Improving Object Detection in Art Images Using Only Style Transfer \thanks{The research presented here has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727040 (the GIFT project).
\IEEEauthorblockN{David Kadish
Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object's texture over its shape. In this paper we propose and evaluate a process for training neural networks to localize objects --- specifically people --- in art images. We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer. This dataset is used to fine-tune a Faster R-CNN object detection network, which is then tested on the existing \gls{pa testing dataset. The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.
glossary.tex
\newacronym{dpm}{DPM}{Deformable Parts Model}
\newacronym{mil}{MIL}{Multiple Instance Learning}
\newacronym{wsod}{WSOD}{Weakly Supervised Object Detection}

\newacronym{ai}{AI}{artificial intelligence}


% Datasets
% \newacronym{pa}{PA}{People-Art}
\newglossaryentry{pa}{
    name={People-Art},
    description={DEF}
}
\newacronym{sin}{SIN}{Stylized-ImageNet}
\newglossaryentry{sc}{
    name={StyleCOCO},
    description={DEF}
}

% Metrics
\newacronym{map}{mAP}{mean average precision}
\newacronym{iou}{IoU}{intersection over union}
\newglossaryentry{ap}{
    name={AP},
    description={COCO mAP metric}
}
\newglossaryentry{ap5}{
    name={\ensuremath{\textrm{AP}^{50}}},
    description={COCO mAP metric at 50\% overlap}
}
\newglossaryentry{ap75}{
    name={\ensuremath{\textrm{AP}^{75}}},
    description={COCO mAP metric at 75\% overlap}
}

% NNs
\newacronym{nn}{NN}{Neural Network}
\newacronym{cnn}{CNN}{Convolutional Neural Network}
\newacronym{rcnn}{R-CNN}{Region Based Convolutional Neural Network}
\newacronym{frcnn}{Fast R-CNN}{Fast Region Based Convolutional Neural Network}
\newacronym{frrcnn}{Faster R-CNN}{Faster Region Based Convolutional Neural Network}

\newglossaryentry{rn50}{
    name={ResNet-50},
    description={}
}
\newglossaryentry{rn152}{
    name={ResNet-152},
    description={}
}

\newacronym{sgd}{SGD}{stochastic gradient descent}

\newglossaryentry{exp_ntrain}{
    name={Experiment 1},
    description={DEF}
}
\newglossaryentry{exp_ap}{
    name={Experiment 2},
    description={DEF}
}
main.tex
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\title{Improving Object Detection in Art Images Using Only Style Transfer
\thanks{The research presented here has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727040 (the GIFT project).}
}

\author{\IEEEauthorblockN{David Kadish}
\IEEEauthorblockA{\textit{Digital Design Department} \\
\textit{IT University of Copenhagen}\\
Copenhagen, Denmark \\
davk@itu.dk}
\and
\IEEEauthorblockN{Sebastian Risi}
\IEEEauthorblockA{\textit{Digital Design Department} \\
\textit{IT University of Copenhagen}\\
Copenhagen, Denmark \\
sebr@itu.dk}
\and
\IEEEauthorblockN{Anders Sundnes Løvlie}
\IEEEauthorblockA{\textit{Digital Design Department} \\
\textit{IT University of Copenhagen}\\
Copenhagen, Denmark \\
asun@itu.dk}
}

\maketitle

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\input{sections/discussion}

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abstract.tex
\begin{abstract}
Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings.
This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object's texture over its shape.
In this paper we propose and evaluate a process for training neural networks to localize objects --- specifically people --- in art images.
We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer.
This dataset is used to fine-tune a Faster R-CNN object detection network, which is then tested on the existing \gls{pa} testing dataset.
The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.
\end{abstract}
background.tex
\section{Background}

A perpetual problem in \gls{ai} is that the creation of large-scale, high-quality datasets is time-consuming and expensive~\citep{Chang2017}.
For some tasks an effort has been made to compile a canonical dataset that can be widely used by the \gls{ai} community~\citep{Deng2010,Lin2014}; for less common tasks the challenge persists.

% Labelled datasets of art images do exist (see \cref{tab:art_dbs}), but most contain only tagged images where the image is labelled with a descriptor, but not its exact location within the image.
% These are useful for training image classifiers, but not object detectors which require localization information, often in the form of object bounding boxes.
% The Behance Artistic Media dataset (\url{https://bam-dataset.org/}) has over 2.5 million images produced by Adobe Behance users that have a mix of tags and captions~\citep{Wilber2017}.
% PhotoArt50 \citep{wu2014learning}.
% Paintings Dataset (\url{https://www.robots.ox.ac.uk/~vgg/data/paintings/}) \citep{} 10K tagged.

\begin{table}[t]
\centering
\caption{Labelled art image datasets}
\begin{tabular}{lrrrl} \toprule
\textbf{Name}      & \textbf{Images} & \textbf{Labels} & \textbf{Classes} & \textbf{Task}    \\ \midrule
Paintings Database & 10000           & 8629            & 10                  & Classification   \\
BAM                & 2500000         & 393000          & n/a                 & Classification   \\
Photo-Art-50       & $\sim$5000      & $\sim$5000      & 50                  & Classification   \\
PACS               & 9991            & n/a             & 7                   & Classification   \\
People-Art         & 4631            & 3487            & 1                   & Obj. Detection   \\ \bottomrule    
\end{tabular}
\label{tab:art_dbs}
\end{table}

The major datasets for object detection training are comprised mainly of photographs.
This leads to the cross-depiction problem~\citep{Cai2015}, where \glspl{nn} struggle to correctly identify objects depicted in different styles --- they may be able to pick out a car in a photograph, but struggle with a pencil sketch of the same vehicle.
% One approach to this is to try to make \gls{ai} systems behave more like humans.
% Humans are able to determine that something is a person whether they are looking at a person in real life, a photograph, a cartoon, a painting, or a line drawing of a stick figure.
\citeauthor{Cai2015} attempt to tackle this problem using neural networks pre-trained on a large database of photographs and fine-tuned on a smaller dataset of labelled artwork.
Achieving a maximum \gls{ap5} score of 0.4, they conclude that deep learning has yet to demonstrate that it is able to solve the cross-depiction problem.

One of the potential reasons for this is that neural networks are not good at recognizing shapes.
They base their decisions mainly on the texture of the objects that they are examining~\citep{Geirhos2018}.
While neural networks tend to be biased towards textures, humans tend to exhibit a strong bias towards shape~\citep{Geirhos2018}.
Artistic representations tend to also be shape-biased, making artwork especially difficult for most neural networks to process.

\subsection{Datasets}
The problem of detecting objects in artwork has proven to be particularly challenging.
For image classification --- categorizing the overall content of an image --- a large training set can be created using metadata like image titles, descriptions, and artist- and crowd-generated tags, as in the Behance Artistic Media (BAM) dataset ~\citep{Wilber2017}.
Object detection, however, requires training data with labelled bounding boxes which indicate where in an image an object is localized, and few sources of this type of data exist (see \cref{tab:art_dbs}).

The main data source used for object detection in artwork is the \gls{pa} dataset, originally described by \citet{Cai2015}.
\Gls{pa} consists of a set of images of artwork in which people have been labelled with bounding boxes. The dataset includes a training set, a validation set and a test set.
\Cref{tab:pa_stats} shows the breakdown of total images, positive images (containing people) and labelled people in the dataset.
The dataset encompasses a wide range of artistic styles, but is notably small for training a deep neural network.

\begin{table}[t]
\caption{Art image object detection datasets}
\begin{center}
\begin{tabular}{llrrr} \toprule
\textbf{Dataset}  & \textbf{Subset} & \textbf{Images} & \textbf{Positive} & \textbf{People} \\ \midrule
\multirow{3}{*}{People-Art} & training & 1,627 & 521 & 1,324 \\
& validation & 1,387 & 442 & 1,080 \\
& testing & 1,617 & 520 & 1,083 \\
\\
\multirow{2}{*}{StyleCOCO} & training & 58,672 & 58,672 & 239,845 \\
& validation & 2,688 & 2,688 & 10,997 \\
\bottomrule
\end{tabular}
\label{tab:pa_stats}
\end{center}
\end{table}

\subsection{Object detection}
Despite the limited availability of training data, a number of efforts have been made to detect people using deep neural networks --- and a variety of other strategies --- in the \gls{pa} test set.
\citet{Cai2015} used \glspl{rcnn} pre-trained on ImageNet and attempted fine-tuning them on the VOC2007 dataset and \gls{pa}.
\citet{Westlake2016} produced a similar attempt, using \gls{frcnn} with a variety of architectures also pre-trained on ImageNet and fine-tuned on VOC 2007 or \gls{pa}.
\citet{Redmon2016} also tested the YOLO network on \gls{pa}, in order to see how well it performed in a cross-depiction test.

Evaluating these efforts is non-trivial, but the standard metrics are a set of average precision and recall measurements that assess the performance of the detector at different levels of bounding box overlap with the labelled box.
The classic metric was devised as part of the PASCAL VOC challenge (Everingham et al. 2010) and is the \gls{map} with a 50\% overlap between bounding boxes.
The results of these trials using this metric are shown in \cref{tab:aps_from_others} and \cref{fig:ap_compare}.
The \gls{frcnn} network using a VGG16 backbone, pre-trained on ImageNet and fine-tuned on the \gls{pa} training set performed the best, scoring 0.58 in the \gls{ap5} test.

Is this a good result?
It can be difficult to know what to compare this to.
Obviously, a score of 1.0 would be desirable, but likely unrealistic.
The best score for person detection in photographs in the latest COCO detection challenge was 0.85\footnote{From \url{https://cocodataset.org/\#detection-leaderboard}, this is the Chal17 score for \texttt{person-person} category by \texttt{Megvii (Face++)}.}, but this detector was trained to look for objects in 80 different categories --- a much more difficult task than just searching for people.
Clearly there is room for improvement.
data.tex
\section{Dataset Generation using Style Transfer}
%We had been attempting to improve the object recognition rates of a \gls{frrcnn} network on a dataset of artwork when we came across \citet{Geirhos2018}.
%They found that \glspl{cnn} trained on ImageNet were biased toward identifying images by texture rather than shape, but a network trained on stylized images from the ImageNet dataset (\acrlong{sin}) could be coaxed toward learning a more shape-based representation of images.
%Given that artistic representations are often shape-oriented, we thought that using this as a backbone for a \gls{frrcnn} network might improve the performance of said network on artwork.
%
%In the end, we observed little-to-no improvement using the shape-oriented backbone, however, the work in \citet{Geirhos2018} gave us another idea.
%\citet{Geirhos2018} had applied a style transfer to the images from ImageNet to generate a set of images for training \gls{sin}.
%If we could use a similar process on a database of images that was already labelled for object detection, we could create a massive, pre-labelled dataset of images that look like artwork.

Given the assertion of \citet{Geirhos2018} that \glspl{cnn} trained on ImageNet were generally texture-biased, we first tried using their \acrlong{sin}-trained \gls{rn50} network as a backbone for a \gls{frrcnn} object detector to see if it would improve over a backbone trained on ImageNet.
This failed to yield a significant improvement, but the process inspired an alternative use of style transfer.
\citet{Geirhos2018} had applied a style transfer to the images from ImageNet to generate a set of images they call \gls{sin}.
If we could use a similar process on a database of images that was already labelled for object detection, we could create a massive, pre-labelled dataset of images that could be used for object detection in art images.

\subsection{Style transfer}
AdaIn style transfer~\citep{Huang2017} is a neural network that takes two input images and applies the style of one to the content of the other.
It is the mechanism used in \citet{Geirhos2018} to create the stylized dataset that they call \acrlong{sin}.

We follow the basic process described in \citet{Geirhos2018}, using the Painter by Numbers dataset from Kaggle\footnote{\url{https://www.kaggle.com/c/painter-by-numbers/} (accessed 14 Oct 2020).} as the source of stylized images, modifying each input image with a single style with a stylization weight of 1 and not changing the size or crop of the input files.
The stylization script used is from \url{https://github.com/bethgelab/stylize-datasets}.

For the source images, we chose the COCO 2017 dataset --- a library of 123,287 photographs with bounding box object detection labels~\citep{Lin2014}.
The resulting dataset is referred to as StyleCOCO.

\subsection{StyleCOCO}
% Characterization from https://colab.research.google.com/drive/1hCL5lU3MIM35-pS9itLwbCq9mJdIBoUV#scrollTo=x1GknLrHF-vH
% Training set: 58671 images of people with 239845 annotations.
% Validation set: 2688 images of people with 10997 annotations.
% No negative images.


\newcommand\imggridwidth{0.19\textwidth}
\begin{figure*}[h]
     \centering
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000032887-stylized-85467.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000032887_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000065798-stylized-95355.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000065798_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000319534-stylized-25570.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000319534_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000360661-stylized-65687.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000360661_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000393226-stylized-10725.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000393226_original.jpg}
         \centering
     \end{subfigure}
     \newline
    %  \vspace{0.5em}
     \newline
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000401446-stylized-57760.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000401446_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000458755-stylized-10429.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000458755_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000475191-stylized-58066.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000475191_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000508101-stylized-13502.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000508101_original.jpg}
         \centering
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/examples/000000540932-stylized-89561.jpg}
         \\
         \includegraphics[width=\textwidth]{images/examples/000000540932_original.jpg}
         \centering
     \end{subfigure}
     \hfill
        \caption{Sample images from the StyleCOCO dataset (above) paried with the original image from the COCO dataset (below)}
        \label{fig:samples}
        %TODO S: Put the art images as well...
        %TODO: Bounding boxes on some images...
\end{figure*}

StyleCOCO is a subset of the original COCO dataset~\citep{Lin2014} with images stylized to look like artwork using AdaIn style transfer.
It consists of a training set of \n{} images and a validation set of 2,688 images\footnote{The set of images is smaller than the full COCO dataset because we excluded images that did not contain a labelled person.}.
The images are annotated for object detection for the person class and bounding box and segmentation information is retained.
A total of 239,845 annotated people are found in the training set and a further 10,997 are found in the validation set.

Images from the original COCO dataset containing no people were removed from the dataset as the pixels not containing people provide sufficient negative examples for neural network training.
No testing set was stylized as the dataset was only intended for training a neural network to be tested on data from the \gls{pa} dataset.
A breakdown of the number of images and labelled people is shown in \cref{tab:pa_stats} and sample images from the StyleCOCO dataset are shown in \cref{fig:samples}.
discussion.tex

\section{Discussion}

\subsection{Failure Modes}
The leap in performance achieved with fine-tuning on \gls{sc} is itself useful, but it is just as interesting to examine the modes of failure of the method.
We have studied a variety of images to attempt to understand the \gls{nn}'s failings, and the examples in \cref{fig:results_images} illustrate some relevant issues.
Higher levels of abstraction such as that found in image D seem to confound the \gls{nn} --- especially in terms of the shape of the people.
It also failed to detect stick figures in another example image.

This is likely due to the way in which style transfer modifies images, largely shifting the colour and texture of the people in them without significantly warping their shapes.
This means that while the \gls{frrcnn} network is fine-tuned with people depicted in a wide range of colours and textures, it has not seen this combination of shapes as a person before and is therefore unable to recognize it as such.

Visual complexity appears to also pose a challenge for \gls{nn}.
In images A and B (\cref{fig:results_images}), the \gls{nn} returns some wildly inaccurate bounding boxes and outright misses some of the people in the image.
In B in particular, the resulting bounding boxes are dispersed almost randomly throughout the image.
B also points to another likely source of error in the data.

