Siwar Bengamra
email: bengamra.siwar@gmail.com

Olfa Mzoughi
email: olfa.mzoughi@gmail.com

André Bigand
email: bigand@univ-littoral.fr

Ezzeddine Zagrouba
email: e.zagrouba@gmail.com

A comprehensive survey on object detection in Visual Art: taxonomy and challenge

Keywords: Computer vision, Painting, Object detection, Deep learning, Explainability Table 1 Major computer vision applications for visual art analysis and understanding. Art history problems Computer vision approaches References

Cultural heritage data plays a key role in the understanding of past human history and culture, enriches the present and prepares the future. A wealth of information is buried in artwork images that can be extracted via digitization and analysis. While a huge number of methods exists, a deep review of the literature concerning object detection in visual art is still lacking. In this study, after reviewing several related papers, a comprehensive review is presented, including (i) an overview of major computer vision applications for visual art, (ii) a presentation of previous related surveys, (iii) a comprehensive overview of relevant object detection methods for artistic images. Considering the studied object detection methods, we propose a new taxonomy based on the supervision learning degree, the adopted framework, the adopted methodology (classical or deep-learning based method), the type of object to detect and the depictive style of the painting images. Then the several challenges for object detection in artistic images are described and the proposed ways of solving some encountered problems are discussed. A comprehensive survey on object detection in Visual Art

In addition, available artwork datasets and metrics for object detection performance evaluation are presented. Finally, we provide potential future directions to improve object detection performances in paintings.

Introduction

Cultural heritage images present an important source of information, as it reflects the bond to the past, enriches the present, and informs the future. Transmitting these heritages to the next generations is an important issue on the name of humanity to protect the history and the identity of the nations [START_REF] Yakar | Gis and three-dimensional modeling for cultural heritages[END_REF]. With the rapid development of computer vision and multimedia technologies, a wide range of interesting applications have grown to analyze, understand and create visual artworks (i.e artistic images). A brief summary of computer vision applications is given in Table 1. Among recent developments, the Generative Adversarial Networks (GAN) have succeed to generate novel paintings simulating a given artwork's style (e.g. Vincent van Gogh, Gustave Dore, Monet or Cezanne) [START_REF] Chen | Gated-gan: Adversarial gated networks for multi-collection style transfer[END_REF][START_REF] Gao | Rpd-gan: Learning to draw realistic paintings with generative adversarial network[END_REF][START_REF] Gatys | Image style transfer using convolutional neural networks[END_REF][START_REF] Goenaga | A critique of contemporary artificial intelligence art: Who is edmond de belamy?[END_REF][START_REF] Liu | Improved generative adversarial network and its application in image oil painting style transfer[END_REF][START_REF] Tan | Artgan: Artwork synthesis with conditional categorical gans[END_REF][START_REF] Yi | Apdrawinggan: Generating artistic portrait drawings from face photos with hierarchical gans[END_REF][START_REF] Zhang | Ai painting: an aesthetic painting generation system[END_REF][START_REF] Zhu | Unpaired image-to-image translation using cycle-consistent adversarial networks[END_REF]. This process is called image style transfer. As example, the work "Portrait of Edmond de Belamy" presented in [START_REF] Goenaga | A critique of contemporary artificial intelligence art: Who is edmond de belamy?[END_REF], is the first Artificial Intelligence painting created using the GAN algorithm [START_REF] Goodfellow | Generative adversarial networks[END_REF] which learned from a dataset of 15,000 portraits painted between the 14th to the 20th one. it is worth noting that most of the researches are conducted on automatic recognizing and classifying artworks based on artists [START_REF] Cetinic | Automated painter recognition based on image feature extraction[END_REF][START_REF] Keren | Object recognition supported by user interaction for service robots[END_REF][START_REF] Ma | From part to whole: Who is behind the painting?[END_REF][START_REF] Sabatelli | Deep transfer learning for art classification problems[END_REF][START_REF] Saleh | Large-scale classification of fine-art paintings: Learning the right metric on the right feature[END_REF][START_REF] Van Noord | Toward discovery of the artist's style: Learning to recognize artists by their artworks[END_REF], styles [START_REF] Arora | Towards automated classification of fineart painting style: A comparative study[END_REF][START_REF] Elgammal | The shape of art history in the eyes of the machine[END_REF][START_REF] Falomir | Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-learn)[END_REF][START_REF] Lecoutre | Recognizing art style automatically in painting with deep learning[END_REF][START_REF] Mensink | The rijksmuseum challenge: Museumcentered visual recognition[END_REF][START_REF] Young-Min | Feature visualization in comic artist classification using deep neural networks[END_REF], genre [START_REF] Hosain | Genre recognition of artworks using convolutional neural network[END_REF]171,[START_REF] Zujovic | Classifying paintings by artistic genre: An analysis of features & classifiers[END_REF], materials [START_REF] Mensink | The rijksmuseum challenge: Museumcentered visual recognition[END_REF][START_REF] Yang | Classification of basic artistic media based on a deep convolutional approach[END_REF][START_REF] Yang | A deep approach for classifying artistic media from artworks[END_REF], years of creation [START_REF] Mensink | The rijksmuseum challenge: Museumcentered visual recognition[END_REF], scenes [START_REF] Florea | Domain transfer for delving into deep networks capacity to de-abstract art[END_REF], artistic media [START_REF] Sabatelli | Deep transfer learning for art classification problems[END_REF][START_REF] Yang | A deep approach for classifying artistic media from artworks[END_REF] or even their associations [START_REF] Cetinic | Fine-tuning convolutional neural networks for fine art classification[END_REF][START_REF] Strezoski | Omniart: Multi-task deep learning for artistic data analysis[END_REF][START_REF] Tan | Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification[END_REF]. Moreover, there are other applications that are interested in indexing and searching artwork databases. The Detection of forgeries in paintings has also gained increasing attention to help art historians in the authentication of copies or disputed paintings [START_REF] Bai | Fake generated painting detection via frequency analysis[END_REF][START_REF] Buchana | Simultaneous forgery identification and localization in paintings using advanced correlation filters[END_REF][START_REF] Folego | From impressionism to expressionism: Automatically identifying van gogh's paintings[END_REF][START_REF] Polatkan | Detection of forgery in paintings using supervised learning[END_REF]. Significant attention is also devoted to image retrieval from digitized artwork collections in order to query similar images [START_REF] Castellano | Towards a tool for visual link retrieval and knowledge discovery in painting datasets[END_REF][START_REF] Castellano | Visual link retrieval and knowledge discovery in painting datasets[END_REF][START_REF] Dominguez | Comparing neural and attractiveness-based visual features for artwork recommendation[END_REF][START_REF] Garcia | How to read paintings: Semantic art understanding with multi-modal retrieval[END_REF][START_REF] Mao | Deepart: Learning joint representations of visual arts[END_REF][START_REF] Messina | Exploring content-based artwork recommendation with metadata and visual features[END_REF][START_REF] Schlecht | Detecting gestures in medieval images[END_REF][139][START_REF] Shen | Discovering visual patterns in art collections with spatially-consistent feature learning[END_REF]. Furthermore, automatic emotion recognition across auditory and visual modalities has received increasing attention in computer vision [START_REF] Hu | Video facial emotion recognition based on local enhanced motion history image and cnn-ctslstm networks[END_REF][START_REF] Mustaqeem | Optimal feature selection based speech emotion recognition using two-stream deep convolutional neural network[END_REF]. So, several tools have been developed for emotion recognition from artworks [START_REF] Achlioptas | ArtEmis: Affective language for visual art[END_REF][START_REF] Lin | Sentiment analysis of painting based on deep learning[END_REF][START_REF] Mohammad | Wikiart emotions: An annotated dataset of emotions evoked by art[END_REF]127,[START_REF] Tan | Cnn models for classifying emotions evoked by paintings[END_REF][START_REF] Yanulevskaya | In the eye of the beholder: employing statistical analysis and eye tracking for analyzing abstract paintings[END_REF]. In fact, these tools facilitate the detection of evoked emotions related to the visual stimuli received from artworks, and possibly provide associated explanations. Another important direction is the clustering of artistic images that can be useful for visual link and knowledge discovery in artwork datasets [START_REF] Castellano | A deep learning approach to clustering visual arts[END_REF][START_REF] Gultepe | Predicting and grouping digitized paintings by style using unsupervised feature learning[END_REF]146,[START_REF] Xie | Unsupervised deep embedding for clustering analysis[END_REF]. The idea of clustering is to arrange artworks in clusters (or groups). So the images in the same cluster are similar according to predefined criteria. Additionally, some computer vision methods [START_REF] Stork | Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature[END_REF][150][151] were proposed to analyse lighting Object detection in artistic images, as one of the most interesting topics, plays important role as it can help art historians in performing several tasks such as portrait analysis [START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF], illuminant position estimation [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF], facial expression dataset annotation [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF], image captioning [START_REF] Lu | Data-efficient image captioning of fine art paintings via virtual-real semantic alignment training[END_REF] or improving augmented reality experiences [START_REF] Smirnov | Deep learning for object detection in fine-art paintings[END_REF]. Although object detection is a classic topic in computer vision, it remains challenging in visual art due to the wide diversity of painting images compared to natural images that makes them difficult to understand or analyse, even by an expert. So, considerable research efforts have been devoted towards developing various methods for the detection of different types of objects in artistic images (e.g. bodies, faces, animals, musical notation and vehicles).

In recent years, advances in deep learning have led to significant improvements in different applications such as image processing [START_REF] Song | Deep learning-based automatic segmentation of images in cardiac radiography: a promising challenge[END_REF][START_REF] Winston | Hybrid deep convolutional neural models for iris image recognition[END_REF][START_REF] Zhu | Traffic sign recognition based on deep learning[END_REF], speech processing [117][START_REF] Mustaqeem | 1d-cnn: Speech emotion recognition system using a stacked network with dilated cnn features[END_REF][119], and video processing [START_REF]Harednet: A deep learning based architecture for autonomous video surveillance by recognizing human actions[END_REF][START_REF] Kumar | Crime activities prediction system in video surveillance by an optimized deep learning framework[END_REF]. The deep learning is a subfield of machine learning, which in turn is a sub-field of artificial intelligence (AI). It involves the use of artificial neural networks with multiple hidden layers to model and solve complex problems. In particular, Convolutional Neural Network (CNN) as a type of deep learning neural network, has created new opportunities in visual art research with automatic tools able to extract, analyze and understand information from artworks. However, training deep neural networks can be challenging, and requires large amounts of data.

While the growth of demand in computer vision and deep learning for visual art, few surveys were published in the field. Table 2 highlights related survey papers. The first survey was the Stork's work [START_REF] Stork | Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature[END_REF] which presents the Table 2 Recent related surveys.

Main focus/ Topic Reference

Computer methods for painting analysis [START_REF] Stork | Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature[END_REF] Computer Vision Algorithms for recognising objects in artwork and in photographs [START_REF] Cai | The cross-depiction problem: Computer vision algorithms for recognising objects in artwork and in photographs[END_REF] Recent developments in computational aesthetics [START_REF] Brachmann | Computational and experimental approaches to visual aesthetics[END_REF] A Survey on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia [START_REF] Chu | invited papers] a survey on multimedia artworks analysis and attractiveness computing in multimedia[END_REF] Machine Learning for Cultural Heritage: A Survey [START_REF] Fiorucci | Machine learning for cultural heritage: A survey[END_REF] Computer Vision Applications for Art History [START_REF] Foka | Computer vision applications for art history: Reflections and paradigms for future research[END_REF] Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview [START_REF] Castellano | A brief overview of deep learning approaches to pattern extraction and recognition in paintings and drawings[END_REF] The use of AI technologies for understanding and creation of art [START_REF] Cetinic | Understanding and creating art with AI: Review and outlook[END_REF] power of several computer techniques to process and understand paintings and drawings. Then, in the survey [START_REF] Stork | Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature[END_REF], the author proposes a categorization of computer vision methods used for automatic analysis of artworks. In [START_REF] Cai | The cross-depiction problem: Computer vision algorithms for recognising objects in artwork and in photographs[END_REF], the authors synthesize current popular algorithms for object recognition in artwork and in photograph. The survey of Brachmann and Redies [START_REF] Brachmann | Computational and experimental approaches to visual aesthetics[END_REF] provides an overview of recent developments and results in the field of computational aesthetics. The recent survey [START_REF] Chu | invited papers] a survey on multimedia artworks analysis and attractiveness computing in multimedia[END_REF] focuses mainly on the evaluation and prediction of multimedia art quality by using deep learning. In [START_REF] Fiorucci | Machine learning for cultural heritage: A survey[END_REF], machine learning algorithms within cultural heritage has been a subject of extensive investigation. In fact, a detailed taxonomy of surveyed works according to supervision degree is presented in order to compare highlighted machine learning methods, their major uses and limitations. The recent survey by Cetinic et al. [START_REF] Cetinic | Understanding and creating art with AI: Review and outlook[END_REF] reviewed artificial intelligence systems for analysis and production of artworks. They presented a detailed overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, computational aesthetics, etc. The work in [START_REF] Foka | Computer vision applications for art history: Reflections and paradigms for future research[END_REF] proposes a short review of computer vision applications studying articles in art history journals from a computer science perspective. In fact, technical details are missing in their study. Finally, Castellano and Vessio [START_REF] Castellano | A brief overview of deep learning approaches to pattern extraction and recognition in paintings and drawings[END_REF] propose a general overview of deep learning methods for pattern extraction and recognition in visual art. Authors in [START_REF] Castellano | A brief overview of deep learning approaches to pattern extraction and recognition in paintings and drawings[END_REF] presented a detailed taxonomy of methods according to the task being solved namely, artwork attribute prediction, information retrieval, content generation, object recognition and detection.

Although several surveys regarding visual arts exist, a deep review of object detection methods in artistic images is still lacking. Object detection in visual arts is currently an area of great research interest. Motivated by the growing literature that has recently emerged on this topic, a review study is required to help interested researchers to quickly and effectively grasp the important concepts and key issues of object detection in artistic images. So this present review intends to provide the reader with a state-of-the-art and future research trends, which could help enter this research field and explore it in details. The contributions of this paper can be summarized as follows:

• We propose in this research review a taxonomy of existing methods, that is extensively and deeply carried out about object detectors for visual art data. • We provide an overview of issues and challenges faced by object detection in artistic images. • We provide a critical review of research findings and give insights for future research directions.

The rest of the paper is organized as follows. Section 2 presents the background of object detectors in artistic images. In this section, we firstly reviewed available artwork datasets and metrics used in this context. Then, we survey the basic principles of the recent works and we describe our proposed new taxonomy of object detection in artistic images. Finally, the various challenges are explained. Section 3 is devoted to provide critical summary of the recent literature and directions for future research. Finally, Section 4 concludes the survey.

Background of object detectors in artistic images

In this section, we firstly review the datasets and the evaluation metrics adopted in the context of object detection methods in artworks. Then, we introduce a new taxonomy of the studied methods according to different aspects namely, methodology of work, framework, supervision learning degree, depictive style and type of objects. Finally, we discuss the challenges of current studies and present possible solutions to overcome the encountered problems.

Main object detection artwork datasets

In object detection, a large number of artwork datasets have been collected and annotated to evaluate and to compare the performances of detectors in artistic images. These specific datasets now open new research perspectives in this particular research field. Table 3 summarizes characteristics of the wellknown datasets used in the studied works on object detection in artistic images, provided in Table 5 with references. For each dataset, the style of the images, the subject classes, the number of images, the type of annotation which could be image level or object level, and the the number of object instances if the dataset contains object level annotations are specified. In fact, the image level annotation is the task of assigning labels (e.g the style or the artist of the painting) to the images. On the other side, the object instance annotation is the process of labeling objects in an image with rectangular bounding boxes. There are multiple formats of bounding boxes, namely COCO and PASCAL VOC. In fact, the COCO format represents the bounding box by the coordinates of its top-left corner along with its width and height. In the PASCAL VOC format, the bounding box is identified by the coordinates of its top-left corner and the coordinates of its bottom-right corner.

For automatic face detection, five datasets of digitized paintings are provided for several tasks. In order to make progress on the visual Turing test [START_REF] Geman | Visual turing test for computer vision systems[END_REF], researchers created a new-dataset MAFD-150 (Modern Art Face Detection) [START_REF]MAFD-150 dataset[END_REF] for face detection containing digitized artworks from modern art that cover much diversity in style and artist ranging from 15th to 20th century. The Artistic-Faces dataset [START_REF]Artistic-faces dataset[END_REF] is also created to evaluate facial landmark detections in artistic portraits. It could be used for creating a style signature for portrait artworks or geometry-aware portrait style transfer. The Artistic-Faces dataset includes artistic portrait of various art genres, styles (ranging from High Renaissance through Cubism to Comics) and 16 different artists, with a large variation in both geometry and texture. In fact, labels related to the artist name, artwork title, style, date and source, in addition to facial landmarks are provided. KaoKore and Kouhon Datasets are also created for automatic face detection from Japanese artworks to help art historians create a new facial expression dataset. The KaoKore dataset [START_REF] Tian | Kaokore: A pre-modern japanese art facial expression dataset[END_REF] is an open dataset of premodern Japanese artworks with annotated metadata for each face. It describes

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-Image level the social status in the pre-modern Japanese society, the genre and the orientation. Nevertheless, the Kouhon dataset is closed within the research group [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF], owned by Shojo-Kouji and digitized by Yugyoji Museum. The artworks of Kouhon were collected from picture scrolls in the 14th century with 346 sheets of paper in 10 volumes. They are characterized by many faces appearing across the scroll. Similarly, researchers created the Tenebrism dataset [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF] to estimate the illuminant position within a painting, and thereby to answer technical questions. This dataset includes artworks in the Tenebrism style of the 17th century. It is worth noting that Tenebrism paintings are challenging for face detection, since they are characterized by violent contrasts of light and dark, and they exhibit large variation in viewpoint, pose and occlusion.

In addition to that, some visual art datasets are created to assess the domain shift problem (i.e. applying natural images-trained detectors to paintings) for different classes in different depictive styles (photographs, drawings, paintings etc.) [START_REF] Crowley | The art of detection[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF]. As an example, the Paintings dataset [START_REF]Paintings dataset[END_REF], a subset of the 'Art UK' dataset is mentioned. It contains oil paintings of different styles (e.g photo-realistic,abstract and impressionist) and eras (18th, 19th and mid-20th century) annotated for popular PASCAL VOC categories. Similarly, the Getty dataset, provided by the Getty Research Institute, used in [START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF], contains 7872 images among which only 156 images are object-level annotated. IconArt dataset in [START_REF]Iconart dataset[END_REF], is also an artwork dataset used for object detection in several works [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF], with challenging domain shifts . It includes painting images from Wikicommons [START_REF]Wikicommons[END_REF] that hold instances from iconographic categories (e.g. Jesus Child, Saint Sebastian), ranging from the 11th to the 20th century.

