75 citations · 89 across the 4 of their papers we have counts for
5 papers
Understanding How MLLMs Describe Artworks Using Token Activation Maps
Nicola Fanelli, Pasquale De Marinis, Raffaele Scaringi +3
Multimodal Large Language Models (MLLMs) describe artworks with remarkable fluency, yet the visual reasoning behind their outputs remains opaque. When an MLLM names a style, identi…
The Myth of Culturally Agnostic AI Models
Eva Cetinic
The paper discusses the potential of large vision-language models as objects of interest for empirical cultural studies. Focusing on the comparative analysis of outputs from two po…
Understanding and Creating Art with AI: Review and Outlook
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 initiativ…
Iconographic Image Captioning for Artworks
Eva Cetinic
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 r…
INDIGO-DataCloud:A data and computing platform to facilitate seamless access to e-infrastructures
DataCloud Collaboration, Davide Salomoni, Isabel Campos +61
This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to…