Xiu-Shen WEI (魏秀参)


Xiu-Shen WEI                                                                                                      “PALM”      “SEU”
Ph.D., Professor
School of Computer Science and Engineering, Southeast University

Office: 503
Email: weixs@seu.edu.cn or weixs.gm@gmail.com

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.

  • 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

Call for Participants


  • Feb. 27, 2024: One paper about unsupervised fine-grained image hashing accepted by CVPR 2024.

  • Dec. 10, 2023: Our IEEE TPAMI survey paper was selected for “首届江苏省自然科学百篇优秀学术成果”!

  • Nov. 4, 2023: One paper about semi-supervised few-shot learning accepted by IEEE TPAMI.

  • Oct. 10, 2023: Very honored to be selected as one of the World’s Top 2% Scientists in 2023.

  • 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.

  • Jun. 6, 2023: Our team won the first place in the SnakeCLEF and PlantTraits tracks of FGVC 2023 global competitions!

  • 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.

  • 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.

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

Tutorial Chair

Workshop Chair

Publicity Chair

Sponsorship Chair

Guest Editor

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



Xiu-Shen WEI
School of Computer Science and Engineering
Southeast University
Nanjing 210096, China


Rm 503, Building of Computer Science and Engineering, Southeast University Road #2, Nanjing 210096, China