Xiu-Shen WEI （魏秀参）
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Xiu-Shen WEI Office: Room 4063-1 |
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！2022年招生开启！（联系邮箱：weixs
njust.edu.cn）
Updates
Dec. 1, 2021: One paper about few-shot fine-grained recognition accepted by AAAI 2022.
Oct. 28, 2021: Our survey paper of Fine-Grained Image Analysis with Deep Learning was accepted by IEEE TPAMI.
Sept. 29, 2021: One paper accepted for Spotlight presentation by NeurIPS 2021.
Aug. 20, 2021: Very honored to serve as a Senior PC Member in AAAI 2022.
Jul. 31, 2021: Two papers accepted by ICCV 2021.
Jul. 2, 2021: Very honored to serve as the Tutorial Chair in ACCV 2022.
Mar. 1, 2021: One paper about long-tailed recognition accepted by CVPR 2021.
Apr. 28, 2020: We release a PyTorch-based library for unsupervised image retrieval by deep CNNs on GitHub!
Jul. 6, 2019: Opened the “Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets” homepage.
Nov. 1, 2018: Published a Chinese book on convolutional neural networks and computer vision.
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.
Fine-Grained Image Analysis with Deep Learning: A Survey.
X.-S. Wei, Y.-Z. Song, O. Mac Aodha, J. Wu, Y. Peng, J. Tang, J. Yang, and S. Belongie.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), in press.
A
-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval. (Spotlight presentation)
X.-S. Wei, Y. Shen, X. Sun, H.-J. Ye, and J. Yang.
Neural Information Processing Systems (NeurIPS’21), Virtual, 2021, pp. xx-xx.
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.
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), 2021, 33(5): 2109-2120.
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.
Professional Activities (Selected)
Young Scientists Committee
Journal of Image and Graphics (中国图象图形学报), Dec. 2020 -
Program Chair / Co-Chair
ACM International Conference on Multimedia (ACM MM) Workshop on Multimedia Understanding with Less Labeling, 2021
International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Long-Tailed Distribution Learning, 2021
International Conference on Computer Vision (ICCV) Workshop on Understanding Social Behavior in Small Group Interactions, 2021
Asian Conference on Computer Vision (ACCV) Workshop on Webly-Supervised Fine-Grained Recognition, 2020
Tutorial Chair
ACCV 2022
Publicity Chair
PRCV 2020
Guest Editor
Pattern Recognition Special Issue on Fine-Grained Object Retrieval, Matching and Ranking, 2021
Journal of Computer Science & Technology Special Section on Self-Learning with Deep Neural Networks, 2021
Area Chair / Senior PC
Program Committee Member
Journal Reviewer
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
International Journal of Computer Vision (IJCV)
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 (中国科学：信息科学)
自动化学报
软件学报
电子学报
中国图象图形学报
…
Correspondence
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
Tel
+86-025-8431-5017 (Ext Number: 8230/ 分机号: 8230)
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