| \documentclass[10pt,twocolumn,letterpaper]{article} | |
| \usepackage{iccv} | |
| \usepackage{times} | |
| \usepackage{epsfig} | |
| \usepackage{graphicx} | |
| \usepackage{amsmath} | |
| \usepackage{amssymb} | |
| \pdfoutput=1 | |
| \usepackage[numbers,sort]{natbib} \usepackage{placeins} \usepackage{afterpage} | |
| \usepackage{adjustbox} \usepackage{multirow} | |
| \renewcommand{\paragraph}[1]{\smallskip\noindent{\bf{#1}}} | |
| \usepackage[customdriver=hpdftex,pagebackref=true,breaklinks=true,letterpaper=true,colorlinks,bookmarks=false]{hyperref} | |
| \hypersetup{pdfauthor={Relja Arandjelovic},pdftitle={Look, Listen and Learn}} | |
| \iccvfinalcopy | |
| \def\iccvPaperID{191} \def\httilde{\mbox{\tt\raisebox{-.5ex}{\symbol{126}}}} | |
| \begin{document} | |
| \title{Look, Listen and Learn} | |
| \author{Relja Arandjelovi\'c$^\dagger$\\ | |
| {\tt\small relja@google.com} | |
| \and | |
| Andrew Zisserman$^{\dagger,*}$\\ | |
| {\tt\small zisserman@google.com} | |
| \and | |
| $^\dagger$DeepMind | |
| \quad\quad\quad | |
| $^*$VGG, Department of Engineering Science, University of Oxford\\ | |
| } | |
| \maketitle | |
| \newcommand{\figTask}{ | |
| \begin{figure}[t] | |
| \begin{center} | |
| \includegraphics[width=\linewidth]{figures/task.eps} | |
| \end{center} | |
| \caption{{\bf Audio-visual correspondence task (AVC)}. | |
| A network should learn to determine whether a pair of | |
| (video frame, short audio clip) correspond to each other or not. | |
| Positives are (frame, audio) extracted from the same time of one video, | |
| while negatives are a frame and audio extracted from different videos. | |
| } | |
| \label{fig:task} | |
| \end{figure} | |
| } | |
| \newcommand{\figNet}{ | |
| \begin{figure}[t] | |
| \vspace{-0.3cm} | |
| \begin{center} | |
| \includegraphics[height=0.5\textheight]{figures/network_vert.eps} | |
| \end{center} | |
| \vspace{-0.2cm} | |
| \caption{{\bf $L^3$-Net architecture}. | |
| Each blocks represents a single layer with text providing more information -- | |
| first row: layer name and parameters, second row: output feature map size. | |
| Layers with a name prefix | |
| \texttt{conv}, \texttt{pool}, \texttt{fc}, \texttt{concat}, \texttt{softmax} | |
| are convolutional, max-pooling, fully connected, concatenation and softmax | |
| layers, respectively. | |
| The listed parameters are: | |
| conv -- kernel size and number of channels, | |
| pooling -- kernel size, | |
| fc -- size of the weight matrix. | |
| The stride of pool layers is equal to the kernel size and there is no padding. | |
| Each convolutional layer is followed by batch normalization \cite{Ioffe15} | |
| and a ReLU nonlinearity, and the first fully connected layer (\texttt{fc1}) | |
| is followed by ReLU. | |
| } | |
| \label{fig:net} | |
| \vspace{-0.3cm} | |
| \end{figure} | |
| } | |
| \newcommand{\tabAvc}{ | |
| \begin{table}[t] | |
| \begin{center} | |
| \small | |
| \begin{tabular}{lcc} | |
| Method & Flickr-SoundNet & Kinetics-Sounds \\ | |
| \hline\hline | |
| Supervised direct & -- & 65\% \\ Supervised pretraining & -- & 74\% \\ | |
| $L^3$-Net & 78\% & 74\% | |
| \end{tabular} | |
| \end{center} | |
| \vspace{-0.2cm} | |
| \caption{{\bf Audio-visual correspondence (AVC) results.} | |
| Test set accuracy on the AVC task for the $L^3$-Net, | |
| and the two supervised baselines on the labelled | |
| Kinetics-Sounds dataset. | |
| The number of positives and negatives is the same, so chance gets 50\%. | |
| All methods are trained on the training set of the respective datasets. | |
| } | |
| \label{tab:avc} | |
| \end{table} | |
| } | |
| \newcommand{\tabSound}{ | |
| \begin{table}[t] | |
| \begin{center} | |
| \small | |
| \hspace*{-0.3cm} | |
| \begin{tabular}{c@{~~~}c} | |
| (a) ESC-50 & (b) DCASE \\ | |
| \begin{adjustbox}{valign=t} | |
| \begin{tabular}{lc} | |
| Method & Accuracy \\ | |
| \hline\hline | |
| SVM-MFCC \cite{Piczak15} & 39.6\% \\ | |
| Autoencoder \cite{Aytar16} & 39.9\% \\ | |
| Random Forest \cite{Piczak15} & 44.3\% \\ | |
| Piczak ConvNet \cite{Piczak15a} & 64.5\% \\ | |
| SoundNet \cite{Aytar16} & 74.2\% \\ | |
| \hline | |
| Ours random & 62.5\% \\ | |
| Ours & {\bf 79.3\%} \\ | |
| \hline | |
| \emph{Human perf.\ \cite{Piczak15}} & \emph{81.3\%} | |
| \end{tabular} | |
| \end{adjustbox} | |
| & | |
| \begin{adjustbox}{valign=t} | |
| \begin{tabular}{lc} | |
| Method & Accuracy \\ | |
| \hline\hline | |
| RG \cite{Rakotomamonjy15} & 69\% \\ | |
| LTT \cite{Li13a} & 72\% \\ | |
| RNH \cite{Roma13} & 77\% \\ | |
| Ensemble \cite{Stowell15} & 78\% \\ | |
| SoundNet \cite{Aytar16} & 88\% \\ | |
| \hline | |
| Ours random & 85\% \\ | |
| Ours & {\bf 93\%} | |
| \end{tabular} | |
| \end{adjustbox} | |
| \end{tabular} | |
| \end{center} | |
| \vspace{-0.2cm} | |
| \caption{{\bf Sound classification.} | |
| ``Ours random'' is an additional baseline which shows the performance | |
| of our network without $L^3$-training. | |
| Our $L^3$-training sets the new state-of-the-art by a large margin | |
| on both benchmarks. | |
| } | |
| \label{tab:sound} | |
| \end{table} | |
| } | |
| \newcommand{\tabImageNet}{ | |
| \begin{table}[t] | |
| \begin{center} | |
| \small | |
| \begin{tabular}{lc}Method & Top 1 accuracy \\ \hline\hline | |
| Random & 18.3\% \\ Pathak \etal \cite{Pathak16} & 22.3\% \\ Kr\"ahenb\"uhl \etal \cite{Krahenbuhl15} & 24.5\% \\ Donahue \etal \cite{Donahue17} & 31.0\% \\ Doersch \etal \cite{Doersch15} & 31.7\% \\ Zhang \etal \cite{Zhang16} (init: \cite{Krahenbuhl15}) & 32.6\% \\ Noroozi and Favaro \cite{Noroozi16} & 34.7\% \\ | |
| \hline | |
| Ours random & 12.9\% \\ Ours & 32.3\% \\ \end{tabular} | |
| \end{center} | |
| \vspace{-0.2cm} | |
| \caption{{\bf Visual classification on ImageNet.} | |
| Following \cite{Zhang16}, our features are evaluated by training a linear | |
| classifier on the ImageNet training set and measuring the classification | |
| accuracy on the validation set. | |
| For more details and discussions see Section \ref{sec:imagenet}. | |
| All performance numbers apart from ours are provided by authors of \cite{Zhang16}, | |
| showing only the best performance for each method over all parameter choices | |
| (\eg Donahue \etal \cite{Donahue17} achieve 27.1\% instead of 31.0\% when taking | |
| features from \texttt{pool5} instead of \texttt{conv3}). | |
| } | |
| \label{tab:imagenet} | |
| \vspace{-0.3cm} | |
| \end{table} | |
| } | |
| \newcommand{\figUnitsV}{ | |
| \def\unitsW{0.1\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure*}[p] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{ccccccccc} | |
| \fntsz Fingerpicking & | |
| \fntsz Lawn mowing & | |
| \fntsz P.\ accordion & | |
| \fntsz P.\ bass guitar & | |
| \fntsz P.\ saxophone & | |
| \fntsz Typing & | |
| \fntsz Bowling & | |
| \fntsz P.\ clarinet & | |
| \fntsz P.\ organ | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_fingerpicking.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_mowing_the_lawn_by_pushing_lawnmower.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_playing_accordion.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_playing_bass_guitar.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_playing_saxophone.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_typing.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_bowling.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_playing_clarinet.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/vision_playing_organ.jpg} | |
| \end{tabular} | |
| \end{center} | |
| \vspace{-0.4cm} | |
| \caption{{\bf Learnt visual concepts (Kinetics-Sounds).} | |
| Each column shows five images that most activate | |
| a particular unit of the 512 in \texttt{pool4} for the vision subnetwork. | |
