Yu Liu's academic page

Executive Director & General Manager, SenseTime
PI, Shanghai AI Lab
Industrial doctoral supervisor, CUHK & SJTU
liuyuisanai@gmail.com

Google Scholar

News

[2024] My team have 19 papers published on NeurIPS/CVPR/TMLR/ICML/ECCV in 2024
[2024] Our AIGC product MiaoHua has garnered users over 3,000,000, with a DAU exceeding 530,000, all within a remarkable 9-day post-launch.
[2024] We are granted the 吴文俊奖 - 科技进步一等奖
[2024] Our OpenDILab achieves 21,000+ stars on GitHub!

[2023] My team have 20 papers published on TPAMI/ICCV/ICLR/NeurIPS/IROS/AAAI in 2023

[2023] My team won the championship of CARLA Autonomous Driving Challenge

[2022] My team have 9 papers published on ECCV/CoRL/NeurIPS/AAAI in 2022

[2022] We release DI-Star, an implementation of AlphaStar in pyTorch, beating pro players with 6000+ MMR.

[2021] My team won the best paper of ICCV21 MFR workshop

[2021] My team won 3 championships of ICCV21 The Masked Face Recognition Challenge

[2021] We release OpenDILab, an open source decision intelligence platform

[2020] 5 papers with 1 oral got published on CVPR/ECCV 2020

[2020] My team won 2 championships of ActivityNet on the Spatio-temporal Action Localization (AVA) track and the Trimmed Activity Recognition (Kinetics) track

[2020] My team won the championship of NIST FRVT 1:N, a 12-million-level commercial facial recognition benchmark held by US government.

[2019] 7 papers with 4 oral presentations published on ICCV/CVPR/AAAI in 2019

[2019] I won 4 champions in 4 ICCV AI challenges:MMIT (solutions), OpenImage Instance Segmentation Challenge (solutions), OpenImage Object Detection Challenge (solutions), LFR 2019 (model and report)

[2019] I was granted the Google PhD Fellowship 2019.

[2018] 4 papers published by CVPR/ECCV/IJCV/AAAI in 2018.

[2017] 6 papers published on CVPR/ICCV/NIPS/AAAI/T-PAMI in 2017.

[2016] We win the 1st place in ECCV-MOT16 Challenge! code

[2016] We win the 1st place in ImageNet Challenge 2016! code

About me

I am in the process of launching a startup venture. Previously, I was the Executive Director of Research and GM at SenseTime Group, spearheading large-scale AIGC and multi-modal interactive models. I led a team of approximately 100 top-tier researchers and developers, utilizing over 4,000 GPUs to drive innovative technology and products. I hold a PhD from MMLab, CUHK, supervised by Prof. Xiaogang Wang, and have won multiple international AI competitions, along with a Google PhD Fellowship.

Working Experience

Executive Director, SenseTime Group. (since Oct. 2019)
Principal Investigator (part-time) at Shanghai AI Lab. (since Jan. 2020)
Research work experiences at SenseTime Group, Google Research and Microsoft Research. (before 2019)

Publications

See full list at Google Scholar.
*equal contribution ⁺corresponding author

▼Large model for Multi-modal generation, AIGC

▼Large-scale Reinforcement Learning, Embodied AI

▼Large-scale Optimiation, Computer Vision

Honors and Awards

Won the 1st prize for scientific and technological progress, CAAI, 2024
Won the 1st place in CARLA Autonomous Driving Challenge 2022
Won the 1st place in ActivityNet 2020, AVA track
Won the 1st place in ActivityNet 2020, Kinetics track
Won the 1st place in NIST FRVT held by US government in 2020, 2021 and 2022
Won the 1st place in ICCV19 Multi-Moments in Time (MIT) Challenge
Won the 1st place in Google OpenImage Object Detection Challenge 2019
Won the 1st place in Google OpenImage Instance Segmentation Challenge 2019
Google PhD Fellowship in 2019 (1/China, 50/world)
Won the 1st place in ICCV19 Lightweight Face Recognition Challenge
Won the 1st place in NIST-FRVT threshold based 1:N track 2018
Won the 1st place in Multiple Objects Tracking Challenge (MOT16) in 2016
Won the 1st place in detection track of ImageNet (ILSVRC) in 2016
Won the best undergraduate dissertation in 2016 (1/230)
IEEE-Microsoft Undergraduate Fellowship in 2016 (40/world)
The Outstanding Winner of Challenge Cup in 2015 (top 1/China)

Projects & Datasets

GitHub

OpenDILab, a generalized Decision Intelligence engine.
X-Temporal, Easily implement SOTA video understanding methods with PyTorch on multiple machines and GPUs
CaffeMex v2.3, a multi-GPU & memory-reduced MAT-Caffe on LINUX and WINDOWS
Labeled Pedestrains in the Wild, a large scale pedestrain re-identification benchmark