Deep generative models, which target reproducing the data distribution to produce novel
images, have made unprecedented advancements in recent years. However, one critical …

Drawing on recent advancements in diffusion models for text-to-image generation, identity-
preserved personalization has made significant progress in accurately capturing specific …

Recent methods are proposed to improve performance of domain adaptation by inferring
domain index under an adversarial variational bayesian framework, where domain index is …

The recent progress in text-to-image models pretrained on large-scale datasets has enabled
us to generate various images as long as we provide a text prompt describing what we want …

Recent deep neural networks (DNNs) have came to rely on vast amounts of training data,
providing an opportunity for malicious attackers to exploit and contaminate the data to carry …

Misinformation is prevalent in various fields such as education, politics, health, etc., causing
significant harm to society. However, current methods for cross-domain misinformation …

SAR images possess unique attributes that present challenges for both human observers
and vision AI models to interpret, owing to their electromagnetic characteristics. The …

The process of estimating and counting tree density using only a single aerial or satellite
image is a difficult task in the fields of photogrammetry and remote sensing. However, it …

Few-shot image generation aims to train generative models using a small number of training
images. When there are few images available for training (eg 10 images), Learning From …

Can a pre-trained generator be adapted to the hybrid of multiple target domains and
generate images with integrated attributes of them? In this work, we introduce a new task …