Given the well-documented~\citep{Buolamwini2018,Dulhanty2019} problem of gender, race, and age biases in machine learning, it is likely that this bias plays a role in the \gls{nn}'s failure on some images.
The \gls{frrcnn} network uses a backbone pre-trained on ImageNet which has documented imbalances in racial representation~\citep{Yang2020} that are shown to lead to biased models~\citep{Steed2020}.

The \gls{nn} presented here likely suffers a second type of bias related to artistic style.
The Painter-by-Numbers database used for style transfer in the creation of \gls{sc} draws most of its image set from \url{WikiArt.org}.
WikiArt catalogues around 250,000 artworks from more that 100 countries\footnote{\url{www.wikiart.org/en/about}}, but it appears to skew towards those living and educated in North America and Europe.
It is reasonable to assume, therefore, that an \gls{nn} trained on \gls{sc} would perform better, for example, on Renaissance paintings than on a print from the Japanese shin-hanga movement.
%TODO asun: It actually looks like a quite interesting example, as the people are mainly just line drawings without texture - referring to the points discussed earlier in background about artwork relying more on shape. https://www.wikiart.org/en/kiyokata-kaburagi/at-the-shore
This was seen in testing as the \gls{nn} performed poorly on Kiyokata Kaburagi's \textit{At the Shore}.

Future work must consider these sources of bias --- the ImageNet backbone, the COCO base image dataset, and the artistic images used for style transfer --- and ways to identify these biases more specifically and mitigate its effects.
One way to more precisely identify the issues would be to test the \gls{nn} against the different image styles contained within the \gls{pa} dataset independently.
This could help to pinpoint the types of images that are difficult for the \gls{nn}.
Examination of saliency maps could also help to illuminate the source of difficulty in identifying people in specific artworks.

\subsection{Applications}
The technique in this paper can be applied to detect objects in non-photographic images, such as in museum collections and online databases.
This type of localization of people can be used to extract more detailed metadata such as the number and relative positions of people in art images.

There is an additional opportunity to extend this process beyond the recognition of people in artwork and into other object categories.
Using this technique it is possible to generate useful training data for all the object categories that are represented in the COCO dataset, thus circumventing one of the main limitations with existing datasets of non-photographic images, which include very few object categories (from 1 in People-Art to 7 in PACS).
\citet{Cai2015} note that objects do not appear with similar frequencies in artwork and people are over-represented, making it easier to collect databases of people in artwork, but harder to collect artwork depicting other minor categories.
If style transfer can be used to train object detectors for people in artwork using the COCO database as pre-labelled data, then it is likely possible to train detectors for the other 79 object categories in COCO as well.

Furthermore, the technique presented here could be used as a bootstrapping technique to build larger datasets of non-photographic images for object detection.
Using a \gls{nn} trained with the stylization technique one could annotate a large dataset (e.g. BAM), which holds 2.5 million images~\citep{Wilber2017}.
These bounding boxes could be verified and corrected using a crowdsourcing approach, resulting in a potentially very large dataset of non-photographic images which could be used to further improve object detection algorithms.

Other application areas for our approach are domains in which users can create their own content such as in video games, social networks, or internet forums.
In the game iNNk~\citep{Villareale2020}, an AI has to guess what object a person is drawing before the other players are able to do so.
For such systems it is challenging to obtain a large enough training set for accurate object detection, and they could therefore benefit from a dataset created using style-transfer.
Other games feature user-generated content that is moderated fully- or semi-automatically to remove or restrict drawn images that are disturbing or inappropriate for the audience.
A system trained on a style-transferred dataset could help to make these AI-driven processes more accurate and attuned to the styles of images produced in a particular game.

\subsection{Future Work}
In this initial exploration of the use of style transferred images for training object detectors in artwork, we used a general database of artistic images from the Painter-by-Numbers dataset to perform style transfer.
However, since the target set of artwork was known in advance it would have been possible to use that set instead.
A specialized version of \gls{sc} could be generated using unlabelled images from the target dataset as the style sources and that database could be used to train a \gls{nn} that is specially tuned to the styles of representation found in the target images.
This could yield additional improvements as the styled images in the training set more closely reflect the eventual targets.

Finally, though not used here, COCO also contains data for object segmentation, person keypoint detection and pose estimation, and detailed scene segmentation.
It would be interesting to see how well these could be translated to artwork using the style transfer method described here.
introduction.tex
\section{Introduction}

Image recognition systems --- especially ones based on deep neural networks --- have improved significantly in recent years~\citep{lecun_deep_2015}, yet they are often brittle and struggle if images are grainy or noisy.
More worryingly, they can be manipulated into making incorrect classifications by malicious pixel changes, sometimes on as little as a single pixel~\citep{su_one_2019}.
% Humans, on the other hand, can identify objects in almost any situation.
Neural networks struggle even more to recognise objects depicted in different styles such as drawings or paintings, something the human mind can do readily even as a small child.
This is known as the cross-depiction problem~\citep{Cai2015,boulton_artistic_2019}.

Meanwhile, in the museum sector there is a growing interest in the use of object detection in artworks~\citep{ciecko2020}, partly for the purpose of improving metadata in order to better support search, e.g. by making it possible to search for visual motifs; and partly in order to set up new creative possibilities for artists and designers, to create innovative art experiences e.g. through apps such as \textit{Smartify} or \textit{Send Me SFMoma}~\citep{mollica_send_2017}.
As is the case with many AI problems, a major challenge for improving object detection in artworks is the lack of large, high quality datasets for training. 

In this paper, we introduce a technique inspired in part by \citet{Geirhos2018}
% to bias vision algorithms away from mainly focusing on the texture of objects but instead start to focus more on their global pattern (i.e. an elephant is an elephant even if it is depicted in pink).
to generate a dataset and train an object detector to locate people in artworks.
\citet{Geirhos2018} used style transfer to demonstrate that neural networks prioritize texture over shape in classifying images and to train a neural network to be more robust to textural distortions such as noise.
% While \citet{Geirhos2018} used style transfer to make object detection on photographic images more robust to noise, we adapt the technique to address the task of detecting objects in artworks.
Here, style transfer is used to address the cross-depiction problem.
We create a large training dataset (\n{} images) of artistically-styled images from already-labelled photographs, train an object detector, and test it on the artwork dataset People-Art~\citep{Cai2015}, which results in a significant improvement on past detection efforts on this dataset.

%Research question: Can object detection in non-photographic artistic images be improved using only style transfer on photographic images, and without training on artistic images?

This research contributes to the field of computer vision by expanding the visual domains available for object detection, for instance making it possible to search for motifs in non-photographic databases. 
Furthermore, it is a step towards developing vision algorithms that have more general representations of objects that resemble the ways humans see objects, allowing for more robust object detection in photos and video (e.g. algorithms that can better handle noise and distortions).

Improving object detection will enable new ways to search and study particular objects in art collections.
This may enable new strategies for doing quantitative research on art history and cultural heritage in the field of digital humanities \citep{berry_digital_2017,manovich_ai_2018,manovich_cultural_2020}.
In combination with existing metadata this would make it possible to search for specific depictions of people, for instance based on the colour of their skin, in order to quantitatively explore depictions of race in art (searching for images based on colour profile is already a well-established technique).
Another use case might be to search the collections of a national museum for depictions of objects from other countries to explore international influences on culture in different historical eras.
Such metadata could enable the design of more engaging experiences with museum collections, e.g. by giving users the means to search for objects relating to their personal interests \citep{ciecko2020,Merritt2017,Bailey2019,Smith2019}.
results.tex

\section{Results}



\subsection{\Gls{exp_ntrain}: Number of training images}
The first experiment tests how the performance of a \gls{nn} in detecting people in an unseen distribution of artistic images shifts depending on the number of stylized training images seen during fine-tuning.
\Cref{fig:ap_n_train} shows the performance of the network using the average precision metrics (\gls{ap}, \gls{ap5}, and \gls{ap75}) defined in the COCO \textit{detection evaluation metrics}\footnote{These metrics are used for all evaluation in this paper and are detailed on the COCO website at \url{https://cocodataset.org/\#detection-eval}}.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{images/plots/ap_scores_by_training_images.pdf}
    \caption{AP scores for networks trained using different numbers of training images in the \gls{sc} dataset and tested on the \gls{pa} dataset. The plot shows the 3 main AP measures (\gls{ap}, \gls{ap5}, and \gls{ap75}). The availability of more training images from \gls{sc} appears to improve the performance of the networks on the \gls{pa} testing set --- rapidly at first and then more gradually as the network sees tens of thousands of images in training.
%	    Performance on the \gls{ap5} metric actually drops very slightly from 32,000 (\gls{ap5}=0.683) to \n{} (\gls{ap5}=0.679) training images, but continues to rise in the other two metrics.
    	At \n{} training images, \gls{ap}=0.36, \gls{ap5}=0.68, and \gls{ap75}=0.33.}
    \label{fig:ap_n_train}
\end{figure}

The precision rises sharply below about 10,000 images, but continue a slow improvement as more images are added to the training set.
On the \gls{ap5} metric, there is actually a small decrease in score between the \gls{nn} trained on 32,000 images and the one trained on \n{} images.
However, the \gls{ap} and \gls{ap75} scores continue to rise.

To understand why, it is important to consider the meaning of the three scores.
\gls{ap5} and \gls{ap75} are \acrlong{map} scores with different \gls{iou} thresholds and AP is the average of a series of scores at different \gls{iou} thresholds from .50 to .95.
The \gls{ap5} score reflects object detections that are less exact than the \gls{ap75} score.
So, it appears as though the network reaches a limit for moderately precise detections somewhere around 32,000 training images.
With more images, it is able to improve the precision of some detections while sacrificing some of the less precise bounding boxes.
Note that the decline in \gls{ap5} is relatively minor (0.4\%) compared to the rise in \gls{ap75} (2.0\%) between the networks trained on 32,000 and \n{} images.
% This confirms the importance of the availability of a large set of labelled images for training object detection models for artistic images.

\renewcommand\imggridwidth{0.19\textwidth}
\begin{figure*}[t]
     \centering
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results_n/image_130_gt.png}
         \caption*{Ground Truth}
     \end{subfigure}
     \hfill
     % Ground truth boxes
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results_n/image_130_0_1000.png}
         \caption*{1000}
     \end{subfigure}
     \hfill
    %  \begin{subfigure}[b]{\imggridwidth}
    %      \centering
    %      \includegraphics[width=\textwidth]{images/results_n/image_130_0_2000.png}
    %  \end{subfigure}
    %  \hfill
    %  \begin{subfigure}[b]{\imggridwidth}
    %      \centering
    %      \includegraphics[width=\textwidth]{images/results_n/image_130_0_4000.png}
    %      \caption*{4000}
    %  \end{subfigure}
    %  \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results_n/image_130_0_8000.png}
         \caption*{8000}
     \end{subfigure}
     \hfill
    %  \begin{subfigure}[b]{\imggridwidth}
    %      \centering
    %      \includegraphics[width=\textwidth]{images/results_n/image_130_0_16000.png}
    %      \caption*{16000}
    %  \end{subfigure}
    %  \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results_n/image_130_0_32000.png}
         \caption*{32000}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results_n/image_130_0.png}
         \caption*{\n{}}
     \end{subfigure}
     \hfill
        \caption{Ground truth (red bounding boxes) and results of person detection (green bounding boxes) with \gls{frrcnn} fine-tuned on \gls{sc} with increasing numbers of training images from 1000 to \n{}. Networks with fewer training images (left) produced far too many candidates. As the networks were able to train with a larger dataset, they narrowed their search and became more accurate overall. Note that in this example the network with 32,000 training images actually outperformed the network with \n{} training images as the latter network added an extra, unmatched candidate.}
        \label{fig:results_different_n}
\end{figure*}


\subsection{\Gls{exp_ap}: Performance on \gls{pa} dataset}

\Gls{exp_ap} tests the performance of the trained networks against the state of the art, as described in literature.
Four previous papers have published results for object detection on the \gls{pa} testing dataset using a variety of methods~\citep{Cai2015,Westlake2016,Redmon2016,Gonthier2020}.
Each reported the PASCAL VOC metric which corresponds to the AP.50 score used here, so a comparison is made based on this value.

\citet{Cai2015} and \citet{Westlake2016} are the creators of the \gls{pa} dataset.
\citet{Cai2015} used a \gls{dpm} as well as 2 R-CNN models pre-trained on ImageNet and fine-tuned on VOC 2012 and \gls{pa}.
\citet{Westlake2016} used Fast R-CNN models with a mix of backbones (CaffeNet, VGG1024, and VGG16) pre-trained on ImageNet and fine-tuned on either VOC 2007 or \gls{pa}, achieving the best result (AP.50=.58) with the VGG16 backbone fine-tuned on \gls{pa}.
A YOLO model was tested on the \gls{pa} dataset by \citet{Redmon2016} with mid-range success (AP.50=0.45) though this model was not fine-tuned on the \gls{pa} dataset nor was it trained or designed to recognize art specifically.
Finally, \citet{Gonthier2020} tested a number of \gls{mil} approaches in a \gls{wsod} task on the \gls{pa} dataset, achieving scores equal to those of \citet{Westlake2016} in their best models.

\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{images/plots/ap50_comparison.pdf}
    \caption{\Gls{ap5} scores on the \gls{pa} testing set for prior work (in blue) and results from this paper (in orange). Includes results from \citet{Cai2015} [a], \citet{Westlake2016} [b], \citet{Redmon2016} [c], \citet{Gonthier2020} [d] and this paper [e]. The method proposed here outperforms past detection efforts on the \gls{pa} dataset for every network trained with more than 4,000 images. The two networks trained with over 32,000 images achieve improvements of 10-percentage-points over the prior state of the art without having been trained on any of the images in the \gls{pa} dataset.}
    \label{fig:ap_compare}
\end{figure}

\begin{table}[t]
    \centering
    \caption{AP scores on the \gls{pa} dataset, best results}
    \begin{tabular}{cccc} \toprule
        \textbf{Paper}    & \textbf{\gls{ap}} & \textbf{\gls{ap5}} & \textbf{\gls{ap75}} \\ \midrule
        \citet{Cai2015} & --- & 0.4 & --- \\ %R-CNN finetuned on peopleart
        \citet{Westlake2016} & --- & 0.58 & --- \\ %Fast R-CNN (VGG16) pre-trained on ImageNet, finetuned on \gls{pa}
        \citet{Redmon2016} & --- & 0.45 & --- \\ %YOLO (vanilla)
        \citet{Gonthier2020} & --- & 0.58 & --- \\ %Polyhedral MI-max (weakly supervised object detection)
        \textbf{This paper (n=\n)} & 0.36 & 0.68 & 0.33 \\ %FasterRCNN pre-trained on ImageNet, finetuned on stylized imagenet
        \bottomrule
    \end{tabular}
    \label{tab:aps_from_others}
\end{table}

The best result from each of the four papers is listed alongside our result in \cref{tab:pa_stats}; full results from those papers\footnote{Most of the papers attempted more than just one method and reported results from all of the trials.} and our tests in \gls{exp_ntrain} are plotted in \cref{fig:ap_compare}.
The approach proposed here achieves a full 10-percentage-point improvement over these methods, with an AP.50 score of $0.68$ on the \gls{pa} testing set, compared to the next best score of $0.58$.
For a more detailed picture of the performance, the precision-recall curve for the network trained with \n{} images is shown in \cref{fig:pr}.

\Cref{fig:results_images} shows some examples of the \gls{nn}'s range of performance on images from the \gls{pa} testing dataset.
The figure includes images where the \gls{nn} was inaccurate (A,B) as well as some where it failed to detect people (C,D).
In other images the \gls{nn} performed relatively well (E,F), labelling the correct number of people with reasonable accuracy, though it seemed to struggle to precisely label people who are occluded by other people.
In a final pair of images it correctly detected people that were unlabelled in the dataset (G,H), outperforming the humans that created the dataset.
In G the \gls{nn} correctly found a faint impression of a person in the left side of the image, though it appears to have been confused by the white space in the middle of the figure's body and has separately labelled the head, the lower body, and the entire person.
In H the \gls{nn} correctly found an additional head that was unlabelled in the right side of the image, but also mislabelled a mark at the top-right corner of the image.


\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{images/plots/precision_recall.pdf}
    \caption{Precision-Recall curve for \gls{ap5} and \gls{ap75} metrics from evaluation on the \gls{pa} dataset for the \gls{frrcnn} network fine-tuned on \n{} images from \gls{sc}.}
    \label{fig:pr}
\end{figure}

\renewcommand\imggridwidth{0.24\textwidth}
\begin{figure*}
     \centering
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_120_0.png}
         \caption*{A}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_60_0.png}
         \caption*{B}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_400_0.png}
         \caption*{C}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_90_0.png}
         \caption*{D}
     \end{subfigure}
     \hfill
    %  \begin{subfigure}[b]{\imggridwidth}
    %      \centering
    %      \includegraphics[width=\textwidth]{images/results/image_240_0.png}
    %  \end{subfigure}
    %  \hfill
     \newline
    %  \begin{subfigure}[b]{\imggridwidth}
    %      \centering
    %      \includegraphics[width=\textwidth]{images/results/image_440_0.png}
    %  \end{subfigure}
    %  \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_430_0.png}
         \caption*{E}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_360_0.png}
         \caption*{F}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_80_0.png}
         \caption*{G}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\imggridwidth}
         \centering
         \includegraphics[width=\textwidth]{images/results/image_420_0.png}
         \caption*{H}
     \end{subfigure}
     \hfill
        \caption{Sample images from the \gls{pa} testing dataset with the ground truth (red bounding boxes) and people detected (green bounding boxes) using \gls{frrcnn} fine-tuned on \n{} images from \gls{sc}. In images A and B, the network did a poor job, failing to detect a number of people and placing wildly inaccurate bounding boxes for others in the busy scenes. Many of the people in images C and D were likely too abstract and the \gls{nn} failed to detect people in them, though it managed to quite accurately find the person in the middle of image C. Images E and F represent pretty good results. Each image has bounding boxes that are imperfectly placed, but both feature the correct number of detections with some bounding boxes that overlap the ground truth almost exactly. Finally, images G and H represent cases where the detector found people in the images that had not been labelled with a ground truth in the dataset, but that were correct nonetheless. These detections show how the \glspl{nn} can occasionally outperform humans at the task of detecting people in artwork. }
        \label{fig:results_images}
\end{figure*}

training.tex
\section{Model Training}
The models employed in this study were trained using the \texttt{PyTorch} library and its built-in \texttt{FasterRCNN} model with a \gls{rn152} backbone pre-trained on ImageNet.
Models were fine-tuned on the StyleCOCO training dataset and evaluated during training using a random subset of 2,000 images from the StyleCOCO validation dataset.
Early stopping was engaged to prevent overfitting of models to the training data.
After fine-tuning was complete, models were tested on the full \gls{pa} test dataset.
The reported AP scores are from this testing phase.

The code for creating the dataset and training and testing the model is available at \url{https://github.com/dkadish/Style-Transfer-for-Object-Detection-in-Art}.
% and the StyleCOCO dataset is available at \url{https://doi.org/10.6084/m9.figshare.13861619}.

\subsection{Experiments}
Two experiments were conducted to test the validity of using stylized photographs to fine-tune a neural network for object detection in artwork.
In the first (\gls{exp_ntrain}), we tested the impact of the number of training images on the performance of the object detector.
We trained 7 models with exponentially\footnote{Following the exponential pattern, the final experiment should have had 64000, but due to the limitations of the COCO dataset there were only \n{} images available, so the entire dataset was used.} increasing amounts of input data, starting at 1000 images.
Images were randomly selected from the full dataset for each model and only those images were used in training.
The models were then tested on the \gls{pa} testing dataset.
These results were compared against each other in order to test the impact of additional training data on model performance.

The second experiment (\gls{exp_ap}) compares performance of a model fine-tuned on StyleCOCO on the \gls{pa} testing dataset with other results reported in the literature.
This experiment establishes the performance of the method detailed here against the state-of-the-art in object detection in artwork.

It is important to note that in both of these experiments, the testing results are achieved without having to use any labelled data from target distribution in training.
The \gls{pa} training datasets are not used in any way to train the \gls{nn} that eventually detects objects in the \gls{pa} testing set.

\subsection{Model and training parameters}
The model used in both experiments is a \gls{frrcnn} network with a \gls{rn152} backbone pre-trained on ImageNet.
Models were fine-tuned using the StyleCOCO training and validation datasets --- or a subset thereof in the case of \gls{exp_ntrain}.
Default training parameters from \texttt{PyTorch} were used except where modifications were shown to improve performance and those non-default parameters are listed below.
2 layers of the backbone were made trainable and the remaining 3 were frozen to retain the pre-trained ImageNet weights. 
Models were trained for 15 epochs, though early stopping (\texttt{patience}=3.0) was employed in order to avoid overfitting.

The model was optimized using \gls{sgd}, using an initial learning rate of 0.005, a momentum of 0.9, and weight decay of 0.0005.
The learning rate was adjusted over the course of the training using a stepped learning rate scheduler, which multiplied the rate by 0.2 every 5 epochs.
A warm-up period of 5,000 iterations was also implemented to ease into the initial learning rate.
The resulting learning rate curve is shown in \cref{fig:learning_rate}.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{images/plots/lr}
    \caption{Learning rate for the model by iteration. The learning rate was adjusted using a stepped scheduler (\texttt{step size}$=2$, $\gamma=0.2$) and a warm-up (5000 iterations) to increase the rate at the beginning of training.}
    \label{fig:learning_rate}
\end{figure}
IEEEabrv.bib
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IEEE Journals


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autos, transportation and vehicles (non-aerospace)
@STRING{IEEE_J_ITS        = "{IEEE} Trans. Intell. Transp. Syst."}
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circuits, signals, systems, audio and controls
@STRING{IEEE_J_STSP       = "{IEEE} J. Sel. Topics Signal Process."}
@STRING{IEEE_J_SPL        = "{IEEE} Signal Process. Lett."}
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@STRING{IEEE_J_AU         = "{IEEE} Trans. Audio"}
@STRING{IEEE_J_AUEA       = "{IEEE} Trans. Audio Electroacoust."}
in 2014 ASL became ASLP
@STRING{IEEE_J_ASLP       = "{IEEE/ACM} Trans. Audio, Speech, Language Process."}
@STRING{IEEE_J_ASL        = "{IEEE} Audio, Speech, Language Process."}
@STRING{IEEE_J_AC         = "{IEEE} Trans. Autom. Control"}
@STRING{IEEE_J_CAS        = "{IEEE} Trans. Circuits Syst."}
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@STRING{IEEE_J_CASI       = "{IEEE} Trans. Circuits Syst. {I}"}
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in 2004 CASI and CASII renamed part title to CASI_RP and CASII_EB, respectively
@STRING{IEEE_J_CASI_RP    = "{IEEE} Trans. Circuits Syst. {I}"}
@STRING{IEEE_J_CASII_EB   = "{IEEE} Trans. Circuits Syst. {II}"}
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@STRING{IEEE_J_ADVP       = "{IEEE} Trans. Adv. Packag."}
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in 2011 CAPT became CPMT
@STRING{IEEE_J_CPMT       = "{IEEE} Trans. Compon. Packag. Manuf. Technol."}
@STRING{IEEE_J_CPMTA      = "{IEEE} Trans. Compon., Packag., Manuf. Technol. {A}"}
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CAD
@STRING{IEEE_J_TCAD       = "{IEEE} J. Technol. Comput. Aided Design"}
@STRING{IEEE_J_CAD        = "{IEEE} Trans. Comput.-Aided Design Integr. Circuits Syst."}



coding, data, information, knowledge
@STRING{IEEE_J_BD         = "{IEEE} Trans. Big Data"}
@STRING{IEEE_J_IFS        = "{IEEE} Trans. Inf. Forensics Security"}
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computers, computation, networking and software
@STRING{IEEE_J_CAL        = "{IEEE} Comput. Archit. Lett."}
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@STRING{IEEE_J_CC         = "{IEEE} Trans. on Cloud Comput."}
@STRING{IEEE_J_CSS        = "{IEEE} Trans. Comput. Social Syst."}
@STRING{IEEE_J_C          = "{IEEE} Trans. Comput."}
@STRING{IEEE_J_CNS        = "{IEEE} Trans. Control Netw. Syst."}
@STRING{IEEE_J_DSC        = "{IEEE} Trans. Depend. Sec. Comput."}
@STRING{IEEE_J_ECOMP      = "{IEEE} Trans. Electron. Comput."}
@STRING{IEEE_J_ETC        = "{IEEE} Trans. Emerg. Topics Comput."}
@STRING{IEEE_J_EVC        = "{IEEE} Trans. Evol. Comput."}
@STRING{IEEE_J_FUZZ       = "{IEEE} Trans. Fuzzy Syst."}
@STRING{IEEE_J_MC         = "{IEEE} Trans. Mobile Comput."}
@STRING{IEEE_J_MSCS       = "{IEEE} Trans. Multi-Scale Comput. Syst."}
@STRING{IEEE_J_NET        = "{IEEE/ACM} Trans. Netw."}
@STRING{IEEE_J_NSE        = "{IEEE} Trans. Netw. Sci. Eng."}
@STRING{IEEE_J_NSM        = "{IEEE} Trans. Netw. Service Manag."}
@STRING{IEEE_J_NN         = "{IEEE} Trans. Neural Netw."}
in 2012 NN became NNLS
@STRING{IEEE_J_NNLS       = "{IEEE} Trans. Neural Netw. Learn. Syst."}
@STRING{IEEE_J_PDS        = "{IEEE} Trans. Parallel Distrib. Syst."}
@STRING{IEEE_J_SC         = "{IEEE} Trans. Serv. Comput."}
@STRING{IEEE_J_SIPN       = "{IEEE} Trans. Signal Inf. Process. Netw."}
@STRING{IEEE_J_SE         = "{IEEE} Trans. Softw. Eng."}



computer graphics, imaging, and multimedia
@STRING{IEEE_J_JDT        = "{IEEE/OSA} J. Display Technol."}
@STRING{IEEE_J_IP         = "{IEEE} Trans. Image Process."}
@STRING{IEEE_J_MM         = "{IEEE} Trans. Multimedia"}
@STRING{IEEE_J_VCG        = "{IEEE} Trans. Vis. Comput. Graphics"}



cybernetics, ergonomics, robots, man-machine, artificial intelligence and automation
@STRING{IEEE_J_JAS        = "{IEEE/CAA} J. Autom. Sinica"}
@STRING{IEEE_J_JRA        = "{IEEE} J. Robot. Autom."}
@STRING{IEEE_J_AFFC       = "{IEEE} Trans. Affect. Comput."}
@STRING{IEEE_J_ASE        = "{IEEE} Trans. Autom. Sci. Eng."}
@STRING{IEEE_J_AMD        = "{IEEE} Trans. Auton. Mental Develop."}
@STRING{IEEE_J_CCN        = "{IEEE} Trans. on Cogn. Commun. Netw."}
in 2015 AMD became CDS
@STRING{IEEE_J_CDS        = "{IEEE} Trans. Cogn. Develop. Syst."}
@STRING{IEEE_J_CIAIG      = "{IEEE} Trans. Comput. Intell. {AI} in Games"}
in 2013 SMCB became CYB
@STRING{IEEE_J_CYB        = "{IEEE} Trans. Cybern."}
@STRING{IEEE_J_H          = "{IEEE} Trans. Haptics"}
@STRING{IEEE_J_HFE        = "{IEEE} Trans. Hum. Factors Electron."}
in 2013 SMCC became HMS
@STRING{IEEE_J_HMS        = "{IEEE} Trans. Human-Mach. Syst."}
@STRING{IEEE_J_MMS        = "{IEEE} Trans. Man-Mach. Syst."}
@STRING{IEEE_J_PAMI       = "{IEEE} Trans. Pattern Anal. Mach. Intell."}
in 1989 JRA became RA
in August 2004, RA split into ASE and RO
@STRING{IEEE_J_RA         = "{IEEE} Trans. Robot. Autom."}
@STRING{IEEE_J_RAL        = "{IEEE} Robot. Autom. Lett."}
@STRING{IEEE_J_RO         = "{IEEE} Trans. Robot."}
@STRING{IEEE_J_SMC        = "{IEEE} Trans. Syst., Man, Cybern."}
@STRING{IEEE_J_SMCA       = "{IEEE} Trans. Syst., Man, Cybern. {A}"}
@STRING{IEEE_J_SMCB       = "{IEEE} Trans. Syst., Man, Cybern. {B}"}
@STRING{IEEE_J_SMCC       = "{IEEE} Trans. Syst., Man, Cybern. {C}"}
in 2012 SMCA became SMCS
@STRING{IEEE_J_SMCS       = "{IEEE} Trans. Syst., Man, Cybern., Syst."}
@STRING{IEEE_J_SSC        = "{IEEE} Trans. Syst. Sci. Cybern."}



earth, wind, fire and water
@STRING{IEEE_J_GRSL       = "{IEEE} Geosci. Remote Sens. Lett."}
@STRING{IEEE_J_GE         = "{IEEE} Trans. Geosci. Electron."}
@STRING{IEEE_J_GRS        = "{IEEE} Trans. Geosci. Remote Sens."}
@STRING{IEEE_J_OE         = "{IEEE} J. Ocean. Eng."}
@STRING{IEEE_J_STARS      = "{IEEE} J. Sel. Topics Appl. Earth Observ. Remote Sens."}



education, engineering, history, IEEE, professional
@STRING{IEEE_J_CJECE      = "Canadian J. Elect. Comput. Eng."}
@STRING{IEEE_J_PROC       = "Proc. {IEEE}"}
@STRING{IEEE_J_RITA       = "{IEEE} Revista Iberoamericana de Technolog{\'{i}}as del Aprendizaje"}
@STRING{IEEE_J_EDU        = "{IEEE} Trans. Educ."}
@STRING{IEEE_J_EM         = "{IEEE} Trans. Eng. Manag."}
@STRING{IEEE_J_EWS        = "{IEEE} Trans. Eng. Writing Speech"}
@STRING{IEEE_J_LT         = "{IEEE} Trans. Learn. Technol."}
@STRING{IEEE_J_PC         = "{IEEE} Trans. Prof. Commun."}



