Luming Tang (唐路明)

I am a Research Scientist at Google DeepMind in New York City. Previously, I was a CS PhD student at Cornell University, advised by Professor Bharath Hariharan. Before that, I received my Bachelor degree in Mathematics and Physics from Tsinghua University.

Email / Resume / GitHub / Google Scholar / Twitter / LinkedIn

Research

My current interests lie at the intersection of machine learning and computer vision, including representation learning and generative models, especially on how to adapt large pre-trained models to tackle challenging real-world problems where data is constrained. Meanwhile, I'm also interested in building vision foundation models.

(* indicates equal contribution)

	RealFill: Reference-Driven Generation for Authentic Image Completion Luming Tang, Nataniel Ruiz, Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E. Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, Michael Rubinstein SIGGRAPH, 2024 (Journal Track) paper / video / project page
	Emergent Correspondence from Image Diffusion Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, Bharath Hariharan NeurIPS*, 2023 paper / video / code / poster / slides / project page
	Magic3D: High-Resolution Text-to-3D Content Creation Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, Tsung-Yi Lin CVPR, 2023 (Highlight)* paper / project page
	Visual Prompt Tuning Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, Ser-Nam Lim ECCV, 2022 paper / video / code
	Few-Shot Classification with Feature Map Reconstruction Networks Davis Wertheimer, Luming Tang, Bharath Hariharan CVPR, 2021 paper / video / code / poster
	Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition Luming Tang, Davis Wertheimer, Bharath Hariharan CVPR, 2020 paper / video / code / slides
	Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder Luming Tang, Yexiang Xue, Di Chen, Carla P. Gomes AAAI, 2018 paper / code / poster / slides
	Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang ICCV, 2017 paper / dataset / poster
	Hierarchical Deep Recurrent Architecture for Video Understanding Luming Tang, Boyang Deng, Haiyu Zhao, Shuai Yi CVPR Workshop on Youtube-8M Large-Scale Video Understanding, 2017 paper / code

Projects

Here're some interesting research or course projects I have worked on.

	Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners Davis Wertheimer, Luming Tang, Bharath Hariharan Tech Report, 2022 arxiv
	Few-Shot Learning in Long-Tailed Settings Davis Wertheimer, Luming Tang, Dhruv Baijal, Pranjal Mittal, Anika Talwar, Bharath Hariharan Tech Report, 2021 paper / code
	Baseline implementation of DrQA and BERT finetuning on SQuAD 2.0 Luming Tang CS 5740 Natural Language Processing, Assignment 4, 2020 code
	An Experimental Evaluation of Optimized Maximum Flow Implementations Junxi Song, Luming Tang, Hongbo Zhang, Xiaoji Zhang (alphabetical order) CS 6820 Analysis of Algorithms, Course Project, 2019 draft
	Garbage Collection Schedule Algorithm Luming Tang, Junxi Song CS 6820 Analysis of Algorithms, Assignment 2 Problem 4, 2019 problem / our proof
	On the Regularization Balance in Autoencoder-Based Generative Models Bin Dai, Luming Tang, David Wipf Tech Report, 2019 draft / supplementary
	OpenNRE: An Open-Source Package for Neural Relation Extraction Tianyu Gao, Xu Han, Shulin Cao, Luming Tang, Yankai Lin, Zhiyuan Liu THUNLP Github, 2018 code

Experience

I am very fortunate to have chance to work in multiple great research groups and spend enjoyable time with so many amazing advisors, mentors and collaborators.

May 2019 - Now: Graphics and Vision Group, Cornell
Graduate Research Assistant, advised by Bharath Hariharan, collaborate with Davis Wertheimer, Menglin Jia, Qianqian Wang, Cheng Perng Phoo
May 2023 - Now: Google Research
Research Intern, hosted by Neal Wadhwa, Nataniel Ruiz, Michael Rubinstein, collaborate with Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E. Jacobs, Yael Pritch, Kfir Aberman
May 2022 - Feb 2023: NVIDIA Research
Research Intern, mentored by Tsung-Yi Lin, collaborated with Chen-Hsuan Lin, Ming-Yu Liu, Xiaohui Zeng, Karsten Kreis, Xun Huang, Jun Gao, Towaki Takikawa
May 2021 - Nov 2021: Perception Group, Waymo
Intern, mentored by Shiwei Sheng, Andy Tsai, Ruichi Yu, collaborated with Frederick Liu, Xu Chen, Charles Qi
Sep 2018 - Dec 2018: Visual Computing Group, Microsoft Research Asia
Research Intern, mentored by David Wipf, collaborated with Bin Dai
Sep 2016 - Jun 2018: Natural Language Processing Lab, Tsinghua
Undergraduate Research Assistant, advised by Zhiyuan Liu
Jun 2017 - Sep 2017: Computational Sustainability Lab, Cornell
Research Intern, advised by Carla Gomes, collaborated with Yexiang Xue, Di Chen
Dec 2016 - Jun 2017: SenseTime
Research Intern, mentored by Shuai Yi, collaborated with Zhongdao Wang, Haiyu Zhao, Boyang Deng

Service

Teaching Assistant:

Spring 2019, CS 4787 Principles of Large-Scale Machine Learning
Summer 2019, CS 2110 OOP and Data Structures
Fall 2019, CS 6670 Graduate Computer Vision

PhD admission committee student volunteer: 2020, 2023

Conference Reviewer: CVPR'21 (Outstanding Reviewer), ICCV'21, CVPR'22, ECCV'22, CVPR'23, ICCV'23, ICML'23, NeurIPS'23, ICLR'24, ICML'24, CVPR'24

Journal Reviewer: TPAMI-SI (Learning with Fewer Labels), IJCV

PC member: AAAI'23

Misc.

Header avatars credit to Zeya Peng.
I love playing soccer and FIFA (FIFA'20 Season Division 1 with Title, FIFA'22 Ultimate Team Division 3).
Meet the most adorable S'more and check out her instagram! Her Chinese name is 屎妹 :) This is her best friend, Shiba a.k.a. 屎宝 lol.
I am from Jiaozuo, a beautiful small city in Henan, China.

Design and source code modified based on Leonid Keselman and Jon Barron's website

last update: Nov, 2023

Luming Tang (唐路明)

Research

RealFill: Reference-Driven Generation for Authentic Image Completion

Emergent Correspondence from Image Diffusion

Magic3D: High-Resolution Text-to-3D Content Creation

Visual Prompt Tuning

Few-Shot Classification with Feature Map Reconstruction Networks

Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

Hierarchical Deep Recurrent Architecture for Video Understanding

Projects

Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners

Few-Shot Learning in Long-Tailed Settings

Baseline implementation of DrQA and BERT finetuning on SQuAD 2.0

An Experimental Evaluation of Optimized Maximum Flow Implementations

Garbage Collection Schedule Algorithm

On the Regularization Balance in Autoencoder-Based Generative Models

OpenNRE: An Open-Source Package for Neural Relation Extraction

Experience

Service

Misc.