For studying the cross-depiction object detection problem (i.e. detect objects regardless the depictive style), many cross-domain datasets (i.e. a collection of images across different domains, photographic images and artworks, containing the same target object classes) are produced such as PhotoArt-50 [START_REF]PhotoArt50 dataset[END_REF] and PeopleArt [START_REF]PeopleArt dataset[END_REF] datasets. The PhotoArt-50 consists of 50 object categories. Each category contains around 100 images with different instances. Approximately half of the images in each class are artworks covering a wide gamut of style. The other imaging are photographic images. The PeopleArt dataset is made of photos, cartoons and artistic images from 41 different movements. It has the single class people. This dataset is particularly challenging due to the high variability in styles and depiction techniques: from Picasso's cubism to Disney's Sleeping Beauty. Other work [START_REF] Ginosar | Detecting people in cubist art[END_REF] has sought to design algorithms that mimic the human visual system for object detection outside the realm of natural images. So the Cubist paintings of Picasso dataset are examples used to depict objects that are not normally seen in nature.

In addition to that, some artwork datasets such as Kotenseki [START_REF]Kotenseki dataset[END_REF] and Al-Qazvin [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF] are created to develop content-based image retrieval systems based on automatic object detection [START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF]. The Al-Qazvin dataset includes Islamic paintings of the 12th century where most of the objects in these paintings are imaginary objects. The Kotenseki is a collection of classical and ancient Japanese paintings. They are comprised of both colored and black-and-white drawings of different objects. In [START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF], only 1104 images were selected to be used. The paintings in Kotenseki dataset are annotated in COCO format for object detection. In addition to object instance annotation, additional informations are provided for Kotenseki images, such as image path, tagname, page of Kotenseki book, title, and tagid. In literature, the WikiArt dataset [START_REF]Wikiart: visual art encyclopedia[END_REF] is also investigated. It represents one of largest online available dataset of digitized paintings from different artists, styles and genres. However, it is not annotated for object detection.

The Brueghel dataset [START_REF]Brueghel dataset[END_REF] is used for the authentication of artworks based object detection. This dataset includes artworks made in different media (e.g. oil, watercolor, chalk) and on different materials (e.g. paper, panel copper), representing a wide variety of scenes (e.g. landscape, religious, still life).

Watercolor2k, Clipart1k and Comic2k datasets are also used in few studied works [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF][START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF]. Watercolor2k is composed of two thousand watercolor paintings made by artists in the 20th century. It contains object instances from 6 categories (i.e. bike, bird, cat, car, dog and person) in common with the PASCAL VOC dataset. It is splitted halfly into training and testing sets. The Clipart1k includes thousand clipart images containing object instances belonging to the same 20 categories of PASCAL VOC. It is splitted into training and test sets. Each includes 500 images. The Comic2k contains two thousand comic images sharing 6 categories with Pascal VOC and splitted halfly into training and testing sets. The CASPApaintings dataset used in [START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF] is the painting subset of the CASPA (Cartoons, Sketches, Paintings) dataset [START_REF]Caspa dataset[END_REF] which covers animal COCO categories.

Recently, the Dataset of cultural sites is created to recognize artworks in cultural sites using images acquired from the visitor [START_REF] Pasqualino | An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites[END_REF]. This can be used to improve augmented reality experiences. This new dataset consists of synthetic and real images of 16 artworks from different points of view.

More Recently, Medieval Musicological Studies Dataset (MMSD) is created and annotated with medieval artworks (paintings or drawings). It holds musical scenes of persons in solo or in group-singing situations, whether accompanied or not by musical instruments. MMSD is used in [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF] to detect signing performances in order to better understand the physical postures of singers, their relationship, and their location inside the building.

Even though museums and cultural institutions constantly make artwork collections available, the choice of the dataset remains application specific, and the most important factors to consider could be the target artistic styles or/and classes, the artwork period and the available annotations (i.e. instance level or image level). For example, it is possible to find a dataset covering the target classes or target style [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF], but only offers image level annotations which are not helpful for object detection purpose. In this case, considerable effort should be made to annotate bounding boxes around objects for its images, which sequentially requires a lot of times.

Evaluation metrics

The results of object detections are bounding boxes around objects with confidence score representing the class probability. Several metrics are provided to evaluate object detection performances in artworks. The four commonly used criteria are Precision, Recall, F-measure (or F1-score) and Average Precision (AP). To compute these metrics, the concept of Intersection Over Union (IoU) is used to determine if a detection result is True Positive (TP), False Positive (FP) or False Negative (FN). In fact, the IoU of two bounding boxes, the bounding box for the ground truth and the predicted bounding box, is the intersection area divided by the union area. A detection result is considered TP if IoU is greater than a predefined threshold, otherwise it is considered as FP. When a ground truth bounding box is present in the image and the object detection model failed to detect it, then it will be considered as FN. True Negative (TN) is every part of the image (i.e. background) where no object is detected. TN is not useful for object detection. In table 4, we describe the metrics aforementioned above whose detection results in artworks are available in mentioned papers.

The precision recall curve (PR-curves) of classes has been drawn in several works [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Crowley | The art of detection[END_REF][START_REF] Ginosar | Detecting people in cubist art[END_REF][START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF][START_REF] Wu | Learning graphs to model visual objects across different depictive styles[END_REF] to show the tradeoff between precision and recall for different thresholds. Indeed, when the precision stays high as its recall increases, the object detector is considered good. So, high area under the curve tends to indicate both high precision and high recall. Hence, the Average Precision AP metric, that consists to estimate the area under the precision recall curve, was considered the popular metric for evaluating object detectors. To do that, the 11-point interpolation approach is commonly used, which averages the maximum precision values at a set of 11 equally spaced recall levels [0, 0.1, 0.2,...,1]. The mAP is computed by taking the average precision over all object categories. In addition to that, the Normalised Mean Error (NME) metric was also adopted in [START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF] to evaluate facial landmark detection in artistic portraits. Finally, training and testing times are computed for some object detectors in artistic images. Thus, we termed ATrT for the average training times and ATeT for average testing times.

It is also worth noting that some computer vision researchers gave attention to the learning process evaluation of their deep learning based object detection models used for painting images. In [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF], authors gave the accuracy and loss curves of the proposed models to control overfitting.

While the training parameters of deep models are adapted during the training phase, the values of the hyper-parameters have to be fixed before the learning phase and nevertheless make an important impact on model's accuracy. The hyper-parameters tuned in the studied papers are mainly the batch size, epoch, learning rate, weight decay and momentum. Indeed, epoch is the number of repetitions of the learning process. The batch size is the number of samples processed by the deep model in one epoch before updating the model parameters. In addition to that, the learning rate is a hyper-parameter that controls the gradient descent algorithm to find the minimum value of the error The fraction of all positive predictions that are true positives.

P =

T rueP ositive(T P ) T rueP ositive(T P )+F alseP ositive(F P ) [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF][START_REF] Crowley | The art of detection[END_REF][START_REF] Ginosar | Detecting people in cubist art[END_REF] Recall (R)

The fraction of all actual positives that are predicted positive.

R =

T rueP ositive(T P ) T rueP ositive(T P )+F alseN egative(F N ) [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF][START_REF] Ginosar | Detecting people in cubist art[END_REF][START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF][START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF] Fmeasure (F)

The harmonic mean of precision and recall. F = 2 × P recision×Recall P recision+Recall [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF][START_REF] Ginosar | Detecting people in cubist art[END_REF][START_REF] Wechsler | Modern art challenges face detection[END_REF] Average Precision (AP)

The average precision at a set of eleven equally spaced recall levels [0,0.1,...,0. The mean Euclidean distance between estimated landmarks and ground truth landmarks divided by the number of points and the normalized distance. [172]

N M E = 1 n n i=1 x i -x *
function for adjusting the weights of the network. The weight decay (commonly called L2 regularization) is a regularization parameter that controls the trade-off between having a powerful model and over-fitting the model. Finally, the momentum controls the rapidity of the learning process. Setting optimal hyper-parameters is one of the main challenge during the training of deep models. In several works [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Ginosar | Detecting people in cubist art[END_REF][START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF][START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF], the authors chose to tune the hyper-parameters of their models using the default values of the original architectures or the same setting used in the original papers. Others researchers proposed the grid-search cross validation method for hyperparameters tuning [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF] by choosing the values that give the most optimal results. For instance, the values of hyper-parameters that minimise the loss function are selected in [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF]. A 3-fold cross validation is also performed in [START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF] for determining the main hyper-parameters of the SVM. Besides, in [START_REF] Smirnov | Deep learning for object detection in fine-art paintings[END_REF], authors proposed a Bayesian optimization strategy [START_REF] Brochu | A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning[END_REF] to efficiently find a good combination of hyper-parameters. In [START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF][START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF][START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF], an analysis of the hyper-parameters of the proposed model on the detection performances was performed. For instance, it was demonstrated in [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF] how the choice of some values for the intersection over union (IoU) that represents an important metric of the proposed method, enables a larger improvement on precision than on recall.

New taxonomy of object detectors in visual art

As previously discussed (section 1), a large number of methods have been developed for object detection in artistic images in the last decade. Fig. 1 shows the trend of this topic in terms of number and publication year of the reviewed papers. It can be seen an increasing number of publications demonstrating the growing interest of the research community on the topic. In order to understand the basic principles of the works investigating object detection in atristic images, a schematic overview is provided in Table 5. The best mean average precision scores (AP 50 ) are given with at least 50% intersection over union (IoU) overlap. For each studied work, we present used artwork dataset, used object detection frameworks, obtained performances and highlights of the work. Fig. 2 illustrates examples of object detection results on different used datasets. Based on the previous meticulous review of papers published for object detection in artistic modalities, five major categories are identified. In fact, the reviewed methods can be classified according to their supervision learning degree into supervised, weakly supervised and unsupervised methods. They could also be categorized according to their methodology of work into two classes: deep-learning based methods and traditional computer vision methods. An especially important separation of the object detection methods in artistic modalities could also be done based on the category of object detector framework adopted in the work: one-stage or two-stages. Additionally, another aspect of dividing the methods is based on the target object to detect: face, person, animal, etc. At last, one crucial aspect that should be taken into consideration is the style of artistic images in which target objects are depicted. Figure 3 presents a mind-map highlighting the different aspects that can be used to classify an object detection method in artistic modality. These aspects should be taken into consideration by practitioners with respect to the problems they want to solve. It is worth noting that this classification of existing methods is not absolute, it depends on the characteristics of the methods. So the existing methods can be classified into many overlapping or non-overlapping categories simultaneously.

Methodology: deep learning vs traditional computer vision methods

Existing methods could be classified into two categories according to the adopted methodology by using classical computer vision techniques or deep learning methods (CNNs). In fact, the classical object detection methods The results are taken from papers cited in Table 5.

consist of traditional hand-crafted features extraction and classification. The hand-crafted features such as edge detection and texture analysis, are designed manually by researchers [START_REF] Gupta | Improved object recognition results using sift and orb feature detector[END_REF]. On the other hand, the CNN is designed to automatically learn high-level deep feature representations for prediction [START_REF] Fujiyoshi | Deep learning-based image recognition for autonomous driving[END_REF]109,121]. One of the main advantages of CNNs is their ability to learn and recognize complex patterns in images without the need for explicit feature engineering. Recently, several techniques are proposed to select optimal features in a CNN to enhance its performance. For example, in [START_REF] Mustaqeem | Optimal feature selection based speech emotion recognition using two-stream deep convolutional neural network[END_REF], authors proposed a two-stream CNN to extract spatial-spectral features which are then fused and fed to the iterative neighborhood component analysis to select the most discriminative optimal features for the final prediction. Before the success of deep convolutional neural networks in artistic images, a variety of traditional methods for object detection based on hand-crafted features have been investigated. Wu et al. [START_REF] Wu | Learning graphs to model visual objects across different depictive styles[END_REF] presented a graph modelling method for object description with multi-labeled nodes and discriminative weights on arcs and nodes to encode relative importance. The visual class models are learned from different depiction styles. For the object detection, a matching algorithm is used to find the graph in the given artistic image that best matches the given visual class model. In [START_REF] Ginosar | Detecting people in cubist art[END_REF], the Deformable Part-based Models (DPM) detection method [START_REF] Felzenszwalb | Object detection with discriminatively trained part-based models[END_REF] has demonstrated the best performances on person detection in paintings compared to other object detectors. The DPM method extracts oriented gradient (HOG) features [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF] at different resolutions since they are invariant to changes in lighting and small deformations, then localizes objects by sweeping the features with support vector machine (SVM) [START_REF] Hearst | Support vector machines[END_REF] classifiers. Similarly, in the same work [START_REF] Ginosar | Detecting people in cubist art[END_REF], authors explored the Poselet detector which leads to good performances on person detection in cubist paintings. The poselet detector [START_REF] Bourdev | Poselets: Body part detectors trained using 3d human pose annotations[END_REF] is a HOG part-based linear support vector machine classifier. Indeed, Poselets are parts of the human pose trained under specific viewpoint and each poselet detection casts a vote towards the full bounding box of the person. Although the poselet detector is robust over a wide range of human poses but it has a high computational demand. The template-based Dalal and Triggs (D&T) method [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF] is also investigated for person detection in [START_REF] Ginosar | Detecting people in cubist art[END_REF], but provides inferior performances compared to DPM [START_REF] Felzenszwalb | Object detection with discriminatively trained part-based models[END_REF] and Poselets [START_REF] Bourdev | Poselets: Body part detectors trained using 3d human pose annotations[END_REF]. The (D&T) method describes objects by histograms of orientations of local edge gradients binned over a dense image grid (HOG) and uses the combined feature vector in a conventional SVM for detection. The algorithm of Viola-Jones Haar face detector is used in [START_REF] Wechsler | Modern art challenges face detection[END_REF] to detect faces on varying art categories (e.g. Impressionism, Fauvism, Pop art, etc.) but provided very low face detection performances ( F1-score = 22%). The Viola-Jones algorithm computes at first the integral image to quickly extract Haar-like features. The AdaBoost algorithm is then used to both select important features and train the classifier. In the literature, the Viola-Jones is considered as one of the best in detection effectiveness/speed of work ratio [START_REF] Peleshko | Research of usage of haar-like features and adaboost algorithm in viola-jones method of object detection[END_REF][START_REF] Winarno | Multiview faces detection using viola-jones method[END_REF]. Recently, in [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF], the authors proposed to adopt a modified version of the SURF (Speeded-Up Robust Features) algorithm to construct a set of description features for every paintings images, mainly scale and orientations features. Then, a matching score between the desired object and the painting images is used to complete the detection process. More recently, in [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF], authors have tested training a model based on histogram of oriented gradients, followed by a support vector machine classifier, to detect signing representations in medieval artworks. The model gives very low precision since it confused many of the other parts of the image as an object, dropping the F1-score to 23.9%.

Recently, deep learning have been intensively employed for detecting objects in artistic images with highly promising results. In the reviewed papers, there are some techniques used to enhance the performance of CNN models such as transfer learning, fine-tuning, data augmentation and style transfer.

Fine-tuning has been widely adopted in transferring learned information from one domain (e.g natural image) to another domain (e.g. painting). This technique involves training a pre-trained CNN on a new smaller dataset. By freezing the lower layers of the CNN and only training the higher layers, the model can learn new feature representations specific to the new dataset. In a very recent work [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF], authors proposed a bi-training technique for performing object detection in medieval artworks. In this work, authors classified object classes in base and novel classes. Base classes are the samples highly represented in the dataset and novel classes are the less represented ones. The first step is to fine-tune the object proposal model (not the classifier) on the whole dataset for potential region proposals. In the second step, authors fine-tune the classifier and bounding box regressor with a particular configuration (i.e. specific weights and learning rate) forcing the model to perform better with the novel classes. In [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF], authors fine-tune the Faster RCNN [START_REF] Ren | Faster r-cnn: Towards realtime object detection with region proposal networks[END_REF] pre-trained on a dataset of photograph faces (AFLW) in different ways. Firstly, face detection improvements are observed by retraining only the higher layers of the model on painting images. Then, authors retrain all layers on the target artwork dataset to have a model that achieves high face detection performances. Another work in [START_REF] Westlake | Detecting people in artwork with CNNs[END_REF] uses a model pre-trained on ImageNet and fine-tunes it on People-Art dataset with different settings in order to choose the one that maximizes the performances. Similarly, authors in [START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF] fine-tune pre-trained model with different configurations (e.g freezing all or some convolutional layers layers, no fine-tuning, training all convolutional layers, etc.) in order to obtain the optimal results. So we remark that transfer learning via fine-tuning has been widely employed by object detectors in artworks. It is worth noting that fine-tuning pre-trained models is better than training models from scratch. To do this, different settings for fine-tuning are investigated in the reviewed papers to determine the optimal configuration of the model. Furthermore, it is worth noting that object detector models based deep learning need large scale datasets annotated with objects. So, in order to avoid the time consuming annotation task, many researchers proposed image style transfer to generate new images that look like artistic ones and will be used for fine-tuning pre-trained models. Image style transfer consists in combining the content of an input image I c with the style of a reference image I s to generate new image I that preserves the content of I c but has the style of I s . The images I c and I s are passed through a pre-trained convolutional neural network (CNN) to extract their content and style features. In fact, the content features capture the high-level features such as structure and positions, while the style features capture the textures, colors, and patterns. The output stylized image I is then optimized to minimize a total loss using gradient descent or other optimization algorithms. Neural style transfer methods are classified into two categories. The first category represents optimization-based methods that transfer the style by iteratively optimizing an image. The second category, called modelbased neural methods, optimizes a generative model offline, and produces the stylized image with a single forward pass. Although image optimisation-based methods are able to yield impressive stylised images, they are computationally expensive. So model-based neural methods speed up the style transfer process.

The optimization-based style transfer method proposed by Gatys et al. [START_REF] Gatys | Image style transfer using convolutional neural networks[END_REF] is employed in [START_REF] Jeon | Object detection in artworks using data augmentation[END_REF][START_REF] Smirnov | Deep learning for object detection in fine-art paintings[END_REF][START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF] to generate stylized images. In fact, in [START_REF] Jeon | Object detection in artworks using data augmentation[END_REF], the COCO dataset is used as a content image, and some images from Brueghel [START_REF]Brueghel dataset[END_REF] and People-Art [START_REF]PeopleArt dataset[END_REF] datasets are used as a style images. In [START_REF] Smirnov | Deep learning for object detection in fine-art paintings[END_REF], authors propose the transfer of artistic style to the natural images in PASCAL VOC dataset. The authors in [START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF] investigate geometric and texture style transfer. The total loss function of the method proposed by Gatys et al. [START_REF] Gatys | Image style transfer using convolutional neural networks[END_REF] is defined by Equation 1.

L total (I s , I c , I) = αL content (I c , I) + βL style (I s , I)

where α and β balance the style and content losses. The content loss is used to measure the distance (e.g squared euclidean distance) between the content features of the I c and I. The style loss is used to measure the difference between the style of I s and the style of I.

In [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF], a model-based style transfer method, called AdaIn (Adaptive Instance Normalization), is used to generate stylized images. The AdaIn loss function involves normalizing the mean and standard deviation of the feature maps F of the content image I c to match those of the style image I s , as shown in the Equation 2.

