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\begin{document}

\mainmatter  % start of an individual contribution

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\title{CS224W Project Report - \\ Understanding Human Actions: An Information Network Perspective of View}

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\titlerunning{CS224W Project Milestone}

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\author{Bangpeng Yao, Bo Wang, Cherhan Foo}
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\institute{Stanford University}

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\toctitle{Lecture Notes in Computer Science}
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\section{Introduction}

Understanding human actions is an important yet challenging problem in many research fields, such as psychology~\cite{Gentner82}, linguistics, and computer vision~\cite{Yao11}. Comparing with object recognition where researchers have started building hierarchical structures for millions of classes in both natural language processing~\cite{Fellbaum98} and computer vision~\cite{Deng09}, little is known about the nature of human actions (e.g. what makes an action to be ``riding a horse''?) as well as the relationship between different actions (intuitively, ``playing guitar'' should be functionally more similar to ``playing violin'' than ``drinking water''). Interestingly, this follows a similar story as the evolution of human brain and languages. It has been reported that infants learn nouns much earlier than verbs~\cite{Gentner82}. One hypothesis is that most of the nouns, which corresponds to objects, refer to existing things and entities in the world that are easily accessible to humans. The verbs, which are closely related to actions, on the other hand, tend to describe the concepts that are more abstract and more diffuse in the feature space.

\begin{figure}[b]
\centering
\includegraphics[width=0.98\linewidth]{./figure/continuous_space.eps}
\caption{Human actions lie in a continuous space, where the human pose can change continuously and convey different meanings.}\label{fig:continuous_space}
\end{figure}

In the field of computer vision, researchers usually regard action understanding as a pure classification problem~\cite{Yao11}. Typically, people have a list of pre-defined actions, and then collect a set of images and annotate the humans that are doing one of the actions. Then the only goal is to design a classifier which is able to automatically assign a class label to each human image.

However, the tasks of understanding human actions is more complicated than simply performing classification. As shown in Figure~\ref{fig:continuous_space}, human actions lie in a continuous space where one action can be more closer to a second action than the others. Furthermore, there are relationships between different actions. As shown in Figure~\ref{fig:pose_demo}, the human poses and objects that the humans are interacting with can make one action related to the other. Furthermore, even within the same action, there are some special distributions of human poses as well as the spatial relationships between objects and humans, and those distributions are different even when the humans are interacting with the same object, as shown in Figure~\ref{fig:bike}.

\begin{figure}[t]
\centering
\includegraphics[width=0.98\linewidth]{./figure/pose_demo.eps}
\caption{Human actions are highly related to human poses and objects. In the left two images, ``riding bike'' and ``riding horse'' are functionally related and the humans have similar poses. In the right two images, both humans are ``playing tennis''. The poses of the two humans are very different, but they both interact with the same object (tennis racket).}\label{fig:pose_demo}
\end{figure}

\begin{figure}[t]
\centering
\includegraphics[width=0.98\linewidth]{./figure/bike.eps}
\caption{The human actions can be different even when the humans interact with the same object. We observe that human poses in ``riding bike'' are usually similar, whereas the poses in ``fixing a bike'' are very different.}\label{fig:bike}
\end{figure}

In this project, we study the properties of different human actions, as well as the relationship between different action images, instead of treating action recognition as a pure classification problem with human-provided class labels. We are interested in the following three questions:
\vspace*{-3pt}
\begin{itemize}
  \item How to model an action image and measure the similarity between two images?
  \item How to measure the similarity between two action classes, hence discover a hierarchical relationship between all actions?
  \item Without class labels, how can we cluster action images in an unsupervised way?
\end{itemize}
\vspace*{-3pt}
 
Specifically, we consider each action as a collection of units of human poses and objects, and use informative network analysis approaches to achieve the above goal.


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\section{Prior Work}


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\section{Algorithm}


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\section{Results and Findings}



\begin{thebibliography}{4}

\bibitem{Activity10} American Time Use Survey (ATUS) Activity Lexicon. Bureau of Labor Statistics, 2010.

\bibitem{Bourdev09} Bourdev, L., Malik, J.: Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations. International Conference on Computer Vision (ICCV), 2009.
    
\bibitem{Deng09} Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
    
\bibitem{Fellbaum98} Fellbaum, C.: An Electronic Lexical Database. Bradford Books, 1998.

\bibitem{Gentner82} Gentner, D.: Why Nouns Are Learned Before Verbs: Linguistic Relativity Versus Natural Partitioning. Language Development, Volume 2: Language Thought and Cluture. Hillsdale, NJ, 1982.

\bibitem{Yao11} Yao, B., Jiang, X., Khosla, A., Lin, A., Guibas, L., Fei-Fei, L.: Human Action Recognition by Learining Bases of Action Attributes and Parts. Internatioinal Conference on Computer Vision (ICCV), 2011.

\end{thebibliography}

\end{document}
