75 citations · 89 across the 4 of their papers we have counts for
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cs.CV2026
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…
cs.CV2021★ 12 cited
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…
cs.CV2021
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…