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 (led by Prof. Jian Yang) and Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, NJUST.

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

In 2014 and 2018, I got my M.Sc. and Ph.D. degree in LAMDA Group at Nanjing University, advised by Prof. Zhi-Hua Zhou and Prof. Jianxin Wu, respectively. From Oct. 2016 to Oct. 2017, I was a visiting scholar in Prof. Chunhua Shen’s group at UoA, Australia.

欢迎有志投身人工智能领域的热忱学子报考我的研究生或本科科研训练及毕设！
2021年度推免招生已开启！（联系邮箱：weixsnjust.edu.cn）

Dec. 2, 2020: One paper about long-tailed recognition accepted by AAAI 2021.
Sept. 25, 2020: One paper about base models accepted by IEEE TNNLS.
Aug. 1, 2020: Two papers accepted to ACM MM 2020: one for webly-supervised fine-grained recognition and one for retrieval toolbox.
Jul. 3, 2020: Two papers accepted to ECCV 2020: one for fine-grained image retrieval and one for object detection.
Jun. 20, 2020: Our team won the first place in the iWildCam track of FGVC 2020 global competitions!
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!
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), Virtual Conference, 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.

Multiple Instance Learning with Emerging Novel Class.
X.-S. Wei, H.-J. Ye, X. Mu, J. Wu, C. Shen, and Z.-H. Zhou.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019, in press.

ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval.
Q. Cui, Q.-Y. Jiang, X.-S. Wei*, W.-J. Li, and O. Yoshie. (Oral presentation)
European Conference on Computer Vision (ECCV’20), Virtual Conference, 2020, pp. xx-xx.

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.

Asian Conference on Computer Vision (ACCV) Workshop on Webly-Supervised Fine-Grained Recognition, 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

+86-025-8431-5017 (Ext Number: 8230/ 分机号: 8230)