21 citations · 71 across the 37 of their papers we have counts for
40 papers
Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference
Jingyi Liu, Cheng Yuan, Lijun He +2
The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditional approaches require massive v…
Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling
Zhening Liu, Rui Song, Yushi Huang +5
3D Gaussian Splatting (3DGS) has emerged as a revolutionary 3D representation. However, its substantial data size poses a major barrier to widespread adoption. While feed-forward 3…
CAS-Spec: Cascade Adaptive Self-Speculative Decoding for On-the-Fly Lossless Inference Acceleration of LLMs
Zhiyuan Ning, Jiawei Shao, Ruge Xu +4
Speculative decoding has become a widely adopted as an effective technique for lossless inference acceleration when deploying large language models (LLMs). While on-the-fly self-sp…
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
Yufei Xue, Yushi Huang, Jiawei Shao +4
Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-language models (VLMs) for multimodal und…
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Jingwen Tong, Wei Guo, Jiawei Shao +4
The rapid evolution of wireless networks presents unprecedented challenges in managing complex and dynamic systems. Existing methods are increasingly facing fundamental limitations…
Mutual Information-Empowered Task-Oriented Communication: Principles, Applications and Challenges
Hongru Li, Songjie Xie, Jiawei Shao +5
Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit…
Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference
Cheng Yuan, Zhening Liu, Jiashu Lv +4
With the rapid development of large multimodal models (LMMs), multimodal understanding applications are emerging. As most LMM inference requests originate from edge devices with li…
Dynamics-Aware Gaussian Splatting Streaming Towards Fast On-the-Fly 4D Reconstruction
Zhening Liu, Yingdong Hu, Xinjie Zhang +4
The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction. Existing approaches mainly rely on full-length multi-view vid…
AI Flow at the Network Edge
Jiawei Shao, Xuelong Li
Recent advancements in large language models (LLMs) and their multimodal variants have led to remarkable progress across various domains, demonstrating impressive capabilities and…
EVA-Gaussian: 3D Gaussian-based Real-time Human Novel View Synthesis under Diverse Multi-view Camera Settings
Yingdong Hu, Zhening Liu, Jiawei Shao +2
Feed-forward based 3D Gaussian Splatting methods have demonstrated exceptional capability in real-time novel view synthesis for human models. However, current approaches are confin…
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Jingwen Tong, Jiawei Shao, Qiong Wu +4
Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particular…
Task-Oriented Communication for Vehicle-to-Infrastructure Cooperative Perception
Jiawei Shao, Teng Li, Jun Zhang
Vehicle-to-infrastructure (V2I) cooperative perception plays a crucial role in autonomous driving scenarios. Despite its potential to improve perception accuracy and robustness, th…
Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
Zhening Liu, Xinjie Zhang, Jiawei Shao +2
With the rapid advancement of stereo vision technologies, stereo image compression has emerged as a crucial field that continues to draw significant attention. Previous approaches…
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
Jiawei Shao, Jingwen Tong, Qiong Wu +4
The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configur…
EdgeLoc: A Communication-Adaptive Parallel System for Real-Time Localization in Infrastructure-Assisted Autonomous Driving
Boyi Liu, Jingwen Tong, Yufan Zhuang
This paper presents EdgeLoc, an infrastructure-assisted, real-time localization system for autonomous driving that addresses the incompatibility between traditional localization me…
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
Zijian Li, Qingyan Guo, Jiawei Shao +4
Retrieval augmented generation has revolutionized large language model (LLM) outputs by providing factual supports. Nevertheless, it struggles to capture all the necessary knowledg…
Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
Hongru Li, Jiawei Shao, Hengtao He +3
Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredi…
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
Zijian Li, Zehong Lin, Jiawei Shao +2
Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing their raw data. However, devices ofte…
Feature Matching Data Synthesis for Non-IID Federated Learning
Zijian Li, Yuchang Sun, Jiawei Shao +3
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server. However, FL encounters…
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao, Zijian Li, Wenqiang Sun +6
Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a pr…
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
Yifei Shen, Jiawei Shao, Xinjie Zhang +5
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper…
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Hongru Li, Wentao Yu, Hengtao He +4
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for dow…
Low-complexity Deep Video Compression with A Distributed Coding Architecture
Xinjie Zhang, Jiawei Shao, Jun Zhang
Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained…
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
Jiawei Shao, Fangzhao Wu, Jun Zhang
While federated learning is promising for privacy-preserving collaborative learning without revealing local data, it remains vulnerable to white-box attacks and struggles to adapt…