349 lines
12 KiB
TeX
349 lines
12 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[nofirafonts]{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{amssymb}
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\usepackage{amsfonts}
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\usepackage{bbm}
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\usepackage{hyperref}
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\usepackage{graphicx}
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\usepackage{xcolor}
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\usepackage{mathtools}
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\RequirePackage[T1]{fontenc}
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\PassOptionsToPackage{sfdefault}{FiraSans}
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\RequirePackage{FiraSans}
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\RequirePackage{FiraMono}
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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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% we wanna use default caleographic alphabet
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\DeclareMathAlphabet{\mathcal}{OMS}{cmbrs}{m}{n}
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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 E.g.:
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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\%/10\% of data labeled
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\end{itemize}
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\item Confidence of prediction [Threshold]
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\item Use pseudo-labels 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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\item scene changes/motion over time
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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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\item localize/identify objects in specific scene
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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 (large model)
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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 (smaller)
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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}{Structure Visualization}
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\includegraphics[scale=.17]{rsc/structure}
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\end{frame}
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\begin{frame}{Performance Perspectives}
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\begin{itemize}
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\item 1\% labeled data + 400 Labels
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\item Kinetics-400 dataset
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\end{itemize}
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\includegraphics[scale=.205]{rsc/performance_comparison}
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\end{frame}
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\section{Give me the math!}
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\begin{frame}{Definitions}
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\begin{itemize}
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\item Labeled data set of size $N_l$\\
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$\mathcal{V} = \{(v_1,y_1), \dots, (v_{N_l}, y_{N_l})\}$
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\item Unlabeled data set of size $N_u$\\
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$\mathcal{U} = \{u_1, \dots, u_{N_u}\}$
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\item in general $\lvert\mathcal{U}\rvert \gg \lvert\mathcal{V}\rvert$\\
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\end{itemize}
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\end{frame}
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\begin{frame}[allowframebreaks]{How existing method \textit{FixMatch} works}
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\begin{itemize}
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\item $B_u \coloneqq \text{Batchsize}$
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\item $\tau \coloneqq \text{Confidence Threshold (Hyperparameter)}$
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\item $F(\mathcal{T}_{\text{strong}}(u_i)) \coloneqq \text{Class distribution}$
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\item $p_i \coloneqq F(\mathcal{T}_{\text{weak}}(u_i))$
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\item $\hat{y}_i \coloneqq \arg \max(p_i) \coloneqq \text{Pseudo Label}$
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\item $\mathcal{H} \coloneqq \text{Cross-entropy loss}$
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\item $\mathcal{L}_u \coloneqq \text{Loss on the unlabeled data}$
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\item $F \coloneqq \text{Model}$
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\item $\mathbbm{1} \coloneqq \text{Indicator Function}$
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\end{itemize}
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\begin{align*}
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\mathcal{L}_u = \frac{1}{B_u} \sum_{i=1}^{B_u} \mathbbm{1}(\max(p_i) \geq \tau) \mathcal{H}(\hat{y}_i,F(\mathcal{T}_{\text{strong}}(u_i)))
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\end{align*}
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\framebreak
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\begin{itemize}
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\item $\mathbbm{1}(\max(p_i) \geq \tau)$
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\begin{itemize}
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\item 'confidence-based masking'
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\item retain label only if largest probability is above threshold
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\item keep only 'high confidence' labels
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\end{itemize}
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\item $\mathcal{H}(\hat{y}_i,F(\mathcal{T}_{\text{strong}}(u_i)))$
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\begin{itemize}
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\item 'consistency regularization'
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\item cross-entropy loss of strong augmented and weak augmented data
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}[allowframebreaks]{CMPL (Cross-Model Pseudo-Labeling)}
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\begin{itemize}
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\item $F(\cdot) \coloneqq \text{Primary backbone}$
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\item $A(\cdot) \coloneqq \text{Auxiliary network}$
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\item Learning on labeled data
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\begin{align*}
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\mathcal{L}_s^F &= \frac{1}{B_l} \sum_{i=1}^{B_l} \mathcal{H}(y_i,F(\mathcal{T}^F_{\text{standard}}(v_i)))\\
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\mathcal{L}_s^A &= \frac{1}{B_l} \sum_{i=1}^{B_l} \mathcal{H}(y_i,A(\mathcal{T}^F_{\text{standard}}(v_i)))
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\end{align*}
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\item $\mathcal{T}^F_{\text{standard}}(v_i) \coloneqq \text{standard augmentations for action recognition}$
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\end{itemize}
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\framebreak
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\begin{itemize}
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\item Learning on unlabeled data
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\begin{align*}
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\mathcal{L}_u^F &= \frac{1}{B_u} \sum_{i=1}^{B_u} \mathbbm{1}(\max(p_i^A) \geq \tau) \mathcal{H}(\hat{y}_i^A,F(\mathcal{T}_{\text{strong}}(u_i)))\\
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\mathcal{L}_u^A &= \frac{1}{B_u} \sum_{i=1}^{B_u} \mathbbm{1}(\max(p_i^F) \geq \tau) \mathcal{H}(\hat{y}_i^F,A(\mathcal{T}_{\text{strong}}(u_i)))\\
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\end{align*}
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\item Complete training objective
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\begin{align*}
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\mathcal{L} = (\mathcal{L}_s^F + \mathcal{L}_s^A) + \lambda(\mathcal{L}_u^F + \mathcal{L}_u^A)
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\end{align*}
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\item $\lambda \coloneqq \text{Balancing coefficient for unsupervised loss}$
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\end{itemize}
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\end{frame}
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\section{Implementation}
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\begin{frame}{Networks}
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\begin{itemize}
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\item Auxiliary Network
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\begin{itemize}
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\item sub-network of primary model
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\item 3D-ResNet18
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\item \textbf{3D-ResNet50x1/4}
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\end{itemize}
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\item Backbone network
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\begin{itemize}
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\item larger version of aux-net
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\item \textbf{3D-ResNet50}
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}{Dataset}
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\begin{itemize}
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\item Kinetics-400
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\begin{itemize}
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\item 400 categories
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\item 240k/20k training/validation samples
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\end{itemize}
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\item UCF-101
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\begin{itemize}
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\item 101 classes
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\item 9.5k/4k training/validation samples
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\end{itemize}
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\item $\approx$10sec every video
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\item 1\% or 10\% labeled subsets balanced sampled from distribution
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\end{itemize}
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\end{frame}
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\begin{frame}{Performance Results}
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\includegraphics[scale=.65]{rsc/results}
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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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