Essentials of Pattern Recognition: An Accessible Approach is now available!

《模式识别》教材中文版现已出版！

Education

Ph. D. in College of Computing, Georgia Institute of Technology, 2009; Advisor Prof. Jim Rehg.
B.S. & M.S., 1999 & 2002, in Nanjing University, China

Career

Services

Publications

Most of my papers are available for download in this Publications page, and here is my Google Scholar Citations profile.
A list of the LAMDA group publications also include my papers.

Teaching

The 2021 version of the Pattern Recognition course to come soon ...

Students

Research

I am mainly interested in computer vision (CV) and machine learning (ML), especially when the computing resources (CPU, GPU, running time, memory, model size, etc.) or data resources (size and distribution of training set, quality of labels and annotations, etc.) are limited. Deep learning (DL) with resource constraints are my current focus.

• CV & ML with limited computing resources
  • Deep network compression, acceleration, and generation
    • CURL: Network compression with only small dataset and/or residual connections: Paper [C51]
    • AutoPruner: Variable ratio channel pruning: Paper [J42]
    • Channel pruning based on activation approximation: Papers [C41], [J37]
    • A simple acceleration trick for detection using deep networks: Paper [C42]
  • Deep learning beyond CNN/RNN/Attention
    • NRS: Nerual random subspace: Paper [J45]
• CV & ML with limited data resources
  • Weakly supervised localization & detection
    • Weakly supervised object localization (WSOL): Paper [C52] (proposing a paradigm shift for WSOL)
    • Object co-localization using deep models: Papers [C39], [J38]
  • Learning with partial, incomplete, noisy, and weak labels
    • Theoretical results and practical algorithm for semi-supervised deep learning: Paper [C50]
    • End-to-end deep learning in the presence of noisy labels: Paper [C48]
    • Using weak labels for recognition: Papers [C38], [J32], [C46]
    • Fine-grained classification and retrieval without using bounding box annotations: Papers [J22], [J31]
  • Dealing with imbalanced & long-tailed data distribution
    • Long-tailed recognition: AAAI'21 paper (bag of tricks for long-tailed recognition)
    • Learning with imbalanced datasets: Papers [C5], [C12], [J6]
    • Imbalance in face detection: Papers [C4], [J4], [J5]
  • Multi-instance learning
    • Scalable MIL: Papers [C33], [J21]
    • MIL with multi-view: Paper [C34]
• Earlier work & other work
  • CV & ML with limited computing resources
    • Kernel approximation (in SVM and beyond): Papers [C10], [C17], [J10], [J19], [C25]
    • Cascade structured classifier and detector: Papers [C3], [C4], [J4], [J5]
    • Creating visual codebooks using additive kernels: Papers [C8], [J8], [J17]
    • High-dimensional visual features and their compact representations: Papers [C28], [C37], [J24]
    • Real time object detection based on HIK: Papers [C10], [C13], [J12]
    • Detection and recognition using sensors beyond camera (RFID, mobile sensor, etc.), and beyond the computer (robot, mobile phone etc.): Papers [C6], [C11],[C15]
    • Visual representation based on the Census Transform: Papers [C7], [C8], [J7], [J14]
  • Actions: Papers [J11], [J13], [C30], [C32] (physics based modeling), [J20] (good practices), [J27] (from single image)
  • Visual Place Categorization, mapping, and navigation: Papers [C9], [C29], my Ph.D. dissertation, [J16]
  • Find the appropriate level of sparsity: [C14]
  • Ensemble learning: [C1], [C2], [J2] (many could be better than all)
  • (Very) early work on faces: Papers [J1], [J3], [J18]

Jianxin Wu (吴建鑫) Professor (中文简介) Department of Computer Science and Technology & School of Artificial Intelligence Nanjing University I am in the LAMDA group. Email: For research related matters (paper, code, review, etc.): wujx2001 {AT} gmail.com Email: For teaching related matters (teaching, admission, hiring, etc.): wujx2001 {AT} nju.edu.cn My email box will treat 126, 163, 189, QQ, Sina, Yeah email addresses as spam. Emails from these addresses will not be read by me.