Xiu-Shen WEI （魏秀参）
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Xiu-Shen WEI Office: 503 |
About Me
Currently, 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！2026年招生开启！（联系邮箱：weixs
seu.edu.cn）
Highlights
本人主编教材——《解析深度学习（第二版）》（入选省部级重点教材和“十四五”国家重点出版物出版规划）于2025年2月正式出版！
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Updates
Mar. 20, 2025: I was honored to be nominated as an Associate Editor for IEEE TIP.
Dec. 26, 2024: One paper about long-tailed recognition accepted by IJCV.
Nov. 22, 2024: I was deeply honored to receive the CSIG Young Scientist Award.
Sep. 27, 2024: I was honored to be nominated as an Associate Editor for IEEE TMM.
Sep. 26, 2024: One paper about long-tailed object detection accepted by NeurIPS 2024.
Sep. 14, 2024: Very honored to serve as an Area Chair in CVPR 2025.
Jul. 13, 2024: One paper about equiangular basis vectors accepted by IJCV.
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 “PyRetri” 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 span several fields, including Computer Vision, Machine Learning, Embodied AI and AI4Science:
Computer Vision enables computers to understand and interpret the visual world from images and videos, aiming to equip machines with human-like or superior visual perception and reasoning. Our research focuses on fine-grained visual analysis, few-shot learning, open dynamic environment recognition, and integrating physical commonsense with visual information.
Machine Learning enables machines to learn and improve performance through data-driven approaches, designing algorithms to solve complex tasks without explicit programming. Our focus includes optimizing for long-tailed distributions, developing multimodal learning mechanisms, and building robust, interpretable intelligent systems.
Embodied AI explores how intelligent agents learn and adapt through interactions with the physical world, enabling them to perceive, reason, and act in complex environments. Our focus is on enhancing autonomous agents’ perception, reasoning, and multimodal interactions by integrating physical commonsense and environmental feedback, advancing decision-making in dynamic settings.
AI4Science applies artificial intelligence to solve scientific problems, aiming to accelerate discovery, optimize experimental design, and enhance computational efficiency. Our focus includes analyzing and modeling quantum computing, DNA nanostructures, and related topics.
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, 入选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.
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition.
X.-S. Wei, X. Sun, Y. Shen, and P. Wang.
International Journal of Computer Vision (IJCV), 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. 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)
Associate Editor
IEEE Transactions on Image Processing, Mar. 2025 -
IEEE Transactions on MultiMedia, Oct. 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
Young Scientists Committee
Fundamental Research, May. 2024 -
Journal of Image and Graphics (中国图象图形学报), Sep. 2024 -
Journal of Image and Graphics (中国图象图形学报), Nov. 2022 - Sep. 2024
Journal of Image and Graphics (中国图象图形学报), Dec. 2020 - Nov. 2022
Rotating Chair
中国图象图形学学会青托俱乐部, Apr. 2024 -
Deputy Secretary-General
中国计算机学会计算机视觉专委会, Dec. 2024 -
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
Area Chair / Senior PC
Program Committee Member
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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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