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# Artwork recommendations guided by foundation models: survey and novel approach

Tetiana Yemelianenko, Iuliia Tkachenko, Tess Masclef, Mihaela Scuturici, Serge Miguet

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## To cite this version:

Tetiana Yemelianenko, Iuliia Tkachenko, Tess Masclef, Mihaela Scuturici, Serge Miguet. Artwork recommendations guided by foundation models: survey and novel approach. Multimedia Tools and Applications, 2025. hal-04829842

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Submitted on 28 Feb 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Artwork recommendations guided by foundation
models: survey and novel approach

Tetiana Yemelianenko*, Iuliia Tkachenko, Tess Masclef,
Mihaela Scuturici, Serge Miguet

Université Lumière Lyon 2, CNRS, Ecole Centrale de Lyon, INSA Lyon,
Universite Claude Bernard Lyon 1, LIRIS, UMR5205, Bron, F-69676,
France.

*Corresponding author(s). E-mail(s): tetiana.yemelianenko@liris.cnrs.fr;
Contributing authors: {name.surname}@liris.cnrs.fr;

Abstract

Museums and art galleries use modern technologies to engage visitors and attract not only specialists but also the general public, by providing recommendations for interactive studying and observing their collections. These recommendations can be created on the base of artwork similarity defined by using fine-tuned deep neural networks. In this paper, we explore the possibilities of fine-tuning foundation models using the Low-Rank-Adaptation fine-tuning technique for the classification task and perform similarity search based on features extracted with fine-tuned models. During fine-tuning, we examined the influence of hyper-parameters on two DINOv2 models' performance and found their reasonable combination according to a number of trainable parameters and performance, achieved on the relatively small artistic dataset. We achieved state-of-the-art accuracy for genre classification on the WikiArt dataset with proposed approach.

**Keywords:** Artwork Classification, LoRA, Foundation Models, DINOv2, Computer Vision

# 1 Introduction

Some of the big museums propose rich annotated collections available on their websites, on the artwork pages diverse information prepared by art experts can be found, like author, style, genre, year and place of creation, technique, and others. Some platforms
introduce interactivity by similar artwork recommendations. While observing museum
collections you can be involved in the process by proposing new similar artworks for
study. These recommendations are often based on the similarity of available artwork's
metadata, less often on visual similarity (Figure 1).

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Artwork recommendations based on visual similarity.</figcaption></figure>

More and more museums propose to the visitors along with the traditional way of
exploring their collections an alternative interactive way using applications on tablets
and smartphones. These tools should retrieve and propose to the visitor a set of
artworks which could be attractive to the user.

Visitors often wish to have more information on a painting encountered in a
museum, to associate it with other similar paintings from the visited museum or vir-
tual collections. This association process could be done through visual similarities
between images, but not only (similarity of the painting style, artist, similarity of
historical context, the similarity of objects present in the image, similarity of compo-
sition, etc.) Visitors are often interested in such predefined recommendations based on
the similarity of the query image to some others images from the museum or virtual
collections.

In the present work we are specifically interested in the tool which permits users to
explore pieces of art in the museum in front of the observed masterpiece, by proposing
additional visual information for the study. The goal is to improve and enrich the
experience of visitors letting them discover different ways to explore other images from
the collections of the visited museum as well as digitized images from open-access
collections.

To obtain similar artworks, content-based image retrieval approach can be used.
This approach requires feature extraction from the query image, then comparison
with the features of images in the dataset and selection of the most similar images
according to the selected similarity measure. The most used approach is based on using
pre-trained or fine-tuned for specific task deep neural network (DNN) as a feature
extractor.

In this paper, we focus on fine-tuning the foundation models for artwork classifi-
cation for further using these models not only for the classification, but for artwork
recommendations based on the paintings similarity calculated on the features extracted
by the fine-tuned model. The contributions of this work are the following:

* We propose a model approach for the recommendations of cultural heritage objects based on visual features. The proposed approach differs from the existing one by the possibility of jointly emerging models separately fine-tuned for the different tasks
(genre, style, artist recognition) according to specific user's preferences. This, on the one hand, permits to diversify artwork recommendations, on the other hand, to stay constrained by visual similarity to the query image and the user's selected criteria.
* We outperformed current state-of-the-art accuracy for genre classification on WikiArt dataset using fine-tuned with Low-Rank-Adaptation (LoRA) technique DINOv2 foundation model.
* The proposed approach improves the final MAP@k metrics for the combined recommendations by style and artist criteria.
* The used approach allows to use of a variety of fine-tuned models with frozen backbone in conditions of limited space on the tablets, because used Parameter Efficient Fine-tuning Technique (PEFT) lets save only specific adapters for each scenario with size in MBs in comparison with fully fine-tuned models with size from hundreds MBs to several GBs.

