VIRTUAL ART SPACES AND MACHINE LEARNING CURATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6802Keywords:
Machine Learning Curation, Virtual Art Spaces, AI-Driven Exhibitions, Digital Curation Ethics, Immersive VR/AR Art ExperiencesAbstract [English]
With the development of machine learning (ML), the concept of virtual space in art is becoming a reality, and it is changing the established curatorial procedures and altering the ways in which audiences experience art. This paper explores the dynamic overlap between ML-driven systems and immersive digital spaces to know how curatorial decision-making, experience of audiences, and interpretation of art is being redefined. The study presents findings of virtual galleries, online archives and interactive display sites based on qualitative and quantitative approaches to assessing how algorithms predict, classify and even create visual artworks. Specific focus is made on the recommendation engines, neural models of style analysis, and generative networks that help to come up with new forms of curators. Simultaneously, the paper examines virtual and augmented reality (VR/AR) as experiential models with a particular focus on how the designs of immersivity, interactivity, and international openness broaden the curatorial practice. These virtual spaces give the viewer a chance to engage more, allowing them to create their own paths in collections of art and disrupting traditional ideas of authorship and originality as well as interpretive power.
References
Ajorloo, S., Jamarani, A., Kashfi, M., Haghi Kashani, M., and Najafizadeh, A. (2024). A Systematic Review of Machine Learning Methods in Software Testing. Applied Soft Computing, 162, 111805. https://doi.org/10.1016/j.asoc.2024.111805 DOI: https://doi.org/10.1016/j.asoc.2024.111805
Alzubaidi, M., Agus, M., Makhlouf, M., Anver, F., Alyafei, K., and Househ, M. (2023). Large-Scale Annotation Dataset for Fetal Head Biometry in Ultrasound Images. Data in Brief, 51, 109708. https://doi.org/10.1016/j.dib.2023.109708 DOI: https://doi.org/10.1016/j.dib.2023.109708
Barglazan, A.-A., Brad, R., and Constantinescu, C. (2024). Image Inpainting Forgery Detection: A Review. Journal of Imaging, 10(2), 42. https://doi.org/10.3390/jimaging10020042 DOI: https://doi.org/10.3390/jimaging10020042
Brambilla, E., Petersen, E., Stendal, K., Sundling, V., MacIntyre, T. E., and Calogiuri, G. (2022). Effects of Immersive Virtual Nature on Nature Connectedness: A Systematic Review Protocol. Digital Health, 8, 20552076221120324. https://doi.org/10.1177/20552076221120324 DOI: https://doi.org/10.1177/20552076221120324
Brauwers, G., and Frasincar, F. (2023). A General Survey on Attention Mechanisms in Deep Learning. IEEE Transactions on Knowledge and Data Engineering, 35, 3279–3298. https://doi.org/10.1109/TKDE.2021.3126456 DOI: https://doi.org/10.1109/TKDE.2021.3126456
Chen, G., Wen, Z., and Hou, F. (2023). Application of Computer Image Processing Technology in Old Artistic Design Restoration. Heliyon, 9, e21366. https://doi.org/10.1016/j.heliyon.2023.e21366 DOI: https://doi.org/10.1016/j.heliyon.2023.e21366
Dobbs, T., and Ras, Z. (2022). On Art Authentication and the Rijksmuseum Challenge: A Residual Neural Network Approach. Expert Systems with Applications, 200, 116933. https://doi.org/10.1016/j.eswa.2022.116933 DOI: https://doi.org/10.1016/j.eswa.2022.116933
Fu, Y., Wang, W., Zhu, L., Ye, X., and Yue, H. (2024). Weakly Supervised Semantic Segmentation Based on Superpixel Affinity. Journal of Visual Communication and Image Representation, 101, 104168. https://doi.org/10.1016/j.jvcir.2024.104168 DOI: https://doi.org/10.1016/j.jvcir.2024.104168
Kaur, H., Pannu, H. S., and Malhi, A. K. (2019). A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and solutions. ACM Computing Surveys, 52, 1–36. https://doi.org/10.1145/3343440 DOI: https://doi.org/10.1145/3343440
Leonarduzzi, R., Liu, H., and Wang, Y. (2018). Scattering Transform and Sparse Linear Classifiers for Art Authentication. Signal Processing, 150, 11–19. https://doi.org/10.1016/j.sigpro.2018.03.012 DOI: https://doi.org/10.1016/j.sigpro.2018.03.012
Messer, U. (2024). Co-Creating Art with Generative Artificial Intelligence: Implications for Artworks and Artists. Computers in Human Behavior: Artificial Humans, 2, 100056. https://doi.org/10.1016/j.chbah.2024.100056 DOI: https://doi.org/10.1016/j.chbah.2024.100056
Nukarinen, T., Rantala, J., Korpela, K., Browning, M. H., Istance, H. O., Surakka, V., and Raisamo, R. (2022). Measures and Modalities in Restorative Virtual Natural Environments: An Integrative Narrative Review. Computers in Human Behavior, 126, 107008. https://doi.org/10.1016/j.chb.2021.107008 DOI: https://doi.org/10.1016/j.chb.2021.107008
Schaerf, L., Postma, E., and Popovici, C. (2024). Art Authentication with Vision Transformers. Neural Computing and Applications, 36, 11849–11858. https://doi.org/10.1007/s00521-023-08864-8 DOI: https://doi.org/10.1007/s00521-023-08864-8
Ünal, A. B., Pals, R., Steg, L., Siero, F. W., and van der Zee, K. I. (2022). Is Virtual Reality A Valid tool for Restorative Environments Research? Urban Forestry and Urban Greening, 74, 127673. https://doi.org/10.1016/j.ufug.2022.127673 DOI: https://doi.org/10.1016/j.ufug.2022.127673
Zaurín, J. R., and Mulinka, P. (2023). Pytorch-Widedeep: A Flexible Package for Multimodal Deep Learning. Journal of Open Source Software, 8, 5027. https://doi.org/10.21105/joss.05027 DOI: https://doi.org/10.21105/joss.05027
Zeng, Z., Zhang, P., Qiu, S., Li, S., and Liu, X. (2024). A Painting Authentication Method Based on Multi-Scale Spatial-Spectral Feature Fusion and Convolutional Neural Network. Computers and Electrical Engineering, 118, 109315. https://doi.org/10.1016/j.compeleceng.2024.109315 DOI: https://doi.org/10.1016/j.compeleceng.2024.109315
Zhang, Z., Sun, K., Yuan, L., Zhang, J., Wang, X., Feng, J., and Torr, P. H. S. (2021). Conditional DETR: A Modularized DETR Framework for Object Detection (arXiv:2108.08902). arXiv. https://arxiv.org/abs/2108.08902
Zhao, S., Fan, Q., Dong, Q., Xing, Z., Yang, X., and He, X. (2024). Efficient Construction And Convergence Analysis of Sparse Convolutional Neural Networks. Neurocomputing, 597, 128032. https://doi.org/10.1016/j.neucom.2024.128032 DOI: https://doi.org/10.1016/j.neucom.2024.128032
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