AdaIn(F (I s ), F (I s )) = σ(F (I s ))( F (I c ) -µF (I c ) σF (I c ) ) + µF (I s ) (2)
Another work [START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF] explored the use of CycleGAN [START_REF] Zhu | Unpaired image-to-image translation using cycle-consistent adversarial networks[END_REF] algorithm to generate clipart, watercolor and comic images from natural images (PASCAL VOC). CycleGAN is capable of learning the mapping between two different domains without the need for paired training data. So, two generators, G CS and G SC , are required to map images from the content domain C (e.g. natural images) to the style Domain S (e.g. clipart images). Two discriminators, D C and D S , are also required to distinguish between real and fake images. CycleGAN minimizes a loss function during training for style transfer, as shown in Equation 3. This loss function includes adversarial loss, cycle-consistency loss and identity loss. The adversarial loss is used to ensure that the generated images are similar to the real images. The cycle-consistency loss ensures that the generated images are consistent with the input images. It is computed by comparing the difference between the input image and the image generated after passing through both the generator networks. The identity loss helps to preserve the content of the original image while transferring the style.

L cycleGAN = L identity + L cycle-consistency + L adv (3)
Finally, in [START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF], authors propose the use of four different functions offered by openCV library [START_REF] Bradski | The opencv library[END_REF] for style transfer namely, edgePreservingFilter, pen-cilSketch, stylization and xphoto.oilPainting. To conclude, it has demonstrated in the several previous works that artistic style transfer enhance object detection performance in digitized fine-art. However, style transfer requires experimentation for adjusting parameters to achieves good results.

Data augmentation is considered as a traditional strategy to artificially increase the size and variability of the original dataset by applying certain transformations to the training artistic images deriving new examples. This can help to improve the generalization performance of the CNN. In fact, the transformations are generally geometric (e.g. flipping, cropping and rotation) or photometric (e.g. contrast changes and color perturbations), and they can be performed at training time (called online or on-the-fly augmentation) or before training (called offline augmentation). Numerous works performed data augmentation for learning the object detector models on artistic images in order to be invariant to these transformations. Consequently, data augmentation helps these models to obtain better accuracy by reducing overfitting and improving generalization. Nevertheless, the training phase will require significant computing time. In [START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF], researchers proposed several data augmentation techniques, namely shearing, zooming, shifting width and height, and flipping. The experimental results in [START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF] demonstrated that data augmentation during the training process could significantly improve the accuracy of the model. Authors in [START_REF] Crowley | The art of detection[END_REF] proposed the use of four data augmentation techniques available in MatConvNet toolbox [START_REF] Vedaldi | Matconvnet: Convolutional neural networks for matlab[END_REF], mainly crop with different configurations and stretch. They showed that augmentation increases the mAP of the model and the stretch technique produces the highest performance. Recently, authors in [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF] investigate the effects of online data augmentation on face detection performances by evaluating separately each technique used. It has demonstrated that data augmentation improve detection performances and the best results are obtained with rotation technique. In general, despite the object detection performance improvement using data augmentation, the choice of the best or appropriate technique remains critical. Thus, it is recommended to perform an in-depth investigation of the influence of data augmentation techniques on object detection performances for optimal choice.

Adopted framework

According to literature, the deep learning frameworks are the most used to detect objects in visual arts. The existing deep learning frameworks are roughly classified into two categories according to their architectures: single-stage and two-stage detectors. The two-stage frameworks have complex architecture and are slow, but still provide more accurate results than one-stage frameworks. Nevertheless, although the one-stage object detectors have simple architecture and save computational time, they suffer accuracy loss, particularly for tiny/small objects which was a challenge for several object detectors in artistic images [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF][START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF]. So, single stage frameworks are recommended for real time applications.

Figure 4 illustrates a schematic description of the two type of object detectors. On one hand, the two-stage object detectors firstly proposes a set of regions of interests (ROIs) by selective search or using Regional Proposal Network (RPN). Then, a classification of each ROI, as well as its regression to the ground-truth locations are performed. The anchor boxes are predefined bounding boxes created at different sizes, aspect ratios, and locations to be used for the ROIs generation in RPN. On other hand, the single stage object detectors use a single feed-forward neural network to learn class probabilities and bounding box coordinates according to the calculation of the loss function. Both types of object detectors have a backbone composed of successive convolutional and pooling (or down-sampling) layers for deep features extraction. In the convolutional layer, the input image is convoluted with a set of learnable filters to extract features such as edges, corners, and textures. The pooling layer reduces the dimensionality of the output by downsampling the features. The ROI pooling layer is used to solve the problem of fixed size of feature maps required for the detection generator. It utilizes max-pooling to convert features inside valid ROIs (i.e. region proposal) into feature maps with fixed size which will be fed then to the fully connected layers for prediction. The purpose of the classification layer is to judge which class a region belongs to. For binary classification, the sigmoid function is used in the classification layer to produce a value between 0 and 1 that represents the class probability. For multi-class classification, the activation function is typically the softmax function, which produces a probability distribution over the possible output classes. The bounding box regression layer adjusts the positions of regions of interest (i.e. four bounding box coordinates) to obtain the final object detection result.

Many works in the studied literature have employed two-stage object detectors. The used frameworks are R-CNN (Region-based CNN) family networks which mainly include original RCNN, Fast RCNN, Faster RCNN, Cascade RCNN and Mask RCNN. For instance, the RCNN network proposed in [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF] performs object detection by using a deep ConvNet to classify object proposals. RCNN was used in [START_REF] Ginosar | Detecting people in cubist art[END_REF] for person detection in abstract paintings but it does not perform well compared to traditional methods. The obtained performance is justified by the fact that RCNN is not a part-based model and over-fits to the natural images. The Fast RCNN [START_REF] Girshick | Fast r-cnn[END_REF] which takes as inputs an entire image and a set of object proposals, learns to classify object proposals and refine their spatial locations. In [START_REF] Westlake | Detecting people in artwork with CNNs[END_REF], a finetuned Fast RCNN based VGG16 gives the best object detection performances on the People-Art Dataset (59% mAP) compared to other object detector models, namely YOLO [129] and DPM [START_REF] Felzenszwalb | Object detection with discriminatively trained part-based models[END_REF]. The Faster RCNN [START_REF] Ren | Faster r-cnn: Towards realtime object detection with region proposal networks[END_REF] improves Fast R-CNN by introducing the Region Proposal Networks (RPN). Faster RCNN has been widely used for object detection in artworks for its strong performances [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Crowley | The art of detection[END_REF][START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF][START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF][START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF][START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF][START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF][START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF]. Indeed, performance analysis were performed in several works for different architectures such as VGG16 and RES-152-COCO, as backbone network of Faster RCNN used for feature extraction. We observed that the residual network (ResNet) appears to be the best architecture for transfer learning by feature extractions [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF]. The two-stage model Cascade RCNN was investigated in [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF] for face detection on the Kouhon dataset. Indeed, the Cascade RCNN is composed of a sequence of detectors trained with increasing IoU thresholds that address the limitations of Faster RCNN as mentioned in [START_REF] Cai | Cascade r-cnn: Delving into high quality object detection[END_REF], to enable high quality object detection. Experimental results in [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF] show that Cascade RCNN yields similar face detection results as Faster RCNN. Recently, the Mask RCNN framework is used in [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF] and compared with YOLO and Faster RCNN for detection of objects (e.g. Lectern, Phylactery) in medieval singing images. The Mask RCNN, proposed in [START_REF] He | Mask r-CNN[END_REF], adopts the same two-stage approach by adding a branch (in the second stage) in parallel with the existing branch for classification and bounding box regression, to predict a binary mask for each RoI. In [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF], Mask RCNN achieves better performances in average precision than Faster RCNN, but it is less accurate than YOLOv4-s.

Other methods such as [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF][START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF][START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF][START_REF] Pasqualino | Unsupervised domain adaptation for object detection in cultural sites[END_REF] explored one-stage object detectors frameworks. YOLO (You Only Look Once), proposed in [129] as a single stage object detector model, was investigated in several related works [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF][START_REF] Jeon | Object detection in artworks using data augmentation[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF][START_REF] Westlake | Detecting people in artwork with CNNs[END_REF] for object detection in artworks. This framework divides an image into S × S grids, and if the center coordinate of the ground truth of an object falls into a grid, the grid is responsible for detecting the object. YOLO is fast by design since it throws out the region proposal generation stage entirely. Several versions of YOLO are proposed to ensure a tradeoff between speed and accuracy. The more highly used in the studied works are YOLOv5 [14], YOLOv4-s [START_REF] Bochkovskiy | Yolov4: Optimal speed and accuracy of object detection[END_REF], YOLOv4-m [START_REF] Bochkovskiy | Yolov4: Optimal speed and accuracy of object detection[END_REF], YOLOv2 or YOLO9000 [START_REF] Redmon | Yolo9000: better, faster, stronger[END_REF] and YOLOFace [START_REF]Yoloface[END_REF]. In [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF], YOLOv4-s provides the best object detection performances on medieval singing dataset (MMSD) compared to Mask RCNN and YOLOv4-m. Experimental results in [START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF] show a varying performance of different YOLO models where each model is trained for a specific type of object (animals, structures, produce and person). The Animal category scored the best performance with 82% mAP. Similarly, YOLOFace, proposed in [START_REF] Chen | Yolo-face: a real-time face detector[END_REF], is a pre-trained model used in [START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF] for the detection of the Face category. In fact, YOLOFace is based on the YOLOv3 architecture trained to predict faces. Furthermore, authors in [START_REF] Jeon | Object detection in artworks using data augmentation[END_REF] applied YOLOv5 pretrained on COCO dataset for object detection in artworks to achieve 40.6% mAP on PeopleArt dataset. In [START_REF] Westlake | Detecting people in artwork with CNNs[END_REF], YOLO was also the best performing object detector model on the Picasso dataset (53% mAP) compared to DPM [START_REF] Felzenszwalb | Object detection with discriminatively trained part-based models[END_REF] and Fast RCNN [START_REF] Girshick | Fast r-cnn[END_REF] models which make it possible to believe that YOLO's design is more robust to abstract forms of art. Moreover, the SSD300 model was also used as one stage object detector in some research works. The SSD300 is a pretrained model of SSD (Single Shot MultiBox Detector) [START_REF] Liu | Ssd: Single shot multibox detector[END_REF] using images with 300 x 300 size resolution. SSD predicts a fixed number of scores and bounding boxes which is more than that of YOLO, followed by a non-maximum suppression step to remove duplicate predictions pointing to the same object. In [START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF], a fine-tuned SSD300 outperforms the best-performing baselines in terms of mAP across all datasets (i.e. Clipart1k, Watercolor2k and Comic2k). In [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF], SSD300 is trained on the KaoKore dataset for face detection on images from Kouhon dataset. The best obtained result of mAP was 72.7%. Finally, the single-stage object detector called RetinaNet [START_REF] Lin | Focal loss for dense object detection[END_REF] was used in the recent work [START_REF] Pasqualino | An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites[END_REF] to detect artworks in cultural sites. Authors showed that RetinaNet is more robust than Faster RCNN to domain shift, in which synthetic images are used for training and real images are used for test.

