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Computer Science

D-Index
52
Citations
15144
World Ranking
4990
National Ranking
2317

Overview

Boqing Gong is a researcher affiliated with Google in the United States. Their primary area of expertise lies within computer science, with a particular focus on computer vision and pattern recognition, artificial intelligence, and related subfields such as radiology, nuclear medicine and imaging, computational mechanics, and aerospace engineering.

Their work spans a range of topics including domain adaptation and few-shot learning, multimodal machine learning applications, advanced neural network applications, human pose and action recognition, COVID-19 diagnosis using AI, adversarial robustness in machine learning, and generative adversarial networks and image synthesis.

Boqing Gong has contributed numerous scholarly papers published mainly in venues such as:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Medical Image Analysis
  • PubMed
  • 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Among the recent papers, key publications include:

  • "VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text," published in 2021 in arXiv (Cornell University)
  • "Contrastive Learning for Label Efficient Semantic Segmentation," published in 2021 at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations," published in 2021 in arXiv (Cornell University)
  • "Smooth Adversarial Training," published in 2020 in arXiv (Cornell University)
  • "Spatiotemporal Contrastive Video Representation Learning," published in 2020 in arXiv (Cornell University)

Boqing Gong has frequently collaborated with a number of co-authors, including Ming-Hsuan Yang, Yandong Li, Liangzhe Yuan, Hartwig Adam, and Liqiang Wang. These collaborations contribute to a broad and diverse research network within their fields.

Best Publications

  • Geodesic flow kernel for unsupervised domain adaptation

    Boqing Gong;Yuan Shi;Fei Sha;Kristen Grauman

  • Large-Scale Long-Tailed Recognition in an Open World

    Ziwei Liu;Zhongqi Miao;Xiaohang Zhan;Jiayun Wang

  • Synthesized Classifiers for Zero-Shot Learning

    Soravit Changpinyo;Wei-Lun Chao;Boqing Gong;Fei Sha

  • An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

    Wei-Lun Chao;Soravit Changpinyo;Boqing Gong;Fei Sha

  • Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

    Yang Zhang;Philip David;Boqing Gong

  • PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

    Yang Zhang;Zixiang Zhou;Philip David;Xiangyu Yue

  • Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

    Boqing Gong;Kristen Grauman;Fei Sha

  • Adversarial Examples Improve Image Recognition

    Cihang Xie;Mingxing Tan;Boqing Gong;Jiang Wang

  • Spatiotemporal Contrastive Video Representation Learning

    Rui Qian;Tianjian Meng;Boqing Gong;Ming-Hsuan Yang

  • Diverse Sequential Subset Selection for Supervised Video Summarization

    Boqing Gong;Wei-Lun Chao;Kristen Grauman;Fei Sha

  • A Fast and Accurate One-Stage Approach to Visual Grounding

    Zhengyuan Yang;Boqing Gong;Liwei Wang;Wenbing Huang

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

    Xiangyu Yue;Yang Zhang;Sicheng Zhao;Alberto Sangiovanni-Vincentelli

  • VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

    Hassan Akbari;Liangzhe Yuan;Rui Qian;Wei-Hong Chuang

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

    Muhammad Abdullah Jamal;Matthew Brown;Ming-Hsuan Yang;Liqiang Wang

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

    Lijie Fan;Wenbing Huang;Chuang Gan;Stefano Ermon

  • MoViNets: Mobile Video Networks for Efficient Video Recognition

    Dan Kondratyuk;Liangzhe Yuan;Yandong Li;Li Zhang

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

    Xiang Wei;Boqing Gong;Zixia Liu;Wei Lu

  • Learning Attributes Equals Multi-Source Domain Generalization

    Chuang Gan;Tianbao Yang;Boqing Gong

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

    Qing Lian;Lixin Duan;Fengmao Lv;Boqing Gong

  • Contrastive Learning for Label Efficient Semantic Segmentation

    Xiangyun Zhao;Raviteja Vemulapalli;Philip Andrew Mansfield;Boqing Gong

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

    Yandong Li;Lijun Li;Liqiang Wang;Tong Zhang

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

    Qing Lian;Fengmao Lv;Lixin Duan;Boqing Gong

Frequent Co-Authors

Fei Sha
Fei Sha Facebook (United States)
Chuang Gan
Chuang Gan University of Massachusetts Amherst
Tianbao Yang
Tianbao Yang Texas A&M University
Deliang Fan
Deliang Fan Johns Hopkins University
Mubarak Shah
Mubarak Shah University of Central Florida
Kristen Grauman
Kristen Grauman The University of Texas at Austin
Wenbing Huang
Wenbing Huang Renmin University of China
Ming-Hsuan Yang
Ming-Hsuan Yang University of California, Merced
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Junzhou Huang
Junzhou Huang The University of Texas at Arlington

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