| Note that these features do not take sound as input. | |
| Videos come from the Kinetics-Sounds test set and the network was trained | |
| on the Kinetics-Sounds train set. | |
| The top row shows the dominant action label for the unit (``P.'' stands for ``playing''). | |
| } | |
| \label{fig:unitsV} | |
| \end{figure*} | |
| } | |
| \newcommand{\figUnitsVmap}{ | |
| \def\unitsW{0.1\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure*}[p] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{ccccccccc} | |
| \fntsz Fingerpicking & | |
| \fntsz Lawn mowing & | |
| \fntsz P.\ accordion & | |
| \fntsz P.\ bass guitar & | |
| \fntsz P.\ saxophone & | |
| \fntsz Typing & | |
| \fntsz Bowling & | |
| \fntsz P.\ clarinet & | |
| \fntsz P.\ organ | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_fingerpicking.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_mowing_the_lawn_by_pushing_lawnmower.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_playing_accordion.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_playing_bass_guitar.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_playing_saxophone.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_typing.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_bowling.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_playing_clarinet.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_vision_playing_organ.png} | |
| \end{tabular} | |
| \end{center} | |
| \vspace{-0.4cm} | |
| \caption{{\bf Visual semantic heatmap (Kinetics-Sounds).} | |
| Examples correspond to the ones in Figure \ref{fig:unitsV}. | |
| A semantic heatmap is obtained as a slice of activations from | |
| \texttt{conv4\_2} of the vision subnetwork that corresponds to the same unit from | |
| \texttt{pool4} as in Figure \ref{fig:unitsV}, | |
| \ie the unit that responds highly to the class in question. | |
| } | |
| \label{fig:unitsVmap} | |
| \end{figure*} | |
| } | |
| \newcommand{\figUnitsA}{ | |
| \def\unitsW{0.1\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure*}[t] | |
| \vspace{-0.3cm} | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{ccccccccc} | |
| \fntsz Fingerpicking & | |
| \fntsz Lawn mowing & | |
| \fntsz P.\ accordion & | |
| \fntsz P.\ bass guitar & | |
| \fntsz P.\ saxophone & | |
| \fntsz Typing & | |
| \fntsz P.\ xylophone & | |
| \fntsz Tap dancing & | |
| \fntsz Tickling | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_fingerpicking.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_mowing_the_lawn_by_pushing_lawnmower.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_playing_accordion.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_playing_bass_guitar.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_playing_saxophone.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_typing.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_playing_xylophone.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_tap_dancing.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/audio_tickling.jpg} | |
| \end{tabular} | |
| \end{center} | |
| \vspace{-0.4cm} | |
| \caption{{\bf Learnt audio concepts (Kinetics-Sounds).} | |
| Each column shows five sounds that most activate | |
| a particular unit in \texttt{pool4} of the audio subnetwork. | |
| Purely for visualization purposes, as it is hard to display sound, | |
| the frame of the video that is aligned with the sound is shown instead | |
| of the actual sound form, | |
| but we stress that no vision is used in this experiment. | |
| Videos come from the Kinetics-Sounds test set and the network was trained | |
| on the Kinetics-Sounds train set. | |
| The top row shows the dominant action label for the unit (``P.'' stands for ``playing''). | |
| } | |
| \label{fig:unitsA} | |
| \vspace{-0.3cm} | |
| \end{figure*} | |
| } | |
| \newcommand{\figUnitsAmap}{ | |
| \def\unitsW{0.11\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure}[t] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{c@{~}c@{~~~}c@{~}c@{~~~}c@{~}c@{~~~}c@{~}c@{~~~}c@{~}c} | |
| \multicolumn{2}{c}{\fntsz Fingerpicking ~~~} & | |
| \multicolumn{2}{c}{\fntsz Lawn mowing ~~~} & | |
| \multicolumn{2}{c}{\fntsz P.\ bass guitar ~~~} & | |
| \multicolumn{2}{c}{\fntsz Tap dancing ~~~} | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/spectrogram_fingerpicking.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_audio_fingerpicking.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/spectrogram_mowing_the_lawn_by_pushing_lawnmower.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_audio_mowing_the_lawn_by_pushing_lawnmower.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/spectrogram_playing_bass_guitar.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_audio_playing_bass_guitar.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/spectrogram_tap_dancing.png} & | |
| \includegraphics[width=\unitsW]{figures/qual/feat_23331/map_audio_tap_dancing.png} | |
| \end{tabular} | |
| \end{center} | |
| \caption{{\bf Audio semantic heatmaps (Kinetics-Sounds).} | |
| Each pair of columns shows a single action class | |
| (top, ``P.'' stands for ``playing''), | |
| five log-spectrograms (left) and spectrogram semantic heatmaps (right) | |
| for the class. | |
| Horizontal and vertical axes correspond to the time and frequency dimensions, | |
| respectively. | |
| A semantic heatmap is obtained as a slice of activations of the unit from | |
| \texttt{conv4\_2} of the audio subnetwork which shows preference for | |
| the considered class. | |
| } | |
| \label{fig:unitsAmap} | |
| \end{figure} | |
| } | |
| \newcommand{\figUnitsVSoundNetA}{ | |
| \def\unitsW{0.144\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure*}[p] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{cccccc} | |
| \multicolumn{2}{c}{Outdoor} & | |
| \multicolumn{2}{c}{Concert} & | |
| \multicolumn{2}{c}{Outdoor sport} | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_022.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_057.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_072.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_318.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_044.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_133.jpg} | |
| \\ \hline \\ | |
| Cloudy sky & | |
| \multicolumn{2}{c}{Sky} & | |
| \multicolumn{2}{c}{Water surface} & | |
| Underwater | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_188.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_417.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_421.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_440.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_471.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_029.jpg} | |
| \\ \hline \\ | |
| Horizon & | |
| Railway & | |
| \multicolumn{2}{c}{Crowd} & | |
| \multicolumn{2}{c}{Text} | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_453.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_181.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_009.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_100.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_010.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_129.jpg} | |
| \end{tabular} | |
| \end{center} | |
| \vspace{-0.4cm} | |
| \caption{{\bf Learnt visual concepts and semantic heatmaps (Flickr-SoundNet).} | |
| Each mini-column shows five images that most activate a particular unit of the 512 in | |
| \texttt{pool4} of the vision subnetwork, and the corresponding heatmap | |
| (for more details see Figures \ref{fig:unitsV} and \ref{fig:unitsVmap}). | |
| Column titles are a subjective names of concepts the units respond to. | |
| } | |
| \label{fig:unitsVSoundNetA} | |
| \end{figure*} | |
| } | |
