Xiu-Shen WEI (魏秀参)


Xiu-Shen WEI                                                                                                                                   “megvii”
Ph.D., Research Director
Megvii Research Nanjing (旷视南京研究院)

Office: 15th Floor, Tower A, No. 6 Xingzhi Rd., Nanjing
Email: weixs.gm@gmail.com or weixiushen@megvii.com

Media: “linkedin” weibo zhihu

About Me

  • I am currently the Research Director of Megvii Research Nanjing, Megvii Technology. We focus on developing novel computer vision systems, creating new deep learning models, publishing high-quality papers and deploying cutting-edge technologies to better serve Megvii's business.


Research Interests

My research interests include some sub-fields of Computer Vision and Machine Learning:

  • Deep Convolutional Neural Networks (DCNN) is a type of feed-forward artificial neural network where the individual neurons are tiled in such a way that they respond to overlapping regions in the visual field, which is widely used in image and video related tasks.

  • Fine-Grained Image Analysis (FGIA) is a hot topic in computer vision and pattern recognition. The goals of FGIA are localizing the fine-grained objects, recognizing the objects’ subordinate categories, retrieving the fine-grained objects and so on.

  • Long-Tailed Distribution Learning (LTDL) deals with real-world datasets displaying skewed distributions with a long tail, i.e., a few classes (a.k.a. head classes) occupy most of the data, while most classes (a.k.a. tail classes) have rarely few samples.

  • General Object Detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos.

  • Weakly Supervised Learning (WSL), especially Multi-Instance Learning (MIL), is a variation on supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances.

  • Bag-of-Words Model (BoW) can be applied to image related tasks by encoding local visual descriptors of one image into a high dimensional vector. In BoW, there also include Vector of the Locally Aggregated Descriptors (VLAD) and Fisher Vector (FV).

My papers are available for download in this Publications page, and here is my Google Scholar Citations profile.

Professional Activities

Journal Reviewer (Selected)

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

  • IEEE Transactions on Image Processing (TIP)

  • Springer Journal of Machine Learning (MLJ)

  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

  • IEEE Transactions on Multimedia (TMM)

  • Elsevier Journal of Neural Networks (NN)

  • Elsevier Journal of Pattern Recognition (PR)

  • SCIENCE CHINA Information Sciences

Conference Reviewer / Program Committee Member (Selected)



Xiu-Shen WEI
Megvii Research Nanjing
Nanjing 210000, China


15th Floor, Tower A, No. 6 Xingzhi Rd., Nanjing