More Recently, the new model SWIN-T [START_REF] Liu | Swin transformer: Hierarchical vision transformer using shifted windows[END_REF] is used in [START_REF] Bekkouch | Adversarial domain adaptation for medieval instrument recognition[END_REF] to detect objects in medieval singing images. This model belongs to the Visual Transformers (ViT) architecture family that is different from the CNN architecture.

In comparison to CNN, ViT exhibits an extraordinary performance with few computational resources. The authors in [START_REF] Bekkouch | Adversarial domain adaptation for medieval instrument recognition[END_REF] have concluded that SWIN-T model is very powerful, but they require more training data.

Supervision learning degree

The studied works on object detection for cultural heritage visual data can be classified into three categories of methods according to the supervision learning degree, namely supervised, weakly-supervised, and unsupervised learning.

In supervised learning, the model is trained on a labeled dataset, and during training, it learns to make predictions by minimizing a loss function using an optimization algorithm such as stochastic gradient descent (SGD). In object detection, the goal is to locate and classify objects in image. So the loss function for many object detection models consists of two components namely, localization loss and classification loss, weighted by hyperparameter that determine the relative importance of each loss (Equation 4). The localization loss measures the error between the predicted bounding boxes and the ground truth bounding boxes of the objects in the image. The classification loss measures the error between the predicted class probabilities and the ground truth class labels of the objects in the image.

Loss = Loss localization + λLoss classif ication (4)
Several works [START_REF] Bekkouch | Adversarial domain adaptation for medieval instrument recognition[END_REF][START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF] propose supervised approaches for object detection in artistic images. In [START_REF] Bekkouch | Adversarial domain adaptation for medieval instrument recognition[END_REF][START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF], the cross-entropy loss function is used as a classification loss and the smooth L1 loss function is used as a regression loss in the used CNN models. The Focal loss is introduced in RetinaNet [START_REF] Lin | Focal loss for dense object detection[END_REF] to address the class imbalance problem in classification. The supervised learning algorithms, Support Vector Machine (SVM) and structured support vector machine (SSVM), are also used in [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF][START_REF] Wu | Learning graphs to model visual objects across different depictive styles[END_REF].

In weakly supervised learning, the model is trained to make predictions despite the lack of complete and accurate labels for the training data. In [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF], the Multiple Instance Learning (MIL) approach is adopted to detect specific classes, such as Nudity, Mary, Jesus as a child or the crucifixion of Jesus, that are not available in photographic images. In this approach, the artistic images are organized into bags, where each bag contains multiple instances. The labels are assigned to bags instead of instances. In [START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF], authors use Weakly Supervised Deep Detection Network (WSDDN) [START_REF] Bilen | Weakly supervised deep detection networks[END_REF] and ContextLocNet [START_REF] Kantorov | Contextlocnet: Contextaware deep network models for weakly supervised localization[END_REF] for training on artwork datasets, where only image level annotations are provided.

In unsupervised learning, the model is trained using unlabeled data. The goal of unsupervised learning is to find patterns and relationships in the data without any prior knowledge. Recently, in [START_REF] Pasqualino | An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites[END_REF] authors investigate the use of unsupervised techniques for artwork detection in cultural sites images. This consists of combining feature alignment techniques based on adversarial learning [START_REF] Ganin | Unsupervised domain adaptation by backpropagation[END_REF] with the RetinaNet architecture [START_REF] Lin | Focal loss for dense object detection[END_REF]. So three discriminators with Gradient Reversal Layer are added to the architecture of RetinaNet to distinguich between real and fake data. The resulting network, called DA-RetinaNet, is then trained to minimize the supervised loss and the discriminator loss. The proposed framework DA-RetinaNet outperforms RetinaNet [START_REF] Lin | Focal loss for dense object detection[END_REF], Strong Weak [START_REF] Saito | Strong-weak distribution alignment for adaptive object detection[END_REF] and DA-Faster RCNN [START_REF] Chen | Domain adaptive faster r-cnn for object detection in the wild[END_REF].

Type of target object

We identify several types (or classes) of target object that are detected in artworks. A recent work has focused on detection of musical representation, namely book hold in the hands or placed on a lectern, unfolded phylactery and musical notation, from medieval artworks [START_REF] Bekkouch | Adversarial domain adaptation for medieval instrument recognition[END_REF][START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF]. Some other works in the literature, such as [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF][START_REF] Mzoughi | Face detection in painting using deep convolutional neural networks[END_REF][START_REF] Srinivasan | Recognizing the royals: Leveraging computerized face recognition for identifying subjects in ancient artworks[END_REF], address face detection in artwork. Other applications were focused on recognising persons [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF][START_REF] Marinescu | Improving object detection in paintings based on time contexts[END_REF] or even specific ones, like Leonardo [158] and other artists [START_REF] Srinivasan | Computerized face recognition in renaissance portrait art: A quantitative measure for identifying uncertain subjects in ancient portraits[END_REF]. In addition, other works were interested in detection of iconographic characters such as Mary, Saint-Sebastian, Jesus as a child or the crucifixion of Jesus [START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF]. Animals were also successfully detected in paintings, such as horse, cock and cow [START_REF] Crowley | The art of detection[END_REF][START_REF] Junger | Object detection. In: Deep learning for computer vision in the art domain: proceedings of the master seminar on practical introduction to deep learning for computer vision[END_REF][START_REF] Marinescu | Improving object detection in paintings based on time contexts[END_REF]. Finally, efforts have been made in [START_REF] Crowley | The art of detection[END_REF] to develop method for detection of objects in different classes (e.g. table, aeroplane and rose) from painting images.

To conclude, it is worth noting that the class of the object influence the object detection performance. For example, it is hard to predict some categories of objects, such as Landforms (e.g. Mountains and Hills) and Produce (e.g. Meat, Fish and Flower), than others. Moreover, we note that low object detection performances are obtained with IconArt dataset due to the semantic complication of the classes. On the other hand, the bike class has scored the best performances in many works, as cited in [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF].

Depictive style

Object detection for visual cultural heritage was explored across different depictive styles of paintings explained by the use of several different datasets. The explored styles used with the studied objects detectors include High Renaissance [START_REF] Wechsler | Modern art challenges face detection[END_REF][START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF], Post-impressionism [START_REF] Wechsler | Modern art challenges face detection[END_REF], Cubism [START_REF] Ginosar | Detecting people in cubist art[END_REF][START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF], Tenebrism [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Mzoughi | Face detection in painting using deep convolutional neural networks[END_REF], Comics and Caricatures [START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF], Expressionism [START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF] and numerous other styles [START_REF] Westlake | Detecting people in artwork with CNNs[END_REF]. Object detection were also applied to pre-modern Japanese artworks [START_REF] Mermet | Face detection on pre-modern japanese artworks using r-CNN and image patching for semi-automatic annotation[END_REF], Al-Qazvin Cosmography book [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF], Japanese old books [START_REF] Sirirattanapol | Deep image retrieval applied on kotenseki ancient japanese literature[END_REF], paintings of medieval and early modern annunciations [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Madhu | Recognizing characters in art history using deep learning[END_REF] and Western artworks [START_REF] Wechsler | Modern art challenges face detection[END_REF]. Using a style transfer model, such as CycleGAN [START_REF] Hu | Tensorflow implementation of cyclegan[END_REF], it was possible to transfer an artistic style of paintings to the content of real world images. However, although many style-transferred datasets are generated in literature [START_REF] Madhu | Recognizing characters in art history using deep learning[END_REF][START_REF] Sarı | Automatic detection and visualization of garment color in western portrait paintings[END_REF][START_REF] Smirnov | Deep learning for object detection in fine-art paintings[END_REF][START_REF] Yaniv | The face of art: landmark detection and geometric style in portraits[END_REF], irregular artifacts are observed in the styletransferred images and the expert opinion remains essential to validate the quality of generated images (i.e. painting-like images).

It should be noted that some styles of artworks require substantially more effort than others for object detection, like the Abstract art or the Tenbrism style. This is surely due to the specific use of shadows in these painting styles. In fact, it has been demonstrated in [START_REF] Wu | Learning graphs to model visual objects across different depictive styles[END_REF] how performances of human and methods degrade with increased painting abstractness.

Challenges

Painting images are generally considered more challenging to analyze and interpret compared to photo-realistic images. They are customarily much harder when it comes to detecting objects. In fact, in addition to classical challenges of object detection in photographic images (e.g. different viewpoints, illuminations, resolutions and occlusions), paintings hold many particularities making their study a more complex task. These are mainly artistic creativity, complexity of scene and lack of large annotated dataset for certain styles.

