activity
20232026
most citedMLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

VersaGauss: A Versatile Framework for Generating Multiphase Dynamics with 3D Gaussians

Ruijie Su, Lingxiao Yang, Xiaohua Xie +1

Recent progress has been made in 3D Gaussian representation for reconstruction, generation, and physical simulation. However, current approaches mainly concentrate on physics-based…

cs.CV2025

Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis

Yanzuo Lu, Yuxi Ren, Xin Xia +6

Distribution Matching Distillation (DMD) is a promising score distillation technique that compresses pre-trained teacher diffusion models into efficient one-step or multi-step stud…

eess.IV2024

CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging

Zixuan Chen, Lingxiao Yang, Jian-Huang Lai +1

Sparse-view computed tomography (SVCT) reconstruction aims to acquire CT images based on sparsely-sampled measurements. It allows the subjects exposed to less ionizing radiation, r…

cs.CV2024

VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation

Zixuan Chen, Ruijie Su, Jiahao Zhu +3

Text-to-3D generation aims to create 3D assets from text-to-image diffusion models. However, existing methods face an inherent bottleneck in generation quality because the widely-u…

cs.CV2024

Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

Yanzuo Lu, Manlin Zhang, Andy J Ma +2

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing method…

cs.CV2023★ 1 cited

MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

Yanzuo Lu, Meng Shen, Andy J Ma +2

Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowl…