AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and
other media using AI algorithms. Due to its wide range of applications and the demonstrated …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

The diffusion model has long been plagued by scalability and quadratic complexity issues,
especially within transformer-based structures. In this study, we aim to leverage the long …

In an effort to further advance semi-supervised generative and classification tasks, we
propose a simple yet effective training strategy called* dual pseudo training*(DPT), built …

To synthesize high-fidelity samples diffusion models typically require auxiliary data to guide
the generation process. However it is impractical to procure the painstaking patch-level …

Object-centric learning aims to represent visual data with a set of object entities (aka slots),
providing structured representations that enable systematic generalization. Leveraging …

Unconditional generation--the problem of modeling data distribution without relying on
human-annotated labels--is a long-standing and fundamental challenge in generative …

This paper strives for image editing via generative models. Flow Matching is an emerging
generative modeling technique that offers the advantage of simple and efficient training …

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 …

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