\documentclass[pmlr]{jmlr}% new name PMLR (Proceedings of Machine Learning Research)

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\usepackage{longtable}% for long tables

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\usepackage[load-configurations=version-1]{siunitx} % newer version
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\newcommand{\cs}[1]{\texttt{\char`\\#1}}
\newcommand{\addcite}{\textcolor{red}{[?]}}
\newcommand{\todo}[1]{\textcolor{red}{\small [#1]}}
\newcommand{\my}[1]{\textcolor{red}{\small [#1 --my]}}
\newcommand{\gazeclr}{\textit{GazeCLR}}
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\theorembodyfont{\upshape}
\theoremheaderfont{\scshape}
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\theoremsep{\newline}
\newtheorem*{note}{Note}

 % change the arguments, as appropriate, in the following:
\jmlrvolume{1}
\jmlryear{2022}
\jmlrworkshop{NeurIPS 2022 Gaze Meets ML Workshop}

\title{Contrastive Representation Learning for Gaze Estimation}

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 % enclose the diacritic in braces, e.g. \Name{{\'E}louise Smith}

 % *** Make sure there's no spurious space before \nametag ***

 % Two authors with the same address
  \author{\Name{Swati Jindal} \Email{swjindal@ucsc.edu} \\
   \Name{Roberto Manduchi} \Email{manduchi@soe.ucsc.edu}\\
   \addr University of California, Santa Cruz\\
   Santa Cruz, CA, 95064, USA}

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%  }

\editor{Editor's name}
%   \editors{List of editors' names}

\begin{document}

\maketitle

\input{content/abstract}

\begin{keywords}
gaze estimation; representation learning; self-supervised learning
\end{keywords}


\input{content/introduction}
\input{content/proposed}
\input{content/experiments}
\input{content/relatedwork}

\section{Conclusion}

We presented \gazeclr{}, a contrastive learning framework for gaze representations using multi-view camera images. Our framework induces invariance and equivariance properties simultaneously in the learned representations and is effective for gaze estimation task in various settings. Furthermore, we showed that \gazeclr{} representations have the potential to be effective across different domain datasets using only a few calibration samples. \gazeclr{} is a general framework for equivariant representation learning and thus can be explored in the future for other geometry-based applications such as head pose estimation.


\acks{This research was supported by the National Eye Institute of the National Institutes of Health (NIH) under award number R01EY030952-01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank Mohit Yadav for the helpful discussions.
}

\bibliography{pmlr-sample}

\appendix

\input{content/supplementary}

\end{document}
