\parencite{oyallon2015deep}.
\end{itemize}
\end{itemize}
\end{frame}

\subsection{Image features}
\begin{frame}{Height based features}
\centering
\includestandalone[mode=buildnew, width=\textwidth]{figures/features/height/scatnet_animated}
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\includestandalone[mode=buildnew, height=.65\textheight]{figures/results/vanilla}

\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<only@1> \texttt{Building errors} on dense scenes: Still to be improved.
\item<only@1> \texttt{Facet errors} always well detected.
\item<only@2> If not better, at least as good as the baseline.
\item<only@2> \phantom{M}
\item \texttt{Building errors} on dense scenes: Still to be improved.
\item \texttt{Facet errors} always well detected.
\end{itemize}
\end{frame}

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\item<only@1> Except \texttt{\acrshort{acr::fib}} on \textbf{Elancourt}.
\item<only@2> Compared to \texttt{Vanilla}.
\item<only@2> Always better to train on \textbf{Elancourt}.
\item<only@3> Compared to the baseline.
\item<only@3> Training independent from origin of model!
\end{itemize}
\end{frame}

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\end{frame}

\subsection*{ScatNet}
\begin{frame}{Our needs}
\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<1-> Global feature vector per image.
\item<2-> Handles:
\begin{itemize}[label=$\blacktriangleright$, font=\color{IGNGreen}]
\item<2-> both depth and RGB images.
\item<3-> edges \& textures.
\item<4-> small dataset.
\end{itemize}
\end{itemize}
\vfill
\uncover<5->{
Proposed solution: \Acrfull{acr::scatnet}~\parencite{mallat2012group}.
}
\end{frame}

\begin{frame}{\texorpdfstring{\acrshort*{acr::scatnet}}{ScatNet} vs. \texorpdfstring{\acrshort*{acr::cnn}}{ConvNet}}
\begin{itemize}[label=\(\blacktriangleright\), font=\color{IGNGreen}]
\item<1-> Filters are hardcoded not learned:
\begin{itemize}
\item[\color{IGNDarkGreen} +]<2-> No need for a large dataset;
\item[\color{IGNDarkGreen} +]<3-> Interpretable;
\item[\color{IGNRed} ---]<4-> Features: not adaptable.
\end{itemize}
\item<5-> Limited to shallow layers:
\begin{itemize}
\item[\color{IGNRed} ---]<6-> Less powerful~\parencite{oyallon2015deep}.
\end{itemize}
\end{itemize}
\end{frame}

\begin{frame}{\texorpdfstring{\acrshort*{acr::scatnet}}{ScatNet}}
\begin{itemize}[label=\(\blacktriangleright\), font=\color{IGNGreen}]
\item<1-> Reverse engineered \acrfull{acr::cnn}~\parencite{mallat2012group};
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