The rest of this paper is organized as follows. Section 2 presents and discusses the state of the art. The proposed approach is presented in Section 3. The experimental results of classification and artwork recommendations are shown in Sections 4.2 and 4.3 respectively. Finally, we conclude this work in Section 5.

# 2 Previous works

## 2.1 Computational approaches for artwork recommendation based on deep neural networks

We start with the review of existing approaches for artwork recommendations when the user's preferences are based on the selected for further study artwork. There are a variety of criteria which can be used for defining similarity in visual arts. In this section, we review possible variants for content-based image retrieval in artistic domain following the list of criteria in Figure 2.

<figure><img src="image_5.png" /><figcaption>**Fig. 2** Criteria for artwork recommendation.</figcaption></figure>

One of the possible ways to retrieve similar paintings is the evaluation of composition similarity through the pose-based analysis of paintings. In the works [1–4] a pose-based retrieval is described, in [5, 6] a more complex analysis of the scene was proposed. Jencek et al. [1] proposed a method for painting retrieval with a similar spatial
configuration of similar poses. This method uses a two-step retrieval procedure based on a pose detector and precise geometric verification. The proposed method allows the discovery of painting-drawing and painting-engraving relations. Zhao et al. [2] created a tool which allows pose-based analyses of paintings. The authors combined two pose estimators to compensate for each other's limitations, then applied a normalization step to correct occlusions and perspective distortions utilizing an average of body poses distinctive for the artist. Springstein et al. [3] studied domain adaption techniques to estimate human poses in art-historical images, and suggested a two-stage approach based on two Transformer models. The authors showed how extracted figure key points can be used in a retrieval scenario and evaluated the quality in a user study. Madhu et al. [5, 6] created a tool for the generation of underlying compositional structures within images. This tool can be used for building an explainable content-based image retrieval of paintings with similar image compositions.

Authors [7–10] are focused on content-based image retrieval using found similar patterns in the paintings.

In [11] authors introduced a dataset for semantic segmentation in paintings and illustrated how their approach can be used to gather all occurrences of a certain class from a given set of paintings. Works [12–14, 14–16] are focused on colour composition analysis, while authors [17–22] proposed solutions for emotion recognition provoked by artworks. In [23, 24] authors worked on discovering artistic influences using DNN. All these approaches could also introduce a possible way for the artwork recommendation for museum visitors.

Another axis of research is focused on recommendations based on collected user preferences [25–28] and aesthetics evaluation [12, 29–31]. In [32] was proposed an approach to refine recommendations according to the specific criteria based on the collected re-rankings. However, the approach that used collected user preferences could not be practical for the applications intended for use directly in the museums because of perception of artworks could vary significantly for the non-expert visitors, except that, the cold start problem exists when for the new user it is not possible to provide personalized recommendations, and its non-realistic scenario that new visitor evaluates sets of paintings in the application before starting the visit.

The artwork recommendations could be based also on using not only visual modality but on a combination of different modalities. Thus, Mathias et al. [33] evaluated artwork importance using textual energy from the description of paintings. Kim et al. [34] built a content detector based on an Inception-V3 model fine-tuned over 40,000 paintings with the words extracted from their titles. In [35–39] authors studied ways of recommendations based on combinations of visual representation of paintings and their textual descriptions.

The most popular axis for artwork recommendations is based on an artwork classification. As this axis is the main focus of our research we observed existing approaches in more detail in the next section. Usually, the recommendations based on this approach are created on the base of features extracted with the fine-tuned DNN models, and then these features are used for calculating the similarity between a query image and images in the dataset.
## 2.2 Artwork classification

Last few years variety of approaches have been proposed for solving fine art classification tasks like defining attributes such as style, genre, artist of the painting and others. Despite the variety of proposed approaches this task still stays rather challenging. Earlier works are based on using extracted hand-crafted low-level features. Gunsel et al. [40] trained SVM classifier for style recognition based on brightness and gradient information of an image and statistics of the grey-level histogram. Zujovic et al. [41] applied different classifiers for genre classification as features used steerable filters and edge information extracted by a canny edge detector, the best accuracy was received for AdaBoost classifier. The description of other approaches based on hand-crafted features can be found in the survey introduced by Brachmann et al. [42]. In this work, we are mostly interested in using DNN for artwork classification tasks.

Most of the works are focused on identifying such attributes as the artist, genre, and style of the painting. Saleh [43] studied the possibilities of using metric learning approaches, low-level visual features (GIST), and high-level semantic features (Classeme, Picodes and Convolutional Neural Network (CNN) based) to learn an optimized similarity measure between paintings for artist, genre, and style classification. The authors used the WikiArt dataset for the received results demonstration. The works [44–47] are focused on artist classification. Kelek et al. [44] tested a using of GoogleNet, DenseNet, ResNet50, ResNet101, and InceptionV3 CNN for the artist classification on the dataset of 797 paintings from 17 artists. The highest result was received with DenseNet network (accuracy 79.36±7.18%), but authors recommend to use InceptionV3 (accuracy 79.26±2.65%) and ResNet50 (accuracy 76.48±5.46%) networks considering training time and file size. Hua et al [45] worked on an artist classification task using the CNN model and Markov random fields (MRF). MRF were used to build the relationship within local image patches. The proposed method was validated on WikiArt (precision 72.89%) and PaintingDb datasets (precision 76.92%). Liao et al. [46] worked on an oil artist recognition based on a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: colour, texture, and spatial layout. The authors used the Painting-91 dataset. Lyberatos et al. [47] used ResNet and RegNet for feature extraction and compared 6 different machine learning classifiers: Logistic Regression, Naive Bayes, K-Nearest Neighbors, Decision Trees, AdaBoost and Support Vector Machines (SVM). The best result for artist classification was received for SVM with 85% accuracy. Cetinic et al. [48] explored different scenarios of CNN fine-tuning for artist, style, genre, and nationality classification on the WikiArt dataset. The received accuracy equals 81.94%, 56.43%, 77.6%, and 58.35% respectively. Zhao et al. [49] proposed using the Big Transfer learning [50] method for genre, style, and artist categorization tasks. Authors used several techniques for dataset augmentation such as random cropping, random horizontal flipping, and Mixup approach [51]. Achieved accuracy for style, genre and artist classification is 71.24%, 82.39%, and 93.50% respectively. Iliadis et al. [52] trained from scratch Vision Transformers and Multi-layer Perceptron Mixer models for artwork style recognition. Authors achieved over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset.
Another group of work is focused on a multi-task learning approach. Strezoski et al. [53] solved the classification task by proposing a multi-task method for learning a shared representation and evaluated their results on four tasks of The Rijksmuseum 2014 Challenge: artist attribution, creation period estimation, material prediction, and type prediction. Authors declared that their OmniArt method speeds up training and testing times. Later other multi-task approaches were proposed based on using not only visual but also contextual information. Among them multitask painting categorization by deep multibranch neural network proposed by Bianco et al. [54] and adaptive multi-task learning approach proposed by Yang et al. [55]. In [54] a multitask architecture was proposed which includes three different branches that process the input image at different scales and perform simultaneously three different tasks: artist, style and genre classification, authors jointly used handcrafted and neural features to improve the classification accuracy. The proposed configuration results in a faster system both at training and at testing time. The authors tested their approach on the MultitaskPainting100k dataset. Yang et al. [55] introduced an adaptive multi-task learning method using a Lagrange multiplier which can be used for the classification tasks. The authors evaluated the quality of classification on the SemArt dataset [36] using such attributes as author, genre, school, and timeframe.

Some authors search for the possibility of using contextual information along with visual features by constructing knowledge graphs. One of the first works which introduced using of an artistic knowledge graph was [56]. The authors proposed two models for capturing artistic context in paintings. They extracted non-visual information from artistic metadata and encoded it in a knowledge graph. In the second model, authors extracted artistic attributes from the images such as author, school, genre, or period with a multi-task learning model. Classification results were compared with other models on the SemArt dataset. Efthymiou et al. [57] proposed a multimodal architecture that integrates Graph Neural Networks and CNN. The authors evaluated the performance of proposed methods on the WikiArt dataset for artwork classification by style, artist, and timeframe and multi-label fine art categorization. El Vaigh et al. [58] worked on a multi-label classification problem, they trained a model using a graph convolutional network, relying on the relationships between entities of the knowledge graph. The evaluation was done on the SemArt and Buddha statues datasets. Zhao et al. [49] built a graph neural network method for art classification that uses features extracted from textual comments about art and on its basis a multitask learning model was built. The method was evaluated on the SemArt dataset. Later Castellano et al. [59] presented an artistic Knowledge Graph built on the base of the WikiArt paintings dataset and artist metadata obtained from DBpedia. The proposed method extracts embeddings from the built knowledge graph and injects them into a Deep Learning model. Achieved accuracy for style classification is 58.58%, for the genre is 72.29%. In this work, the latest bigger version of the WikiArt dataset (in comparison with earlier published works) with a bigger number of styles and genres was used.

