The rapid advancement of generative models facilitating the creation of hyper-realistic
images from textual descriptions has concurrently escalated critical societal concerns such …

Abstract Text-to-image (T2I) diffusion models notably the unCLIP models (eg DALL-E-2)
achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at …

Abstract Text-to-image (T2I) generative models have recently emerged as a powerful tool
enabling the creation of photo-realistic images and giving rise to a multitude of applications …

Multimodal learning has been a field of increasing interest, aiming to combine various
modalities in a single joint representation. Especially in the area of visiolinguistic (VL) …

Despite the recent advances in personalized text-to-image (P-T2I) generative models,
subject-driven T2I remains challenging. The primary bottlenecks include 1) Intensive training …

Compositionality is a critical capability in Text-to-Image (T2I) models, as it reflects their ability
to understand and combine multiple concepts from text descriptions. Existing evaluations of …

We present CUPID: a visualization method for the contextual understanding of prompt‐
conditioned image distributions. CUPID targets the visual analysis of distributions produced …

Abstract Models that learn from data are widely and rapidly being deployed today for real‐
world use, but they suffer from unforeseen failures that limit their reliability. These failures …

In this chapter, we will review the advances that are being made in this new field of
multimodal content generation and also discuss several challenges associated with this …

In the rapidly evolving field of Generative Artificial Intelligence (Gen-AI) for imaging, models
such as DALL· E3 and Stable Diffusion have transitioned from theoretical concepts to …