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 …

This paper provides an overview of some of the most relevant deep learning approaches to
pattern extraction and recognition in visual arts, particularly painting and drawing. Recent …

Diffusion models are increasingly favored over traditional approaches like generative
adversarial networks (GANs) and auto-regressive transformers due to their remarkable …

Iconography in art is the discipline that studies the visual content of artworks to determine
their motifs and themes and to characterize the way these are represented. It is a subject of …

Abstract Visual Question Answering (VQA) is an extremely stimulating and challenging
research area where Computer Vision (CV) and Natural Language Processig (NLP) have …

The image captioning of fine art paintings aims at generating a sentence to describe the
painting content. Compared with photographic images, there are few annotated data of clear …

Abstract The use of Deep Learning and Computer Vision in the Cultural Heritage domain is
becoming highly relevant in the last few years with lots of applications about audio smart …

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 …

Computational image aesthetic evaluation is a computable human aesthetic perception and
judgment realized by machines, which has a significant impact on a variety of applications …

Large-scale deployment of large language models (LLMs) in various applications, such as
chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure …