1 citations · 1 across the 5 of their papers we have counts for
8 papers
ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming
Haoran Liu, Di Wu, Wang Luo +3
Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and computation remains challengi…
Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach
Xunkai Li, Chenxi Wan, Yinlin Zhu +4
Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-At…
TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control
Wei-Jin Huang, Yuan-Ming Li, Kun-Yu Lin +8
Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment slow and memory-intensive. W…
Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach
Yinlin Zhu, Di Wu, Wang Luo +2
Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a promising paradigm for mining kn…
Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
Yinlin Zhu, Di Wu, Yi Zhang +5
Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopte…
RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering
Yinlin Zhu, Di Wu, Ziyu Han +4
Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-f…
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
Yinlin Zhu, Xunkai Li, Di Wu +3
Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks. However, our empir…
Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective
Wang Luo, Di Wu, Hengyuan Na +3
Point cloud completion aims to reconstruct complete 3D shapes from partial observations, which is a challenging problem due to severe occlusions and missing geometry. Despite recen…