Xiu-Shen WEI (魏秀参)

weixs 

Xiu-Shen WEI                                                                                                                                            “NJUST”
Ph.D., Professor
School of Computer Science and Engineering, Nanjing University of Science and Technology

Office: Room 4063-1
Email: weixs@njust.edu.cn or weixs.gm@gmail.com

About Me

  • From Jun. 2020, I am a Professor at PCA Lab and Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology.

  • From Oct. 2017 to Jun. 2020, I served as the Founding Director in Megvii Research Nanjing, Megvii Technology.

  • Self-media: “GoogleScholar” “linkedin” weibo zhihu

Updates

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).

Selected Publications

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)

Correspondence

Mail

Xiu-Shen WEI
School of Computer Science and Engineering
Nanjing University of Science and Technology
Nanjing 210094, China

Office

Rm 4063-1, Building of Computer Science and Engineering, Xiaolingwei 200, Nanjing 210094, China

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