| \newcommand{\figUnitsVSoundNetB}{ | |
| \def\unitsW{0.19\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure*}[b] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{ccccc} | |
| Baby & | |
| Face & | |
| \multicolumn{2}{c}{Head} & | |
| Crowd | |
| \\ | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_030.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_003.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_315.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_173.jpg} & | |
| \includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/vision_009.jpg} | |
| \\ | |
| \end{tabular} | |
| \end{center} | |
| \caption{{\bf Learnt human-related visual concepts and semantic heatmaps (Flickr-SoundNet).} | |
| Each mini-column shows five images that most activate a particular unit of the 512 in | |
| \texttt{pool4} of the vision subnetwork, and the corresponding heatmap | |
| (for more details see Figures \ref{fig:unitsV} and \ref{fig:unitsVmap}). | |
| Column titles are a subjective names of concepts the units respond to. | |
| } | |
| \label{fig:unitsVSoundNetB} | |
| \end{figure*} | |
| } | |
| \newcommand{\figUnitsASoundNetA}{ | |
| \def\unitsW{0.1\linewidth} | |
| \def\fntsz{\scriptsize} | |
| \begin{figure*}[p] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \begin{tabular}{cccccccc} | |
| \multicolumn{2}{c}{Baby voice} & | |
| \multicolumn{2}{c}{Human voice} & | |
| Male voice & | |
| People & | |
| \multicolumn{2}{c}{Crowd} | |
| \\ | |
| \href{http://youtu.be/MrADE51ahHQ}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_174.jpg}} & | |
| \href{http://youtu.be/L-SbBq2xJVo}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_191.jpg}} & | |
| \href{http://youtu.be/i8khQ-MY9Qg}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_187.jpg}} & | |
| \href{http://youtu.be/ET3i_yZm4Ww}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_354.jpg}} & | |
| \href{http://youtu.be/vs5-5u2C-KI}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_476.jpg}} & | |
| \href{http://youtu.be/GBL0475ctoQ}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_474.jpg}} & | |
| \href{http://youtu.be/Ax6Xcqn1Gxc}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_004.jpg}} & | |
| \href{http://youtu.be/Tt41s6xezkM}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_053.jpg}} | |
| \\ \hline \\ | |
| Music & | |
| Concert & | |
| Sport & | |
| Clapping & | |
| Water & | |
| Underwater & | |
| Windy & | |
| Outdoor | |
| \\ | |
| \href{http://youtu.be/A5aEXSUbGjg}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_005.jpg}} & | |
| \href{http://youtu.be/cqQY7TrqQdw}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_479.jpg}} & | |
| \href{http://youtu.be/8BPQd_DytyI}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_472.jpg}} & | |
| \href{http://youtu.be/GlbhKNqNCGw}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_286.jpg}} & | |
| \href{http://youtu.be/GWW5scO468o}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_181.jpg}} & | |
| \href{http://youtu.be/_AR2dCWd3aY}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_041.jpg}} & | |
| \href{http://youtu.be/gyIdY6ncxpc}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_080.jpg}} & | |
| \href{http://youtu.be/7DxHhEewhwA}{\includegraphics[width=\unitsW]{figures/qual/soundnet500k_233004/audio_269.jpg}} | |
| \end{tabular} | |
| \end{center} | |
| \caption{{\bf Learnt audio concepts (Flickr-SoundNet).} | |
| Each mini-column shows sounds that most activate a particular unit of the 512 in | |
| \texttt{pool4} of the audio subnetwork. | |
| Purely for visualization purposes, as it is hard to display sound, | |
| the frame of the video that is aligned with the sound is shown instead | |
| of the actual sound form, | |
| but we stress that no vision is used in this experiment. | |
| Column titles are a subjective names of concepts the units respond to. | |
| Note that for the ``Human voice'', ``Male voice'', ``Crowd'', ``Music'' | |
| and ``Concert'' examples, the respective clips do contain the relevant audio | |
| despite the frame looking as if it is unrelated, \eg the third example in the | |
| ``Concert'' column does contain loud music sounds. | |
| Audio clips containing the five concatenated 1s samples corresponding to each | |
| mini-column are hosted on YouTube and can be reached by clicking on the | |
| respective mini-columns; | |
| \href{https://goo.gl/ohDGtJ}{this YouTube playlist (https://goo.gl/ohDGtJ)} | |
| contains all 16 examples. | |
| } | |
| \label{fig:unitsASoundNetA} | |
| \end{figure*} | |
| } | |
| \newcommand{\figTsne}{ | |
| \begin{figure*}[p] | |
| \begin{center} | |
| \setlength{\tabcolsep}{2pt} | |
| \hspace*{-0.5cm} | |
| \begin{tabular}{c@{\hskip 1cm}cc} | |
| (a) Audio Learnt & | |
| \multirow{4}{*}{ | |
| \rule{0pt}{12cm} | |
| \includegraphics[trim={1cm 2cm 15cm 2.5cm},clip,height=0.4\linewidth]{figures/tsne/audio_15_random.eps} | |
| } & | |
| (b) Audio Random \\ [1em] | |
| \includegraphics[height=0.4\linewidth]{figures/tsne/audio_15_L3.eps} & & | |
| \hspace{-0.3cm}\includegraphics[trim={6.5cm 2.7cm 5cm 2.7cm},clip,height=0.35\linewidth]{figures/tsne/audio_15_random.eps} \\ [5em] | |
| (c) Visual Learnt && | |
| (d) Visual Random \\ [1em] | |
| \includegraphics[height=0.4\linewidth]{figures/tsne/visual_15_L3.eps} & & | |
| \hspace{-2.5cm}\includegraphics[trim={3cm 2cm 3cm 2cm},clip,height=0.33\linewidth]{figures/tsne/visual_15_random.eps} | |
| \end{tabular} | |
| \end{center} | |
| \caption{{\bf t-SNE visualization \cite{Van-der-Maaten08} of learnt representations (Kinetics-Sounds).} | |
| The (a,c) and (b,d) show the two-dimensional t-SNE embeddings for the trained | |
| versus non-trained (\ie network with random weights) $L^3$-Net, respectively. | |
| For visualization purposes only, we colour the t-SNE embeddings using the | |
| Kinetics-Sounds labels, but no labels were used for training the $L^3$-Net. | |
| For clarity and reduced clutter, only a subset of actions (13 classes out of 34) | |
| is shown. Some clearly noticeable clusters are manually highlighted by | |
| enclosing them with ellipses. | |
| Best viewed in colour. | |
| } | |
| \label{fig:tsne} | |
| \end{figure*} | |
| } | |
| \newcommand{\tabNMI}{ | |
| \begin{table}[b!] | |
| \begin{center} | |
| \begin{tabular}{lcc} | |
| Method & Vision & Audio \\ | |
| \hline\hline | |
| Random assignments & 0.165 & 0.165 \\ | |
| Ours random ($L^3$-Net without training) & 0.204 & 0.219 \\ | |
| Ours ($L^3$-Net self-supervised training) & 0.409 & 0.330 | |
| \end{tabular} | |
| \end{center} | |
| \caption{{\bf Clustering quality.} | |
| Normalized Mutual Information (NMI) score between the unsupervised clusterings of | |
| feature embeddings and the Kinetics-Sounds labels. | |
| } | |
| \label{tab:nmi} | |
| \end{table} | |
| } | |
| \begin{abstract} | |
| We consider the question: what can be learnt by looking at and listening to | |
| a large number of unlabelled videos? There is a | |
| valuable, but so far untapped, source of information | |
| contained in the video itself -- the correspondence between the visual | |
| and the audio streams, and we introduce a | |
| novel ``Audio-Visual Correspondence'' learning task that | |
| makes use of this. Training visual and audio networks from scratch, | |
| without any additional supervision other than the raw unconstrained | |
| videos themselves, is shown to successfully solve this task, and, | |
| more interestingly, result in good visual and audio representations. | |
| These features set the new state-of-the-art on two sound classification | |
| benchmarks, and perform on par with the state-of-the-art self-supervised | |
| approaches on ImageNet classification. | |
| We also demonstrate that the network is able to localize objects | |
| in both modalities, as well as perform fine-grained recognition tasks. | |
| \end{abstract} | |
| \vspace{-0.4cm} | |
| \section{Introduction} | |
| Visual and audio events tend to occur together; not always but | |
| often: the movement of fingers and sound of the instrument when a | |
| piano, guitar or drum is played; lips moving and speech when talking; | |
| cars moving and engine noise when observing a street. | |
| The visual and audio events are concurrent in these cases | |
| because there is a common cause. | |
| In this paper we investigate whether we can use this simple | |
| observation to learn about the world both visually and aurally by | |
| simply watching and listening to videos. | |
| We ask the question: what can be learnt by training visual and audio | |
| networks {\em simultaneously} to predict whether visual information | |
| (a video frame) corresponds or not to audio information (a sound snippet)? | |
| This is a looser requirement than that the visual and audio | |
| events occur in sync. It only requires that there is something in | |
| the image that correlates with something in the audio clip -- a car present | |
| in the video frame, for instance, correlating with engine noise; or an exterior shot with the sound of wind. | |
| Our motivation for this work is three fold: first, as in many recent | |
| self-supervision tasks~\cite{Dosovitskiy14,Doersch15,Agrawal15,Wang15,Zhang16,Misra16,Pathak16,Noroozi16}, it | |
| is interesting to learn from a virtually infinite source of free | |
| supervision (video with visual and audio modes in this case) rather | |
| than requiring strong supervision; second, this is a possible source | |
| of supervision that an infant could use as their visual and audio | |
| capabilities develop; third, we want to know what can be learnt, and | |
| how well the networks are trained, for example in the performance of | |
| the visual and audio networks for other tasks. | |
| Of course, we are not the first to make the observation that visual | |
| and audio events co-occur, and to use their concurrence or correlation | |
| as supervision for training a network. In a series of recent and | |
| inspiring papers~\cite{Owens16,Owens16a,Aytar16,Harwath16}, the group | |
| at MIT has investigated precisely this. However, their goal is always | |
| to train a single network for one of the modes, for example, train a | |
| visual network to generate sounds in~\cite{Owens16,Owens16a}; or train | |
| an audio network to correlate with visual outputs | |
| in~\cite{Aytar16,Harwath16}, where the visual networks are pre-trained | |
| and fixed and act as a teacher. In earlier, pre deep-learning, | |
| approaches the observation was used to beautiful effect | |
| in~\cite{Kidron05} showing ``pixels that sound'' (\eg for a guitar) | |
| learnt using CCA. In contrast, we train both visual and audio networks and, | |
| somewhat surprisingly, show that this is beneficial -- in that our | |
| performance improves substantially over that of~\cite{Aytar16} when | |
| trained on the same data. | |
| In summary: our goal is to design a system that is able to learn both | |
| visual and audio semantic information in a completely unsupervised | |
| manner by simply looking at and listening to a large number of | |
| unlabelled videos. To achieve this we introduce a novel | |
| \emph{Audio-Visual Correspondence (AVC)} learning task that is used | |
| to train the two (visual and audio) networks from scratch. | |
| This task is described | |
| in section~\ref{sec:approach}, together with the network architecture | |
| and training procedure. In section~\ref{sec:results} we describe what | |
| semantic information has been learnt, and assess the performance of | |
| the audio and visual networks. We find, which we had not anticipated, | |
| that this task leads to quite fine grained visual and audio | |
| discrimination, \eg into different instruments. In terms of | |
| quantitative performance, the audio network exceed those recently | |
| trained for audio recognition using visual supervision, | |
| and the visual network has similar | |
| performance to those trained for other, purely visual, | |
| self-supervision tasks. Furthermore, we show, as an added benefit, that we are | |
| able to {\em localize} the source of the audio event in the video frame (and | |
| also localize | |
| the corresponding regions of the sound source) using activation | |
| visualization. | |
| In terms of prior work, the most closely related deep learning | |
| approach that we know of is `SyncNet' in~\cite{Chung16a}. However, | |
| \cite{Chung16a}~is aimed at learning to synchronize lip-regions and speech | |
| for lip-reading, rather than the more general video and audio material | |
| considered here for learning semantic representations. | |
| More generally, the AVC task is a form of co-training~\cite{Blum98}, where there | |
| are two `views' of the data, and each view provides complementary information. | |
| In our case the two views are visual and audio (and each can determine | |
| semantic information independently). A similar scenario arises when | |
| the two views are visual and language (text) as | |
| in~\cite{Frome13,Lei15,Socher13} where a common embedding is learnt. However, usually | |
| one (or both) of the networks (for images and text) are pre-trained, in contrast to the | |
| approach taken here where no supervision is required and both | |
| networks are trained from scratch. | |
| \section{Audio-visual correspondence learning} | |
| \label{sec:approach} | |
| \label{sec:avc} | |
| The core idea is to use a valuable but so far | |
| untapped source of information contained in the video itself -- | |
| the correspondence between visual and audio streams available by | |
| virtue of them appearing together at the same time in the same video. | |
| By seeing and hearing many examples of a person playing a violin | |
| and examples of a dog barking, and never, or at least very infrequently, | |
| seeing a violin being played while hearing a dog bark and vice versa, | |
| it should be possible to conclude what a violin and a dog look and sound | |
| like, without ever being explicitly taught what is a violin or a dog. | |
| \figTask | |
| We leverage this for learning by an | |
| \emph{audio-visual correspondence (AVC)} | |
| task, illustrated in Figure \ref{fig:task}. | |
| The AVC task is a simple binary classification task: | |
| given an example video frame and a short audio clip -- decide whether | |
| they correspond to each other or not. The corresponding (positive) pairs | |
| are the ones that are taken at the same time from the same video, | |
| while mismatched (negative) pairs are extracted from different videos. | |
| The only way for a system to solve this task is if it learns | |
| to detect various semantic concepts in both the visual and the audio domain. | |
| Indeed, we demonstrate in Section \ref{sec:resqual} that our network | |
| automatically learns relevant semantic concepts in both modalities. | |
| It should be noted that the task is very difficult. | |
| The network is made to learn visual and audio features and concepts from | |
| scratch without ever seeing a single label. | |
| Furthermore, the AVC task itself is quite hard when done on | |
| completely unconstrained videos -- videos can be very noisy, | |
| the audio source is not necessarily visible in the video | |
| (\eg camera operator speaking, person narrating the video, | |
| sound source out of view or occluded, \etc), | |
| and the audio and visual content can be completely unrelated | |
| (\eg edited videos with added music, very low volume sound, | |
| ambient sound such as wind dominating the audio track despite | |
| other audio events being present, \etc). | |
| Nevertheless, the results in Section \ref{sec:results} show that our network | |
| is able to fairly successfully solve the AVC task, and in the process | |
| learn very good visual and audio representations. | |
| \subsection{Network architecture} | |
| \label{sec:network} | |
| \figNet | |
| To tackle the AVC task, we propose the | |
| network structure shown in Figure \ref{fig:net}. | |
| It has three distinct parts: the vision and the audio subnetworks | |
| which extract visual and audio features, respectively, | |
| and the fusion network which takes these features into account to | |
| produce the final decision on whether the visual and audio signals correspond. | |
| Here we describe the three parts in more detail. | |
| \paragraph{Vision subnetwork.} | |
| The input to the vision subnetwork is a $224 \times 224$ colour image. | |
| We follow the VGG-network \cite{Simonyan15} design style, | |
| with $3 \times 3$ convolutional filters, | |
| and $2 \times 2$ max-pooling layers with stride $2$ and no padding. | |
| The network can be segmented into four blocks of conv+conv+pool layers | |
| such that inside each block the two conv layers have the same number of filters, | |
| while consecutive blocks have doubling filter numbers: 64, 128, 256 and 512. | |
| At the very end, max-pooling is performed across all spatial locations | |
| to produce a single 512-D feature vector. | |
| Each conv layer is followed by batch normalization \cite{Ioffe15} and | |
| a ReLU nonlinearity. | |
| \paragraph{Audio subnetwork.} | |
| The input to the audio subnetwork is a 1 second sound clip converted | |
| into a log-spectrogram (more details are provided later in this section), | |
| which is thereafter treated as a greyscale | |
| $257 \times 199$ image. | |
| The architecture of the audio subnetwork is identical to the vision one | |
| with the exception that input pixels are 1-D intensities instead of 3-D | |
| colours and therefore the \texttt{conv1\_1} filter sizes are $3\times$ smaller | |
| compared to the vision subnetwork. The final audio feature is also 512-D. | |
| \paragraph{Fusion network.} | |
| The two 512-D visual and audio features are concatenated into a 1024-D | |
| vector which is passed through the fusion network to produce a | |
| 2-way classification output, namely, whether the vision and audio correspond | |
| or not. | |
| It consists of two fully connected layers, with ReLU in between them, | |
| and the intermediate feature size of 128-D. | |
| \subsection{Implementation details} | |
| \paragraph{Training data sampling.} | |
| A non-corresponding frame-audio pair is compiled by randomly sampling two | |
| different videos and picking a random frame from one and a random 1 second | |
| audio clip from the other. | |
| A corresponding frame-audio pair is created by sampling a random video, | |
| picking a random frame in that video, and then picking a random | |
| 1 second audio clip that overlaps in time with the sampled frame. | |
| This provides additional training samples compared to simply sampling | |
| the 1 second audio with the frame at its mid-point. | |
| We use standard data augmentation techniques for images: | |
| each training image is uniformly scaled such that the smallest | |
| dimension is equal to 256, followed by random cropping into $224 \times 224$, | |
| random horizontal flipping, and brightness and saturation jittering. | |
| Audio is only augmented by changing the volume up to 10\% randomly but | |
| consistently across the sample. | |
| \paragraph{Log-spectrogram computation.} | |
| The 1 second audio is resampled to 48 kHz, and a spectrogram is computed | |
| with window length of 0.01 seconds and a half-window overlap; | |
| this produces 199 windows with 257 frequency bands. | |
| The response map is passed through a logarithm | |
| before feeding it into the | |
| audio subnetwork. | |
| \paragraph{Training procedure.} | |
| We use the Adam optimizer \cite{Kingma15}, weight decay $10^{-5}$, | |
| and perform a grid search on the learning rate, although $10^{-4}$ | |
| usually works well. | |
| The network was trained on 16 GPUs in parallel with synchronous training | |
| implemented in TensorFlow, where each worker processed a 16-element batch, | |
| thus making the effective batch size of 256. | |
| For a training set of 400k 10 second videos, the network is trained | |
| for two days, during which it has seen 60M frame-audio pairs. | |
| \section{Results and discussion} | |
| \label{sec:results} | |
| Our ``look, listen and learn'' network ($L^3$-Net) approach is evaluated and examined | |
| in multiple ways. | |
| First, the performance of the network on the audio-visual correspondence | |
| task itself is investigated, and compared to supervised baselines. | |
| Second, the quality of the learnt visual and audio features is tested | |
| in a transfer learning setting, on visual and audio classification tasks. | |
| Finally, we perform a qualitative analysis of what the network has learnt. | |
| We start by introducing the datasets used for training. | |
| \subsection{Datasets} | |
| \label{sec:datasets} | |
| Two video datasets are used for training the networks: | |
| Flickr-SoundNet and Kinetics-Sounds. | |
| \paragraph{Flickr-SoundNet \cite{Aytar16}.} | |
| This is a large unlabelled dataset of completely unconstrained videos | |
| from Flickr, compiled by searching for popular tags, but no tags | |
| or any sort of additional information apart from the videos themselves | |
| are used. | |
| It contains over 2 million videos but for practical reasons we | |
| use a random subset of 500k videos | |
| (400k training, 50k validation and 50k test) | |
| and only use the first 10 seconds of each video. | |
| This is the dataset that is used for training the $L^3$-Net | |
| for the transfer learning experiments in | |
| Sections \ref{sec:resaudio} and \ref{sec:resvisual}. | |
| \paragraph{Kinetics-Sounds.} | |
| While our goal is to learn from completely unconstrained videos, | |
| having a labelled dataset is useful for quantitative evaluation. | |
| For this purpose we took a subset (much smaller than Flickr-SoundNet) of | |
| the Kinetics dataset \cite{Kay17}, which contains YouTube videos | |
| manually annotated for human actions using Mechanical Turk, and cropped | |
| to 10 seconds around the action. | |
| The subset contains 19k 10 second video clips | |
| (15k training, 1.9k validation, 1.9k test) | |
| formed by filtering the Kinetics dataset for 34 human action | |
| classes, which have been chosen to be potentially manifested | |
| visually and aurally, such as | |
| playing various instruments (guitar, violin, xylophone, \etc), | |
| using tools (lawn mowing, shovelling snow, \etc), | |
| as well as performing miscellaneous actions | |
| (tap dancing, bowling, laughing, singing, blowing nose, \etc); | |
| the full list is given in appendix \ref{sec:kineticssounds}. | |
| Although this dataset is fairly clean by construction, it still contains | |
| considerable noise, \eg the bowling action is often accompanied by loud music | |
| at the bowling alley, human voices (camera operators or video narrations) | |
| often masks the sound of interest, and many videos contain sound tracks | |
| that are completely unrelated to the visual content | |
| (\eg music montage for a snow shovelling video). | |
| \subsection{Audio-visual correspondence} | |
| First we evaluate the performance of our method on the task it was trained | |
| to solve -- deciding whether a frame and a 1 second audio clip | |
