Boqing Gong

Principal Researcher
Tencent AI Lab, Seattle


I am a Principal Researcher of Tencent AI Lab at Seattle, working on machine learning and computer vision. Before joining Tencent, I was a tenure-track Assistant Professor in University of Central Florida (UCF). My research in UCF was supported in part by an NSF CRII award (so-PI, received in 2016) and an NSF BIGDATA award (PI, received in 2017), both of which were the first of their kinds ever granted to UCF. I actively serve on NSF panels and the program committees of computer vision conferences (CVPR, ICCV, ECCV, etc.) and machine learning conferences (ICML, NIPS, AISTATS, etc.). I was an area chair of IEEE WACV'18 and a mentor of its PhD forum. In 2015, I received a Ph.D. degree in Computer Science from the University of Southern California, where my work was partially supported by the Viterbi Fellowship.

Recent research interests: domain adaptation, reinforcement learning, and visual analytics of objects, human activities, scenes, and their attributes.

What's New

  • 2018
  • 2017
  • 2016
  • 2015
Eight Papers Published in 2018

Three at IEEE CVPR, two at ECCV, one at ICLR, one at ACM TOMM, and one at IEEE WACV.

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11/2018: Talk at INFORMS Special Session on Stochastic Optimization Methods and Approximation Theory in Machine Learning

"The Multiple Shades of Dropout for Discriminative and Generative Deep Neural Networks". Dropout, which independently zeros out the outputs of neurons at random, has become one of the most popular techniques in training deep neural networks due to its simplicity and remarkable effectiveness. This talk reveals multiple shades of dropout for both discriminative and generative deep neural networks, mainly covering our following works: [Li et al., NIPS'16] and [Wei, Gong, et al., ICLR'18].

Slides coming soon
06/2018: Talk at CVPR The 2nd Workshop on Visual Understanding by Learning from Web Data

"Learning from Web Data and Adapting Beyond It", which inlcudes the Web data of 1) noisy labels, 2) accurate labels, and 3) multi-modalities by semi-supervised learning, curriculum learning, and kernel mean matching, respectively. It mainly covers our following works: [Ding et al., WACV'18], [Wei, Gong, et al., ICLR'18], [Gan et al., CVPR'18], [Zhang et al., ICCV'17], [Gan et al., ECCV'16], and [Sharghi et al., ECCV'16].

read more: Slides
05/2018: Be serving as an Area Chair for IEEE WACV 2019
01/2018: Joined Tencent AI Lab (Seattle)
Seven Papers Published in 2017

Two at CVPR, two at ICCV, two book chapters, and one at ACII.

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10/2017: Talk at Department of Computer Engineering, University of California at Santa Cruz

"Domain Adaptation for Robust Visual Recognition". It is about our following works: [Gong, et al., ICML'13], [Gan et al., ECCV'16], [Gan et al., CVPR'16], and [Zhang et al., ICCV'17].

read more: slides
08/2017: Received the Third NSF Award as a PI

"BIGDATA: IA: Distributed Semi-Supervised Training of Deep Models and Its Applications in Video Understanding" (transferred to Mubarak Shah and Liqiang Wang). This project investigates semi-supervised training of deep neural network models using large-scale labeled and unlabeled data in a distributed fashion.

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07/2017: Selected as One of the Outstanding Reviewers by the IEEE CVPR 2017 Organization Committee
05/2017: Received the Second Gift Grant from Adobe Research

for our researcho on "Face Detector Adaptation without Forgetting".

Served as a panelist for two NSF panels in 2016
Eight Papers Published in 2016

Three at IEEE CVPR, three at ECCV, one at NIPS, and one at Symposium Multemedia.

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10/2016: Received the Second NSF Award as a Co-PI

Project: "Collaborative Research: Florida-IT-Pathways to Success (Flit-Path)".

10/2016: Received a Gift Grant from Adobe Research

for our researcho on "User-Guided Visual Analytics".

09/2016: Talk at the Army Research Office / Information Science Institute Workshop on Multi-Modal Data Analysis for Human Activity Detection

"Domain Adaptation Approaches to Human Activity Detection, Recognition, and Summarization".

03/2016: Received My First NSF Award as a So-PI

"CRII: RI: Multi-Source Domain Generalization Approaches to Visual Attribute Detection". This project investigates how to accurately and robustly detect attributes from images (videos, and 3D data), with the goal of developing and publicly providing effective attribute detection tools.

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12/2015: Talk at the AI Seminar of Information Science Institute, University of Southern California
11/2015: Talk at the ICDM Workshop on Practical Transfer Learning

"Reshaping Datasets for Unsupervised Domain Adaptation", which is about our following works: [Gong et al., CVPR'12], [Gong, et al., ICML'13], and [Gong et al., NIPS'13].

read more: Slides
08/2015: Joined University of Central Florida as an Assistant Professor (tenure-track)
06/2015: Defended Ph.D. thesis


Ph.D. in Computer Science

University of Southern California

Master of Philosophy in Information Engineering

The Chinese University of Hong Kong

Bachelor in Eletronic Information Engineering

University of Science and Technology of China


A research scientist in machine learning and computer vision, I am interested in developing novel algorithms to understand objects, human activities, scenes, and their attributes.

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