Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the
task of visual generative modeling. The network employs a bipartite structure that enables …

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to
obtain, particularly in medical imaging, where annotations also require expert knowledge …

We introduce the GANformer2 model, an iterative object-oriented transformer, explored for
the task of generative modeling. The network incorporates strong and explicit structural …

Brain magnetic resonance imaging segmentation is a recent and still popular research area.
Good and accurate segmentation results play an important role in the diagnosis of cancer or …

Existing text-to-image generation approaches have set high standards for photorealism and
text-image correspondence, largely benefiting from web-scale text-image datasets, which …

Semantic image synthesis aims to generate photo realistic images given a semantic
segmentation map. Despite much recent progress, training them still requires large datasets …

Deep generative models have various content creation applications such as graphic design,
e-commerce, and virtual try-on. However, current works mainly focus on synthesizing …

Due to the difficulty and labor-consuming nature of getting highly accurate or matting
annotations, there only exists a limited amount of highly accurate labels available to the …

In multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main
bottleneck for the performance of learning-based methods. To tackle this challenge …