## 2.3 Parameter Efficient Fine-tuning with LoRA

Modern tablets propose quite a big amount of space for storage but we should save not only fine-tuned for each task model (for modern transformers architecture size can
vary from hundreds of MBs to several GBs) but also a dataset and indexed files for image retrieval, therefore it's crucial to find a possibility to reduce the sizes of saved fine-tuned models.

One of the techniques which can be used for it is Parameter Efficient Fine-tuning (PEFT) [60]. PEFT is a group of techniques for efficient adaptation of large pre-trained models without full fine-tuning of all model parameters. Fine-tuning of foundation models can be challenging because of the model sizes. During full fine-tuning on relatively small artwork datasets, we can quickly observe overfitting because of the big amount of parameters which should be trained.

Due to the relatively small size of artistic datasets (in comparison to the natural image datasets on which modern transformers were trained), it is quite difficult to fine-tune transformer models without overfitting. Using the LoRA technique permits to train a small subset of parameters which preserves from quick overfitting. After fine-tuning only this small subset of parameters could be saved, which permits to use one saved pre-trained model and many small fine-tuned for different tasks LoRA modules. This saves space for storing fine-tuned models and gives flexibility.

<figure><img src="image_6.png" /><figcaption>**Fig. 3** Vision Transformers (ViT) model overview with injected LoRA matrices. $W_q^*$ ($W_k^*$, $W_v^*$) – frozen weight matrices, $A_q$, $B_q$ ($A_k$, $B_k$, $A_v$, $B_v$) – trainable LoRA matrices.</figcaption></figure>

LoRA fine-tuning technique uses frozen pre-trained model weights and injects trainable rank decomposition matrices into selected layers of the transformer architecture. This technique greatly reduces the number of trainable parameters [61]. The idea of the method is that during the training a pre-trained weight matrix $W^{dk}$ is frozen and does not receive gradient updates, while low-rank decomposition matrices $A^{rk}$ and $B^{dr}$ contain trainable parameters, so $W + \Delta W = W + BA$, where $d$ is a dimension size of a transformer layer, and $r$ the rank of LoRA matrices. Thus, we do not train the original weights but add some extra weights to the model, and then train their. The authors declared that LORA can be applied to any subset of weight matrices in a neural network. For the transformer architecture, these are four weight matrices in
the self-attention module $W_q, W_k, W_v, W_o$ and two in the MLP module. For simplicity, we follow the paper and treat $W_q, W_k$, and $W_v$ as single matrices with dimension $d \times d$, although typically output dimension is sliced into attention heads [61].

Initially, the LoRA technique was proposed for fine-tuning Large Language Models, but it could be adapted for fine-tuning vision transformer models also [62]. Figure 3 illustrates the process of fine-tuning the transformer model using the LoRA technique.

# 3 Proposed approach for artwork recommendations

For creating recommendations based on visual similarity, we used a commonly used approach with a combination of transfer learning technique and fine-tuning for specific tasks. The idea behind transfer learning is to use a model which was trained on a large and general enough dataset on another small specific dataset.

As the base models, we used two pre-trained DINOv2 ViT-B/14 and DINOv2 ViT-L/14 models [63] with a total number of 86M and 304M parameters respectively. These models are distilled versions of the trained ViT model [64] with 1B parameters. DINOv2 belongs to the family of foundation models, i.e. large DNN pre-trained on massive datasets. Foundation models are trained on diverse datasets, generally with unsupervised learning, and they are capable to adapt with high accuracy to a broad range of tasks. DINOv2 produces universal features applicable for solving image-level and pixel-level visual tasks, such as classification, image retrieval, semantic segmentation, depth estimation, and others. DINOv2 method uses self-supervised learning for training computer vision models. The models were trained on a dataset of 142 millions images.

Checkpoints of modern transformer models are quite big, for example, the size of checkpoint for DINO ViT-B/14 distilled model equals 330 MBs, and for DINO ViT-L/14 - 1.1 GB. If you want to use fine-tuned for different task models for the inference or feature extraction, you should store every fine-tuned model on the device. Our application must not be dependent on the Internet connection in the museum, and all data: code, user's data, dataset, fine-tuned models, and index files for similarity search must be kept on the device. Due to the relatively big size of the models, this can be challenging. We can not reduce the size of the dataset or index files, but we can reduce the size of fine-tuned models. Thus, we propose to use the LoRA technique for fine-tuning, this permits to store only small adapter files for each task and one checkpoint of the pre-trained model, which reduces the size of stored fine-tuned models significantly. The proposed approach is illustrated in the Figure 4.