| correspond (Section \ref{sec:avc}). | |
| For the Kinetics-Sounds dataset which contains labelled videos, | |
| we also evaluate two supervised baselines in order to gauge how well | |
| the AVC training compares to supervised training. | |
| \paragraph{Supervised baselines.} | |
| For both baselines we first train vision and audio networks independently | |
| on the action classification task, and then combine them in two different ways. | |
| The vision network has an identical feature extraction trunk as our vision | |
| subnetwork (Section \ref{sec:network}), | |
| on top of which two fully connected layers | |
| are attached (sizes: $512 \times 128$ and $128 \times 34$) to perform | |
| classification into the 34 Kinetics-Sounds classes. | |
| The audio classification network is constructed analogously. | |
| The {\bf direct combination} baseline computes the audio-video correspondence | |
| score as the similarity of class score distributions of the two networks, | |
| computed as the scalar product between the 34-D network softmax outputs, | |
| and decides that audio and video are in correspondence if the score is larger | |
| than a threshold. | |
| The motivation behind this baseline is that if the vision network believes the | |
| frame contains a dog while the audio network is confident it hears a violin, | |
| then the (frame, audio) pair is unlikely to be in correspondence. | |
| The {\bf supervised pretraining} baseline takes the feature extraction | |
| trunks from the two trained networks, assembles them into our network | |
| architecture by concatenating the features and adding two fully connected | |
| layers (Section \ref{sec:network}). The weights of the feature extractors | |
| are frozen and the fully connected layers are trained on the AVC task | |
| in the same manner as our network. | |
| This is the strongest baseline as it directly corresponds to our method, | |
| but with features learnt in a fully supervised manner. | |
| \tabAvc | |
| \paragraph{Results and discussion.} | |
| Table \ref{tab:avc} shows the results on the AVC task. | |
| The $L^3$-Net achieves 74\% and 78\% on the two datasets, where chance is 50\%. | |
| It should be noted that the task itself is quite hard due to the | |
| unconstrained nature of the videos (Section \ref{sec:avc}), | |
| as well as due to the very local input data which lacks context -- | |
| even humans find it hard to judge whether an isolated frame and | |
| an isolated single second of audio correspond; | |
| informal human tests indicated that humans are only a few percent better than the $L^3$-Net. | |
| Furthermore, the supervised baselines do not beat the $L^3$-Net | |
| as ``supervised pretraining'' performs on par with it, while | |
| ``supervised direct combination'' works significantly worse | |
| as, unlike ``supervised pretraining'', it has not been trained for the AVC task. | |
| \subsection{Audio features} | |
| \label{sec:resaudio} | |
| In this section we evaluate the power of the audio representation that emerges | |
| from the $L^3$-Net approach. | |
| Namely, the $L^3$-Net audio subnetwork trained on Flickr-SoundNet | |
| is used to extract features from | |
| 1 second audio clips, and the effectiveness of these features is evaluated | |
| on two standard sound classification benchmarks: ESC-50 and DCASE. | |
| \paragraph{Environmental sound classification (ESC-50) \cite{Piczak15}.} | |
| This dataset contains 2000 audio clips, 5 seconds each, equally balanced | |
| between 50 classes. These include animal sounds, natural soundscapes, | |
| human non-speech sounds, interior/domestic sounds, and exterior/urban noises. | |
| The data is split into 5 predefined folds and performance is measured in terms | |
| of mean accuracy over 5 leave-one-fold-out evaluations. | |
| \paragraph{Detection and classification of acoustic scenes and events (DCASE) \cite{Stowell15}.} | |
| We consider the scene classification task of the challenge which contains | |
| 10 classes | |
| (bus, busy street, office, open air market, park, quiet street, | |
| restaurant, supermarket, tube, tube station), | |
| with 10 training and 100 test clips per class, where each clip is 30 seconds long. | |
| \tabSound | |
| \tabImageNet | |
| \paragraph{Experimental procedure.} | |
| To enable a fair direct comparison with the current state-of-the-art, | |
| Aytar \etal \cite{Aytar16}, | |
| we follow the same experimental setup. | |
| Multiple overlapping subclips are extracted from each recording and described | |
| using our features. For 5 second recordings from ESC-50 we extract 10 | |
| equally spaced 1 second subclips, while for the 6 times longer DCASE recordings, | |
| 60 subclips are extracted per clip. | |
| The audio features are obtained by max-pooling the last convolutional layer | |
| of the audio subnetwork (\texttt{conv4\_2}), before the ReLU, | |
| into a $4 \times 3 \times 512 = 6144$ dimensional representation | |
| (the \texttt{conv4\_2} outputs are originally $16 \times 12 \times 512$). | |
| The features are preprocessed using z-score normalization, \ie shifted and | |
| scaled to have a zero mean and unit variance. | |
| A multi-class one-vs-all linear SVM is trained, and at test time the class scores | |
| for a recording are computed as the mean over the class scores for its subclips. | |
| \paragraph{Results and discussion.} | |
| Table \ref{tab:sound} shows | |
| the results on ESC-50 and DCASE. On both benchmarks we convincingly beat the previous | |
| state-of-the-art, SoundNet \cite{Aytar16}, by 5.1\% and 5\% absolute. | |
| For ESC-50 we reduce the gap between the previous best result and the human | |
| performance by 72\% while for DCASE we reduce the error by 42\%. | |
| The results are especially impressive as SoundNet uses | |
| two vision networks trained in a fully supervised manner on | |
| ImageNet and Places2 as teachers for the audio network, while we learn | |
| both the vision and the audio networks without any supervision whatsoever. | |
| Note that we train our networks with a random subset of the SoundNet videos | |
| for efficiency purposes, so it is possible that further gains can be achieved | |
| by using all the available training data. | |
| \subsection{Visual features} | |
| \label{sec:resvisual} | |
| \label{sec:imagenet} | |
| In this section we evaluate the power of the visual representation that emerges | |
| from the $L^3$-Net approach. | |
| Namely, the $L^3$-Net vision subnetwork trained on Flickr-SoundNet | |
| is used to extract features from | |
| images, and the effectiveness of these features is evaluated | |
| on the ImageNet large scale visual recognition challenge 2012 \cite{Russakovsky15}. | |
| \paragraph{Experimental procedure.} | |
| We follow the experimental setup of Zhang \etal \cite{Zhang16} | |
| where features are extracted from $256 \times 256$ images | |
| and used to | |
| perform linear classification on ImageNet. | |
| As in \cite{Zhang16}, | |
| we take \texttt{conv4\_2} features after ReLU and perform max-pooling | |
| with equal kernel and stride sizes until feature dimensionality is below 10k; | |
| in our case this results in $4 \times 4 \times 512 = 8192$-D features. | |
| A single fully connected layer is added to perform linear classification | |
| into the 1000 ImageNet classes. All the weights are frozen to their $L^3$-Net-trained | |
| values, apart from the final classification layer which is trained with | |
| cross-entropy loss on the ImageNet training set. | |
| The training procedure | |
| (data augmentation, learning rate schedule, label smoothing) | |
| is identical to \cite{Szegedy16}, the only differences being that we use | |
| the Adam optimizer instead of RMSprop, | |
| and a $256 \times 256$ input image instead of $299 \times 299$ | |
| as it fits our architecture better and to be consistent with \cite{Zhang16}. | |
| \figUnitsV | |
| \figUnitsVmap | |
| \afterpage{\FloatBarrier} | |
| \paragraph{Results and discussion.} | |
| Classification accuracy on the ImageNet validation set is shown in | |
| Table \ref{tab:imagenet} and contrasted with other unsupervised and | |
| self-supervised methods. | |
| We also test the performance of random features, | |
| \ie our $L^3$-Net architecture without AVC training but with a trained classification | |
| layer. | |
| Our $L^3$-Net-trained features achieve 32.3\% accuracy which is on par with other | |
| state-of-the-art self-supervised methods of \cite{Doersch15,Zhang16,Donahue17,Noroozi16}, | |
| while convincingly beating random initialization, | |
| data-dependent initialization \cite{Krahenbuhl15}, and | |
| Context Encoders \cite{Pathak16}. | |
| It should be noted that these methods use the AlexNet \cite{Krizhevsky12} architecture | |
| which is different to ours, | |
| so the results are not fully comparable. | |
| On the one hand, our architecture when trained from scratch in its entirety | |
| achieves a higher performance (59.2\% vs AlexNet's 51.0\%). | |
| On the other hand, it is deeper which makes it harder to train | |
| as can be seen from the fact that our random features perform worse | |
| than theirs (12.9\% vs AlexNet's 18.3\%), and that all competing methods hit peak | |
| performance when they use earlier layers | |
| (\eg \cite{Donahue17} drops from 31.0\% to 27.1\% | |
| when going from \texttt{conv3} to \texttt{pool5}). | |
| In fact, when measuring the improvement achieved due to AVC or self-supervised | |
| training versus | |
| the performance of the network with random initialization, | |
| our AVC training beats all competitors. | |
| Another important fact to consider is that \emph{all} | |
| competing methods actually use ImageNet images when | |
| training. Although they do not make use of the labels, | |
| the underlying image statistics are the same: | |
| objects are fairly central in the image, and the networks have seen, | |
| for example, abundant images of 120 breads of dogs and thus potentially | |
| learnt their distinguishing features. | |
| In contrast, we use a completely separate source of training data in the | |
| form of frames from Flickr videos -- here the objects are in general not | |
| centred, it is likely that the network has never seen a ``Tibetan terrier'' | |
| nor the majority of other fine-grained categories. | |
| Furthermore, video frames have vastly different low-level statistics to | |
| still images, with strong artefacts such as motion blur. | |
| With these factors hampering our network, it is impressive that our | |
| visual features $L^3$-Net-trained on Flickr videos | |
| perform on par with self-supervised state-of-the-art trained on ImageNet. | |
| \subsection{Qualitative analysis} | |
| \label{sec:resqual} | |
| In this section we analyse what is it that the network has learnt. | |
| We visualize the results on the test set of the Kinetics-Sounds and Flickr-SoundNet datasets, | |
| so the network has not seen the videos during training. | |
| \subsubsection{Vision features} | |
| To probe what the vision subnetwork has learnt, we pick a particular `unit' | |
| in \texttt{pool4} (\ie a component of the 512 dimensional \texttt{pool4} vector) and rank the test images by its magnitude. | |
| Figure \ref{fig:unitsV} shows the images from Kinetics-Sounds that activate particular | |
| units in \texttt{pool4} the most (\ie are ranked highest by its magnitude). As can be | |
| seen, the vision subnetwork has automatically learnt, without any | |
| explicit supervision, to recognize semantic entities such as guitars, | |
| accordions, keyboards, clarinets, bowling alleys, lawns or lawnmowers, | |
| \etc. Furthermore, it has learnt finer-grained categories as well as | |
| it is able to distinguish between acoustic and bass guitars | |
| (``fingerpicking'' is mostly associated with acoustic guitars). | |
| Figure \ref{fig:unitsVmap} shows heatmaps for the Kinetics-Sounds images in | |
| Figure~\ref{fig:unitsV}, obtained by simply displaying the spatial | |
| activations of the corresponding vision unit (\ie if the $k$ component of | |
| \texttt{pool4} is chosen, then the $k$ channel of \texttt{conv4\_2} is displayed -- since the $k$ component is just the spatial max over this channel | |
| (after ReLU)). | |
| Objects are successfully | |
| detected despite significant clutter and occlusions. It is | |
| interesting to observe the type of cues that the network decides to | |
| use, \eg the ``playing clarinet'' unit, instead of trying to detect | |
| the entire clarinet, seems to mostly activate on the interface between | |
| the player's face and the clarinet. | |
| Figures \ref{fig:unitsVSoundNetA} and \ref{fig:unitsVSoundNetB} show | |
| visual concepts learnt by the $L^3$-Net on the Flickr-SoundNet dataset. | |
| It can be seen that the network learns to recognize many scene categories | |
| (Figure \ref{fig:unitsVSoundNetA}), | |
| such as outdoors, concert, water, sky, crowd, text, railway, \etc. | |
| These are useful for the AVC task as, for example, crowds indicate a large | |
| event that is associated with a distinctive sound as well | |
| (\eg a football game), text indicates narration, and outdoors scenes are | |
| likely to be accompanied with wind sounds. | |
| It should be noted that though at first sight some categories seem trivially | |
| detectable, it is not the case; for example, ``sky'' detector is not equivalent to | |
| the ``blueness'' detector as it only fires on ``sky'' and not on ``water'', | |
| and furthermore there are separate units sensitive to ``water surface'' | |
| and to ``underwater'' scenes. | |
| The network also learns to detect people as user uploaded content is | |
| substantially people-oriented -- Figure \ref{fig:unitsVSoundNetB} shows | |
| the network has learnt to distinguish between babies, adults and crowds. | |
| \figUnitsVSoundNetA | |
| \subsubsection{Audio features} | |
| Figure \ref{fig:unitsA} shows what particular audio units | |
| are sensitive to in the Kinetics-Sounds dataset. | |
| For visualization purposes, instead of showing | |
| the sound form, we display the video frame that corresponds to the sound. | |
| It can be seen that the audio subnetwork, again without any supervision, | |
| manages to learn various semantic entities, as well as perform fine-grained | |
| classification (``fingerpicking'' vs ``playing bass guitar''). | |
| Note that some units are naturally confused -- the ``tap dancing'' unit also | |
| responds to ``pen tapping'', while the ``saxophone'' unit is sometimes | |
| confused with a ``trombone''. These are reasonable mistakes, especially | |
| when taking into account that the sound input is only one second in length. | |
| The audio concepts learnt on the Flickr-SoundNet dataset (Figure \ref{fig:unitsASoundNetA}) follow | |
| the same pattern as the visual ones -- the network learns to distinguish | |
| various scene categories such as water, underwater, outdoors and windy scenes, | |
| as well as human-related concepts like baby and human voices, crowds, \etc. | |
| Figure \ref{fig:unitsAmap} shows spectrograms and their semantic heatmaps, | |
| illustrating that our $L^3$-Net learns to detect audio events. | |
| For example, it shows clear preference for low frequencies when detecting | |
| bass guitars, attention to wide frequency range when detecting lawnmowers, | |
| and temporal `steps' when detecting fingerpicking and tap dancing. | |
| \figUnitsVSoundNetB | |
