83 citations · 100 across the 10 of their papers we have counts for
6 papers · 1 filter
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
Ling Yang, Zixiang Zhang, Zhilong Zhang +6
Flow matching (FM) is a general framework for defining probability paths via Ordinary Differential Equations (ODEs) to transform between noise and data samples. Recent approaches a…
Structure-Guided Adversarial Training of Diffusion Models
Ling Yang, Haotian Qian, Zhilong Zhang +2
Diffusion models have demonstrated exceptional efficacy in various generative applications. While existing models focus on minimizing a weighted sum of denoising score matching los…
Distribution-Aware Data Expansion with Diffusion Models
Haowei Zhu, Ling Yang, Jun-Hai Yong +5
The scale and quality of a dataset significantly impact the performance of deep models. However, acquiring large-scale annotated datasets is both a costly and time-consuming endeav…
Retrieval-Augmented Generation for AI-Generated Content: A Survey
Penghao Zhao, Hailin Zhang, Qinhan Yu +7
Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (…
Contextualized Diffusion Models for Text-Guided Image and Video Generation
Ling Yang, Zhilong Zhang, Zhaochen Yu +4
Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion m…
Improving Diffusion-Based Image Synthesis with Context Prediction
Ling Yang, Jingwei Liu, Shenda Hong +5
Diffusion models are a new class of generative models, and have dramatically promoted image generation with unprecedented quality and diversity. Existing diffusion models mainly tr…