08/2025: [New] Serve as Area Chair for CVPR 2026.
07/2025: [New] Released Apple Foundation Models [Tech report].
07/2025: [New] One paper accepted to COLM 2025!
06/2025: [New] Serve as Area Chair for AAAI 2026.
05/2025: We released UniGen.
05/2025: We released StreamBridge.
03/2025: We released SlowFast-LLaVA-1.5.
01/2025: One paper accepted to ICLR 2025!
Hi, I am Mingze. I am a Machine Learning Engineer at Apple. My research interests lie primarily in the area of computer vision and deep learning, and my current focus is on building unified encoders and LLMs for diverse modalities (text, image, video, and 3D) and tasks (understanding and generation). Before joining Apple, I also worked or interned at Cruise AI, Amazon AWS AI, and Microsoft Research.
I received my Ph.D. degree in Computer Science from Indiana University, advised by Prof. David Crandall in 2020. I was a visiting student researcher at Georgia Institute of Technology, working with Prof. Dhruv Batra and Prof. Devi Parikh, in 2018. Before that, I received my Master's degree in Computer Science from Indiana University and my Bachelor's degree in Software Engineering from Jilin University.
(*Equal Contribution, †Corresponding Author)
UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation
Rui Tian*, Mingfei Gao*, Mingze Xu*, Jiaming Hu, Jiasen Lu, Zuxuan Wu, Yinfei Yang, Afshin Dehghan
Technical Report, 2025
StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant
Haibo Wang, Bo Feng, Zhengfeng Lai, Mingze Xu, Shiyu Li, Weifeng Ge, Afshin Dehghan, Meng Cao, Ping Huang
Technical Report, 2025
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding
Mingze Xu*, Mingfei Gao*, Shiyu Li*, Jiasen Lu, Zhe Gan, Zhengfeng Lai, Meng Cao, Kai Kang, Yinfei Yang, Afshin Dehghan
Conference on Language Modeling (COLM), 2025
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Haotian Zhang*, Mingfei Gao*, Zhe Gan*, Philipp Dufter, Nina Wenzel, Forrest Huang, Dhruti Shah, Xianzhi Du, Bowen Zhang, Yanghao Li,
Sam Dodge, Keen You, Zhen Yang, Aleksei Timofeev, Mingze Xu, Hong-You Chen, Jean-Philippe Fauconnier,
Zhengfeng Lai, Haoxuan You, Zirui Wang, Afshin Dehghan, Peter Grasch, Yinfei Yang
International Conference on Learning Representations (ICLR), 2025
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
Mingze Xu*, Mingfei Gao*, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, Afshin Dehghan
Technical Report, 2024
SkeleTR: Towards Skeleton-based Action Recognition in the Wild
Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, Alessandro Bergamo
IEEE International Conference on Computer Vision (ICCV), 2023
An In-depth Study of Stochastic Backpropagation
Jun Fang, Mingze Xu†, Hao Chen, Bing Shuai, Zhuowen Tu, Joseph Tighe
Conference on Neural Information Processing Systems (NeurIPS), 2022
MeMOT: Multi-Object Tracking with Memory
Jiarui Cai, Mingze Xu†, Wei Li, Yuanjun Xiong, Wei Xia, Zhuowen Tu, Stefano Soatto
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Oral)
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
Feng Cheng, Mingze Xu†, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Li, Wei Xia
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Oral)
TubeR: Tubelet Transformer for Video Action Detection
Jiaojiao Zhao*, Yanyi Zhang*, Xinyu Li*, Hao Chen, Shuai Bing, Mingze Xu, Chunhui Liu, Kaustav Kundu, Yuanjun Xiong, Davide Modolo, Ivan Marsic, Cees Snoek, Joseph Tighe
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Oral)
DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos
Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, David Crandall
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2022
Stepwise Goal-Driven Networks for Trajectory Prediction
Chuhua Wang*, Yuchen Wang*, Mingze Xu, David Crandall
IEEE Robotics and Automation Letters (RA-L), 2022
Long Short-Term Transformer for Online Action Detection
Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Xia, Zhuowen Tu, Stefano Soatto
Conference on Neural Information Processing Systems (NeurIPS), 2021 (Spotlight)
Learning Self-Consistency for Deepfake Detection
Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, Wei Xia
IEEE International Conference on Computer Vision (ICCV), 2021 (Oral)
Temporal Recurrent Networks for Online Action Detection
Mingze Xu*, Mingfei Gao*, Yi-Ting Chen, Larry Davis, David Crandall
IEEE International Conference on Computer Vision (ICCV), 2019
StartNet: Online Detection of Action Start in Untrimmed Videos
Mingfei Gao, Mingze Xu, Larry Davis, Richard Socher, Caiming Xiong
IEEE International Conference on Computer Vision (ICCV), 2019
Embodied Amodal Recognition: Learning to Move to Perceive Objects
Jianwei Yang*, Zhile Ren*, Mingze Xu, Xinlei Chen, David Crandall, Devi Parikh, Dhruv Batra
IEEE International Conference on Computer Vision (ICCV), 2019
Unsupervised Traffic Accident Detection in First-Person Videos
Mingze Xu*, Yu Yao*, Yuchen Wang, David Crandall, Ella Atkins
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Yu Yao, Mingze Xu, Chiho Choi, David Crandall, Ella Atkins, Behzad Dariush
IEEE International Conference on Robotics and Automation (ICRA), 2019
Joint Person Segmentation and Identification in Synchronized First- and Third-Person Videos
Mingze Xu, Chenyou Fan, Yuchen Wang, Michael Ryoo, David Crandall
European Conference on Computer Vision (ECCV), 2018
Last update: 08/2025