6 citations · 7 across the 8 of their papers we have counts for
4 papers · 1 filter
Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection
Yunqing Hu, Zheming Yang, Chang Zhao +1
Traditional object detection methods face performance degradation challenges in complex scenarios such as low-light conditions and heavy occlusions due to a lack of high-level sema…
MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Zheming Yang, Qi Guo, Yunqing Hu +4
Multimodal large language models (MLLMs) enable powerful cross-modal inference but impose significant computational and latency burdens, posing severe challenges for deployment in…
EC2MoE: Adaptive End-Cloud Pipeline Collaboration Enabling Scalable Mixture-of-Experts Inference
Zheming Yang, Yunqing Hu, Sheng Sun +1
The Mixture-of-Experts (MoE) paradigm has emerged as a promising solution to scale up model capacity while maintaining inference efficiency. However, deploying MoE models across he…
CDIO: Cross-Domain Inference Optimization with Resource Preference Prediction for Edge-Cloud Collaboration
Zheming Yang, Wen Ji, Qi Guo +7
Currently, massive video tasks are processed by edge-cloud collaboration. However, the diversity of task requirements and the dynamics of resources pose great challenges to efficie…