Proposed approach can be divided in two main stages.

**Preparation stage.** We start from the fine-tuning of DINOv2 models for genre, style and artist classification tasks. Next, we extract features for all images in the dataset using fine-tuned models as feature extractors. Due to the big size of the dataset and computational power of tablets, the exhaustive nearest neighbour search is not practical, user will not be satisfied if he must wait several minutes on the retrieved results. The good alternative is using n Approximate Nearest Neighbour (ANN) algorithm for efficient search. ANN algorithm allows a real-time search of similar images,
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Retrieval stage of the proposed approach.</figcaption></figure>

with a quality close to an exact algorithm. As in [65], we used ANNOY (Approximate Nearest Neighbour Oh Yeah)<fnref n="1" /> for similar images search. This is a tree-based algorithm created by Spotify for music recommendations. ANNOY is efficient in high dimensional spaces, it permits to use static files as indexes, this gives the possibility to share index files across processes. So, at the next step we use ANNOY for creating index files for the similarity search based on the euclidean distances for the each set (genre, style and artist) of the extracted features.

**Retrieval stage.** At this stage for the request painting we retrieve similar paintings for each of three criteria of similarity (based on genre, style and artist) using previously created ANNOY indexes. The distributions of euclidean distances is illustrated in Figure 5. Given that the distribution are different, we need first to normalize distances and only after that combine them. For normalization we calculate mean and standard deviation of distances for each criteria separately, then normalize distances using calculated values. After normalization, we combine lists of similar paintings using normalized distances with selected weights of criteria. Next, final list is sorted by increasing of calculated distances.

We illustrated the proposed approach using three criteria, but it is possible to use more criteria with their different combinations according to user preferences. For adding new criteria you need to prepare LoRA adapters, they let flexibly switch between different fine-tuned models, and you need to prepare beforehand ANNOY index for similarity search. Also, as the base pre-trained model can be used other vision models. The proposed approach gives a possibility to switch flexibly between the models and combine them for the inference and similar images retrieval.

<footnote n="1">¹https://github.com/spotify/annoy</footnote>
<figure><img src="image_8.png" /><figcaption>Fig. 5 Distribution of distances for the models fine-tuned for genre, style and artist classification.</figcaption></figure>

# 4 Dataset and results

## 4.1 WikiArt dataset

In our work we used the publicly available WikiArt<fnref n="2" /> dataset, which is, to the best of our knowledge, the most used dataset for the art classification tasks. The dataset contains more than 130000 digitized paintings from the 15th to 20th centuries, metadata of the dataset includes 27 different styles (Romanticism, Baroque, Impressionism, etc.), 10 painting genres (landscape, portrait, still life, etc.), and more than 1000 artists.

For the fair comparison, we used a subset of 81444 paintings from the WikiArt dataset first presented in [66] paper, because this subset is commonly used in the papers. For genre classification, dataset was filtered to exclude paintings with unknown genre, final dataset contains 64993 paintings. For style classification, we used the original dataset. For artist classification we followed earlier works and selected 23 artists with the highest number of paintings in the dataset. Figure 6 demonstrates the distribution of classes in the dataset.

## 4.2 Genre, style and artist classification

In our work we tested several fine-tuning scenarios:

* With dataset augmentation using random cropping and random flipping of training data.

* With dataset augmentation only for the classes with the smallest number of elements using random cropping and random flipping of training data.

* With different values of rank of LoRA matrices: 4, 8, 16, 32.

<footnote n="2">²https://www.wikiart.org/</footnote>
<figure><img src="image_9.png" /><figcaption>Fig. 6 Genre, style and artist class distribution in WikiArt dataset.</figcaption></figure>

* With applying LoRA to the query, key and value weight matrices on attention layers, and additionally with fully connected and MLP layers of the base model.

The training was done for 40 epochs, we used 80% of the dataset as training data, 10% as validation, and 10% as test data. To avoid overfitting, we applied to the training data several image transformations which do not change significantly the original image because it is important for artwork classification. We used random cropping preserving minimum 80% of images with the probability of cropping equals 0.5, random horizontal flipping with the probability 0.5, and randomly made enhancing of contrast with the factor equals to 3 and the probability 0.5. The size of all images was reduced to 224x224 pixels. For model fine-tuning, we used the Hugging Face PEFT library [62]. Networks were fine-tuned with training batch size 64, Adam with weight decay optimizer, learning rate 0.00001, and 0.1 dropout probability for LoRA layers.

After a series of experiments, we can conclude that dataset augmentation with cropping and using random flipping slightly improves accuracy, but the difference in the accuracy values with data augmentation and without are not significant, considering that training on augmented dataset takes longer, we decided not to add changed images to the dataset, but use flipping, cropping and contrast enhancing only to add variety to the training data to prevent overfitting. Similarly, using a variation of dataset augmentation only for classes with a small number of elements improves insignificantly the value of accuracy and could not be considered reasonable.

We trained the models for values of rank LoRA matrices 4, 8, 16, 32, and as expected, we received better accuracy with higher values of LoRA matrices rank *r*. For the value *r* = 32, we received the highest accuracy among all our experiments with different *r*, but higher values of *r* result in more trainable parameters and, consequently, longer time for training. For that reason, we selected *r* = 16 for all other experiments. We experimented with applying the LoRA technique to different attention layers matrices, but we observed overfitting during training in case we used key matrices. The same effect was observed while applying LoRA not only to the attention layers matrices but to the fully connected layers also. Overfitting can be elucidated
as the necessity with higher rank to learn more parameters, making the task more challenging, particularly on a relatively small WikiArt dataset.

<table><caption>**Table 1** Genre classification results: comparison of the state-of-the-art results with our proposed approach based on DINOv2 and LoRA</caption><thead><tr><th>Paper</th><th>Size of WikiArt dataset used</th><th>Accuracy, %</th><th>Comment</th></tr></thead><tbody><tr><td>Saleh et al. [43]</td><td>63691</td><td>60.28</td><td>Features extracted with CNN and classification was done with SVM model</td></tr><tr><td>Tan et al. [66]</td><td>64993</td><td>74.14</td><td>Fine-tuned CNN model</td></tr><tr><td>Cetinic et al. [48]</td><td>86087</td><td>77.6</td><td>Fine-tuned CNN model In this work different genres category was used</td></tr><tr><td>Zhong et al. [67]</td><td>64993</td><td>76.27</td><td>Fine-tuned CNN model</td></tr><tr><td>Zhao et al. [49]</td><td>28760</td><td>78.03</td><td>Fine-tuned ViT model and Graph Attention Network (GAT)</td></tr><tr><td>Castellano et al. [59]</td><td>135038</td><td>71.23</td><td>ResNet and GAT</td></tr><tr><td>Zhao et al. [68]</td><td>64993</td><td>69.73</td><td>ViT model</td></tr><tr><td>Zhao et al. [68]</td><td>64993</td><td>65.92</td><td>Big transfer model BiT-S</td></tr><tr><td>Zhao et al. [68]</td><td>64993</td><td>80.88</td><td>Big transfer model BiT-M</td></tr><tr><td>Wu et al. [69]</td><td>81445</td><td>82.39</td><td>Pre-trained CLIP model</td></tr><tr><td>Wu et al. [69]</td><td>81445</td><td>71.56</td><td>CLIP model fine-tuned on WikiArt dataset</td></tr><tr><td>Ours</td><td>64993</td><td>80.43</td><td>DINOv2 ViT-B/14 fine-tuned with LoRA</td></tr><tr><td>Ours</td><td>64993</td><td>80.45</td><td>DINOv2 ViT-L/14 fine-tuned with LoRA</td></tr><tr><td>Ours</td><td>64993</td><td>83.24</td><td>DINOv2 ViT-B/14 fine-tuned with LoRA</td></tr></tbody></table>

After all experiments we chose the rank of LoRA matrices $r = 16$, learning rate=0.00001, and applied LoRA only to the query and value weight matrices. We repeated our experiment 5 times for the smaller DINOv2 ViT-B/14 model and 3 times for the bigger model DINOv2 ViT-L/14. In Table 1 you can find obtained average values of accuracy for all experiments as well as the results of the previous studies. In Table 1 we report the performance of our fine-tuned model with respect to the state-of-the-art accuracy on the WikiArt dataset. We report average accuracy for the series of independent runs of our fine-tuned models (5 and 3 for the DINOv2 ViT-B/14 and DINOv2 ViT-L/14 respectively). The results show that our DINOv2 ViT-L/14 fine-tuned model outperforms the current state-of-the-art reported for the WikiArt dataset in [68].

