In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and
Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence …

Recently style transfer has received a lot of attention. While much of this research has aimed
at speeding up the processing, the approaches are still lacking from a principled, art …

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically
solved by learning a joint embedding space where the semantic content shared between …

Sketchformer is a novel transformer-based representation for encoding free-hand sketches
input in a vector form, ie as a sequence of strokes. Sketchformer effectively addresses …

This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-
based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to …

Two primary input modalities prevail in image retrieval: sketch and text. While text is widely
used for inter-category retrieval tasks sketches have been established as the sole preferred …

Free-hand sketches are highly illustrative, and have been widely used by humans to depict
objects or stories from ancient times to the present. The recent prevalence of touchscreen …

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a
particular photo instance given a user's query sketch. Its widespread applicability is however …

Zero-shot sketch-based image retrieval typically asks for a trained model to be applied as is
to unseen categories. In this paper, we question to argue that this setup by definition is not …

The recent focus on Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) has shifted
towards generalising a model to new categories without any training data from them. In real …