electromagnetics, antennas, EMI, magnetics and microwave
@STRING{IEEE_J_AWPL       = "{IEEE} Antennas Wireless Propag. Lett."}
@STRING{IEEE_J_MAGL       = "{IEEE} Magn. Lett."}
@STRING{IEEE_J_MGWL       = "{IEEE} Microw. Guided Wave Lett."}
@STRING{IEEE_J_MWCL       = "{IEEE} Microw. Wireless Compon. Lett."}
@STRING{IEEE_J_RFIC       = "{IEEE} {RFIC} J."}
@STRING{IEEE_J_RFID       = "{IEEE} {RFID} J."}
@STRING{IEEE_J_AP         = "{IEEE} Trans. Antennas Propag."}
@STRING{IEEE_J_EMC        = "{IEEE} Trans. Electromagn. Compat."}
@STRING{IEEE_J_MAG        = "{IEEE} Trans. Magn."}
@STRING{IEEE_J_MTT        = "{IEEE} Trans. Microw. Theory Techn."}
@STRING{IEEE_J_RFI        = "{IEEE} Trans. Radio Freq. Interference"}
@STRING{IEEE_J_TTHZ       = "{IEEE} Trans. {THz} Sci. Technol."}
@STRING{IEEE_J_TJMJ       = "{IEEE} Transl. J. Magn. Jpn."}



energy, power and conversion
@STRING{IEEE_J_PHOT       = "{IEEE} J. Photovolt."}
@STRING{IEEE_J_PEL        = "{IEEE} Power Electron. Lett."}
@STRING{IEEE_J_PETS       = "{IEEE} Power Energy Technol. Syst. J."}
@STRING{IEEE_J_ESTPE      = "{IEEE} Trans. Emerg. Sel. Topics Power Electron."}
@STRING{IEEE_J_EC         = "{IEEE} Trans. Energy Convers."}
@STRING{IEEE_J_PWRAS      = "{IEEE} Trans. Power App. Syst."}
@STRING{IEEE_J_PWRD       = "{IEEE} Trans. Power Del."}
@STRING{IEEE_J_PWRE       = "{IEEE} Trans. Power Electron."}
@STRING{IEEE_J_PWRS       = "{IEEE} Trans. Power Syst."}
@STRING{IEEE_J_SG         = "{IEEE} Trans. Smart Grid"}
@STRING{IEEE_J_STE        = "{IEEE} Trans. Sustain. Energy"}



industrial, commercial and consumer
@STRING{IEEE_J_PSE        = "{IEEE} J. Product Safety Eng."}
@STRING{IEEE_J_APPIND     = "{IEEE} Trans. Appl. Ind."}
@STRING{IEEE_J_BC         = "{IEEE} Trans. Broadcast."}
@STRING{IEEE_J_BCTV       = "{IEEE} Trans. Broadcast Television Receivers"}
@STRING{IEEE_J_CE         = "{IEEE} Trans. Consum. Electron."}
@STRING{IEEE_J_IE         = "{IEEE} Trans. Ind. Electron."}
@STRING{IEEE_J_IECI       = "{IEEE} Trans. Ind. Electron. Contr. Instrum."}
@STRING{IEEE_J_IA         = "{IEEE} Trans. Ind. Appl."}
@STRING{IEEE_J_IGA        = "{IEEE} Trans. Ind. Gen. Appl."}
@STRING{IEEE_J_IINF       = "{IEEE} Trans. Ind. Informat."}



instrumentation and measurement
@STRING{IEEE_J_IM         = "{IEEE} Trans. Instrum. Meas."}



insulation and materials
@STRING{IEEE_J_JEM        = "{IEEE/TMS} J. Electron. Mater."}
@STRING{IEEE_J_DEI        = "{IEEE} Trans. Dielectr. Electr. Insul."}
@STRING{IEEE_J_EI         = "{IEEE} Trans. Electr. Insul."}



mechanical
@STRING{IEEE_J_MEMS       = "J. Microelectromech. Syst."}
in 2014 MEMS became MEMSI
@STRING{IEEE_J_MEMSI      = "J. Microelectromech. Syst."}
@STRING{IEEE_J_MECH       = "{IEEE/ASME} Trans. Mechatronics"}



medical and biological
@STRING{IEEE_J_BHI        = "{IEEE} J. Biomed. Health Inform."}
@STRING{IEEE_J_TEHM       = "{IEEE} J. Transl. Eng. Health Med."}
@STRING{IEEE_J_LS         = "{IEEE} Life Sci. Lett."}
@STRING{IEEE_J_RBME       = "{IEEE} Rev. Biomed. Eng."}
@STRING{IEEE_J_BCAS       = "{IEEE} Trans. Biomed. Circuits Syst."}
@STRING{IEEE_J_BMELC      = "{IEEE} Trans. Bio-Med. Electron."}
B-ME later dropped the hyphen and became the BME
@STRING{IEEE_J_BME        = "{IEEE} Trans. Biomed. Eng."}
@STRING{IEEE_J_B-ME       = "{IEEE} Trans. Bio-Med. Eng."}
@STRING{IEEE_J_CBB        = "{IEEE/ACM} Trans. Comput. Biol. Bioinformatics"}
@STRING{IEEE_J_ITBM       = "{IEEE} Trans. Inf. Technol. Biomed."}
@STRING{IEEE_J_ME         = "{IEEE} Trans. Med. Electron."}
@STRING{IEEE_J_MI         = "{IEEE} Trans. Med. Imag."}
@STRING{IEEE_J_MBSC       = "{IEEE} Trans. Mol. Biol. Multi-Scale Commun."}
@STRING{IEEE_J_NB         = "{IEEE} Trans. Nanobiosci."}
@STRING{IEEE_J_NSRE       = "{IEEE} Trans. Neural Syst. Rehabil. Eng."}
@STRING{IEEE_J_RE         = "{IEEE} Trans. Rehabil. Eng."}



optics, lightwave and photonics
@STRING{IEEE_J_JLT        = "J. Lightw. Technol."}
@STRING{IEEE_J_OCN        = "{IEEE} J. Opt. Commun. Netw."}
@STRING{IEEE_J_PJ         = "{IEEE} Photon. J."}
@STRING{IEEE_J_PTL        = "{IEEE} Photon. Technol. Lett."}



physics, electrons, nanotechnology, nuclear and quantum electronics
@STRING{IEEE_J_EDL        = "{IEEE} Electron Device Lett."}
@STRING{IEEE_J_EDS        = "{IEEE} J. Electron Devices Soc."}
@STRING{IEEE_J_JQE        = "{IEEE} J. Quantum Electron."}
@STRING{IEEE_J_JSTQE      = "{IEEE} J. Sel. Topics Quantum Electron."}
@STRING{IEEE_J_ENANO      = "{IEEE} Nanotechnol. Express"}
@STRING{IEEE_J_ED         = "{IEEE} Trans. Electron Devices"}
@STRING{IEEE_J_NANO       = "{IEEE} Trans. Nanotechnol."}
@STRING{IEEE_J_NS         = "{IEEE} Trans. Nucl. Sci."}
@STRING{IEEE_J_PS         = "{IEEE} Trans. Plasma Sci."}



reliability
@STRING{IEEE_J_DMR        = "{IEEE} Trans. Device Mater. Rel."}
@STRING{IEEE_J_R          = "{IEEE} Trans. Rel."}



semiconductors, superconductors, electrochemical and solid state
@STRING{IEEE_J_ESSL       = "{IEEE/ECS} Electrochem. Solid-State Lett."}
@STRING{IEEE_J_JSSC       = "{IEEE} J. Solid-State Circuits"}
@STRING{IEEE_J_ASC        = "{IEEE} Trans. Appl. Supercond."}
@STRING{IEEE_J_SM         = "{IEEE} Trans. Semicond. Manuf."}



sensors
@STRING{IEEE_J_SENSOR     = "{IEEE} Sensors J."}



VLSI
@STRING{IEEE_J_VLSI       = "{IEEE} Trans. {VLSI} Syst."}






IEEE Magazines and Online Publications


@STRING{IEEE_O_ACC        = "{IEEE} Access"}
@STRING{IEEE_M_AES        = "{IEEE} Aerosp. Electron. Syst. Mag."}
@STRING{IEEE_M_HIST       = "{IEEE} Ann. Hist. Comput."}
@STRING{IEEE_M_AP         = "{IEEE} Antennas Propag. Mag."}
@STRING{IEEE_M_ASSP       = "{IEEE} {ASSP} Mag."}
@STRING{IEEE_M_CHINAC     = "China Commun."}
@STRING{IEEE_M_CD         = "{IEEE} Circuits Devices Mag."}
@STRING{IEEE_M_CAS        = "{IEEE} Circuits Syst. Mag."}
@STRING{IEEE_M_COM        = "{IEEE} Commun. Mag."}
@STRING{IEEE_M_COMSOC     = "{IEEE} Commun. Soc. Mag."}
@STRING{IEEE_O_CSTO       = "{IEEE} Commun. Surveys Tuts."}
@STRING{IEEE_M_CIM        = "{IEEE} Comput. Intell. Mag."}
CSEM changed to CSE in 1999
@STRING{IEEE_M_CSE        = "{IEEE} Comput. Sci. Eng."}
@STRING{IEEE_M_CSEM       = "{IEEE} Comput. Sci. Eng. Mag."}
@STRING{IEEE_M_C          = "Computer"}
@STRING{IEEE_M_CAP        = "{IEEE} Comput. Appl. Power"}
@STRING{IEEE_M_CGA        = "{IEEE} Comput. Graph. Appl."}
@STRING{IEEE_M_CONC       = "{IEEE} Concurrency"}
@STRING{IEEE_M_CS         = "{IEEE} Control Syst. Mag."}
in 2012 DTC became DT
@STRING{IEEE_M_DT         = "{IEEE} Des. Test"}
@STRING{IEEE_M_DTC        = "{IEEE} Des. Test. Comput."}
@STRING{IEEE_O_DSO        = "{IEEE} Distrib. Syst. Online"}
@STRING{IEEE_M_EI         = "{IEEE} Electr. Insul. Mag."}
@STRING{IEEE_M_ETF        = "{IEEE} Electrific. Mag."}
@STRING{IEEE_M_EMC        = "{IEEE} Electromagn. Compat."}
@STRING{IEEE_M_ETR        = "{IEEE} ElectroTechnol. Rev."}
@STRING{IEEE_M_EMR        = "{IEEE} Eng. Manag. Rev."}
@STRING{IEEE_M_EMB        = "{IEEE} Eng. Med. Biol. Mag."}
@STRING{IEEE_M_EXP        = "{IEEE} Expert"}
@STRING{IEEE_M_GRS        = "{IEEE} Geosci. Remote Sens. Mag."}
@STRING{IEEE_M_IA         = "{IEEE} Ind. Appl. Mag."}
@STRING{IEEE_M_IE         = "{IEEE} Ind. Electron. Mag."}
@STRING{IEEE_M_IM         = "{IEEE} Instrum. Meas. Mag."}
@STRING{IEEE_M_IS         = "{IEEE} Intell. Syst."}
@STRING{IEEE_M_ITS        = "{IEEE} Intell. Transp. Syst. Mag."}
@STRING{IEEE_M_IC         = "{IEEE} Internet Comput."}
@STRING{IEEE_M_ITP        = "{IEEE} {IT} Prof."}
@STRING{IEEE_M_MICRO      = "{IEEE} Micro"}
@STRING{IEEE_M_MW         = "{IEEE} Microw. Mag."}
@STRING{IEEE_M_MM         = "{IEEE} Multimedia"}
@STRING{IEEE_M_NANO       = "{IEEE} Nanotechnol. Mag."}
@STRING{IEEE_M_NET        = "{IEEE} Netw."}
IEEE's editorial manual lists "Pers. Commun.",
but "Personal Commun. Mag." seems to be what is used in the journals
@STRING{IEEE_M_PCOM       = "{IEEE} Personal Commun. Mag."}
@STRING{IEEE_M_PVC        = "{IEEE} Pervasive Comput."}
@STRING{IEEE_M_POT        = "{IEEE} Potentials"}
@STRING{IEEE_M_PEL        = "{IEEE} Power Electron. Mag."}
CAP and PER merged to form PE in 2003
@STRING{IEEE_M_PE         = "{IEEE} Power Energy Mag."}
@STRING{IEEE_M_PER        = "{IEEE} Power Eng. Rev."}
@STRING{IEEE_M_PULSE      = "{IEEE} Pulse"}
@STRING{IEEE_M_RA         = "{IEEE} Robot. Autom. Mag."}
@STRING{IEEE_M_SAP        = "{IEEE} Security Privacy"}
@STRING{IEEE_M_SP         = "{IEEE} Signal Process. Mag."}
@STRING{IEEE_M_S          = "{IEEE} Softw."}
@STRING{IEEE_M_SSC        = "{IEEE} Solid-State Circuits Mag."}
@STRING{IEEE_M_SPECT      = "{IEEE} Spectr."}
@STRING{IEEE_M_SMC        = "{IEEE} Syst., Man, Cybern. Mag."}
@STRING{IEEE_M_TS         = "{IEEE} Technol. Soc. Mag."}
@STRING{IEEE_M_VT         = "{IEEE} Veh. Technol. Mag."}
@STRING{IEEE_M_WC         = "{IEEE} Wireless Commun."}
@STRING{IEEE_M_TODAY      = "Today's Engineer"}





--
EOF

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@article{Kuhns1967,
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@misc{Fried1968,
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@misc{Morris1884,
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@book{Kagan2011a,
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@incollection{Stiles2012,
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@book{Foster2011,
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@article{JonMccormack,
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@article{Rykiel1989,
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@article{Stephan,
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@article{Boulton2019,
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@article{boulton_artistic_2019,
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@phdthesis{Maggs2014,
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@misc{Mohan2017,
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@article{Munchen2007,
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@article{Beason2019,
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@article{sebe2006authentic,
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@article{SEBE2007,
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@article{Dell2014,
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    year = {2014},
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    author = {Dell, Anthony I. and Bender, John A. and Branson, Kristin and Couzin, Iain D. and de Polavieja, Gonzalo G. and Noldus, Lucas P.J.J. and P{\'{e}}rez-Escudero, Alfonso and Perona, Pietro and Straw, Andrew D. and Wikelski, Martin and Brose, Ulrich},
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    keywords = {Automated image-based tracking, Behavior, Bio-logging, Ecological interactions, Tracking}
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@article{Noordam2005,
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    year = {2005},
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@inproceedings{Cohn2004a,
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    year = {2004},
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@article{Pantic2000,
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@inproceedings{Faria2012,
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    year = {2012},
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@phdthesis{Biadsy2011,
    title = {{Automatic Dialect and Accent Recognition and its Application to Speech Recognition}},
    year = {2011},
    author = {Biadsy, F},
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@article{fasel2003automatic,
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    issn = {0031-3203}
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@article{Feng2006,
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    author = {Feng, Xiaoyi and Lv, Baohua and Li, Zhen and Zhang, Jiling},
    pages = {726–733},
    publisher = {Springer},
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@article{Rocha2010,
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    issn = {01681699}
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@article{Wang2017,
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    author = {Wang, Guan and Sun, Yu and Wang, Jianxin and Zhang, Haiyan},
    month = {1},
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    volume = {2017},
    url = {https://www.hindawi.com/journals/cin/2017/2917536/},
    doi = {10.1155/2017/2917536},
    issn = {1687-5265}
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@article{Bukhari2012,
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@article{samal1992automatic,
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    keywords = {ARTIFICIAL INTELLIGENCE, CLASSIFICATION, COMPUTER SCIENCE, ELECTRICAL {\&}amp, ELECTRONIC, ENGINEERING, FACE DETECTION, FACE IDENTIFICATION, FACIAL EXPRESSIONS, FACIAL FEATURES, IDENTIFICATION}
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@article{Liu1995,
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    year = {1995},
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@inproceedings{Kadish2016,
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@misc{creativecommons2012,
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@article{Liu2010,
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@article{Morton2010a,
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@article{Bradfer-Lawrence2019,
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@misc{HLT2014,
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@misc{Tinguely1960,
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@article{Glaser2008,
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@book{Hundertwasser1997,
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@article{Crabtree2008,
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@article{Rajashekara2005,
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@inproceedings{Kadish2017,
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@inproceedings{Beesley2016,
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@misc{Beesley2010a,
    title = {{Hylozoic Ground}},
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@misc{Beesley2010b,
    title = {{Hylozoic Ground}},
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@misc{Beuys,
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@article{Ciencia,
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    author = {Ciencia, I A and El, Moderna and Fenom, Mecanicismo and Sid, Nuncio},
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@article{Gomez2018,
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@article{busso2008iemocap,
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@misc{Keung2016,
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@misc{Velasquez,
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@inproceedings{Lovell2011,
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@article{Ross2018,
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@article{Ross2018a,
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@inproceedings{Buechley2010,
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@article{Huebner2010,
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@book{Broeckman2016,
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@book{murphy2012,
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@inproceedings{Zoo2015,
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@misc{Karelse,
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@misc{Debatty2016,
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@misc{Karelse2015,
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@misc{Ganson,
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@article{Norris2014,
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@book{Platt2009,
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@book{Karvinen2014,
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}

