Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Transformers, the dominant architecture for natural language processing, have also recently
attracted much attention from computational visual media researchers due to their capacity …

Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This
capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI …

Guided image synthesis enables everyday users to create and edit photo-realistic images
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited
customization options hinder users from accurately describing desired outputs. For example …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

A neural radiance field (NeRF) is a scene model supporting high-quality view synthesis,
optimized per scene. In this paper, we explore enabling user editing of a category-level …

Deep generative models have become increasingly effective at producing realistic images
from randomly sampled seeds, but using such models for controllable manipulation of …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

We propose a novel method for unsupervised image-to-image translation, which
incorporates a new attention module and a new learnable normalization function in an end …