Curating art exhibitions using machine learning

• URL: http://arxiv.org/abs/2506.19813v1
• Date: Tue, 24 Jun 2025 17:25:03 GMT
• Title: Curating art exhibitions using machine learning
• Abstract summary: We present a series of artificial models based on machine learning techniques.<n>We focus on the last 25 years of past exhibitions at the Metropolitan Museum of Art in New York.<n>Our four artificial intelligence models achieve a reasonable ability at imitating these various curators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Art curatorship has always been mostly the subjective work of human experts, who, with extensive knowledge of many and diverse artworks, select a few of those to present in communal spaces, spaces that evolved into what we now call art galleries. There are no hard and fast set of rules on how to select these artworks, given a theme which either is presented to the art curator or constructed by her/him. Here we present a series of artificial models -- a total of four related models -- based on machine learning techniques (a subset of artificial intelligence) that attempt to learn from existing exhibitions which have been curated by human experts, in order to be able to do similar curatorship work. We focus exclusively on the last 25 years of past exhibitions at the Metropolitan Museum of Art in New York, due to the quality of the data available and the physical and time limitations of our research. Our four artificial intelligence models achieve a reasonable ability at imitating these various curators responsible for all those exhibitions, with various degrees of precision and curatorial coherence. In particular, we can conclude two key insights: first, that there is sufficient information in these exhibitions to construct an artificial intelligence model that replicates past exhibitions with an accuracy well above random choices; second, that using feature engineering and carefully designing the architecture of modest size models can make them as good as those using the so-called large language models such as GPT in a brute force approach. We also believe, based on small attempts to use the models in out-of-sample experiments, that given more much more data, it should be possible for these kinds of artificial intelligence agents to be closer and closer to the aesthetic and curatorial judgment of human art curators.

Related papers

• Guava: An Effective and Universal Harness for Embodied Manipulation [74.34187069605844]
  We develop an end-to-end training pipeline that distills embodied manipulation capabilities into a 4B open-source model.<n>Results suggest that a well-designed harness can serve as a scalable, model-agnostic interface for embodied manipulation.
  arXiv  Detail & Related papers  (2026-06-16T18:09:26Z)
• Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series [15.763018008675083]
  We introduce four model series specifically designed to meet industrial requirements.<n>The DistilQwen model collection comprises: (1) slow-thinking models, optimized for reasoning tasks that require high accuracy; (2) two series of adaptive-thinking models, which dynamically adjust reasoning strategies based on input tasks to maximize efficiency across diverse scenarios; and (3) distilled reward models, which enable further reinforcement learning of reasoning models using distilled knowledge.
  arXiv  Detail & Related papers  (2025-11-03T09:00:51Z)
• UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models [62.76435672183968]
  We introduce a novel framework, namely UNIFORM, for knowledge transfer from a diverse set of off-the-shelf models into one student model.<n>We propose a dedicated voting mechanism to capture the consensus of knowledge both at the logit level and at the feature level.<n>Experiments demonstrate that UNIFORM effectively enhances unsupervised object recognition performance compared to strong knowledge transfer baselines.
  arXiv  Detail & Related papers  (2025-08-27T00:56:11Z)
• ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models [61.55816738318699]
  We propose a novel method for data-use auditing in the text-to-image generation model.<n>ArtistAuditor employs a style extractor to obtain the multi-granularity style representations and treats artworks as samplings of an artist's style.<n>The experimental results on six combinations of models and datasets show that ArtistAuditor can achieve high AUC values.
  arXiv  Detail & Related papers  (2025-04-17T16:15:38Z)
• Diffusion Models for Robotic Manipulation: A Survey [8.215325350337126]
  Diffusion generative models have demonstrated remarkable success in visual domains such as image and video generation.<n>They have also emerged as a promising approach in robotics, especially in robot manipulations.<n>This survey provides a comprehensive review of state-of-the-art diffusion models in robotic manipulation.
  arXiv  Detail & Related papers  (2025-04-11T11:01:11Z)
• Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution [1.8435193934665342]
  We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 500 years of Western paintings.<n>Our findings reveal that contextual information differentiates between artistic periods, styles, and individual artists more successfully than formal elements.<n>Our generative experiment, infusing prospective contexts into historical artworks, successfully reproduces the evolutionary trajectory of artworks.
  arXiv  Detail & Related papers  (2025-03-15T10:45:04Z)
• A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles [0.0]
  This paper presents a critical assessment of the style replication capabilities of contemporary generative models.<n>We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance.<n>The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past.
  arXiv  Detail & Related papers  (2025-02-21T07:00:06Z)
• PATCH: a deep learning method to assess heterogeneity of artistic practice in historical paintings [0.0]
  In the Renaissance and Early Modern period, paintings were largely produced by master painters directing workshops of apprentices.<n>Information on how different workshops were managed and the processes by which artworks were created remains elusive.
  arXiv  Detail & Related papers  (2025-02-04T01:05:12Z)
• Understanding the World's Museums through Vision-Language Reasoning [49.976422699906706]
  Museums serve as vital repositories of cultural heritage and historical artifacts spanning diverse epochs, civilizations, and regions.<n>We collect and curate a large-scale dataset of 65M images and 200M question-answer pairs in the standard museum catalog format for exhibits from all around the world.<n>We train two VLMs from different categories: the BLIP model, with vision-language aligned embeddings, but lacking the expressive power of large language models, and the LLaVA model, a powerful instruction-tuned LLM enriched with vision-language reasoning capabilities.
  arXiv  Detail & Related papers  (2024-12-02T10:54:31Z)
• Opt-In Art: Learning Art Styles Only from Few Examples [50.60063523054282]
  We show that it is possible to adapt a model trained without paintings to an artistic style, given only few examples.<n>Surprisingly, our findings suggest that high-quality artistic outputs can be achieved without prior exposure to artistic data.
  arXiv  Detail & Related papers  (2024-11-29T18:59:01Z)
• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
  Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
  arXiv  Detail & Related papers  (2024-08-01T11:52:56Z)
• Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition [83.13280812128411]
  Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models. We present a comprehensive framework that provides a unified view of these three phenomena, focusing on the competition between memorization and generalization circuits.
  arXiv  Detail & Related papers  (2024-02-23T08:14:36Z)
• Masked Modeling for Self-supervised Representation Learning on Vision and Beyond [69.64364187449773]
  Masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training. We elaborate on the details of techniques within masked modeling, including diverse masking strategies, recovering targets, network architectures, and more. We conclude by discussing the limitations of current techniques and point out several potential avenues for advancing masked modeling research.
  arXiv  Detail & Related papers  (2023-12-31T12:03:21Z)
• Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases [98.35348038111508]
  This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision) The core of our analysis delves into the distinct visual comprehension abilities of each model. Our findings illuminate the unique strengths and niches of both models.
  arXiv  Detail & Related papers  (2023-12-22T18:59:58Z)
• Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model [74.62272538148245]
  We show that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other. We investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation.
  arXiv  Detail & Related papers  (2023-10-26T17:59:46Z)
• State of the Art on Diffusion Models for Visual Computing [191.6168813012954]
  This report introduces the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model. We also give a comprehensive overview of the rapidly growing literature on diffusion-based generation and editing. We discuss available datasets, metrics, open challenges, and social implications.
  arXiv  Detail & Related papers  (2023-10-11T05:32:29Z)
• Model Synthesis for Zero-Shot Model Attribution [26.835046772924258]
  generative models are shaping various fields such as art, design, and human-computer interaction.<n>We propose a model synthesis technique, which generates numerous synthetic models mimicking the fingerprint patterns of real-world generative models.<n>Our experiments demonstrate that this fingerprint extractor, trained solely on synthetic models, achieves impressive zero-shot generalization on a wide range of real-world generative models.
  arXiv  Detail & Related papers  (2023-07-29T13:00:42Z)
• ArtGPT-4: Towards Artistic-understanding Large Vision-Language Models with Enhanced Adapter [19.830089364830066]
  ArtGPT-4 is a large vision-language model tailored to address the limitations of existing models in artistic comprehension. It can render images with an artistic-understanding and convey the emotions they inspire, mirroring human interpretation.
  arXiv  Detail & Related papers  (2023-05-12T14:04:30Z)
• Towards mapping the contemporary art world with ArtLM: an art-specific NLP model [0.0]
  We present a generic Natural Language Processing framework (called ArtLM) to discover the connections among contemporary artists based on their biographies. With extensive experiments, we demonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score. We also provide a visualisation and a qualitative analysis of the artist network built from ArtLM's outputs.
  arXiv  Detail & Related papers  (2022-12-14T09:26:07Z)
• Data-driven Approaches to Surrogate Machine Learning Model Development [3.2734466030053175]
  We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions and transfer learning. We see a significant improvement in model performance through the combination of all three techniques.
  arXiv  Detail & Related papers  (2022-10-06T01:30:11Z)
• Inter-model Interpretability: Self-supervised Models as a Case Study [0.2578242050187029]
  We build on a recent interpretability technique called Dissect to introduce textitinter-model interpretability We project 13 top-performing self-supervised models into a Learned Concepts Embedding space that reveals proximities among models from the perspective of learned concepts. The experiment allowed us to categorize the models into three categories and revealed for the first time the type of visual concepts different tasks requires.
  arXiv  Detail & Related papers  (2022-07-24T22:50:18Z)
• Semi-supervised Human Pose Estimation in Art-historical Images [9.633949256082763]
  We propose a novel approach to estimate human poses in art-language images. Our approach achieves significantly better results than methods that use pre-trained models or style transfer.
  arXiv  Detail & Related papers  (2022-07-06T21:20:58Z)
• Knowledge distillation: A good teacher is patient and consistent [71.14922743774864]
  There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. We identify certain implicit design choices, which may drastically affect the effectiveness of distillation. We obtain a state-of-the-art ResNet-50 model for ImageNet, which achieves 82.8% top-1 accuracy.
  arXiv  Detail & Related papers  (2021-06-09T17:20:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.