Xiu-Shen WEI （魏秀参）
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Xiu-Shen WEI Office: 503 |
About Me
From Oct. 2023, I am a Professor at Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications of Ministry of Education, School of Computer Science and Engineering, Southeast University.
From Jun. 2020 to Oct. 2023, I am a Professor at 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.
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！2025年招生开启！（联系邮箱：weixs
seu.edu.cn）
Updates
Jul. 13, 2024: One paper about equiangular basis vectors accepted by IJCV.
Jun. 6, 2024: Our team won the first place in the SnakeCLEF and FathomNet tracks of FGVC 2024 global competitions!
Jun. 2, 2024: One paper about fine-grained recognition with incomplete multi-modalities accepted by IEEE TIP.
Feb. 27, 2024: One paper about unsupervised fine-grained image hashing accepted by CVPR 2024.
Nov. 4, 2023: One paper about semi-supervised few-shot learning accepted by IEEE TPAMI.
Sep. 22, 2023: One paper about fine-grained learning accepted by NeurIPS 2023.
Jul. 20, 2023: One paper about large-scale fine-grained image retrieval accepted by IEEE TPAMI.
Nov. 1, 2022: We release a PyTorch-based library “Hawkeye” for Fine-Grained Recognition by deep CNNs on GitHub!
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.
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.
Large-Scale Image Retrieval involves the retrieval of images from vast databases, often utilizing advanced techniques and deep learning models to achieve high-performance image retrieval, including retrieval accuracy, speed, and storage efficiency.
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.
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. (Highly cited paper by ESI, 首届江苏省自然科学百篇优秀学术成果)
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), 2022, 44(12): 8927-8948.
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval.
X.-S. Wei, Y. Shen, X. Sun, P. Wang, and Y. Peng.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023, 45(11): 13904-13920.
Negatives Make A Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning.
X.-S. Wei, H.-Y. Xu, Z. Yang, C.-L. Duan, and Y. Peng.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024, 46(4): 2091-2103.
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. 5720-5730.
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning. (Spotlight presentation)
X.-S. Wei, H.-Y. Xu, F. Zhang, Y. Peng, and W. Zhou.
Neural Information Processing Systems (NeurIPS’22), New Orleans, 2022, pp. 14489-14500.
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval. (Highly cited paper by ESI)
X.-S. Wei, J.-H. Luo, J. Wu, and Z.-H. Zhou.
IEEE Transactions on Image Processing (TIP), 2017, 26(6): 2868-2881.
Multi-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.
Prototype-based Classifier Learning for Long-Tailed Visual Recognition.
X.-S. Wei, S.-L. Xu, H. Chen, L. Xiao, and Y. Peng.
SCIENCE CHINA Information Sciences (中国科学：信息科学), 2022, 65(6): 160105.
RPC: A Large-Scale and Fine-Grained Retail Product Checkout Dataset.
X.-S. Wei*, Q. Cui, L. Yang, P. Wang, L. Liu, and J. Yang.
SCIENCE CHINA Information Sciences (中国科学：信息科学), 2022, 65(9): 197101.
Professional Activities (Selected)
Young Scientists Committee
Fundamental Research, May. 2024 -
Journal of Image and Graphics (中国图象图形学报), Nov. 2022 -
Journal of Image and Graphics (中国图象图形学报), Dec. 2020 - Nov. 2022
Program Chair / Co-Chair
Large-Scale Natural Biology Hashing (iBioHash) Competition associated with CVPR24-FGVC11, 2024
Large-Scale Natural Biology Hashing (iBioHash) Competition associated with CVPR23-FGVC10, 2023
Asian Conference on Computer Vision (ACCV) Workshop on Challenges of Fine-Grained Image Analysis, 2022
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
Workshop Chair
Publicity Chair
CCIG 2025
PRCV 2020
Sponsorship Chair
CCBR 2024
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)
Scientific Reports
IEEE Transactions on Image Processing (TIP)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
Springer Journal of Machine Learning (MLJ)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
IEEE Transactions on Multimedia (TMM)
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
Southeast University
Nanjing 210096, China
Office
Rm 503, Building of Computer Science and Engineering, Southeast University Road #2, Nanjing 210096, China
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