Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

Recently, diffusion models have emerged as a new paradigm for generative models.
Despite the success in domains using continuous signals such as vision and audio …

Temporal action segmentation is crucial for understanding long-form videos. Previous works
on this task commonly adopt an iterative refinement paradigm by using multi-stage models …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

Diffusion models, a family of generative models based on deep learning, have become
increasingly prominent in cutting-edge machine learning research. With distinguished …

Diffusion frameworks have achieved comparable performance with previous state-of-the-art
image generation models. This paper proposes DiffusionInst, a novel framework …

Latent space EBMs, also known as energy-based priors, have drawn growing interests in
the field of generative modeling due to its flexibility in the formulation and strong modeling …