Painting images are often seen as a form of creative expression. Unlike photo-realistic images, paintings are man-made representations of real-life objects, animals, peoples, scenes or also an inspiration of them as a result of creative imagination. They can incorporate abstract elements and stylized object representations, allowing the artist to express their emotions and ideas in a unique and subjective way that cannot be captured by a camera. Creativity can be expressed in terms of a particular or exaggerated composition of elements such as texture, form, shape, color, tone and line. To these elements, several artistic concepts such as movement, unity, variety, harmony, pattern, balance and contrast besides to art elements (such as thickness of brush strokes, media (e.g. oil, watercolor) and painting materials (e.g. paper, copper) [START_REF] Elgammal | Quantifying creativity in art networks[END_REF] are added. This leads to significant differences in geometry, texture and color of the artistically rendered objects reflecting time-specific dimensions (e.g. hairstyle, shape of faces, physical postures, clothes worn, etc.) and different artist purposes. Therefore, an interesting related issue posed by the wide variation in the visual appearance exhibited by the same object across different styles, is the high intra-class variations. For instance, in figure 5-a, we show the representation of persons across different styles to exhibit the significant visual differences between instances of the same class, revealing the high intra-class variations. The figure 5-b illustrate also the wide variation in visual appearance exhibited by the horse across different depictive styles.

The high visual complexity of scenes (figure 5-c) appears to also pose a challenge for object detection in paintings. It was proven that it is difficult to detect objects in busy scenes characterized by small regions of interest, especially when the style of the images is very different from recent photos [START_REF] Ibrahim | Few-shot object detection: Application to medieval musicological studies[END_REF][START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF]. The inherent complexity of the style can also be seen in the image surrounded by the green dashed bounding box in figure 5-c). In fact, the extreme contrast between light and dark areas introduces an element of mystery and ambiguity bringing an element of drama [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Mzoughi | Face detection in painting using deep convolutional neural networks[END_REF]. Moreover, the detection of abstract objects (like people in the pink dashed bounding box in figure 5-c) represents also a complex task [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF].

Another major challenge is the lack of large annotated data with high quality digitized paintings for training. Even though a large number of artwork datasets were continuously published, the available images remain content, technique, artist or style dependent. Various works have been proposed to overcome the lack of large-scale annotated paintings and hence make use of the promising deep neural networks as explained in section 2.3.

The occlusion of depicted objects was also one of the critical issue yet to be solved for object detection in painting [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF][START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Crowley | The art of detection[END_REF][START_REF] Westlake | Detecting people in artwork with CNNs[END_REF]. Indeed, painting images often present overlapping, occluded or truncated objects (blue and green bounding boxes in Figure 5-e). Generally, occlusion in object detection task occurs under three situations: self-occlusion, inter-object occlusions or occlusion by background. Self-occlusion arises when a part of the object occlude another whereas inter-object occlusion occurs when two objects occlude each other. The occlusion by the background occurs when a structure in the background occludes the object. A new occlusion situation is considered in artistic images when the painter depict an occlusion that is obviously intended. For example, in Figure 5-e, several faces (surrounded by green-bounding boxes) appear occluded by the painter.

Finally, we notice that objects with a variety of viewpoints and sizes (red bounding boxes in figure 5-c, e andf) make the object detection in artistic images a challenge to existing methods [START_REF] Al-Yasiri | A new approach for object detection, recognition and retrieving in painting images[END_REF][START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF][START_REF] Crowley | The art of detection[END_REF][START_REF] Mzoughi | Face detection in painting using deep convolutional neural networks[END_REF].

Discussion and future directions

Considering the reviewed literature, it is clear that object detection plays an increasingly important role in visual art analysis. Despite the improvements that have been realised using deep learning neural networks for the other art history problems such as style, genre and artist recognition, these deep neural network models still face challenges when it comes to identifying objects in paintings. It was worth pointing out that painting images are the result of creativity. In fact, they can incorporate abstract elements and stylized representations of objects besides to a variety of brush stroke, shapes, textures and lighting conditions. In addition, they may depict complex and intricate scenes with multiple elements, characters, and details. These artistic characteristics, coupled with the small number of available images per style, have made the task of object detection more difficult than in photo-realistic images. This problem was stated in literature as the cross-depiction problem [START_REF] Cai | The cross-depiction problem: Computer vision algorithms for recognising objects in artwork and in photographs[END_REF]. A primary question that arises in this context is why the human mind is capable of recognizing objects depicted in various artistic styles, whereas computers are unable to do so with equal ease. [START_REF] Cai | Beyond photo-domain object recognition: Benchmarks for the cross-depiction problem[END_REF] have shown that using object representations (expressed within the BoW-SIFT feature space), there is a larger variance across photo and art domains than there is in each one alone. [START_REF] Geirhos | Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness[END_REF] have demonstrated that CNNs are surprisingly biased toward texture, using Ima-geNet dataset, whereas human rely heavily on shape similarity among objects for shape identification and recognition. As a result, researchers should explore new techniques in order to improve object detection accuracy in paintings. We will next discuss some of the promising research directions for object detection in paintings.

In our context, domain generalization aims at finding objects regardless they are photographed, painted, drawn, etc. Considering neural networks, constructing a generalized object detection models require a huge number of data in different depictive styles. Such goal remains largely untapped due to the limited availability of paintings in digital form or even the scarcity of certain styles besides to the labor-intensive process of manually annotating them. As stated throughout this review, existing object detectors for paintings are generally initiated from a model pre-trained on photographs since they are largely more abundant in comparison with paintings. They are then fine-tuned using transfer learning. Different transfer learning strategies and configurations were investigated for paintings towards studying the generalization abilities of models. For example, in [START_REF] Gonthier | Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts[END_REF] authors have presented a comparison between different feature extraction methods, loss functions and model' hyperparameters. [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF] have also evaluated different backbone feature extraction architectures towards enhancing object detection results of Faster RCNN in the Tenerism Style. In addition, transfer learning approaches are generally associated with data augmentation techniques to encounter data scarcity in paintings. These latters include mainly standard augmentation techniques [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF], generative adversarial networks [START_REF] Sindel | Artfacepoints: High-resolution facial landmark detection in paintings and prints[END_REF] and neural style transfer [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF]. It is worth noting that despite the improvement of detection results that has been achieved, crucial difficulties lie ahead. Particularly, certain artwork styles are more difficult than others for object detection. [START_REF] Gonthier | Weakly supervised object detection in artworks[END_REF][START_REF] Inoue | Cross-domain weaklysupervised object detection through progressive domain adaptation[END_REF] have shown that performance varies depending on the used artwork style. In fact, the more different is the target style from photograph, the more difficult become the object detection task. Particularly, It has been stated that difficulties increase also along with high level of painting abstractness [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF][START_REF] Wu | Learning graphs to model visual objects across different depictive styles[END_REF]. Generally, abstract paintings are highly deviated from visual reality. Recognizable elements that could be found within abstract painting are generally distorted or defined using composition of simplified shapes. Existing style transfer techniques [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF][START_REF] Wu | Learning graphs to model visual objects across different depictive styles[END_REF] can make it possible to shift the color and texture but not shapes. This drawback explains the performance degradation of object detection in abstract paintings. Another complex style that were stated in literature is the Tenebrism style. The mysterious and dramatic illumination with violent contrasts of light and dark and the dominance of darkness make the task of face detection particularly difficult [START_REF] Bengamra | New challenges of face detection in paintings based on deep learning[END_REF]. Besides to style complexity, another source of bias that should also been investigated is related to the training sources themselves. Both the photograph-based datasets (such as ImageNet or COCO dataset which were used for pre-training) and the painting datasets (which were used for transfer learning) hold biases related to local context and cultural disparities. For example, [START_REF] Kadish | Improving object detection in art images using only style transfer[END_REF] have claimed that his model trained on StyleCOCO (obtained by style transfer from COCO dataset) performs better on Renaissance paintings than on a print from the Japanese shin-hanga movement. This was explained by the predominance of training images from North America and Europe. To this end, it becomes important to establish a detailed quantitative evaluation of object detection among different image styles, different objects and different datasets separately to pinpoint difficult ones. Another way of research that could be investigated is to design specific approaches for each difficult style. For instance, studying opportunities to deploy low-light enhancement techniques to improve object detection in the Tenebrist Style. On the other hand, another important line of research is to construct models that generalize well to novel test-domain. This could benefit from multi-task style and object identification architectures or also from recent stable diffusion image generators [START_REF] Rombach | High-resolution image synthesis with latent diffusion models[END_REF]. Finally, it is still important to continue developing new digital large-scale datasets for paintings.

The use of eXplainable AI (XAI) methods could also be efficient to produce explanations and reasons for decisions made by object detectors in paintings.

A first attempt in this direction was established in [START_REF] Bengamra | Towards explainability in using deep learning for face detection in paintings[END_REF]. In this work, the D-RISE method, a perturbation-based method that were designed for generating visual explanations of object detection, were used to examine saliency maps of Faster-RCNN face detection model in the Tenebrism style. A first investigation has proven that face detection in Tenebrism heavily rely on context unlike photograph-based face detection models. This is encouraging to persue a comprehensive study to understand principal modes of failures and to interpret the features learned by painting-based object detection models.

Another research axis that could be explored to improve object detection models for paintings is related to combining neural network models with semantic metadata information associated with them. Recently, a first try was realised by [START_REF] Marinescu | Improving object detection in paintings based on time contexts[END_REF] were time-specific dimensions were associated with images to detect the most probable objects that fit the time period of the painting. In this direction, other contextual constraints such as cultural and geographical dimensions could be investigated for better object detection in paintings.

Conclusion

The continued and vigorous progress of computer vision has made a great advent in the development of methods for visual art analysis, particularly due to deep learning techniques. This review provides a comprehensive overview of computer vision applications in visual art. Among these applications, object detection in paintings represents a task that remains acute nowadays. In this paper, a review of the recent progress in this field was presented. We broadly categorized the methods into two trends, classical machine learning based methods and deep learning based methods. We list the highlights and challenges of each one, and we show that deep learning based methods can bring new tools for art history studies. This survey will enables new ways to investigate visual art object detection and new strategies for doing quantitative research on art history and visual cultural heritage. It has been concluded that significant effort will be required in the future to tackle the challenges and open issues with object detection in visual art.

i 2 d

 2 d = normalized distance (interocular distance or e minimal bounding box size) n= number of facial landmarks x i and x * i are ground-truth points and model prediction points, respectively.

Fig. 1

 1 Fig.1Count of papers published in object detection from artworks (above) and year of publication (below).

Fig. 2

 2 Photo-art 50Kotenseki Comic2K Watercolor2k

Fig. 3

 3 Fig. 3 Taxonomy mind-map of object detection methods in artistic images.

Fig. 4

 4 Fig. 4 Basic architectures of single stage and two-stage object detection frameworks.

Fig. 5

 5 Fig. 5 Five different object detection Challenges in painting. (a) et (b) Difficulties to detect objects depicted by artists: significant differences with photographic images and wide intraclass variation. (c) Detection of objects in complex scenes. (d) The scarcity of paintings annotated in the form of bounding boxes around objects. (e) The problem of occlusion in object detection. (f) Detection of objects viewed from different viewpoints.

Table 3

 3 Detailed list of artwork datasets used for object detection.

	Dataset		Artisitc Style		Subject matter		Total	Total	Annotation
									images	instances
	Artistic-		High Renaissance	Face			160	68	facial	Image	level
	Faces		through Cubism				landmarks	and	Facial
			to Comics					per image	landmarks
	Al-Qazvin		Islamic painting 10 categories includ-	67	29	Object
						ing devils, angels and	level (COCO
						amphibian animals.	Format)
	Brueghel		Flemish(Bruegel	5 categories (carts, cows,	1587	273	Object
			family)			windmills, rowboats and	level (COCO
						sailbaots)		Format)
	CASPA		Cartoons,		8 categories: bear, bird,	1391	2834	Object
	paintings		Sketches,		cat, cow, dog, elephant,	level (COCO
			Paintings		giraffe, horse, sheep, and	Format)
						zebra
	Clipart1k		Clipart			20 categories including	1K	3165	Object level
						person, horse and plant	(PASCAL VOC Format)
	Comic2k		Comic			6 categories including	2K	6389	Image	and
						bike, bird, cat, car, dog	Object level
						and person		(PASCAL VOC Format)
	Dataset	of	Not available		16 objects including	75.3K	-	Image level
	cultural					sculptures,	paintings
	sites					and books
	Getty		Drawings, Paint-	>22	including	child	7872	-	Image level
			ing			Jesus,	Mary,	fruit,
						horse, flower and face
	IconArt		Paintings		7 classes: Jesus Child,	6K	3009	Image	and
						Saint Sebastian, Angel,	object level
						Crucifixion, Nudity and Ruins	Mary,	(PASCAL VOC Format)
	KaoKore		Pre-modern		Face			1470	8573	Image	and
			Japanese					Object
									level (COCO
									Format)
	Kotenseki		classical		and	6 classes: person, floor,	3126	609631	Image	and
			ancient Japanese	shoji, tree, roof and ani-	Object level
			style			mal			(CCOCO Format)
	Kouhon		Japanese(owned	Face			346	-	Image level
			by Shojo-Kouji)
	MAFD-150 29 styles belong-	Face			150	398	Object level
			ing to Modern art				(PASCAL VOC Format)
	MMSD		Medieval music	Phylactery, Folio, Book,	Not avail-	693	Object level
						Altar et Lectern	able
	Paintings		Different		10 classes: Aeroplane,	8629	-	Image level
			styles	(e.g.	bird, boat, chair, cow,
			photo-realistic,	table, dog, horse, sheep
			abstract	and	and train
			impressionism)
	PeopleArt		Photo,	cartoon	People			4.6K	> 4.6K	Object level
			and 41 styles from [2]				(PASCAL VOC Format)
	PhotoArt-50 Photo,		oil	50 classes including per-	≈ 100 for	> 5K	Object
			painting, draw-	son, horse, car, bike,	each class	level (COCO
			ing,	cartoon,	giraffe and face.	Format)
			stick-figures, etc.
	Picasso		Cubism			People			218	-	Image level
	Tenebrism		Tenebism		Face			409	1159	Object level
									(PASCAL VOC
									Format)
	Watercolor2k Watercolor		6 classes: bike, bird, cat,	2K	3315	Image	and
						car, dog and person	Object level
									(PASCAL VOC
									Format)
	WikiArt		27 styles includ-	Landscape,	Interior,
			ing	Abstract,	face, person, etc.
			Baroque	and
			Byzantine

Table 4

 4 Metrics used for object detection in artworks.

	Metric	Description	Formula	Papers
	Precision
	(P)

Table 5 :

 5 Brief overview of various research works on object detection in painting images.

	Year/Ref Artwork	Used framework Highlights	Performances
	Dataset
	2022/[88] MMSD	YOLOv4 (m/s),	A dataset collection and	AP50=83.62%
			Faster RCNN,	annotation,	Bi-training
			Mask	RCNN,	object detection model
			SWIN-T		based finetuning
	2022/[78] PeopleArt,	Faster RCNN	An extension of [77],	AP50=60%,
	IconArt,			Transfer	learning	of	R=94%
	Watercolor2k,			pretrained CNN, A poly-
	Clipart1k,			hedral	separation	for
	Comic2k			classification
	and	CASPA
	paintings

Table 5 -

 5 Continued from previous page

	Year/Ref Artwork	Used framework Highlights		Performances
	Dataset
	2021/[94] PeopleArt	Faster-RCNN	AdaIn Style transfer for	AP50=68%
				artistically-styled images
				generation,	Finetuning
				the model
	2021/[22] Tenebrism	Faster RCNN	Collection and annotation	AP50=86.51%
				of new dataset, Fine-
				tuning pretrained Faster
				RCNN, Application of
				several data augmenta-
				tion techniques (rotation,
				flipping).
	2021/[93] Getty, IconArt Faster-RCNN,	Style transfer for artwork-	AP50=82%
		NudeNet,	like images generation,
		YOLOFace,	Fine-tuning	Faster
		YOLO9000,	RCNN.
		ResNe(X)t and
		WBF
	2021/[124]Dataset of cul-	DA-RetinaNet,	New	Dataset	cre-	AP50=55.54%
	tural site [123]	RetinaNet,	ation,	Proposition	of
		Faster RCNN,	DA-RetinaNet
		DA-Faster-
		RCNN,
		Strong-Weak
	2020/[91] Brueghel, Peo-	YOLO		Neural	style	transfer,	AP50=40.6%
	pleArt			Data	augmentation,
				Training object detector.
	2020/[113]KaoKore,	SSD300, Faster	Image		patching,	AP50=82.9%,
	Kouhon	RCNN, Cascade	object	detection	in	R=91.1%
		RCNN		each	patch,	Non
				Maximum-Suppression
	2019/[162]MAFD-150	Four face detec-	New artwork dataset cre-	F<35%
		tors	including	ation, performance eval-
		Viola & Jones	uation of face detectors,
				challenges of face detec-
				tion in artistic images
	2019/[172]Artistic-Faces Facial landmark	Artistic data augmenta-	NME=2.01
		detection frame-	tion, Heat-Maps Network
		work [177]	for landmark detection,
				Landmarks	correction
				using a pre-trained point
				distribution model (PDM)
	2019/[77] IconArt, Peo-	Faster RCNN ,	New database creation,	AP50=85.2%
	pleArt, Water-	MI-max frame-	Multiple instance learning
	color2k	work		classification coupled with
				Faster RCNN
	2018/[16] Al-Qazvin	SURF detector,	Improved SURF algo-	F=81%,
		Hash Function	rithm	for	features	P=87%,
				extraction, Hash func-	R= 77%,
				tion to index features in	ATeT = 1.4
				database			sec to 4.3 sec
	2018/[90] Clipart1k,	Faster RCNN,	New dataset construction,	AP50=86.3%
	Watercolor2k,	SSD300, YOLO	CycleGAN for generation
	Comic2k			of artistic images, Fine-
				tuning pretrained fully
				supervised detector
	2018/[144]Paintings,	VGG-19	Artistic Style Transfer,	AP50=82%
	WikiArt	architecture	Transfer learning, Train-
				ing CNN models

Table 5 -

 5 Continued from previous page

	Year/Ref Artwork	Used framework Highlights				Performances
	Dataset
	2018/[122]Tenebrism	AlexNet		Finetuning		pre-	AP50=25.89%
				trained	model,	Data
				augmentation.
	2017/[143]Kotenseki	Faster RCNN	Fine-tuning		Faster	AP50=98.57%
				RCNN, L2 Normalization,
				Data augmentation.
	2016/[163]PeopleArt,	Fast	RCNN,	Collection and annotation	AP50=59%
	Picasso	DPM, YOLO	of a new dataset, Fine-
				tuning pre-trained model.
	2016/[52] Paintings	Faster RCNN	Data	augmentation,	P=100%,
				Application	of	a	AP50=59%
				pre-trained Faster RCNN.
	2015/[73] Picasso	Dalal	and	Comparison	of	object	P=44.4%,
		Triggs (D&T),	detectors				R=48.6%,
		DPM, Poselets,					F=45.8%,
		R-CNN						AP50=37.8%
	2014/[166]PhotoArt-50	DPM		Model visual objects with	AP50=89.1%,
				graphs				ATrT = 4 to
								5 min,
								ATeT= 4.5 to
								5 min

Data availability

All data analysed during this study are available at locations cited in the reference section.

Conflict of Interest

The authors declare that they have no conflict of interest.