10 citations · 21 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation
Ye Zhu, Yu Wu, Kyle Olszewski +3
Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis. A key desidera…
Supplementing Missing Visions via Dialog for Scene Graph Generations
Zhenghao Zhao, Ye Zhu, Xiaoguang Zhu +2
Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks. However, the classic task…
Quantized GAN for Complex Music Generation from Dance Videos
Ye Zhu, Kyle Olszewski, Yu Wu +4
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos. Our proposed framework takes dan…
Learning Audio-Visual Correlations from Variational Cross-Modal Generation
Ye Zhu, Yu Wu, Hugo Latapie +2
People can easily imagine the potential sound while seeing an event. This natural synchronization between audio and visual signals reveals their intrinsic correlations. To this end…