Boqing Gong

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Google ∙ University of Southern California ∙ Microsoft ∙ University of Central Florida

Principal Researcher in Tencent AI Lab at Seattle, Assistanf Professor at University of Florida from 2015 to 2017

Featured Co-authors

Jiebo Luo
120 publications
Ming-Hsuan Yang
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Alan Yuille
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Stefano Ermon
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Tong Zhang
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Yang Zhang
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Joshua B. Tenenbaum
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Dahua Lin
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Cho-Jui Hsieh
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Quoc V. Le
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Liwei Wang
78 publications

Improving Object Detection with Selective Self-supervised Self-training

We study how to leverage Web images to augment human-curated object dete...
07/17/2020 ∙ by Yandong Li, et al. ∙ 32 ∙ share

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Complete Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

We study an unsupervised domain adaptation problem for the semantic labe...
07/16/2020 ∙ by Li Yi, et al. ∙ 0 ∙ share

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Smooth Adversarial Training

It is commonly believed that networks cannot be both accurate and robust...
06/25/2020 ∙ by Cihang Xie, et al. ∙ 13 ∙ share

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When Ensembling Smaller Models is More Efficient than Single Large Models

Ensembling is a simple and popular technique for boosting evaluation per...
05/01/2020 ∙ by Dan Kondratyuk, et al. ∙ 22 ∙ share

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Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model

We study how to train a student deep neural network for visual recogniti...
03/31/2020 ∙ by Dongdong Wang, et al. ∙ 2 ∙ share

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Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

Object frequency in the real world often follows a power law, leading to...
03/24/2020 ∙ by Muhammad Abdullah Jamal, et al. ∙ 5 ∙ share

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MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Adversarial training is one of the most popular ways to learn robust mod...
01/08/2020 ∙ by Runtian Zhai, et al. ∙ 4 ∙ share

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Look, Listen, and Act: Towards Audio-Visual Embodied Navigation

A crucial aspect of mobile intelligent agents is their ability to integr...
12/25/2019 ∙ by Chuang Gan, et al. ∙ 2 ∙ share

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Adversarial Examples Improve Image Recognition

Adversarial examples are commonly viewed as a threat to ConvNets. Here w...
11/21/2019 ∙ by Cihang Xie, et al. ∙ 24 ∙ share

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Compound Domain Adaptation in an Open World

Existing works on domain adaptation often assume clear boundaries betwee...
09/08/2019 ∙ by Ziwei Liu, et al. ∙ 5 ∙ share

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Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data

We propose to harness the potential of simulation for the semantic segme...
09/02/2019 ∙ by Xiangyu Yue, et al. ∙ 7 ∙ share

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach

We propose a new approach, called self-motivated pyramid curriculum doma...
08/26/2019 ∙ by Qing Lian, et al. ∙ 13 ∙ share

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A Fast and Accurate One-Stage Approach to Visual Grounding

We propose a simple, fast, and accurate one-stage approach to visual gro...
08/18/2019 ∙ by Zhengyuan Yang, et al. ∙ 1 ∙ share

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Defending Against Adversarial Attacks Using Random Forests

As deep neural networks (DNNs) have become increasingly important and po...
06/16/2019 ∙ by Yifan Ding, et al. ∙ 1 ∙ share

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NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

Powerful adversarial attack methods are vital for understanding how to c...
05/01/2019 ∙ by Yandong Li, et al. ∙ 10 ∙ share

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Large-Scale Long-Tailed Recognition in an Open World

Real world data often have a long-tailed and open-ended distribution. A ...
04/10/2019 ∙ by Ziwei Liu, et al. ∙ 10 ∙ share

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Synthesized Policies for Transfer and Adaptation across Tasks and Environments

The ability to transfer in reinforcement learning is key towards buildin...
04/05/2019 ∙ by Hexiang Hu, et al. ∙ 6 ∙ share

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Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

Real-time traffic volume inference is key to an intelligent city. It is ...
02/25/2019 ∙ by Xianfeng Tang, et al. ∙ 6 ∙ share

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A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

During the last half decade, convolutional neural networks (CNNs) have t...
12/24/2018 ∙ by Yang Zhang, et al. ∙ 0 ∙ share

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Classifier and Exemplar Synthesis for Zero-Shot Learning

Zero-shot learning (ZSL) enables solving a task without the need to see ...
12/16/2018 ∙ by Soravit Changpinyo, et al. ∙ 0 ∙ share

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Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate p...
08/09/2018 ∙ by Lijie Fan, et al. ∙ 6 ∙ share

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Improving Sequential Determinantal Point Processes for Supervised Video Summarization

It is now much easier than ever before to produce videos. While the ubiq...
07/28/2018 ∙ by Aidean Sharghi, et al. ∙ 4 ∙ share

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Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy

Deep convolution neural network has achieved great success in many artif...
07/20/2018 ∙ by Zhezhi He, et al. ∙ 2 ∙ share

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Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions

Recent studies have shown that deep neural networks (DNNs) are vulnerabl...
07/18/2018 ∙ by Adnan Siraj Rakin, et al. ∙ 2 ∙ share

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How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization

The large volume of video content and high viewing frequency demand auto...
07/11/2018 ∙ by Yandong Li, et al. ∙ 8 ∙ share

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End-to-End Learning of Motion Representation for Video Understanding

Despite the recent success of end-to-end learned representations, hand-c...
04/02/2018 ∙ by Lijie Fan, et al. ∙ 0 ∙ share

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End-to-End Video Captioning with Multitask Reinforcement Learning

Although end-to-end (E2E) learning has led to promising performance on a...
03/21/2018 ∙ by Lijun Li, et al. ∙ 0 ∙ share

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Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

Despite being impactful on a variety of problems and applications, the g...
03/05/2018 ∙ by Xiang Wei, et al. ∙ 0 ∙ share

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A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

The recent success of deep neural networks is powered in part by large-s...
02/08/2018 ∙ by Yifan Ding, et al. ∙ 0 ∙ share

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Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples

Deep learning algorithms and networks are vulnerable to perturbed inputs...
02/05/2018 ∙ by Adnan Siraj Rakin, et al. ∙ 0 ∙ share

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Robust Pre-Processing: A Robust Defense Method Against Adversary Attack

Deep learning algorithms and networks are vulnerable to perturbed inputs...
02/05/2018 ∙ by Adnan Siraj Rakin, et al. ∙ 0 ∙ share

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VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

Rich and dense human labeled datasets are among the main enabling factor...
08/15/2017 ∙ by Chuang Gan, et al. ∙ 0 ∙ share

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Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

During the last half decade, convolutional neural networks (CNNs) have t...
07/29/2017 ∙ by Yang Zhang, et al. ∙ 0 ∙ share

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Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach

Recent years have witnessed a resurgence of interest in video summarizat...
07/16/2017 ∙ by Aidean Sharghi, et al. ∙ 0 ∙ share

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Fast Zero-Shot Image Tagging

The well-known word analogy experiments show that the recent word vector...
05/31/2016 ∙ by Yang Zhang, et al. ∙ 0 ∙ share

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Synthesized Classifiers for Zero-Shot Learning

Given semantic descriptions of object classes, zero-shot learning aims t...
03/02/2016 ∙ by Soravit Changpinyo, et al. ∙ 0 ∙ share

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Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural ne...
02/06/2016 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Large-Margin Determinantal Point Processes

Determinantal point processes (DPPs) offer a powerful approach to modeli...
11/06/2014 ∙ by Boqing Gong, et al. ∙ 0 ∙ share

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