Confusion matrices for two selected models DINO ViT-B/14 and DINO ViT-L/14 are shown in Figure 7. In Table 2 we report precision, recall and F1-score for each class. The received results show that the fine-tuned model performs better on abstract painting, landscape, portrait and still life classes, this could be explained the distinct visual appearance of that classes. However, for the illustration class we received the lowest values of accuracy and recall. This class is often confused with "genre painting" and "sketch and study" classes. This confusion could be explained for the "genre"
<figure><img src="image_10.png" /><figcaption>**Fig. 7** Confusion matrices for genre classification: a) DINO ViT-B/14 model), b) DINO ViT-L/14 model.</figcaption></figure>

<table><caption>**Table 2** Genre classification results</caption><thead><tr><td>Class</td><td>Precision</td><td>Recall</td><td>F1-score</td><td>Support</td></tr></thead><tbody><tr><td>Abstract painting</td><td>0.9165</td><td>0.9738</td><td>0.9443</td><td>496</td></tr><tr><td>Cityscape</td><td>0.7947</td><td>0.7912</td><td>0.7930</td><td>455</td></tr><tr><td>Genre painting</td><td>0.7618</td><td>0.7478</td><td>0.7547</td><td>1146</td></tr><tr><td>Illustration</td><td>0.7234</td><td>0.5312</td><td>0.6126</td><td>192</td></tr><tr><td>Landscape</td><td>0.8842</td><td>0.8983</td><td>0.8912</td><td>1258</td></tr><tr><td>Nude painting</td><td>0.7571</td><td>0.8325</td><td>0.7930</td><td>191</td></tr><tr><td>Portrait</td><td>0.8529</td><td>0.8901</td><td>0.8711</td><td>1401</td></tr><tr><td>Religious painting</td><td>0.8410</td><td>0.8179</td><td>0.8293</td><td>692</td></tr><tr><td>Sketch and study</td><td>0.7131</td><td>0.6554</td><td>0.6830</td><td>383</td></tr><tr><td>Still life</td><td>0.9247</td><td>0.9021</td><td>0.9133</td><td>286</td></tr></tbody></table>

class by representation in this class paintings which illustrate scenes from everyday life, and for the "sketch and study" class by the presence in this class the graphic works which potentially could be confused with monochrome illustrations.

Figure 8 illustrates the process of training by graphics of training and validation loss functions. We observe that the losses gradually decrease during the time, and we can conclude that the models improve their performances without overfitting.

The number of trainable parameters and the size of the final adapter file which should be used for the inference in combination with the base model with frozen weights are shown in the Table 3. For using fine-tuned with LoRA technique models you only need one backbone of base model and set of trained LoRA adapters with sizes in MBs for each task, in comparison with the full fine-tuning when you need for the inference and feature extraction fine-tuned for each task models with sizes in GBs. Hence, LoRA technique permits to use on the devices with limited storage capacity significantly bigger number of fine-tuned models.

For the style and artist classification we received accuracy 67.01% and 83.57% respectively. This is lower than current state-of-the-art values (71.24%, 93.50%), but this accuracy is higher in the comparison with the obtained results for other
<figure><img src="image_11.png" /><figcaption>Fig. 8 Training and validation losses: a) DINO ViT-B/14 model), b) DINO ViT-L/14 model.</figcaption></figure>

<table><caption>**Table 3** Size of the LoRA configuration file and the number of trainable parameters</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Checkpoint size</th><th colspan="2">Trainable number of parameters</th><th rowspan="2">Size of the adapter file</th></tr><tr><th>Pre-trained model</th><th>Model fine-tuned with LoRA technique</th></tr></thead><tbody><tr><td>ViT-B/14 (query, value weight matrices), r=16</td><td>330 MB</td><td>86.6M, 100%</td><td>0.6M, 0.69%</td><td>2.43 MB</td></tr><tr><td>ViT-L/14 (query, value weight matrices), r=16</td><td>1.1 GB</td><td>304.4M, 100%</td><td>1.6M, 0.52%</td><td>6.39 MB</td></tr><tr><td>ViT-B/14 (query, value, fully-connected and MLP weight matrices), r=16</td><td>330 MB</td><td>86.6M, 100%</td><td>2.7M, 2.99%</td><td>10.7 MB</td></tr><tr><td>ViT-L/14 (query, value, fully-connected and MLP weight matrices), r=16</td><td>1.1 GB</td><td>304.4M, 100%</td><td>7M, 2.28%</td><td>12.54 MB</td></tr></tbody></table>

transformer-based model [52]. Possibly, the results can be improved by using more sophisticated dataset augmentation technique.

## 4.3 Combined recommendations

In this section we tested the proposed approach (section 3.2) for artwork recommendations based on combined similarity by genre, style and artist criteria.

