Xiu-Shen WEI （魏秀参）

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

Office: Room 4063-1
Email: weixsnjust.edu.cn or weixs.gmgmail.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.

I got my Ph.D. in LAMDA Group at Nanjing University in 2018, advised by Prof. Jianxin Wu. Before that, I received my B.Sc. degree in Computer Science and Technology in 2012, and got my M.Sc. degree in 2014 under the supervision of Prof. Zhi-Hua Zhou.

From Oct. 2016 to Oct. 2017, I was a visiting scholar in Prof. Chunhua Shen’s group at The University of Adelaide, Australia.

Jun. 28, 2020: After three amazing years at Megvii, I have joined Nanjing University of Science and Technology as a Professor.
Jun. 20, 2020: Our team won the first place in the iWildCam track of FGVC 2020 global competitions!
May. 13, 2020: Very delighted to participate as an invited speaker in the FGVC7 workshop associated with CVPR 2020.
Apr. 28, 2020: We release a PyTorch-based library for unsupervised image retrieval by deep CNNs on GitHub!
Apr. 21, 2020: Our work (SCDA) about fine-grained image retrieval was selected as the highly cited paper by ESI!
Feb. 27, 2020: Two papers accepted to CVPR 2020: one for long-tailed recognition and one for domain adaptive object detection.
Feb. 20, 2020: Co-organized a workshop on Robust Vision Challenge at ECCV 2020.
Jul. 6, 2019: Opened the “Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets” homepage.
Jun. 12, 2019: Our team won the first place in both iNaturalist and Herbarium tracks of FGVC 2019 global competition!
Nov. 1, 2018: Published a Chinese book on convolutional neural networks and computer vision.

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.

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.
(This work was the highly cited paper by ESI, which received enough citations to place it in the top 1% of its academic field.)
X.-S. Wei, J.-H. Luo, J. Wu, and Z.-H. Zhou.
IEEE Transactions on Image Processing (TIP), 2017, 26(6): 2868-2881.

BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition. (Oral presentation)
(This work was the winner solution of the iNaturalist 2019 global competition.)
B. Zhou, Q. Cui, X.-S. Wei*, and Z.-M. Chen.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), Seattle, WA, 2020, pp. 9719-9728.

Piecewise Classifier Mappings: Learning Fine-Grained Learners for Novel Categories with Few Examples.
X.-S. Wei, P. Wang, L. Liu, C. Shen, and J. Wu.
IEEE Transactions on Image Processing (TIP), 2019, 28(12): 6116-6125.

Exploring Categorical Regularization for Domain Adaptive Object Detection.
C. Xu, X. Zhao, X. Jin, and X.-S. Wei*.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), Seattle, WA, 2020, pp. 11724-11733.

Multi-Label Image Recognition with Graph Convolutional Networks.
Z.-M. Chen, X.-S. Wei*, Peng Wang, and Y. Guo.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), Long Beach, CA, 2019, pp. 5177-5186.

An Empirical Study on Image Bag Generators for Multi-Instance Learning.
X.-S. Wei, and Z.-H. Zhou.
Machine Learning, 2016, 105(2): 155-198.

Deep Descriptor Transforming for Image Co-Localization.
X.-S. Wei, C.-L. Zhang, Y. Li, C.-W. Xie, J. Wu, C. Shen, and Z.-H. Zhou.
International Joint Conference on Artificial Intelligence (IJCAI’17), Melbourne, Australia, 2017, pp. 3048-3054.

CVPR 2017, 2018, 2019, 2020, ICCV 2017, 2019, ECCV 2018, NeurIPS 2016, 2018, 2020, IJCAI 2018, 2019, 2020, AAAI 2016, 2017, 2018, 2019, 2020, ACCV 2018, 2020, BMVC 2019, 2020, WACV 2021, PRCV 2019, 2020.

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

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