Generative adversarial networks conditioned on textual image descriptions are capable of
generating realistic-looking images. However, current methods still struggle to generate …

Recently there is an increasing interest in scene generation within the research community.
However, models used for generating scene layouts from textual description largely ignore …

Generating realistic images of complex visual scenes becomes challenging when one
wishes to control the structure of the generated images. Previous approaches showed that …

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

For decades, co-relating different data domains to attain the maximum potential of machines
has driven research, especially in neural networks. Similarly, text and visual data (images …

Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared
with many traditional Machine Learning (ML) models in diverse fields. However, adversarial …

In this paper, we study graph-to-image generation conditioned exclusively on scene graphs,
in which we seek to disentangle the veiled semantics between knowledge graphs and …

When translating text inputs into layouts or images, existing works typically require explicit
descriptions of each object in a scene, including their spatial information or the associated …

Videos of actions are complex signals containing rich compositional structure in space and
time. Current video generation methods lack the ability to condition the generation on …

Rigid pavement design is a high-dimensional optimization problem, involving several
variables and design considerations. The existing machine learning (ML) design models are …