AI and Artworks: Object Detection, Image Classification and Iconographic Analysis

Session

Models

16 Apr 2026, 09:30

Seminar room 1&2 (Postsparkasse)

19. Meta-Acervos

Thiago Hersan (Parsons School of Design), Dr Giselle Beiguelman (University of São Paulo), Dr Ana Gonçalves Magalhães (University of São Paulo)

16/04/2026, 09:30

Talk

In this talk we'll present our methodology and prototype for a meta-collection system called [Meta-Acervos][7] that uses different AI models and computer vision techniques to recombine existing archive metadata. We’ll share the challenges we faced when using pre-trained models and how we addressed questions of vocabulary and legibility in the art history field.

This work was carried out...

25. A Benchmark Dilemma: Between Representativeness and Domain-Specificity

Dr Marta Kipke (Center for Humanities Computing, Aarhus University), Louise Brix Pilegaard Hansen (Center for Humanities Computing, Aarhus University)

16/04/2026, 09:50

Talk

When is a machine learning model performing well? From a computer science perspective, this question can be answered quite simply with evaluation metrics. A statistically well-performing model forms the basis for any further research, in real-world, as well as in humanities domains. However, domain-specific tasks and in-depth case studies, such as those in digital art history, often reveal...

24. Learning from Small Data: Adapting Pretrained Diffusion Models for 17th-Century Painting

Dr Katarina Mohar (Research Centre of the Slovenian Academy of Sciences and Arts, ZRC SAZU), Dr Rok Vrabič (University of Ljubljana)

16/04/2026, 10:10

Talk

Most AI models used in art-historical analysis or image generation are trained on large photographic datasets whose statistical structure differs fundamentally from painted images. This raises a key methodological problem: how can such pre-trained models be adapted to small, historically specific corpora while retaining interpretive reliability?

This paper presents results from the ongoing...