5 papers
Personalizing Text-to-Image Generation to Individual Taste
Anne-Sofie Maerten, Juliane Verwiebe, Shyamgopal Karthik +3
Modern text-to-image (T2I) models generate high-fidelity visuals but remain indifferent to individual user preferences. While existing reward models optimize for "average" human ap…
From paintbrush to pixel: A review of deep neural networks in AI-generated art
Anne-Sofie Maerten, Derya Soydaner
This paper delves into the fascinating field of AI-generated art and explores the various deep neural network architectures and models that have been utilized to create it. From th…
On the Role of Individual Differences in Current Approaches to Computational Image Aesthetics
Li-Wei Chen, Ombretta Strafforello, Anne-Sofie Maerten +2
Image aesthetic assessment (IAA) evaluates image aesthetics, a task complicated by image diversity and user subjectivity. Current approaches address this in two stages: Generic IAA…
Have Large Vision-Language Models Mastered Art History?
Ombretta Strafforello, Derya Soydaner, Michiel Willems +2
The emergence of large Vision-Language Models (VLMs) has established new baselines in image classification across multiple domains. We examine whether their multimodal reasoning ca…
LAPIS: A novel dataset for personalized image aesthetic assessment
Anne-Sofie Maerten, Li-Wei Chen, Stefanie De Winter +2
We present the Leuven Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA). It is the first dataset with images of artworks that i…