Lu Ming

I am a researcher focusing on AI Scientist (AI4AI) in Model Optimization (e.g., Quantization, Knowledge Distillation, Token Compression, etc.) for inference systems.

Previously, I worked on neural fields and computer vision models (2012-2022).

I obtained my Ph.D at Department of Electronic Engineering, Tsinghua University, where I was advised by Prof. Zhang Li. I have been working on Computer Vision and Model Optimization at Intel Labs China since 2015, under the supervision of Dr. Yao Anbang.

I have published over 50 papers in top-tier journals and conference proceedings. I also have about 30 PCT/US/EP patents approved for filing. Some of my works have been featured in Intel's GPU/CPU, Chris Lee's MV, and the opening ceremony of the 2022 Winter Olympic Games.

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ModelOpt Papers (as project leader)
MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders
Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, Shanghang Zhang
Conference on Computer Vision and Pattern Recognition (CVPR), 2025

Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs
Qizhe Zhang, Aosong Cheng, Ming Lu, Zhiyong Zhuo, Minqi Wang, Jiajun Cao, Shaobo Guo, Qi She, Shanghang Zhang
International Conference on Computer Vision (ICCV), 2025

Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs
Qizhe Zhang, Mengzhen Liu, Lichen Li, Ming Lu, Yuan Zhang, Junwen Pan, Qi She, Shanghang Zhang
Conference on Neural Information Processing Systems (NeurIPS), 2025

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation
Chengyu Bai, Yuming Li, Zhongyu Zhao, Jintao Chen, Peidong Jia, Qi She, Ming Lu, Shanghang Zhang
arXiv, 2025

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang
Conference on Computer Vision and Pattern Recognition (CVPR), 2023

A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution
Huicheng Pi, Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Shunli Zhang
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023

Efficient Meta-Tuning for Content-Aware Neural Video Delivery
Xiaoqi Li, Jiaming Liu, Shizun Wang, Cheng Lyu, Ming Lu, Yurong Chen, Anbang Yao, Yandong Guo, Shanghang Zhang
European Conference on Computer Vision (ECCV), 2022

Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Shizun Wang, Jiaming Liu, Kaixin Chen, Xiaoqi Li, Ming Lu, Yandong Guo
European Conference on Computer Vision (ECCV Oral), 2022

Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Jiaming Liu, Ming Lu, Kaixin Chen, Xiaoqi Li, Shizun Wang, Zhaoqing Wang, Enhua Wu, Yurong Chen, Chuang Zhang, Ming Wu
International Conference on Computer Vision (ICCV), 2021

Deep Likelihood Network for Image Restoration With Multiple Degradation Levels
Yiwen Guo, Ming Lu, Wangmeng Zuo, Changshui Zhang, Yurong Chen
Transactions on Image Processing (TIP), 2021

SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
Shizun Wang, Ming Lu, Kaixin Chen, Jiaming Liu, Xiaoqi Li, Ming Wu
British Machine Vision Conference (BMVC), 2021

A Closed-Form Solution to Universal Style Transfer
Ming Lu, Hao Zhao, Anbang Yao, Yurong Chen, Feng Xu, Zhang Li
International Conference on Computer Vision (ICCV), 2019

Decoder Network over Lightweight Reconstructed Feature for Fast Semantic Style Transfer
Ming Lu, Hao Zhao, Anbang Yao, Feng Xu, Yurong Chen, Li Zhang
International Conference on Computer Vision (ICCV), 2017

Emotion-preserving Blendshape Update with Real-time Face Tracking
Zhibo Wang, Jingwang Ling, Chengzeng Feng, Ming Lu, Feng Xu
Transactions on Visualization and Computer Graphics (TVCG), 2020

Real-time 3D Eyelids Tracking from Semantic Edges
Quan Wen, Feng Xu, Ming Lu, Jun-Hai Yong
ACM Transactions on Graphics (TOG), 2017