World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
38153
World Ranking
4172
National Ranking
561

Overview

Jifeng Dai is affiliated with Tsinghua University in China and has made significant contributions to the field of computer science, particularly within computer vision and pattern recognition. Their research spans several subfields, including artificial intelligence, cardiology and cardiovascular medicine, aerospace engineering, and electrical and electronic engineering.

The scientist's research focuses on several key topics:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Vision and Imaging

Jifeng Dai has published extensively in high-impact venues, collaborating frequently with researchers such as Yu Qiao, Xizhou Zhu, Hongsheng Li, Lewei Lu, and Wenhai Wang. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Science China Information Sciences

Some recent papers authored or co-authored by Jifeng Dai include:

  • Deformable DETR: Deformable Transformers for End-to-End Object Detection (2020, arXiv)
  • MMDetection: Open MMLab Detection Toolbox and Benchmark (2024, arXiv)
  • Exploring Cross-Image Pixel Contrast for Semantic Segmentation (2021, 2021 IEEE/CVF International Conference on Computer Vision)
  • Fast Convergence of DETR with Spatially Modulated Co-Attention (2021, 2021 IEEE/CVF International Conference on Computer Vision)
  • Vision Transformer Adapter for Dense Predictions (2022, arXiv)

The scientist's work involves developing advanced neural network architectures, including transformers adapted for vision tasks, and exploring novel methods for object detection and semantic segmentation. Their contributions span both theoretical developments and practical implementations in machine learning toolboxes.

Overall, Jifeng Dai's research portfolio demonstrates a blend of interdisciplinary scientific inquiry and focused expertise in advanced computational techniques within computer science, particularly addressing challenges in visual data processing and multimodal learning.

Best Publications

  • Deformable Convolutional Networks

    Jifeng Dai;Haozhi Qi;Yuwen Xiong;Yi Li

  • R-FCN: Object Detection via Region-based Fully Convolutional Networks

    Jifeng Dai;Yi Li;Kaiming He;Jian Sun

  • Deformable DETR: Deformable Transformers for End-to-End Object Detection

    Xizhou Zhu;Weijie Su;Lewei Lu;Bin Li

  • Deformable ConvNets V2: More Deformable, Better Results

    Xizhou Zhu;Han Hu;Stephen Lin;Jifeng Dai

  • Instance-Aware Semantic Segmentation via Multi-task Network Cascades

    Jifeng Dai;Kaiming He;Jian Sun

  • Relation Networks for Object Detection

    Han Hu;Jiayuan Gu;Zheng Zhang;Jifeng Dai

  • BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

    Jifeng Dai;Kaiming He;Jian Sun

  • ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

    Di Lin;Jifeng Dai;Jiaya Jia;Kaiming He

  • Fully Convolutional Instance-Aware Semantic Segmentation

    Yi Li;Haozhi Qi;Jifeng Dai;Xiangyang Ji

  • InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

    Unknown

  • VL-BERT: Pre-training of Generic Visual-Linguistic Representations

    Weijie Su;Xizhou Zhu;Yue Cao;Bin Li

  • MMDetection: Open MMLab Detection Toolbox and Benchmark.

    Kai Chen;Jiaqi Wang;Jiangmiao Pang;Yuhang Cao

  • Deep Feature Flow for Video Recognition

    Xizhou Zhu;Yuwen Xiong;Jifeng Dai;Lu Yuan

  • Flow-Guided Feature Aggregation for Video Object Detection

    Xizhou Zhu;Yujie Wang;Jifeng Dai;Lu Yuan

  • Planning-oriented Autonomous Driving

    Unknown

  • An Empirical Study of Spatial Attention Mechanisms in Deep Networks

    Xizhou Zhu;Dazhi Cheng;Zheng Zhang;Stephen Lin

  • Exploring Cross-Image Pixel Contrast for Semantic Segmentation

    Wenguan Wang;Tianfei Zhou;Fisher Yu;Jifeng Dai

  • Convolutional feature masking for joint object and stuff segmentation

    Jifeng Dai;Kaiming He;Jian Sun

  • BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

    Unknown

  • Instance-Sensitive Fully Convolutional Networks

    Jifeng Dai;Kaiming He;Yi Li;Shaoqing Ren

  • Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

    Guolei Sun;Wenguan Wang;Jifeng Dai;Jifeng Dai;Luc Van Gool

  • Towards High Performance Video Object Detection

    Xizhou Zhu;Jifeng Dai;Lu Yuan;Yichen Wei

  • Fast Convergence of DETR with Spatially Modulated Co-Attention

    Peng Gao;Minghang Zheng;Xiaogang Wang;Jifeng Dai

Frequent Co-Authors

Yichen Wei
Yichen Wei Microsoft Research Asia (China)
Kaiming He
Kaiming He Facebook (United States)
Jian Sun
Jian Sun Megvii
Han Hu
Han Hu Microsoft Research Asia (China)
Hongsheng Li
Hongsheng Li Chinese University of Hong Kong
Lu Yuan
Lu Yuan Microsoft (United States)
Wenguan Wang
Wenguan Wang Zhejiang University
Gao Huang
Gao Huang Tsinghua University
Ying Nian Wu
Ying Nian Wu University of California, Los Angeles
Stephen Lin
Stephen Lin Microsoft Research Asia (China)

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