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\begin{document}

\title{User-Guided Global Explanations for Deep Image Recognition: A User Study}



\author[1]{Mandana Hamidi-Haines}%
\author[2]{Zhongang Qi}%
\author[1]{Alan Paul Fern}%
\author[1]{Li Fuxin}%
\author[1]{Prasad Tadepalli}%
\affil[1]{Oregon State University}%
\affil[2]{Tencent}%


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  \date{May 17, 2024}


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\begin{abstract}
We study a user-guided approach for producing global explanations of
deep networks for image recognition. The global explanations are
produced with respect to a test data set and give the overall frequency
of different ``recognition reasons'' across the data. Each reason
corresponds to a small number of the most significant human-recognizable
visual concepts used by the network. The key challenge is that the
visual concepts cannot be predetermined and those concepts will often
not correspond to existing vocabulary or have labelled data sets. We
address this issue via an interactive-naming interface, which allows
users to freely cluster significant image regions in the data into
visually similar concepts. Our main contribution is a user study on two
visual recognition tasks. The results show that the participants were
able to produce a small number of visual concepts sufficient for
explanation and that there was significant agreement among the concepts,
and hence global explanations, produced by different participants.%
\end{abstract}%



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