3 citations · 3 across the 8 of their papers we have counts for
9 papers
Task-Oriented Visual Feature Compression via Residual Vector Quantization for Device-Edge Multimodal Inference
Luning Pang, Cheng Yuan, Jiawei Shao +2
Large multimodal models (LMMs) support diverse visual understanding and reasoning tasks but are often impractical to run entirely on resource-constrained devices. Device-edge co-in…
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…
Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noise
Cheng Yuan, Zhenyu Jia, Jiawei Shao +1
Static scene videos, such as surveillance feeds and videotelephony streams, constitute a dominant share of storage consumption and network traffic. However, both traditional standa…
Privacy-Aware Camera 2.0 Technical Report
Huan Song, Shuyu Tian, Ting Long +5
With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound…
Beyond Static Artifacts: A Forensic Benchmark for Video Deepfake Reasoning in Vision Language Models
Zheyuan Gu, Qingsong Zhao, Yusong Wang +6
Current Vision-Language Models (VLMs) for deepfake detection excel at identifying spatial artifacts but overlook a critical dimension: temporal inconsistencies in video forgeries.…
A Real-Time Privacy-Preserving Behavior Recognition System via Edge-Cloud Collaboration
Huan Song, Shuyu Tian, Junyi Hao +4
As intelligent sensing expands into high-privacy environments such as restrooms and changing rooms, the field faces a critical privacy-security paradox. Traditional RGB surveillanc…
The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models
Dakuan Lu, Jiaqi Zhang, Cheng Yuan +2
Recent advances in large language models (LLMs) have been largely driven by scaling laws for individual models, which predict performance improvements as model parameters and data…
Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression
Cheng Yuan, Jiawei Shao, Xuelong Li
Recent years have witnessed the rapid advancements of large language models (LLMs) and their expanding applications, leading to soaring demands for computational resources. The wid…
AI Flow: Perspectives, Scenarios, and Approaches
Hongjun An, Wenhan Hu, Sida Huang +11
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…