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Xiaohan Ding

Xiaohan Ding (丁霄汉) is a senior researcher at Tencent AI Lab (Shenzhen, China) since 2022. He obtained Ph.D from Tsinghua University supervised by Prof. Guiguang Ding.

Email  /  Google Scholar  /  Zhi Hu  /  DBLP


  • 02/2023, RepOptimizer has been accepted by ICLR 2023!
  • 10/2022, The methodology of Re-parameterization have been used in PP-YOLOE+, which outperformed YOLOv6 and YOLOv7!
  • 08/2022, RepVGG and the methodology of Re-parameterization (including our recently proposed RepOptimizer) have been used in both YOLOv6 and YOLOv7!
  • 07/2022, the Structural Re-parameterization Universe has got around 5000 stars on GitHub.
  • 08/2021, two papers of the Structural Re-parameterization Universe (RepMLPNet and RepLKNet) has been accepted by CVPR 2022.
  • 08/2021, one paper of the Structural Re-parameterization Universe has been accepted by ICCV 2021.
  • 06/2021, RepVGG ranks 5th on Twitter #CVPR hot trends during the conference!
  • 03/2021, two papers of the Structural Re-parameterization Universe have been accepted by CVPR 2021.
  • Research Projects

    I mostly focus on foundation model (i.e., backbone) design and optimization. Some of my works have been used as fundamental tools in academia and industry. I take great pleasure in introducing the Structural Re-parameterization Universe to you.

  • The Structural Re-parameterization Universe
  • Representative: RepVGG GitHub (2800 stars) | Paper

    I proposed Structural Re-parameterization, a methodology that converts a structure into another via transforming the parameters. I started to investigate into it since ACNet (ICCV 2019) and continued to work on it during my internship with Dr. Xiangyu Zhang at MEGVII Technology. I started to use Structural Re-parameterization to term this methodology since RepVGG (CVPR 2021) and Diverse Branch Block (CVPR 2021). In the following works, I broadened and deepened this research direction.

  • ACNet (ICCV 2019): a building block to improve CNN without any extra inference costs.
  • RepVGG (CVPR 2021): proposed Structural Re-parameterization and used it to build a super simple but powerful architecture.
  • DBB (CVPR 2021): proposed a more powerful building block and analyzed why Strutural Re-parameterization worked.
  • ResRep (ICCV 2021): a state-of-the-art channel pruning method using Structural Re-parameterization.
  • RepMLPNet (CVPR 2022): vision MLP with local priors injected with Structural Re-parameterization. Still no inference costs!
  • RepLKNet (CVPR 2022): studied very large kernel (e.g., 31x31) design in modern CNNs. Outperformed Transformers on segmentation and object detection. Used Structural Re-parameterization to further improve the performance. My favorite work of 2022.
  • RepOptimizer: generalized the idea to Gradient Re-parameterization, which facilitates efficient training of very simple (RepOpt-VGG, which is plain even during training) and quantization-friendly models. Already used in YOLOv6 and deployed in business.
  • Model Compression and Acceleration
  • From 2017 to 2019, I was doing research at Tsinghua University and published several papers on model compression and acceleration. Please see my Google Scholar Profile.

    Three Representative Research Papers

  • RepVGG: Making VGG-style ConvNets Great Again
    Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun
    CVPR 2021 | paper | code and models

  • Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
    Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou, Jungong Han, Guiguang Ding, Jian Sun
    CVPR 2022 | paper | code and models

  • Three Selected Awards

  • 2022, outstanding Ph.D dissertation of Tsinghua University

  • 2021, Intel Scholarship

  • 2019, Baidu Scholarship

  • Many thanks go to Dr. Yunhe Wang, who shared the source code of his homepage.