For creating combined recommendations we need to retrieve images similar to the request artwork separately for each criterion, then combine them in one list and sort by increasing the euclidean distances from the query artwork. Figure 9 illustrates request painting, paintings retrieved based on genre, style and artist similarity, and the combined list of recommended paintings using these criteria with equal weights. In the same way, it is possible to combine several criteria with different weights for
<figure><img src="image_12.png" /><figcaption>**Fig. 9** Artwork recommendations based on the combination of genre, style and artist criteria. Paintings for which attributes differ from request painting are marked with frames: green for paintings with genre different from the request painting, blue for style, and red for artist.</figcaption></figure>

each of them if needed. Combined recommendations permit diversify the final list of recommended paintings. In some cases, we observed the improvement in the order of paintings in the final list in comparison with the original lists of retrieved images (irrelevant items are located towards the end of the list), and sometimes the final list is more relevant to the request painting according to the selected criteria. To verify if this difference is common or not, we calculated values of MAP@5, MAP@10 and MAP@15 metrics for the recommended lists received for each criterion and for the combined lists. MAP@k is a quality metric that evaluates the ability of the recommender system to return relevant items in the top-k results while placing more relevant items at the top. Next, we used a two-sided paired samples t-test to verify that the differences between means of AP@k are statistically significant (here AP@k is average precision at k). Received results (Table 4) show that for style and artist criteria the differences are statistically significant, and we can confirm that combined recommendations are better aligned with these two selected criteria.

It could be explained by the procedure of two-step retrieval. First, we use the ANNOY algorithm for narrowing our search, this is an approximate search, so potentially paintings which are closer to the requested painting could be not present in the pre-selected lists of similar paintings. In the second step we combine lists received by each criteria in one list and calculate exact distances (euclidean or cosine) for the pre-selected lists, then re-order the final combined list. Possibly, this improvement is caused also by the presence of artists in the dataset with well-established styles and similar paintings from both artist and style lists complement each other.
<table><caption>**Table 4** Comparison of MAP@k metrics for lists of similar paintings by genre, style, artist criteria, and combined list</caption><thead><tr><th></th><th>MAP@5</th><th>MAP@10</th><th>MAP@15</th></tr></thead><tbody><tr><td>Genre</td><td>0.8114</td><td>0.7911</td><td>0.7768</td></tr><tr><td>Genre in combined list</td><td>0.8088</td><td>0.7884</td><td>0.773</td></tr><tr><td>t-test, p-value for genre</td><td>t=1.335<br>p=0.182</td><td>t=1.783<br>p=0.075</td><td>t=2.749<br>p=0.006</td></tr><tr><td>Style</td><td>0.7739</td><td>0.7474</td><td>0.7262</td></tr><tr><td>Style in combined list</td><td>0.8033</td><td>0.7796</td><td>0.7594</td></tr><tr><td>t-test, p-value for style</td><td>t=-13.938<br>p=6.6e-44</td><td>t=-18.774<br>p=8.1e-78</td><td>t=-21.628<br>p=2.7e-102</td></tr><tr><td>Artist</td><td>0.8041</td><td>0.7844</td><td>0.769</td></tr><tr><td>Artist in combined list</td><td>0.8214</td><td>0.8002</td><td>0.7835</td></tr><tr><td>t-test, p-value for artist</td><td>t=-10.38<br>p=3.6e-25</td><td>t=-12.715<br>p=7.3e-37</td><td>t=-14.162<br>p=2.9e-45</td></tr></tbody></table>

# 5 Conclusion

In this work we used PEFT technique to fine-tune foundation models for classification task on artistic dataset. Using the LoRA technique for fine-tuning permits to store small adapter files for each task and only one checkpoint of the pre-trained model, this let reduce significantly the size of stored models on the device and let to switch flexibly between the fine-tuned models for doing inference or image retrieval. We achieved state-of-the-art accuracy for the genre classification on WikiArt dataset by studying the impact of hyperparameters on the training process. The proposed approach permits to combine different fine-tuned models and diversify recommendations of artworks for the visitors based on their current preferences. We showed that combined recommendations are slightly improved the final MAP@k metrics by style and artist criteria. Future work will focus on the using other type of metadata in the purpose of reducing so called “intention gap” between the users’ desired results and the actual artwork recommendations.

**Acknowledgements.** We would like to thank the PAUSE-ANR Ukraine program: Ukrainian scientists support to supporting the scientific stay of T. Yemelianenko in the LIRIS laboratory.

# Declarations

**Funding** This work was funded by a French national research agency with grant ANR-20-CE38-0017.

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