1 citations · 1 across the 11 of their papers we have counts for
13 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…
DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training
Xucong Wang, Zhe Zhao, Liheng Yu +3
Reinforcement learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for training coding agents, where the execution feedback from compilation and tests provides…
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…
Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Scheduling
Xianzhi Zhang, Yue Xu, Yinlin Zhu +4
Multi-modal large language model (MLLM) inference scheduling enables strong response quality under practical and heterogeneous budgets, beyond what a homogeneous single-backend set…
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…
Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs
Yinlin Zhu, Di Wu, Xu Wang +2
Text-attributed graphs (TAGs) associate nodes with textual attributes and graph structure, enabling GNNs to jointly model semantic and structural information. Although effective on…
Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach
Yinlin Zhu, Di Wu, Xianzhi Zhang +4
Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to train graph foundation models, an…
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…
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
Yinlin Zhu, Xunkai Li, Jishuo Jia +3
Recent advances in graph machine learning have shifted to data-centric paradigms, driven by two emerging fields: (1) Federated graph learning (FGL) enables multi-client collaborati…
Federated Continual Graph Learning
Yinlin Zhu, Miao Hu, Di Wu
Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting,…
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Yinlin Zhu, Xunkai Li, Zhengyu Wu +3
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfor…