Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …

Inspired by the ability of StyleGAN to generate highly re-alistic images in a variety of
domains, much recent work hasfocused on understanding how to use the latent spaces …

Technologies related to artificial intelligence (AI) have a strong impact on the changes of
research and creative practices in visual arts. The growing number of research initiatives …

We investigate the capability of a transformer pretrained on natural language to generalize
to other modalities with minimal finetuning--in particular, without finetuning of the self …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Score-based generative models and diffusion probabilistic models have been successful at
generating high-quality samples in continuous domains such as images and audio …

The high dimensionality of images presents architecture and sampling-efficiency challenges
for likelihood-based generative models. Previous approaches such as VQ-VAE use deep …

The softmax operator is a crucial component of large language models (LLMs), which have
played a transformative role in computer research. Due to the centrality of the softmax …

While Attention has come to be an important mechanism in deep learning, there remains
limited intuition for why it works so well. Here, we show that Transformer Attention can be …