General world models represent a crucial pathway toward achieving Artificial General
Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual …

Recent studies have shown that the denoising process in (generative) diffusion models can
induce meaningful (discriminative) representations inside the model, though the quality of …

Recent studies have demonstrated that diffusion models can generate high-quality samples,
but their quality heavily depends on sampling guidance techniques, such as classifier …

Diffusion Models are popular generative modeling methods in various vision tasks, attracting
significant attention. They can be considered a unique instance of self-supervised learning …

We present Visual AutoRegressive modeling (VAR), a new generation paradigm that
redefines the autoregressive learning on images as coarse-to-fine" next-scale prediction" or" …

Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion
process to encode data into a simple Gaussian distribution. However, encoding a complex …

Multi-modal magnetic resonance imaging (MRI) provides rich, complementary information
for analyzing diseases. However, the practical challenges of acquiring multiple MRI …

In this work, we propose Visual-Predictive Instruction Tuning (VPiT)-a simple and effective
extension to visual instruction tuning that enables a pretrained LLM to quickly morph into an …

We introduce BiGR, a novel conditional image generation model using compact binary
latent codes for generative training, focusing on enhancing both generation and …

Contrastive learning with the nearest neighbor has proved to be one of the most efficient self-
supervised learning (SSL) techniques by utilizing the similarity of multiple instances within …