10 citations · 21 across the 11 of their papers we have counts for
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DETER: Detecting Edited Regions for Deterring Generative Manipulations
Sai Wang, Ye Zhu, Ruoyu Wang +3
Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or "deep fakes". However, deep fake d…
A Sampling-Based Domain Generalization Study with Diffusion Generative Models
Ye Zhu, Yu Wu, Duo Xu +3
In this work, we investigate the domain generalization capabilities of diffusion models in the context of synthesizing images that are distinct from the training data. Instead of f…
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
Ruoyu Wang, Yongqi Yang, Zhihao Qian +2
Originating from the diffusion phenomenon in physics that describes particle movement, the diffusion generative models inherit the characteristics of stochastic random walk in the…
Boundary Guided Learning-Free Semantic Control with Diffusion Models
Ye Zhu, Yu Wu, Zhiwei Deng +2
Applying pre-trained generative denoising diffusion models (DDMs) for downstream tasks such as image semantic editing usually requires either fine-tuning DDMs or learning auxiliary…