A key aspect of text-to-image personalization methods is the manner in which the target
concept is represented within the generative process. This choice greatly affects the visual …

Recently, diffusion models have emerged as a powerful class of generative models. Despite
their success, there is still limited understanding of their semantic spaces. This makes it …

In the field of Few-Shot Image Generation (FSIG) using Deep Generative Models (DGMs),
accurately estimating the distribution of target domain with minimal samples poses a …

We are perceiving and communicating with the world in a multisensory manner, where
different information sources are sophisticatedly processed and interpreted by separate …

Originating from the diffusion phenomenon in physics that describes particle movement, the
diffusion generative models inherit the characteristics of stochastic random walk in the data …

Diffusion models (DMs) synthesize high-quality images in various domains. However,
controlling their generative process is still hazy because the intermediate variables in the …

It is well known that many open-released foundational diffusion models have difficulty in
generating images that substantially depart from average brightness despite such images …

This paper explores the potential of leveraging language priors learned by text-to-image
diffusion models to address ambiguity and visual nuisance in monocular depth estimation …

While the current trend in the generative field is scaling up towards larger models and more
training data for generalized domain representations, we go the opposite direction in this …

The stochastic structural plane of a rock mass is the key factor controlling the stability of rock
mass. Obtaining the distribution of stochastic structural planes within a rock mass is crucial …