@book{Hartman2014,
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    author = {Lozano-Hemmer, Rafael},
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}

@misc{LozanoHemmer2007,
    title = {{Pulse Room}},
    year = {2007},
    author = {Lozano-Hemmer, Rafael},
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@inproceedings{Lindley2016,
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@article{BellonMaurel2017,
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@misc{ri2012,
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@article{Santillo2007b,
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@misc{Escher1953,
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@misc{Abramovic1974,
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@inproceedings{Kuznetsov2010,
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@article{Landgraf2016,
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@inproceedings{Kadish2019,
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@inproceedings{VonMammen2016,
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@article{Warren-Rhodes2007,
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@inproceedings{Ball2015,
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@book{Herath2016,
    title = {{Robots and Art}},
    year = {2016},
    editor = {Herath, Damith and Kroos, Christian and {Stelarc}},
    series = {Cognitive Science and Technology},
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    doi = {10.1007/978-981-10-0321-9}
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@incollection{BenAri2018,
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@article{Dumyahn2011c,
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@article{Pijanowski2011,
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@article{Truax2011a,
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@article{Truax2011b,
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    keywords = {Acoustic complexity, biophony, circadian rhythm, eclipse, ephemeral, land use, soundscape, urban development, urbanisation, urban–rural gradient, wildland}
}

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@inproceedings{Cohen2009,
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}

@book{Kadish2012a,
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    keywords = {emotional music synthesis, empathic interaction, facial expression recognition}
}

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@article{Aguilar2014,
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@article{campbell2003peak,
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@article{Schroder2008,
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@book{Mataric2007a,
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@misc{Aleklett2005,
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@article{Joergensen2019,
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  2014, vol. 8693 LNCS, no. PART 5, pp. 740--755.

\bibitem[Wilber et~al.(2017)Wilber, Fang, Jin, Hertzmann, Collomosse, and
  Belongie]{Wilber2017}
M.~J. Wilber, C.~Fang, H.~Jin, A.~Hertzmann, J.~Collomosse, and S.~Belongie,
  ``{BAM! The Behance Artistic Media Dataset for Recognition Beyond
  Photography},'' in \emph{2017 IEEE International Conference on Computer
  Vision (ICCV)}, vol. 2017-Octob.\hskip 1em plus 0.5em minus 0.4em\relax IEEE,
  oct 2017, pp. 1211--1220.

\bibitem[Westlake et~al.(2016)Westlake, Cai, and Hall]{Westlake2016}
N.~Westlake, H.~Cai, and P.~Hall, ``{Detecting people in artwork with CNNs},''
  in \emph{Lecture Notes in Computer Science (including subseries Lecture Notes
  in Artificial Intelligence and Lecture Notes in Bioinformatics)}, vol. 9913
  LNCS.\hskip 1em plus 0.5em minus 0.4em\relax Springer Verlag, 2016, pp.
  825--841.

\bibitem[Redmon et~al.(2016)Redmon, Divvala, Girshick, and Farhadi]{Redmon2016}
J.~Redmon, S.~Divvala, R.~Girshick, and A.~Farhadi, ``{You only look once:
  Unified, real-time object detection},'' in \emph{Proceedings of the IEEE
  Computer Society Conference on Computer Vision and Pattern Recognition}, vol.
  2016-December.\hskip 1em plus 0.5em minus 0.4em\relax IEEE Computer Society,
  dec 2016, pp. 779--788.

\bibitem[Huang and Belongie(2017)]{Huang2017}
X.~Huang and S.~Belongie, ``{Arbitrary Style Transfer in Real-Time with
  Adaptive Instance Normalization},'' in \emph{Proceedings of the IEEE
  International Conference on Computer Vision}, vol. 2017-October.\hskip 1em
  plus 0.5em minus 0.4em\relax Institute of Electrical and Electronics
  Engineers Inc., dec 2017, pp. 1510--1519.

\bibitem[Gonthier et~al.(2020)Gonthier, Ladjal, and Gousseau]{Gonthier2020}
N.~Gonthier, S.~Ladjal, and Y.~Gousseau, ``{Multiple instance learning on deep
  features for weakly supervised object detection with extreme domain
  shifts},'' aug 2020, arXiv:2008.01178.

\bibitem[Buolamwini and Gebru(2018)]{Buolamwini2018}
J.~Buolamwini and T.~Gebru, ``{Gender Shades: Intersectional Accuracy
  Disparities in Commercial Gender Classification},'' in \emph{Proceedings of
  Machine Learning Research}, S.~A. Friedler and C.~W. Abstract, Eds.,
  vol.~81.\hskip 1em plus 0.5em minus 0.4em\relax New York, USA: PMLR, jan
  2018, pp. 1--15.

\bibitem[Dulhanty and Wong(2019)]{Dulhanty2019}
C.~Dulhanty and A.~Wong, ``{Auditing ImageNet: Towards a Model-driven Framework
  for Annotating Demographic Attributes of Large-Scale Image Datasets},'' may
  2019, arXiv:1905.01347.

\bibitem[Yang et~al.(2020)Yang, Qinami, Fei-Fei, Deng, and
  Russakovsky]{Yang2020}
K.~Yang, K.~Qinami, L.~Fei-Fei, J.~Deng, and O.~Russakovsky, ``{Towards fairer
  datasets},'' in \emph{Proceedings of the 2020 Conference on Fairness,
  Accountability, and Transparency}.\hskip 1em plus 0.5em minus 0.4em\relax New
  York, NY, USA: ACM, jan 2020, pp. 547--558.

\bibitem[Steed and Caliskan(2021)]{Steed2020}
R.~Steed and A.~Caliskan, ``{Image Representations Learned With Unsupervised
  Pre-Training Contain Human-like Biases},'' in \emph{Proceedings of the 2021
  ACM Conference on Fairness, Accountability, and Transparency}.\hskip 1em plus
  0.5em minus 0.4em\relax New York, NY, USA: ACM, mar 2021, pp. 701--713.

\bibitem[Villareale et~al.(2020)Villareale, Acosta-Ruiz, Arcaro, Fox, Freed,
  Gray, L{\"{o}}we, Nuchprayoon, Sladek, Weigelt, Li, Risi, and
  Zhu]{Villareale2020}
J.~Villareale, A.~V. Acosta-Ruiz, S.~A. Arcaro, T.~Fox, E.~Freed, R.~C. Gray,
  M.~L{\"{o}}we, P.~Nuchprayoon, A.~Sladek, R.~Weigelt, Y.~Li, S.~Risi, and
  J.~Zhu, ``{iNNk},'' in \emph{Extended Abstracts of the 2020 Annual Symposium
  on Computer-Human Interaction in Play}.\hskip 1em plus 0.5em minus
  0.4em\relax New York, NY, USA: ACM, nov 2020, pp. 33--37.

\end{thebibliography}

main.bib


%@{nourls,
%}

@article{lecun_deep_2015,
  author =        {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  journal =       {Nature},
  month =         {may},
  number =        {7553},
  pages =         {436--444},
  title =         {{Deep learning}},
  volume =        {521},
  year =          {2015},
  doi =           {10.1038/nature14539},
  issn =          {0028-0836},
}

@article{su_one_2019,
  author =        {Su, Jiawei and Vargas, Danilo Vasconcellos and
                   Sakurai, Kouichi},
  journal =       {IEEE Transactions on Evolutionary Computation},
  month =         {oct},
  number =        {5},
  pages =         {828--841},
  title =         {{One Pixel Attack for Fooling Deep Neural Networks}},
  volume =        {23},
  year =          {2019},
  doi =           {10.1109/TEVC.2019.2890858},
  issn =          {1089-778X},
}

@unpublished{Cai2015,
  author =        {Cai, Hongping and Wu, Qi and Corradi, Tadeo and
                   Hall, Peter},
  month =         {may},
  title =         {{The Cross-Depiction Problem: Computer Vision
                   Algorithms for Recognising Objects in Artwork and in
                   Photographs}},
  year =          {2015},
  abstract =      {The cross-depiction problem is that of recognising
                   visual objects regardless of whether they are
                   photographed, painted, drawn, etc. It is a
                   potentially significant yet under-researched problem.
                   Emulating the remarkable human ability to recognise
                   objects in an astonishingly wide variety of depictive
                   forms is likely to advance both the foundations and
                   the applications of Computer Vision. In this paper we
                   benchmark classification, domain adaptation, and deep
                   learning methods; demonstrating that none perform
                   consistently well in the cross-depiction problem.
                   Given the current interest in deep learning, the fact
                   such methods exhibit the same behaviour as all but
                   one other method: they show a significant fall in
                   performance over inhomogeneous databases compared to
                   their peak performance, which is always over data
                   comprising photographs only. Rather, we find the
                   methods that have strong models of spatial relations
                   between parts tend to be more robust and therefore
                   conclude that such information is important in
                   modelling object classes regardless of appearance
                   details.},
  note = 		  {arXiv:1505.00110}
}

@unpublished{boulton_artistic_2019,
  author =        {Boulton, Padraig and Hall, Peter},
  journal =       {arXiv:1907.12622 [cs]},
  month =         {jul},
  title =         {{Artistic Domain Generalisation Methods are Limited
                   by their Deep Representations}},
  year =          {2019},
  abstract =      {The cross-depiction problem refers to the task of
                   recognising visual objects regardless of their
                   depictions; whether photographed, painted, sketched,
                   {\{}$\backslash$em etc{\}}. In the past, some
                   researchers considered cross-depiction to be domain
                   adaptation (DA). More recent work considers
                   cross-depiction as domain generalisation (DG), in
                   which algorithms extend recognition from one set of
                   domains (such as photographs and coloured artwork) to
                   another (such as sketches). We show that fixing the
                   last layer of AlexNet to random values provides a
                   performance comparable to state of the art DA and DG
                   algorithms, when tested over the PACS benchmark. With
                   support from background literature, our results lead
                   us to conclude that texture alone is insufficient to
                   support generalisation; rather, higher-order
                   representations such as structure and shape are
                   necessary.},
  annote =        {arXiv: 1907.12622},
  note = 		  {arXiv:1907.12622}
}

@inproceedings{ciecko2020,
  address =       {Online},
  author =        {Ciecko, Brendan},
  booktitle =     {Museums and the Web 2020},
  publisher =     {Museums and the Web},
  title =         {{AI Sees What? The Good, the Bad, and teh Ugly of
                   Machine Vision for Museum Collections}},
  year =          {2020},
                  bad-and-the-ugly-of-machine-vision-for-museum-collections/},
}

@inproceedings{mollica_send_2017,
address = {Vancouver, BC},
author = {Mollica, Jay},
booktitle = {MW18: Museums and the Web 2018},
mendeley-groups = {phd/artnet},
title = {{Send Me SFMOMA}},
year = {2018}
}


@unpublished{Geirhos2018,
  author =        {Geirhos, Robert and Rubisch, Patricia and
                   Michaelis, Claudio and Bethge, Matthias and
                   Wichmann, Felix A. and Brendel, Wieland},
  month =         {nov},
  title =         {{ImageNet-trained CNNs are biased towards texture;
                   increasing shape bias improves accuracy and
                   robustness}},
  year =          {2018},
  abstract =      {Convolutional Neural Networks (CNNs) are commonly
                   thought to recognise objects by learning increasingly
                   complex representations of object shapes. Some recent
                   studies suggest a more important role of image
                   textures. We here put these conflicting hypotheses to
                   a quantitative test by evaluating CNNs and human
                   observers on images with a texture-shape cue
                   conflict. We show that ImageNet-trained CNNs are
                   strongly biased towards recognising textures rather
                   than shapes, which is in stark contrast to human
                   behavioural evidence and reveals fundamentally
                   different classification strategies. We then
                   demonstrate that the same standard architecture
                   (ResNet-50) that learns a texture-based
                   representation on ImageNet is able to learn a
                   shape-based representation instead when trained on
                   "Stylized-ImageNet", a stylized version of ImageNet.
                   This provides a much better fit for human behavioural
                   performance in our well-controlled psychophysical lab
                   setting (nine experiments totalling 48,560
                   psychophysical trials across 97 observers) and comes
                   with a number of unexpected emergent benefits such as
                   improved object detection performance and previously
                   unseen robustness towards a wide range of image
                   distortions, highlighting advantages of a shape-based
                   representation.},
  note = 		  {arXiv:1811.12231}
}

@book{berry_digital_2017,
  address =       {Cambridge, UK and Malden, MA},
  author =        {Berry, David M and Fagerjord, Anders},
  publisher =     {Polity Press},
  title =         {{Digital humanities}},
  year =          {2017},
}

@book{manovich_ai_2018,
  address =       {Moscow},
  author =        {Manovich, Lev},
  publisher =     {Strelka Press},
  title =         {{AI Aesthetics}},
  year =          {2018},
}

@book{manovich_cultural_2020,
  address =       {Cambridge, MA},
  author =        {Manovich, Lev},
  publisher =     {The MIT Press},
  title =         {{Cultural Analytics}},
  year =          {2020},
  abstract =      {A book at the intersection of data science and media
                   studies, presenting concepts and methods for
                   computational analysis of cultural data. How can we
                   see a billion images? What analytical methods can we
                   bring to bear on the astonishing scale of digital
                   culture—the terabytes of photographs shared on
                   social media every day, the hundreds of millions of
                   songs created by twenty million musicians on Sound
                   Cloud, the content of four billion Pinterest boards?
                   In Cultural Analytics, Lev Manovich presents concepts
                   and methods for computational analysis of cultural
                   data, with a particular focus on visual media.
                   Drawing on more than a decade of research and
                   projects from his own lab, Manovich—the founder of
                   the field of cultural analytics—offers a gentle,
                   nontechnical introduction to selected key concepts of
                   data science and discusses the ways that our society
                   uses data and algorithms.Manovich offers examples of
                   computational cultural analysis and discusses the
                   shift from “new media” to “more media”;
                   explains how to turn cultural processes into
                   computational data; and introduces concepts for
                   exploring cultural datasets using data visualization
                   as well as other recently developed methods for
                   analyzing image and video datasets. He considers both
                   the possibilities and the limitations of
                   computational methods, and how using them challenges
                   our existing ideas about culture and how to study it.
                   Cultural Analytics is a book of media theory. Arguing
                   that before we can theorize digital culture, we need
                   to see it, and that, because of its scale, to see it
                   we need computers, Manovich provides scholars with
                   practical tools for studying contemporary media.},
  annote =        {Publisher: The MIT Press},
}

@electronic{Merritt2017,
  author =        {Merritt, Elizabeth},
  organization =     {American Alliance of Museums},
  month =         {may},
  title =         {{Artificial Intelligence The Rise Of The Intelligent
                   Machine}},
  year =          {2017},
  url =           {https://www.aam-us.org/2017/05/01/artificial-intelligence-
                  the-rise-of-the-intelligent-machine/},
}

@electronic{Bailey2019,
  author =        {Bailey, Jason},
  organization =     {Artnome},
  month =         {apr},
  title =         {{Solving Art's Data Problem - Part One, Museums}},
  year =          {2019},
  url =           {https://www.artnome.com/news/2019/4/29/solving-arts-data-
                  problem-part-one-museums},
}

@electronic{Smith2019,
  author =        {Smith, Jonas Heide},
  organization =     {SMK Open},
  month =         {sep},
  title =         {{SMK's collection search levels up}},
  year =          {2019},
  url =           {https://medium.com/smk-open/smks-collection-search-levels-
                  up-cf8e967e9346},
}

@inproceedings{Chang2017,
  address =       {New York, NY, USA},
  author =        {Chang, Joseph Chee and Amershi, Saleema and
                   Kamar, Ece},
  booktitle =     {Proceedings of the 2017 CHI Conference on Human
                   Factors in Computing Systems},
  month =         {may},
  pages =         {2334--2346},
  publisher =     {ACM},
  title =         {{Revolt}},
  volume =        {2017-May},
  year =          {2017},
  abstract =      {Crowdsourcing provides a scalable and efficient way
                   to construct labeled datasets for training machine
                   learning systems. However, creating comprehensive
                   label guidelines for crowd-workers is often
                   prohibitive even for seemingly simple concepts.
                   Incomplete or ambiguous label guidelines can then
                   result in differing interpretations of concepts and
                   inconsistent labels. Existing approaches for
                   improving label quality, such as worker screening or
                   detection of poor work, are ineffective for this
                   problem and can lead to rejection of honest work and
                   a missed opportunity to capture rich interpretations
                   about data. We introduce Revolt, a collaborative
                   approach that brings ideas from expert annotation
                   workflows to crowd-based labeling. Revolt eliminates
                   the burden of creating detailed label guidelines by
                   harnessing crowd disagreements to identify ambiguous
                   concepts and create rich structures (groups of
                   semantically related items) for post-hoc label
                   decisions. Experiments comparing Revolt to
                   traditional crowdsourced labeling show that Revolt
                   produces high quality labels without requiring label
                   guidelines in turn for an increase in monetary cost.
                   This up front cost, however, is mitigated by Revolt's
                   ability to produce reusable structures that can
                   accommodate a variety of label boundaries without
                   requiring new data to be collected. Further
                   comparisons of Revolt's collaborative and
                   non-collaborative variants show that collaboration
                   reaches higher label accuracy with lower monetary
                   cost.},
  doi =           {10.1145/3025453.3026044},
  isbn =          {9781450346559},
}

@inproceedings{Deng2010,
  author =        {Deng, Jia and Dong, Wei and Socher, Richard and
                   Li, Li-Jia and {Kai Li} and {Li Fei-Fei}},
  booktitle =     {2009 IEEE Conference on Computer Vision and Pattern
                   Recognition},
  month =         {jun},
  pages =         {248--255},
  publisher =     {IEEE},
  title =         {{ImageNet: A large-scale hierarchical image
                   database}},
  year =          {2009},
  abstract =      {The explosion of image data on the Internet has the
                   potential to foster more sophisticated and robust
                   models and algorithms to index, retrieve, organize
                   and interact with images and multimedia data. But
                   exactly how such data can be harnessed and organized
                   remains a critical problem. We introduce here a new
                   database called ldquoImageNetrdquo, a large-scale
                   ontology of images built upon the backbone of the
                   WordNet structure. ImageNet aims to populate the
                   majority of the 80,000 synsets of WordNet with an
                   average of 500-1000 clean and full resolution images.
                   This will result in tens of millions of annotated
                   images organized by the semantic hierarchy of
                   WordNet. This paper offers a detailed analysis of
                   ImageNet in its current state: 12 subtrees with 5247
                   synsets and 3.2 million images in total. We show that
                   ImageNet is much larger in scale and diversity and
                   much more accurate than the current image datasets.
                   Constructing such a large-scale database is a
                   challenging task. We describe the data collection
                   scheme with Amazon Mechanical Turk. Lastly, we
                   illustrate the usefulness of ImageNet through three
                   simple applications in object recognition, image
                   classification and automatic object clustering. We
                   hope that the scale, accuracy, diversity and
                   hierarchical structure of ImageNet can offer
                   unparalleled opportunities to researchers in the
                   computer vision community and beyond.},
  doi =           {10.1109/CVPR.2009.5206848},
  isbn =          {978-1-4244-3992-8},
}

@incollection{Lin2014,
  author =        {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and
                   Hays, James and Perona, Pietro and Ramanan, Deva and
                   Doll{\'{a}}r, Piotr and Zitnick, C. Lawrence},
  booktitle =     {Lecture Notes in Computer Science (including
                   subseries Lecture Notes in Artificial Intelligence
                   and Lecture Notes in Bioinformatics)},
  number =        {PART 5},
  pages =         {740--755},
  publisher =     {Springer Verlag},
  title =         {{Microsoft COCO: Common Objects in Context}},
  volume =        {8693 LNCS},
  year =          {2014},
  abstract =      {We present a new dataset with the goal of advancing
                   the state-of-the-art in object recognition by placing
                   the question of object recognition in the context of
                   the broader question of scene understanding. This is
                   achieved by gathering images of complex everyday
                   scenes containing common objects in their natural
                   context. Objects are labeled using per-instance
                   segmentations to aid in precise object localization.
                   Our dataset contains photos of 91 objects types that
                   would be easily recognizable by a 4 year old. With a
                   total of 2.5 million labeled instances in 328k
                   images, the creation of our dataset drew upon
                   extensive crowd worker involvement via novel user
                   interfaces for category detection, instance spotting
                   and instance segmentation. We present a detailed
                   statistical analysis of the dataset in comparison to
                   PASCAL, ImageNet, and SUN. Finally, we provide
                   baseline performance analysis for bounding box and
                   segmentation detection results using a Deformable
                   Parts Model. {\textcopyright} 2014 Springer
                   International Publishing.},
  doi =           {10.1007/978-3-319-10602-1_48},
  issn =          {16113349},
                  1{\_}48 http://link.springer.com/10.1007/978-3-319-10602-
                  1{\_}48},
}

@inproceedings{Wilber2017,
  author =        {Wilber, Michael J. and Fang, Chen and Jin, Hailin and
                   Hertzmann, Aaron and Collomosse, John and
                   Belongie, Serge},
  booktitle =     {2017 IEEE International Conference on Computer Vision
                   (ICCV)},
  month =         {oct},
  pages =         {1211--1220},
  publisher =     {IEEE},
  title =         {{BAM! The Behance Artistic Media Dataset for
                   Recognition Beyond Photography}},
  volume =        {2017-Octob},
  year =          {2017},
  abstract =      {Computer vision systems are designed to work well
                   within the context of everyday photography. However,
                   artists often render the world around them in ways
                   that do not resemble photographs. Artwork produced by
                   people is not constrained to mimic the physical
                   world, making it more challenging for machines to
                   recognize.,,This work is a step toward teaching
                   machines how to categorize images in ways that are
                   valuable to humans. First, we collect a large-scale
                   dataset of contemporary artwork from Behance, a
                   website containing millions of portfolios from
                   professional and commercial artists. We annotate
                   Behance imagery with rich attribute labels for
                   content, emotions, and artistic media. Furthermore,
                   we carry out baseline experiments to show the value
                   of this dataset for artistic style prediction, for
                   improving the generality of existing object
                   classifiers, and for the study of visual domain
                   adaptation. We believe our Behance Artistic Media
                   dataset will be a good starting point for researchers
                   wishing to study artistic imagery and relevant
                   problems. This dataset can be found at
                   https://bam-dataset.org/},
  doi =           {10.1109/ICCV.2017.136},
  isbn =          {978-1-5386-1032-9},
  issn =          {15505499},
}

@inproceedings{Westlake2016,
  author =        {Westlake, Nicholas and Cai, Hongping and Hall, Peter},
  booktitle =     {Lecture Notes in Computer Science (including
                   subseries Lecture Notes in Artificial Intelligence
                   and Lecture Notes in Bioinformatics)},
  pages =         {825--841},
  publisher =     {Springer Verlag},
  title =         {{Detecting people in artwork with CNNs}},
  volume =        {9913 LNCS},
  year =          {2016},
  abstract =      {CNNs have massively improved performance in object
                   detection in photographs. However research into
                   object detection in artwork remains limited. We show
                   state-of-the-art performance on a challenging
                   dataset, People-Art, which contains people from
                   photos, cartoons and 41 different artwork movements.
                   We achieve this high performance by finetuning a CNN
                   for this task, thus also demonstrating that training
                   CNNs on photos results in overfitting for photos:
                   only the first three or four layers transfer from
                   photos to artwork. Although the CNN's performance is
                   the highest yet, it remains less than 60{\%} AP,
                   suggesting further work is needed for the
                   cross-depiction problem.},
  doi =           {10.1007/978-3-319-46604-0_57},
  isbn =          {9783319466033},
  issn =          {16113349},
                  0{\_}57},
}

@inproceedings{Redmon2016,
  author =        {Redmon, Joseph and Divvala, Santosh and
                   Girshick, Ross and Farhadi, Ali},
  booktitle =     {Proceedings of the IEEE Computer Society Conference
                   on Computer Vision and Pattern Recognition},
  month =         {dec},
  pages =         {779--788},
  publisher =     {IEEE Computer Society},
  title =         {{You only look once: Unified, real-time object
                   detection}},
  volume =        {2016-December},
  year =          {2016},
  abstract =      {We present YOLO, a new approach to object detection.
                   Prior work on object detection repurposes classifiers
                   to perform detection. Instead, we frame object
                   detection as a regression problem to spatially
                   separated bounding boxes and associated class
                   probabilities. A single neural network predicts
                   bounding boxes and class probabilities directly from
                   full images in one evaluation. Since the whole
                   detection pipeline is a single network, it can be
                   optimized end-to-end directly on detection
                   performance. Our unified architecture is extremely
                   fast. Our base YOLO model processes images in
                   real-time at 45 frames per second. A smaller version
                   of the network, Fast YOLO, processes an astounding
                   155 frames per second while still achieving double
                   the mAP of other real-time detectors. Compared to
                   state-of-the-art detection systems, YOLO makes more
                   localization errors but is less likely to predict
                   false positives on background. Finally, YOLO learns
                   very general representations of objects. It
                   outperforms other detection methods, including DPM
                   and R-CNN, when generalizing from natural images to
                   other domains like artwork.},
  doi =           {10.1109/CVPR.2016.91},
  isbn =          {9781467388504},
  issn =          {10636919},
}

@inproceedings{Huang2017,
  author =        {Huang, Xun and Belongie, Serge},
  booktitle =     {Proceedings of the IEEE International Conference on
                   Computer Vision},
  month =         {dec},
  pages =         {1510--1519},
  publisher =     {Institute of Electrical and Electronics Engineers
                   Inc.},
  title =         {{Arbitrary Style Transfer in Real-Time with Adaptive
                   Instance Normalization}},
  volume =        {2017-October},
  year =          {2017},
  abstract =      {Gatys et al. recently introduced a neural algorithm
                   that renders a content image in the style of another
                   image, achieving so-called style transfer. However,
                   their framework requires a slow iterative
                   optimization process, which limits its practical
                   application. Fast approximations with feed-forward
                   neural networks have been proposed to speed up neural
                   style transfer. Unfortunately, the speed improvement
                   comes at a cost: the network is usually tied to a
                   fixed set of styles and cannot adapt to arbitrary new
                   styles. In this paper, we present a simple yet
                   effective approach that for the first time enables
                   arbitrary style transfer in real-time. At the heart
                   of our method is a novel adaptive instance
                   normalization (AdaIN) layer that aligns the mean and
                   variance of the content features with those of the
                   style features. Our method achieves speed comparable
                   to the fastest existing approach, without the
                   restriction to a pre-defined set of styles. In
                   addition, our approach allows flexible user controls
                   such as content-style trade-off, style interpolation,
                   color {\&} spatial controls, all using a single
                   feed-forward neural network.},
  doi =           {10.1109/ICCV.2017.167},
  isbn =          {9781538610329},
  issn =          {15505499},
}

@unpublished{Gonthier2020,
  author =        {Gonthier, Nicolas and Ladjal, Sa{\"{i}}d and
                   Gousseau, Yann},
  month =         {aug},
  title =         {{Multiple instance learning on deep features for
                   weakly supervised object detection with extreme
                   domain shifts}},
  year =          {2020},
  abstract =      {Weakly supervised object detection (WSOD) using only
                   image-level annotations has attracted a growing
                   attention over the past few years. Whereas such task
                   is typically addressed with a domain-specific
                   solution focused on natural images, we show that a
                   simple multiple instance approach applied on
                   pre-trained deep features yields excellent
                   performances on non-photographic datasets, possibly
                   including new classes. The approach does not include
                   any fine-tuning or cross-domain learning and is
                   therefore efficient and possibly applicable to
                   arbitrary datasets and classes. We investigate
                   several flavors of the proposed approach, some
                   including multi-layers perceptron and polyhedral
                   classifiers. Despite its simplicity, our method shows
                   competitive results on a range of publicly available
                   datasets, including paintings (People-Art, IconArt),
                   watercolors, cliparts and comics and allows to
                   quickly learn unseen visual categories.},
  note = 		  {arXiv:2008.01178}
}

@inproceedings{Buolamwini2018,
  address =       {New York, USA},
  author =        {Buolamwini, Joy and Gebru, Timnit},
  booktitle =     {Proceedings of Machine Learning Research},
  editor =        {Friedler, Sorelle A. and Abstract, Christo Wilson},
  month =         {jan},
  pages =         {1--15},
  publisher =     {PMLR},
  title =         {{Gender Shades: Intersectional Accuracy Disparities
                   in Commercial Gender Classification}},
  volume =        {81},
  year =          {2018},
  abstract =      {Recent studies demonstrate that machine learning
                   algorithms can discriminate based on classes like
                   race and gender. In this work, we present an approach
                   to evaluate bias present in automated facial analysis
                   algorithms and datasets with respect to phe-notypic
                   subgroups. Using the dermatologist approved
                   Fitzpatrick Skin Type classification system, we
                   characterize the gender and skin type distribution of
                   two facial analysis benchmarks, IJB-A and Adience. We
                   find that these datasets are overwhelmingly composed
                   of lighter-skinned subjects (79.6{\%} for IJB-A and
                   86.2{\%} for Adience) and introduce a new facial
                   analysis dataset which is balanced by gender and skin
                   type. We evaluate 3 commercial gender classification
                   systems using our dataset and show that
                   darker-skinned females are the most misclassified
                   group (with error rates of up to 34.7{\%}). The
                   maximum error rate for lighter-skinned males is
                   0.8{\%}. The substantial disparities in the accuracy
                   of classifying darker females, lighter females,
                   darker males, and lighter males in gender
                   classification systems require urgent attention if
                   commercial companies are to build genuinely fair,
                   transparent and accountable facial analysis
                   algorithms.},
  issn =          {2640-3498},
}

@unpublished{Dulhanty2019,
  author =        {Dulhanty, Chris and Wong, Alexander},
  month =         {may},
  publisher =     {arXiv},
  title =         {{Auditing ImageNet: Towards a Model-driven Framework
                   for Annotating Demographic Attributes of Large-Scale
                   Image Datasets}},
  year =          {2019},
  note = 		  {arXiv:1905.01347}
}

@inproceedings{Yang2020,
  address =       {New York, NY, USA},
  author =        {Yang, Kaiyu and Qinami, Klint and Fei-Fei, Li and
                   Deng, Jia and Russakovsky, Olga},
  booktitle =     {Proceedings of the 2020 Conference on Fairness,
                   Accountability, and Transparency},
  month =         {jan},
  pages =         {547--558},
  publisher =     {ACM},
  title =         {{Towards fairer datasets}},
  year =          {2020},
  doi =           {10.1145/3351095.3375709},
  isbn =          {9781450369367},
}

@inproceedings{Steed2020,
abstract = {Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to face recognition. But do unsupervised computer vision models automatically learn implicit patterns and embed social biases that could have harmful downstream effects? For the first time, we develop a novel method for quantifying biased associations between representations of social concepts and attributes in images. We find that state-of-the-art unsupervised models trained on ImageNet, a popular benchmark image dataset curated from internet images, automatically learn racial, gender, and intersectional biases. We replicate 8 of 15 documented human biases from social psychology, from the innocuous, as with insects and flowers, to the potentially harmful, as with race and gender. For the first time in the image domain, we replicate human-like biases about skin-tone and weight. Our results also closely match three hypotheses about intersectional bias from social psychology. When compared with statistical patterns in online image datasets, our findings suggest that machine learning models can automatically learn bias from the way people are stereotypically portrayed on the web.},
address = {New York, NY, USA},
archivePrefix = {arXiv},
arxivId = {2010.15052},
author = {Steed, Ryan and Caliskan, Aylin},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
doi = {10.1145/3442188.3445932},
eprint = {2010.15052},
file = {:Users/davk/Documents/Mendeley Desktop/2020 - Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases.pdf:pdf},
isbn = {9781450383097},
mendeley-groups = {phd/artnet},
month = {mar},
pages = {701--713},
publisher = {ACM},
title = {{Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases}},
year = {2021}
}


@inproceedings{Villareale2020,
  address =       {New York, NY, USA},
  author =        {Villareale, Jennifer and Acosta-Ruiz, Ana V. and
                   Arcaro, Samuel Adam and Fox, Thomas and Freed, Evan and
                   Gray, Robert C. and L{\"{o}}we, Mathias and
                   Nuchprayoon, Panote and Sladek, Aleksanteri and
                   Weigelt, Rush and Li, Yifu and Risi, Sebastian and
                   Zhu, Jichen},
  booktitle =     {Extended Abstracts of the 2020 Annual Symposium on
                   Computer-Human Interaction in Play},
  month =         {nov},
  pages =         {33--37},
  publisher =     {ACM},
  title =         {{iNNk}},
  year =          {2020},
  abstract =      {This paper presents iNNK, a multiplayer drawing game
                   where human players team up against an NN. The
                   players need to successfully communicate a secret
                   code word to each other through drawings, without
                   being deciphered by the NN. With this game, we aim to
                   foster a playful environment where players can, in a
                   small way, go from passive consumers of NN
                   applications to creative thinkers and critical
                   challengers.},
  doi =           {10.1145/3383668.3419858},
  isbn =          {9781450375870},
}


nourls.bib
%@IEEEtranBSTCTL{nourls,
%    CTLuse_url = "no"
%}
references.bib


%@{nourls,
%}

@article{lecun_deep_2015,
  author =        {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  journal =       {Nature},
  month =         {may},
  number =        {7553},
  pages =         {436--444},
  title =         {{Deep learning}},
  volume =        {521},
  year =          {2015},
  doi =           {10.1038/nature14539},
  issn =          {0028-0836},
}

@article{su_one_2019,
  author =        {Su, Jiawei and Vargas, Danilo Vasconcellos and
                   Sakurai, Kouichi},
  journal =       {IEEE Transactions on Evolutionary Computation},
  month =         {oct},
  number =        {5},
  pages =         {828--841},
  title =         {{One Pixel Attack for Fooling Deep Neural Networks}},
  volume =        {23},
  year =          {2019},
  doi =           {10.1109/TEVC.2019.2890858},
  issn =          {1089-778X},
}

@unpublished{Cai2015,
  author =        {Cai, Hongping and Wu, Qi and Corradi, Tadeo and
                   Hall, Peter},
  month =         {may},
  title =         {{The Cross-Depiction Problem: Computer Vision
                   Algorithms for Recognising Objects in Artwork and in
                   Photographs}},
  year =          {2015},
  abstract =      {The cross-depiction problem is that of recognising
                   visual objects regardless of whether they are
                   photographed, painted, drawn, etc. It is a
                   potentially significant yet under-researched problem.
                   Emulating the remarkable human ability to recognise
                   objects in an astonishingly wide variety of depictive
                   forms is likely to advance both the foundations and
                   the applications of Computer Vision. In this paper we
                   benchmark classification, domain adaptation, and deep
                   learning methods; demonstrating that none perform
                   consistently well in the cross-depiction problem.
                   Given the current interest in deep learning, the fact
                   such methods exhibit the same behaviour as all but
                   one other method: they show a significant fall in
                   performance over inhomogeneous databases compared to
                   their peak performance, which is always over data
                   comprising photographs only. Rather, we find the
                   methods that have strong models of spatial relations
                   between parts tend to be more robust and therefore
                   conclude that such information is important in
                   modelling object classes regardless of appearance
                   details.},
  note = 		  {arXiv:1505.00110}
}

@unpublished{boulton_artistic_2019,
  author =        {Boulton, Padraig and Hall, Peter},
  journal =       {arXiv:1907.12622 [cs]},
  month =         {jul},
  title =         {{Artistic Domain Generalisation Methods are Limited
                   by their Deep Representations}},
  year =          {2019},
  abstract =      {The cross-depiction problem refers to the task of
                   recognising visual objects regardless of their
                   depictions; whether photographed, painted, sketched,
                   {\{}$\backslash$em etc{\}}. In the past, some
                   researchers considered cross-depiction to be domain
                   adaptation (DA). More recent work considers
                   cross-depiction as domain generalisation (DG), in
                   which algorithms extend recognition from one set of
                   domains (such as photographs and coloured artwork) to
                   another (such as sketches). We show that fixing the
                   last layer of AlexNet to random values provides a
                   performance comparable to state of the art DA and DG
                   algorithms, when tested over the PACS benchmark. With
                   support from background literature, our results lead
                   us to conclude that texture alone is insufficient to
                   support generalisation; rather, higher-order
                   representations such as structure and shape are
                   necessary.},
  annote =        {arXiv: 1907.12622},
  note = 		  {arXiv:1907.12622}
}

@inproceedings{ciecko2020,
  address =       {Online},
  author =        {Ciecko, Brendan},
  booktitle =     {Museums and the Web 2020},
  publisher =     {Museums and the Web},
  title =         {{AI Sees What? The Good, the Bad, and teh Ugly of
                   Machine Vision for Museum Collections}},
  year =          {2020},
                  bad-and-the-ugly-of-machine-vision-for-museum-collections/},
}

@inproceedings{mollica_send_2017,
address = {Vancouver, BC},
author = {Mollica, Jay},
booktitle = {MW18: Museums and the Web 2018},
mendeley-groups = {phd/artnet},
title = {{Send Me SFMOMA}},
year = {2018}
}


@unpublished{Geirhos2018,
  author =        {Geirhos, Robert and Rubisch, Patricia and
                   Michaelis, Claudio and Bethge, Matthias and
                   Wichmann, Felix A. and Brendel, Wieland},
  month =         {nov},
  title =         {{ImageNet-trained CNNs are biased towards texture;
                   increasing shape bias improves accuracy and
                   robustness}},
  year =          {2018},
  abstract =      {Convolutional Neural Networks (CNNs) are commonly
                   thought to recognise objects by learning increasingly
                   complex representations of object shapes. Some recent
                   studies suggest a more important role of image
                   textures. We here put these conflicting hypotheses to
                   a quantitative test by evaluating CNNs and human
                   observers on images with a texture-shape cue
                   conflict. We show that ImageNet-trained CNNs are
                   strongly biased towards recognising textures rather
                   than shapes, which is in stark contrast to human
                   behavioural evidence and reveals fundamentally
                   different classification strategies. We then
                   demonstrate that the same standard architecture
                   (ResNet-50) that learns a texture-based
                   representation on ImageNet is able to learn a
                   shape-based representation instead when trained on
                   "Stylized-ImageNet", a stylized version of ImageNet.
                   This provides a much better fit for human behavioural
                   performance in our well-controlled psychophysical lab
                   setting (nine experiments totalling 48,560
                   psychophysical trials across 97 observers) and comes
                   with a number of unexpected emergent benefits such as
                   improved object detection performance and previously
                   unseen robustness towards a wide range of image
                   distortions, highlighting advantages of a shape-based
                   representation.},
  note = 		  {arXiv:1811.12231}
}

@book{berry_digital_2017,
  address =       {Cambridge, UK and Malden, MA},
  author =        {Berry, David M and Fagerjord, Anders},
  publisher =     {Polity Press},
  title =         {{Digital humanities}},
  year =          {2017},
}

@book{manovich_ai_2018,
  address =       {Moscow},
  author =        {Manovich, Lev},
  publisher =     {Strelka Press},
  title =         {{AI Aesthetics}},
  year =          {2018},
}

@book{manovich_cultural_2020,
  address =       {Cambridge, MA},
  author =        {Manovich, Lev},
  publisher =     {The MIT Press},
  title =         {{Cultural Analytics}},
  year =          {2020},
  abstract =      {A book at the intersection of data science and media
                   studies, presenting concepts and methods for
                   computational analysis of cultural data. How can we
                   see a billion images? What analytical methods can we
                   bring to bear on the astonishing scale of digital
                   culture—the terabytes of photographs shared on
                   social media every day, the hundreds of millions of
                   songs created by twenty million musicians on Sound
                   Cloud, the content of four billion Pinterest boards?
                   In Cultural Analytics, Lev Manovich presents concepts
                   and methods for computational analysis of cultural
                   data, with a particular focus on visual media.
                   Drawing on more than a decade of research and
                   projects from his own lab, Manovich—the founder of
                   the field of cultural analytics—offers a gentle,
                   nontechnical introduction to selected key concepts of
                   data science and discusses the ways that our society
                   uses data and algorithms.Manovich offers examples of
                   computational cultural analysis and discusses the
                   shift from “new media” to “more media”;
                   explains how to turn cultural processes into
                   computational data; and introduces concepts for
                   exploring cultural datasets using data visualization
                   as well as other recently developed methods for
                   analyzing image and video datasets. He considers both
                   the possibilities and the limitations of
                   computational methods, and how using them challenges
                   our existing ideas about culture and how to study it.
                   Cultural Analytics is a book of media theory. Arguing
                   that before we can theorize digital culture, we need
                   to see it, and that, because of its scale, to see it
                   we need computers, Manovich provides scholars with
                   practical tools for studying contemporary media.},
  annote =        {Publisher: The MIT Press},
}

@electronic{Merritt2017,
  author =        {Merritt, Elizabeth},
  organization =     {American Alliance of Museums},
  month =         {may},
  title =         {{Artificial Intelligence The Rise Of The Intelligent
                   Machine}},
  year =          {2017},
  url =           {https://www.aam-us.org/2017/05/01/artificial-intelligence-
                  the-rise-of-the-intelligent-machine/},
}

@electronic{Bailey2019,
  author =        {Bailey, Jason},
  organization =     {Artnome},
  month =         {apr},
  title =         {{Solving Art's Data Problem - Part One, Museums}},
  year =          {2019},
  url =           {https://www.artnome.com/news/2019/4/29/solving-arts-data-
                  problem-part-one-museums},
}

@electronic{Smith2019,
  author =        {Smith, Jonas Heide},
  organization =     {SMK Open},
  month =         {sep},
  title =         {{SMK's collection search levels up}},
  year =          {2019},
  url =           {https://medium.com/smk-open/smks-collection-search-levels-
                  up-cf8e967e9346},
}

@inproceedings{Chang2017,
  address =       {New York, NY, USA},
  author =        {Chang, Joseph Chee and Amershi, Saleema and
                   Kamar, Ece},
  booktitle =     {Proceedings of the 2017 CHI Conference on Human
                   Factors in Computing Systems},
  month =         {may},
  pages =         {2334--2346},
  publisher =     {ACM},
  title =         {{Revolt}},
  volume =        {2017-May},
  year =          {2017},
  abstract =      {Crowdsourcing provides a scalable and efficient way
                   to construct labeled datasets for training machine
                   learning systems. However, creating comprehensive
                   label guidelines for crowd-workers is often
                   prohibitive even for seemingly simple concepts.
                   Incomplete or ambiguous label guidelines can then
                   result in differing interpretations of concepts and
                   inconsistent labels. Existing approaches for
                   improving label quality, such as worker screening or
                   detection of poor work, are ineffective for this
                   problem and can lead to rejection of honest work and
                   a missed opportunity to capture rich interpretations
                   about data. We introduce Revolt, a collaborative
                   approach that brings ideas from expert annotation
                   workflows to crowd-based labeling. Revolt eliminates
                   the burden of creating detailed label guidelines by
                   harnessing crowd disagreements to identify ambiguous
                   concepts and create rich structures (groups of
                   semantically related items) for post-hoc label
                   decisions. Experiments comparing Revolt to
                   traditional crowdsourced labeling show that Revolt
                   produces high quality labels without requiring label
                   guidelines in turn for an increase in monetary cost.
                   This up front cost, however, is mitigated by Revolt's
                   ability to produce reusable structures that can
                   accommodate a variety of label boundaries without
                   requiring new data to be collected. Further
                   comparisons of Revolt's collaborative and
                   non-collaborative variants show that collaboration
                   reaches higher label accuracy with lower monetary
                   cost.},
  doi =           {10.1145/3025453.3026044},
  isbn =          {9781450346559},
}

@inproceedings{Deng2010,
  author =        {Deng, Jia and Dong, Wei and Socher, Richard and
                   Li, Li-Jia and {Kai Li} and {Li Fei-Fei}},
  booktitle =     {2009 IEEE Conference on Computer Vision and Pattern
                   Recognition},
  month =         {jun},
  pages =         {248--255},
  publisher =     {IEEE},
  title =         {{ImageNet: A large-scale hierarchical image
                   database}},
  year =          {2009},
  abstract =      {The explosion of image data on the Internet has the
                   potential to foster more sophisticated and robust
                   models and algorithms to index, retrieve, organize
                   and interact with images and multimedia data. But
                   exactly how such data can be harnessed and organized
                   remains a critical problem. We introduce here a new
                   database called ldquoImageNetrdquo, a large-scale
                   ontology of images built upon the backbone of the
                   WordNet structure. ImageNet aims to populate the
                   majority of the 80,000 synsets of WordNet with an
                   average of 500-1000 clean and full resolution images.
                   This will result in tens of millions of annotated
                   images organized by the semantic hierarchy of
                   WordNet. This paper offers a detailed analysis of
                   ImageNet in its current state: 12 subtrees with 5247
                   synsets and 3.2 million images in total. We show that
                   ImageNet is much larger in scale and diversity and
                   much more accurate than the current image datasets.
                   Constructing such a large-scale database is a
                   challenging task. We describe the data collection
                   scheme with Amazon Mechanical Turk. Lastly, we
                   illustrate the usefulness of ImageNet through three
                   simple applications in object recognition, image
                   classification and automatic object clustering. We
                   hope that the scale, accuracy, diversity and
                   hierarchical structure of ImageNet can offer
                   unparalleled opportunities to researchers in the
                   computer vision community and beyond.},
  doi =           {10.1109/CVPR.2009.5206848},
  isbn =          {978-1-4244-3992-8},
}

@incollection{Lin2014,
  author =        {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and
                   Hays, James and Perona, Pietro and Ramanan, Deva and
                   Doll{\'{a}}r, Piotr and Zitnick, C. Lawrence},
  booktitle =     {Lecture Notes in Computer Science (including
                   subseries Lecture Notes in Artificial Intelligence
                   and Lecture Notes in Bioinformatics)},
  number =        {PART 5},
  pages =         {740--755},
  publisher =     {Springer Verlag},
  title =         {{Microsoft COCO: Common Objects in Context}},
  volume =        {8693 LNCS},
  year =          {2014},
  abstract =      {We present a new dataset with the goal of advancing
                   the state-of-the-art in object recognition by placing
                   the question of object recognition in the context of
                   the broader question of scene understanding. This is
                   achieved by gathering images of complex everyday
                   scenes containing common objects in their natural
                   context. Objects are labeled using per-instance
                   segmentations to aid in precise object localization.
                   Our dataset contains photos of 91 objects types that
                   would be easily recognizable by a 4 year old. With a
                   total of 2.5 million labeled instances in 328k
                   images, the creation of our dataset drew upon
                   extensive crowd worker involvement via novel user
                   interfaces for category detection, instance spotting
                   and instance segmentation. We present a detailed
                   statistical analysis of the dataset in comparison to
                   PASCAL, ImageNet, and SUN. Finally, we provide
                   baseline performance analysis for bounding box and
                   segmentation detection results using a Deformable
                   Parts Model. {\textcopyright} 2014 Springer
                   International Publishing.},
  doi =           {10.1007/978-3-319-10602-1_48},
  issn =          {16113349},
                  1{\_}48 http://link.springer.com/10.1007/978-3-319-10602-
                  1{\_}48},
}

@inproceedings{Wilber2017,
  author =        {Wilber, Michael J. and Fang, Chen and Jin, Hailin and
                   Hertzmann, Aaron and Collomosse, John and
                   Belongie, Serge},
  booktitle =     {2017 IEEE International Conference on Computer Vision
                   (ICCV)},
  month =         {oct},
  pages =         {1211--1220},
  publisher =     {IEEE},
  title =         {{BAM! The Behance Artistic Media Dataset for
                   Recognition Beyond Photography}},
  volume =        {2017-Octob},
  year =          {2017},
  abstract =      {Computer vision systems are designed to work well
                   within the context of everyday photography. However,
                   artists often render the world around them in ways
                   that do not resemble photographs. Artwork produced by
                   people is not constrained to mimic the physical
                   world, making it more challenging for machines to
                   recognize.,,This work is a step toward teaching
                   machines how to categorize images in ways that are
                   valuable to humans. First, we collect a large-scale
                   dataset of contemporary artwork from Behance, a
                   website containing millions of portfolios from
                   professional and commercial artists. We annotate
                   Behance imagery with rich attribute labels for
                   content, emotions, and artistic media. Furthermore,
                   we carry out baseline experiments to show the value
                   of this dataset for artistic style prediction, for
                   improving the generality of existing object
                   classifiers, and for the study of visual domain
                   adaptation. We believe our Behance Artistic Media
                   dataset will be a good starting point for researchers
                   wishing to study artistic imagery and relevant
                   problems. This dataset can be found at
                   https://bam-dataset.org/},
  doi =           {10.1109/ICCV.2017.136},
  isbn =          {978-1-5386-1032-9},
  issn =          {15505499},
}

@inproceedings{Westlake2016,
  author =        {Westlake, Nicholas and Cai, Hongping and Hall, Peter},
  booktitle =     {Lecture Notes in Computer Science (including
                   subseries Lecture Notes in Artificial Intelligence
                   and Lecture Notes in Bioinformatics)},
  pages =         {825--841},
  publisher =     {Springer Verlag},
  title =         {{Detecting people in artwork with CNNs}},
  volume =        {9913 LNCS},
  year =          {2016},
  abstract =      {CNNs have massively improved performance in object
                   detection in photographs. However research into
                   object detection in artwork remains limited. We show
                   state-of-the-art performance on a challenging
                   dataset, People-Art, which contains people from
                   photos, cartoons and 41 different artwork movements.
                   We achieve this high performance by finetuning a CNN
                   for this task, thus also demonstrating that training
                   CNNs on photos results in overfitting for photos:
                   only the first three or four layers transfer from
                   photos to artwork. Although the CNN's performance is
                   the highest yet, it remains less than 60{\%} AP,
                   suggesting further work is needed for the
                   cross-depiction problem.},
  doi =           {10.1007/978-3-319-46604-0_57},
  isbn =          {9783319466033},
  issn =          {16113349},
                  0{\_}57},
}

@inproceedings{Redmon2016,
  author =        {Redmon, Joseph and Divvala, Santosh and
                   Girshick, Ross and Farhadi, Ali},
  booktitle =     {Proceedings of the IEEE Computer Society Conference
                   on Computer Vision and Pattern Recognition},
  month =         {dec},
  pages =         {779--788},
  publisher =     {IEEE Computer Society},
  title =         {{You only look once: Unified, real-time object
                   detection}},
  volume =        {2016-December},
  year =          {2016},
  abstract =      {We present YOLO, a new approach to object detection.
                   Prior work on object detection repurposes classifiers
                   to perform detection. Instead, we frame object
                   detection as a regression problem to spatially
                   separated bounding boxes and associated class
                   probabilities. A single neural network predicts
                   bounding boxes and class probabilities directly from
                   full images in one evaluation. Since the whole
                   detection pipeline is a single network, it can be
                   optimized end-to-end directly on detection
                   performance. Our unified architecture is extremely
                   fast. Our base YOLO model processes images in
                   real-time at 45 frames per second. A smaller version
                   of the network, Fast YOLO, processes an astounding
                   155 frames per second while still achieving double
                   the mAP of other real-time detectors. Compared to
                   state-of-the-art detection systems, YOLO makes more
                   localization errors but is less likely to predict
                   false positives on background. Finally, YOLO learns
                   very general representations of objects. It
                   outperforms other detection methods, including DPM
                   and R-CNN, when generalizing from natural images to
                   other domains like artwork.},
  doi =           {10.1109/CVPR.2016.91},
  isbn =          {9781467388504},
  issn =          {10636919},
}

@inproceedings{Huang2017,
  author =        {Huang, Xun and Belongie, Serge},
  booktitle =     {Proceedings of the IEEE International Conference on
                   Computer Vision},
  month =         {dec},
  pages =         {1510--1519},
  publisher =     {Institute of Electrical and Electronics Engineers
                   Inc.},
  title =         {{Arbitrary Style Transfer in Real-Time with Adaptive
                   Instance Normalization}},
  volume =        {2017-October},
  year =          {2017},
  abstract =      {Gatys et al. recently introduced a neural algorithm
                   that renders a content image in the style of another
                   image, achieving so-called style transfer. However,
                   their framework requires a slow iterative
                   optimization process, which limits its practical
                   application. Fast approximations with feed-forward
                   neural networks have been proposed to speed up neural
                   style transfer. Unfortunately, the speed improvement
                   comes at a cost: the network is usually tied to a
                   fixed set of styles and cannot adapt to arbitrary new
                   styles. In this paper, we present a simple yet
                   effective approach that for the first time enables
                   arbitrary style transfer in real-time. At the heart
                   of our method is a novel adaptive instance
                   normalization (AdaIN) layer that aligns the mean and
                   variance of the content features with those of the
                   style features. Our method achieves speed comparable
                   to the fastest existing approach, without the
                   restriction to a pre-defined set of styles. In
                   addition, our approach allows flexible user controls
                   such as content-style trade-off, style interpolation,
                   color {\&} spatial controls, all using a single
                   feed-forward neural network.},
  doi =           {10.1109/ICCV.2017.167},
  isbn =          {9781538610329},
  issn =          {15505499},
}

@unpublished{Gonthier2020,
  author =        {Gonthier, Nicolas and Ladjal, Sa{\"{i}}d and
                   Gousseau, Yann},
  month =         {aug},
  title =         {{Multiple instance learning on deep features for
                   weakly supervised object detection with extreme
                   domain shifts}},
  year =          {2020},
  abstract =      {Weakly supervised object detection (WSOD) using only
                   image-level annotations has attracted a growing
                   attention over the past few years. Whereas such task
                   is typically addressed with a domain-specific
                   solution focused on natural images, we show that a
                   simple multiple instance approach applied on
                   pre-trained deep features yields excellent
                   performances on non-photographic datasets, possibly
                   including new classes. The approach does not include
                   any fine-tuning or cross-domain learning and is
                   therefore efficient and possibly applicable to
                   arbitrary datasets and classes. We investigate
                   several flavors of the proposed approach, some
                   including multi-layers perceptron and polyhedral
                   classifiers. Despite its simplicity, our method shows
                   competitive results on a range of publicly available
                   datasets, including paintings (People-Art, IconArt),
                   watercolors, cliparts and comics and allows to
                   quickly learn unseen visual categories.},
  note = 		  {arXiv:2008.01178}
}

@inproceedings{Buolamwini2018,
  address =       {New York, USA},
  author =        {Buolamwini, Joy and Gebru, Timnit},
  booktitle =     {Proceedings of Machine Learning Research},
  editor =        {Friedler, Sorelle A. and Abstract, Christo Wilson},
  month =         {jan},
  pages =         {1--15},
  publisher =     {PMLR},
  title =         {{Gender Shades: Intersectional Accuracy Disparities
                   in Commercial Gender Classification}},
  volume =        {81},
  year =          {2018},
  abstract =      {Recent studies demonstrate that machine learning
                   algorithms can discriminate based on classes like
                   race and gender. In this work, we present an approach
                   to evaluate bias present in automated facial analysis
                   algorithms and datasets with respect to phe-notypic
                   subgroups. Using the dermatologist approved
                   Fitzpatrick Skin Type classification system, we
                   characterize the gender and skin type distribution of
                   two facial analysis benchmarks, IJB-A and Adience. We
                   find that these datasets are overwhelmingly composed
                   of lighter-skinned subjects (79.6{\%} for IJB-A and
                   86.2{\%} for Adience) and introduce a new facial
                   analysis dataset which is balanced by gender and skin
                   type. We evaluate 3 commercial gender classification
                   systems using our dataset and show that
                   darker-skinned females are the most misclassified
                   group (with error rates of up to 34.7{\%}). The
                   maximum error rate for lighter-skinned males is
                   0.8{\%}. The substantial disparities in the accuracy
                   of classifying darker females, lighter females,
                   darker males, and lighter males in gender
                   classification systems require urgent attention if
                   commercial companies are to build genuinely fair,
                   transparent and accountable facial analysis
                   algorithms.},
  issn =          {2640-3498},
}

@unpublished{Dulhanty2019,
  author =        {Dulhanty, Chris and Wong, Alexander},
  month =         {may},
  publisher =     {arXiv},
  title =         {{Auditing ImageNet: Towards a Model-driven Framework
                   for Annotating Demographic Attributes of Large-Scale
                   Image Datasets}},
  year =          {2019},
  note = 		  {arXiv:1905.01347}
}

@inproceedings{Yang2020,
  address =       {New York, NY, USA},
  author =        {Yang, Kaiyu and Qinami, Klint and Fei-Fei, Li and
                   Deng, Jia and Russakovsky, Olga},
  booktitle =     {Proceedings of the 2020 Conference on Fairness,
                   Accountability, and Transparency},
  month =         {jan},
  pages =         {547--558},
  publisher =     {ACM},
  title =         {{Towards fairer datasets}},
  year =          {2020},
  doi =           {10.1145/3351095.3375709},
  isbn =          {9781450369367},
}

@inproceedings{Steed2020,
abstract = {Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to face recognition. But do unsupervised computer vision models automatically learn implicit patterns and embed social biases that could have harmful downstream effects? For the first time, we develop a novel method for quantifying biased associations between representations of social concepts and attributes in images. We find that state-of-the-art unsupervised models trained on ImageNet, a popular benchmark image dataset curated from internet images, automatically learn racial, gender, and intersectional biases. We replicate 8 of 15 documented human biases from social psychology, from the innocuous, as with insects and flowers, to the potentially harmful, as with race and gender. For the first time in the image domain, we replicate human-like biases about skin-tone and weight. Our results also closely match three hypotheses about intersectional bias from social psychology. When compared with statistical patterns in online image datasets, our findings suggest that machine learning models can automatically learn bias from the way people are stereotypically portrayed on the web.},
address = {New York, NY, USA},
archivePrefix = {arXiv},
arxivId = {2010.15052},
author = {Steed, Ryan and Caliskan, Aylin},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
doi = {10.1145/3442188.3445932},
eprint = {2010.15052},
file = {:Users/davk/Documents/Mendeley Desktop/2020 - Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases.pdf:pdf},
isbn = {9781450383097},
mendeley-groups = {phd/artnet},
month = {mar},
pages = {701--713},
publisher = {ACM},
title = {{Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases}},
year = {2021}
}


@inproceedings{Villareale2020,
  address =       {New York, NY, USA},
  author =        {Villareale, Jennifer and Acosta-Ruiz, Ana V. and
                   Arcaro, Samuel Adam and Fox, Thomas and Freed, Evan and
                   Gray, Robert C. and L{\"{o}}we, Mathias and
                   Nuchprayoon, Panote and Sladek, Aleksanteri and
                   Weigelt, Rush and Li, Yifu and Risi, Sebastian and
                   Zhu, Jichen},
  booktitle =     {Extended Abstracts of the 2020 Annual Symposium on
                   Computer-Human Interaction in Play},
  month =         {nov},
  pages =         {33--37},
  publisher =     {ACM},
  title =         {{iNNk}},
  year =          {2020},
  abstract =      {This paper presents iNNK, a multiplayer drawing game
                   where human players team up against an NN. The
                   players need to successfully communicate a secret
                   code word to each other through drawings, without
                   being deciphered by the NN. With this game, we aim to
                   foster a playful environment where players can, in a
                   small way, go from passive consumers of NN
                   applications to creative thinkers and critical
                   challengers.},
  doi =           {10.1145/3383668.3419858},
  isbn =          {9781450375870},
}

