29 citations · 46 across the 25 of their papers we have counts for
11 papers · 1 filter
MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models
Keyan Zhou, Zecheng Tang, Lingfeng Ming +9
The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guar…
Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition
Zichen Liang, Jingjing Fei, Jie Wang +6
Recent advances in multimodal large language models (MLLMs) have been primarily evaluated on general-purpose benchmarks, while their applications in domain-specific scenarios, such…
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…
SAEC: Scene-Aware Enhanced Edge-Cloud Collaborative Industrial Vision Inspection with Multimodal LLM
Yuhao Tian, Zheming Yang
Industrial vision inspection requires high accuracy under stringent resource constraints, yet existing approaches face a fundamental trade-off. Multimodal LLMs (MLLMs) deliver stro…
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…
CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention
Qingbin Li, Rongkun Xue, Jie Wang +8
Recent advances in Reinforcement Learning with Verified Reward (RLVR) have driven the emergence of more sophisticated cognitive behaviors in large language models (LLMs), thereby e…
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…
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Yufei Zhan, Ziheng Wu, Yousong Zhu +10
Despite notable advancements in multimodal reasoning, leading Multimodal Large Language Models (MLLMs) still underperform on vision-centric multimodal reasoning tasks in general sc…
Recursive Offloading for LLM Serving in Multi-tier Networks
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Heterogeneous device-edge-cloud computing infrastructures have become widely adopted in telecommunication operators and Wide Area Networks (WANs), offering multi-tier computational…
Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models
Guanghao Zhou, Panjia Qiu, Cen Chen +4
The application of reinforcement learning (RL) to enhance the reasoning capabilities of Multimodal Large Language Models (MLLMs) constitutes a rapidly advancing research area. Whil…
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…