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Akademický článek

A Deep Learning Perspective on Beauty, Sentiment, and Remembrance of Art

Autor: Eva Cetinic, Tomislav Lipic, Sonja Grgic

Publikováno v: IEEE Access, Vol 7, Pp 73694-73710 (2019)

With the emergence of large digitized fine art collections and the successful performance of deep learning techniques, new research prospects unfold in the intersection of artificial intelligence and art. In order to explore the applicability of deep

Externí odkaz: https://doaj.org/article/c68ef3c961564070833f8d43291e2305

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Akademický článek

Towards Generating and Evaluating Iconographic Image Captions of Artworks

Autor: Eva Cetinic

Publikováno v: Journal of Imaging, Vol 7, Iss 8, p 123 (2021)

To automatically generate accurate and meaningful textual descriptions of images is an ongoing research challenge. Recently, a lot of progress has been made by adopting multimodal deep learning approaches for integrating vision and language. However,

Externí odkaz: https://doaj.org/article/41992b590a3942e3aff51442aa0c701d

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Understanding and Creating Art with AI: Review and Outlook

Autor: Eva Cetinic, James She

Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f85a28935240d69634ce02dcf359788b

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Iconographic Image Captioning for Artworks

Autor: Eva Cetinic

Publikováno v: Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687953
ICPR Workshops (3)

Image captioning implies automatically generating textual descriptions of images based only on the visual input. Although this has been an extensively addressed research topic in recent years, not many contributions have been made in the domain of ar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4d1207a7643ce3aa4199c8baf357856
https://doi.org/10.1007/978-3-030-68796-0_36

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Learning the Principles of Art History with convolutional neural networks

Autor: Tomislav Lipic, Eva Cetinic, Sonja Grgic

Understanding the historical transformation of artistic styles implies the recognition of different stylistic properties. From a computer vision perspective, stylistic properties represent complex image features. In our work we explore the use of con

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3721ad4b6c6a61b637abbd57ba28d682
https://www.bib.irb.hr/1033866

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Digital repository as a service: automatic deployment of an Invenio-based repository using TOSCA orchestration and Apache Mesos

Autor: Alberto Brigandì, Davide Salomoni, Davor Davidović, Miguel Caballer, Giacinto Donvito, Germán Moltó, Marica Antonacci, Eva Cetinic

Publikováno v: EPJ Web of Conferences, Vol 214, p 07023 (2019)
EPJ web of conferences

In the framework of the H2020 INDIGO-DataCloud project, we have implemented an advanced solution for the automatic deployment of digital data repositories based on Invenio, the digital library framework developed by CERN. Exploiting cutting-edge tech

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::778799f70cb961bcdf72d0e3133588dd
https://www.bib.irb.hr/1020617

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How Convolutional Neural Networks Remember Art

Autor: Eva Cetinic, Tomislav Lipic, Sonja Grgic

Publikováno v: IWSSIP

Inspired by the successful performance of Convolutional Neural Networks (CNN) in automatically predicting complex image properties such as memorability, in this work we explore their transferability to the domain of art images. We employ a CNN model

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b8d80e7a4469fd313be97588590093d
https://doi.org/10.1109/iwssip.2018.8439497

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Fine-tuning Convolutional Neural Networks for fine art classification

Autor: Sonja Grgic, Tomislav Lipic, Eva Cetinic

Publikováno v: Expert Systems with Applications

The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77e3d31e98295348c7f234914da97da6
https://doi.org/10.1016/j.eswa.2018.07.026

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INDIGO-DataCloud: a platform to facilitate seamless access to e-infrastructures

Autor: Sandro Fiore, João Martins, Daniele Spiga, Paola Nassisi, Giacinto Donvito, Alessandro Costa, J. Marco de Lucas, Marcin Plociennik, Tomasz Zok, Andrea Ceccanti, Isabel Campos, Cristina Duma, L. Alves, Marco Antonio Tangaro, João Murta Pina, M. Viljoen, Michal Urbaniak, Marcus Hardt, Luciano Gaido, Davide Salomoni, Y. Chen, Álvaro López-García, Federico Zambelli, Alexandre M. J. J. Bonvin, Louis Antonelli, Lukasz Dutka, Bas Wegh, P. Solagna, Cosimo Palazzo, Luděk Matyska, Eva Sciacca, Mario David, Zeynep Kurkcuoglu, B. Ertl, Davor Davidović, Eva Cetinic, S. Vallero, S. Gallozzi, Valentina Zaccolo, Marica Antonacci, Riccardo Bruno, Fernando Aguilar, P. Fuhrman, S. Bagnasco, Zdeněk Šustr, Germán Moltó, Jorge Gomes, Marco Fargetta, Ignacio Blanquer, Lara Lloret, Alessandra Nuzzo, Pablo Orviz, Roberto Barbera

Publikováno v: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Journal of grid computing
Journal of Grid Computing
Journal of grid computing 16(3), 381-408 (2018). doi:10.1007/s10723-018-9453-3
Digital.CSIC. Repositorio Institucional del CSIC
Journal of grid computing 16 (2018): 381–408. doi:10.1007/s10723-018-9453-3
info:cnr-pdr/source/autori:Salomoni D.; Campos I.; Gaido L.; de Lucas J.M.; Solagna P.; Gomes J.; Matyska L.; Fuhrman P.; Hardt M.; Donvito G.; Dutka L.; Plociennik M.; Barbera R.; Blanquer I.; Ceccanti A.; Cetinic E.; David M.; Duma C.; Lopez-Garcia A.; Molto G.; Orviz P.; Sustr Z.; Viljoen M.; Aguilar F.; Alves L.; Antonacci M.; Antonelli L.A.; Bagnasco S.; Bonvin A.M.J.J.; Bruno R.; Chen Y.; Costa A.; Davidovic D.; Ertl B.; Fargetta M.; Fiore S.; Gallozzi S.; Kurkcuoglu Z.; Lloret L.; Martins J.; Nuzzo A.; Nassisi P.; Palazzo C.; Pina J.; Sciacca E.; Spiga D.; Tangaro M.; Urbaniak M.; Vallero S.; Wegh B.; Zaccolo V.; Zambelli F.; Zok T./titolo:INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures/doi:10.1007%2Fs10723-018-9453-3/rivista:Journal of grid computing/anno:2018/pagina_da:381/pagina_a:408/intervallo_pagine:381–408/volume:16
Journal of Grid Computing, 16(3), 381. Springer Netherlands

Journal of grid computing 16(3), 381 - 408 (2018). doi:10.1007/s10723-018-9453-3
This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fff434ecd8e8abf21f03d8fa9b85ad7
http://hdl.handle.net/10261/168382

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Genre classification of paintings

Autor: Sonja Grgic, Eva Cetinic

Publikováno v: 2016 International Symposium ELMAR.

Extensive digitization efforts in the recent years have led to a large increase of digitized and online available fine-art collections. With digitization of artworks, we aim to preserve all those valuable evidences of various human creative expressio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd1d307acb5547772c3cfd1e976717d4
https://doi.org/10.1109/elmar.2016.7731786

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