235 lines
7.1 KiB
TeX
235 lines
7.1 KiB
TeX
\documentclass[usenames,dvipsnames]{beamer}
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%----------------------------------------------------------------------------------------
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% Struktur und Pointer Referat
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% 20.04.2020
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%----------------------------------------------------------------------------------------
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\usetheme{focus}
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\usepackage[utf8]{inputenc}
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\usepackage{booktabs}
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\usepackage{amsmath}
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\usepackage{hyperref}
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\usepackage{graphicx}
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\usepackage{listings}
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\usepackage{xcolor}
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% Farbdefinitionen
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\definecolor{backgroundcoloreq}{RGB}{180,140,0}
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\definecolor{codegreen}{rgb}{0,0.6,0}
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\definecolor{codegray}{rgb}{0.5,0.5,0.5}
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\definecolor{codepurple}{rgb}{0.58,0,0.82}
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\definecolor{codeorange}{RGB}{190,100,0}
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\lstset{
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language=C,
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basicstyle=\ttfamily,
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numbers=left,
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numberstyle=\tiny,
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tabsize=4,
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columns=fixed,
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showstringspaces=false,
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showtabs=false,
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breaklines=true,
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keepspaces,
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morekeywords={std},
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keywordstyle=\color{blue}\ttfamily,
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stringstyle=\color{red}\ttfamily,
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commentstyle=\color{OliveGreen!85}\ttfamily,
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numberstyle=\tiny\color{codegray},
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basicstyle=\footnotesize\selectfont\ttfamily,
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% keyword highlighting
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classoffset=1, % starting new class
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otherkeywords={>,<,.,;,-,+,!,=,~,:,[,],NULL,&},
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morekeywords={>,<,.,;,-,+,!,=,~,:,[,],NULL,&},
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keywordstyle=\color{codeorange},
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classoffset=0
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}
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%----------------------------------------------------------------------------------------
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% TITLE SLIDE
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%----------------------------------------------------------------------------------------
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\title{Cross-Model Pseudo-Labeling}
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\subtitle{for Semi-Supervised Action Recognition}
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\author{Lukas Heiligenbrunner}
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\date{\today}
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%------------------------------------------------
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\begin{document}
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%------------------------------------------------
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\begin{frame}
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\maketitle
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\end{frame}
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%----------------------------------------------------------------------------------------
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% SECTION 1
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%----------------------------------------------------------------------------------------
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% todo pic of action
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\section{The goal}
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\begin{frame}{The goal}
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\begin{itemize}
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\item train model
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\item recognize action of person
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\item from video [$\approx$10sec]
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\item eg.:
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\begin{itemize}
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\item brushing hair
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\item riding bike
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\item dancing
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\item playing violin
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\end{itemize}
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\item as generic as possible
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\end{itemize}
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\end{frame}
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%----------------------------------------------------------------------------------------
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% SECTION 2
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%----------------------------------------------------------------------------------------
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\section{The Problem} % Section title slide, unnumbered
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%------------------------------------------------
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\begin{frame}{Missing Labels}
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\begin{itemize}
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\item Supervised action recoginition
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\begin{itemize}
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\item lots of labeled samples necessary
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\item videos
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\end{itemize}
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\item Labeling Samples very expensive
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\begin{itemize}
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\item Avoid!
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\end{itemize}
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\item Tremendous amount of unlabled data
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\begin{itemize}
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\item YouTube
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\end{itemize}
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\item using semi-supervised learning might be benefitial
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\end{itemize}
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\end{frame}
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%------------------------------------------------
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\begin{frame}{What's all about Semi supervised?}
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\begin{itemize}
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\item Supervised learning
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\begin{itemize}
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\item Data samples
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\item Target labels
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\item Each sample is associated to target label
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\end{itemize}
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\item Unsupervised learning
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\begin{itemize}
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\item Data samples
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\item target is to find patterns in data
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\item without supervision
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\end{itemize}
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\item Semi-Supervised learning
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\begin{itemize}
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\item combination of both
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\item have labeled \& unlabeled data
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\item labeled data guides learning process
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\item unlabled helps to gain additional information
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\item goal is performance improvement
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\end{itemize}
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\end{itemize}
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\end{frame}
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%------------------------------------------------
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\begin{frame}[allowframebreaks]{What's already been done}
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\begin{itemize}
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\item Pseudo-labeling
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\item Train model on labeled data
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\begin{itemize}
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\item Eg. 1\% of data labeled
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\end{itemize}
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\item Confidence of prediction
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\item If high enough
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\item Use to predict unlabeled data
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\end{itemize}
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\framebreak
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\begin{itemize}
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\item quantity and quality of pseudo-labels
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\item significant impact on main model accuracy!
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\item we want to improve pseudo-label framework as much as possible
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\end{itemize}
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\end{frame}
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%----------------------------------------------------------------------------------------
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% SECTION 2
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%----------------------------------------------------------------------------------------
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\section{Cross-Model Pseudo-Labeling}
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\begin{frame}[allowframebreaks]{Papers approach}
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\begin{itemize}
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\item Based on complementary-representations of model
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\item Models of different size
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\item Different structural-bias $\rightarrow$ different category-wise performance
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\item Small model
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\begin{itemize}
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\item lower capacity
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\item better captures temporal dynamics in recognizing actions
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\end{itemize}
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\item Large model
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\begin{itemize}
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\item better learns spatial semantics
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\item to distinguish different action instances
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\end{itemize}
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\end{itemize}
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\framebreak
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\begin{itemize}
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\item Cross-Model Pseudo-Labeling
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\item Primary backbone
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\item Supplemented by lightweight auxiliary network
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\begin{itemize}
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\item Different structure
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\item Fewer channels
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\end{itemize}
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\item Different representation of data complements primary backbone
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\end{itemize}
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\end{frame}
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\begin{frame}{Performance glance}
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todo the pic of the performance graph
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\end{frame}
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% --- THE END
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\begin{frame}[focus]
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Thanks for your Attention!
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\end{frame}
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%----------------------------------------------------------------------------------------
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% CLOSING/SUPPLEMENTARY SLIDES
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%----------------------------------------------------------------------------------------
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\appendix
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\begin{frame}{Sources}
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\nocite{*} % Display all references regardless of if they were cited
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\bibliography{sources}
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\bibliographystyle{plain}
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\end{frame}
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\end{document}
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