25 citations · 45 across the 8 of their papers we have counts for
10 papers
Nested AutoRegressive Models
Hongyu Wu, Xuhui Fan, Zhangkai Wu +1
AutoRegressive (AR) models have demonstrated competitive performance in image generation, achieving results comparable to those of diffusion models. However, their token-by-token i…
VALA: Learning Latent Anchors for Training-Free and Temporally Consistent
Zhangkai Wu, Xuhui Fan, Zhongyuan Xie +2
Recent advances in training-free video editing have enabled lightweight and precise cross-frame generation by leveraging pre-trained text-to-image diffusion models. However, existi…
FAME: Fairness-aware Attention-modulated Video Editing
Zhangkai Wu, Xuhui Fan, Zhongyuan Xie +3
Training-free video editing (VE) models tend to fall back on gender stereotypes when rendering profession-related prompts. We propose \textbf{FAME} for \textit{Fairness-aware Atten…
FigBO: A Generalized Acquisition Function Framework with Look-Ahead Capability for Bayesian Optimization
Hui Chen, Xuhui Fan, Zhangkai Wu +1
Bayesian optimization is a powerful technique for optimizing expensive-to-evaluate black-box functions, consisting of two main components: a surrogate model and an acquisition func…
SCoT: Unifying Consistency Models and Rectified Flows via Straight-Consistent Trajectories
Zhangkai Wu, Xuhui Fan, Hongyu Wu +1
Pre-trained diffusion models are commonly used to generate clean data (e.g., images) from random noises, effectively forming pairs of noises and corresponding clean images. Distill…
A Survey on Pre-Trained Diffusion Model Distillations
Xuhui Fan, Zhangkai Wu, Hongyu Wu
Diffusion Models~(DMs) have emerged as the dominant approach in Generative Artificial Intelligence (GenAI), owing to their remarkable performance in tasks such as text-to-image syn…
Marked Temporal Bayesian Flow Point Processes
Hui Chen, Xuhui Fan, Hengyu Liu +1
Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. They have appeared in various rea…
Federated Neural Nonparametric Point Processes
Hui Chen, Xuhui Fan, Hengyu Liu +5
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a…
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks
Zhangkai Wu, Xuhui Fan, Jin Li +3
The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discret…
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
Hui Chen, Hengyu Liu, Zhangkai Wu +2
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for f…