2 citations · 2 across the 15 of their papers we have counts for
5 papers · 1 filter
GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting
Tiantian Li, Xinjie Zhang, Xingtong Ge +4
Implicit neural representations (INRs) have achieved remarkable success in image representation and compression, but they require substantial training time and memory. Meanwhile, r…
Never compromise with vulnerabilities: a comprehensive survey on AI governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan +62
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, t…
Fast Training-free Perceptual Image Compression
Ziran Zhu, Tongda Xu, Minye Huang +5
Training-free perceptual image codec adopt pre-trained unconditional generative model during decoding to avoid training new conditional generative model. However, they heavily rely…
SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
Xingtong Ge, Xin Zhang, Tongda Xu +4
The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from co…
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
Tongda Xu, Xiyan Cai, Xinjie Zhang +7
Recent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is…