| \figUnitsA | |
| \figUnitsASoundNetA | |
| \afterpage{\FloatBarrier} | |
| \subsubsection{Versus random features} | |
| Could the results in Figures | |
| \ref{fig:unitsV}, | |
| \ref{fig:unitsVmap}, | |
| \ref{fig:unitsVSoundNetA}, | |
| \ref{fig:unitsVSoundNetB}, | |
| \ref{fig:unitsA} | |
| \ref{fig:unitsASoundNetA}, | |
| and | |
| \ref{fig:unitsAmap} | |
| simply be obtained by chance due to examining a large number of units, | |
| as colourfully illustrated by the dead salmon experiment | |
| \cite{Bennett09}? It is unlikely as there are only 512 units in | |
| \texttt{pool4} to choose from, and many of those were found to be | |
| highly correlated with a semantic concept. Nevertheless, we repeated | |
| the same experiment with a random network (\ie a network that has not | |
| been trained), and have failed to find such correlation. In more | |
| detail, we examined how many out of the action classes in | |
| Kinetics-Sounds have a unit in \texttt{pool4} which shows high | |
| preference for the class. For the vision subnetwork the preference is | |
| determined by ranking all images by their unit activation, and | |
| retaining the top 5; if 4 out of these 5 images correspond to | |
| one class, then that class is deemed to have a high-preference for the | |
| unit (a similar procedure is carried out for the audio subnetwork | |
| using spectrograms). Our trained vision and audio networks have | |
| high-preference units for 10 and 11 out of a possible 34 action | |
| classes, respectively, compared to 1 and 1 for the random vision and | |
| audio networks. Furthermore, if the threshold for deeming a unit to | |
| be high-preference is reduced to 3, our trained vision and audio | |
| subnetworks cover 23 and 20 classes, respectively, compared to the 4 | |
| and 3 of a random network, respectively. These results confirm that | |
| our network has indeed learnt semantic features. | |
| Furthermore, | |
| Figure \ref{fig:tsne} shows the comparison between the trained and the | |
| non-trained (\ie network with random weights) $L^3$-Net representations | |
| for the visual and the audio modalities, on the Kinetics-Sounds dataset, | |
| using the t-SNE visualization \cite{Van-der-Maaten08}. | |
| It is clear that training for the audio-visual correspondence task | |
| produces representations that have a semantic meaning, as videos containing | |
| the same action classes | |
| often cluster together, while the random network's representations do not | |
| exhibit any clustering. | |
| There is still a fair amount of confusion in the representations, | |
| but this is expected as no class-level supervision is provided | |
| and classes can be very alike. | |
| For example, an organ and a piano are quite visually similar as they | |
| contain keyboards, and the visual difference between a bass guitar and an | |
| acoustic guitar is also quite fine; these similarities are reflected | |
| in the closeness or overlap of respective clusters in | |
| Figure \ref{fig:tsne}(c) | |
| (\eg as noted earlier, ``fingerpicking'' is mostly associated with acoustic guitars). | |
| \tabNMI | |
| We also evaluate the quality of the $L^3$-Net embeddings by clustering them | |
| with k-means into 64 clusters and reporting the Normalized Mutual Information | |
| (NMI) score between the clusters and the ground truth action classes. | |
| Results in Table \ref{tab:nmi} confirm the emergence of semantics as | |
| the $L^3$-Net embeddings outperform the best random baselines by 50-100\%. | |
| The t-SNE visualization also shows some interesting features, | |
| such as the ``typing'' class being divided into two clusters in the visual | |
| domain. Further investigation reveals that all frames in one cluster | |
| show both a keyboard and hands, while the second cluster contains | |
| much fewer hands. Separating these two cases can be a good indication of | |
| whether the typing action is happening at the moment captured by the | |
| (frame, 1 second sound clip) pair, and thus whether the typing sound | |
| is expected to be heard. Furthermore, we found that the ``typing'' | |
| audio samples appear in three clusters -- the two fairly pure clusters | |
| (outlined in Figure \ref{fig:tsne}(a)) correspond to strong typing sounds | |
| and talking while typing, respectively, and the remaining cluster, | |
| which is very impure and intermingled with other action classes, | |
| mostly corresponds to silence and background noise. | |
| \figUnitsAmap | |
| \figTsne | |
| \section{Discussion} | |
| We have shown that the network trained for the AVC task achieves | |
| superior results on sound classification to recent methods | |
| that pre-train and fix the visual networks (one each for ImageNet and Scenes), | |
| and we conjecture that the reason for this is that the additional freedom | |
| of the visual network allows the learning to better take advantage of the | |
| opportunities offered by the variety of visual information in the video | |
| (rather than be restricted to seeing only through the eyes of the pre-trained | |
| network). | |
| Also, the visual features that emerge from the $L^3$-Net | |
| are on par with the state-of-the-art among self-supervised approaches. | |
| Furthermore, | |
| it has been demonstrated that the network automatically learns, | |
| in both modalities, | |
| fine-grained distinctions such as bass versus acoustic guitar or | |
| saxophone versus clarinet. | |
| The localization visualization results are reminiscent of the classic | |
| highlighted pixels in~\cite{Kidron05}, except in our case we do not just learn | |
| the few pixels that move (concurrent with the sound) but instead are able | |
| to learn extended regions corresponding to the instrument. | |
| We motivated this work by considering {\em correlation} of video and audio | |
| events. However, we believe there is additional information in \emph{concurrency} | |
| of the two streams, as concurrency is stronger than correlation | |
| because the events need to be | |
| synchronised (of course, if events are concurrent then they will | |
| correlate, but not vice versa). Training for concurrency will require | |
| video (multiple frames) as input, rather than a single video frame, | |
| but it would be interesting to explore what more is gained from this | |
| stronger condition. | |
| In the future, | |
| it would be interesting to learn | |
| from the recently released large dataset of videos curated according to | |
| {\em audio}, rather than visual, events~\cite{Gemmeke17} and see what subtle | |
| visual semantic categories are discovered. | |
| {\small | |
| \bibliographystyle{ieee} | |
| \bibliography{bib/shortstrings,bib/vgg_local,bib/vgg_other,bib/to_add} | |
| } | |
| \appendix | |
| \section{Kinetics-Sounds} | |
| \label{sec:kineticssounds} | |
| The 34 action classes taken from the Kinetics dataset \cite{Kay17} to form | |
| the Kinetics-Sounds dataset \ref{sec:datasets} are: | |
| blowing nose, | |
| bowling, | |
| chopping wood, | |
| ripping paper, | |
| shuffling cards, | |
| singing, | |
| tapping pen, | |
| typing, | |
| blowing out, | |
| dribbling ball, | |
| laughing, | |
| mowing the lawn by pushing lawnmower, | |
| shoveling snow, | |
| stomping, | |
| tap dancing, | |
| tapping guitar, | |
| tickling, | |
| fingerpicking, | |
| patting, | |
| playing accordion, | |
| playing bagpipes, | |
| playing bass guitar, | |
| playing clarinet, | |
| playing drums, | |
| playing guitar, | |
| playing harmonica, | |
| playing keyboard, | |
| playing organ, | |
| playing piano, | |
| playing saxophone, | |
| playing trombone, | |
| playing trumpet, | |
| playing violin, | |
| playing xylophone. | |
| \end{document} | |