Dr Yuanbo Wu

Senior Lecturer, Computer Science

l.y.wu@swansea.ac.uk

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Office - 223
Second Floor
Computational Foundry
Bay Campus

Available For Postgraduate Supervision

About

Dr Lin Yuanbo Wu received her PhD from The University of New South Wales, Kensington, Sydney, Australia in 2014. Dr Wu has broad research interests across computer vision tasks and machine learning with strong motivation to address real-world challenges. Dr Wu is specialized in video content understanding (such as video instance/panoptic/object segmentation, object detection/tracking), object re-identification, incremental learning, Generative AI, and AI-based advancement for medical study. Dr Wu is currently serving as the Associate Editor with IEEE Trans on Neural Networks and Learning Systems (IF: 14.255), IEEE Trans on Multimedia (IF: 8.182) and IEEE Trans on Big Data (IF: 4.271). Dr Wu is a Senior Member of IEEE.

Areas Of Expertise

Computer vision
Object detection
Multi-object tracking
Instance and panoptic segmentation
Machine learning
AI for bioscience

Career Highlights

Research

Publications:

Pseudo-Pair Based Self-Similarity Learning for Unsupervised Person Re-Identification. Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen, Zongyuan Ge, Farid Boussaid, Mohammed Bennamoun, Jialie Shen, Yuanbo Wu. Journal article1/1/2022IEEE Transactions on Image Processing314803-4816
10.1109/tip.2022.3186746

Publications

Research Highlights

Wu, Y., Liu, D., Zhang, W., Chen, D., Ge, Z., Boussaid, F., Bennamoun, M., & Shen, J. (2022). Pseudo-Pair Based Self-Similarity Learning for Unsupervised Person Re-Identification. IEEE Transactions on Image Processing, 31, 4803-4816.
https://doi.org/10.1109/tip.2022.3186746, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61771
Hu, C., Xie, X., & Wu, Y. (2023). Face reenactment via generative landmark guidance. Image and Vision Computing, 130, 104611
https://doi.org/10.1016/j.imavis.2022.104611, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62159
Ji, Z., Hu, J., Liu, D., Wu, Y., & Zhao, Y. (2022). Asymmetric Cross-Scale Alignment for Text-Based Person Search. IEEE Transactions on Multimedia, 0, 1-11.
https://doi.org/10.1109/tmm.2022.3225754, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62251

All Research

Journal Articles

Hu, C., Xie, X., & Wu, Y. (2023). Face reenactment via generative landmark guidance. Image and Vision Computing, 130, 104611
https://doi.org/10.1016/j.imavis.2022.104611, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62159
Ji, Z., Hu, J., Liu, D., Wu, Y., & Zhao, Y. (2022). Asymmetric Cross-Scale Alignment for Text-Based Person Search. IEEE Transactions on Multimedia, 0, 1-11.
https://doi.org/10.1109/tmm.2022.3225754, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62251
Wu, Y., Liu, D., Zhang, W., Chen, D., Ge, Z., Boussaid, F., Bennamoun, M., & Shen, J. (2022). Pseudo-Pair Based Self-Similarity Learning for Unsupervised Person Re-Identification. IEEE Transactions on Image Processing, 31, 4803-4816.
https://doi.org/10.1109/tip.2022.3186746, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61771

Taught Modules

CSC371 Advanced Object-Oriented Programming

This module will give an advanced look at object-oriented programming (OOP) languages. It will investigate how OOP languages and libraries evolved to meet the needs of developers, overcoming the challenges involved in application development using non-OOP languages. The module introduces the programming languages C and C++, including low-level aspects of programming that are usually abstracted away in languages like Java. By starting with C, students will gain an in-depth understanding of the need for OOP concepts before moving on to studying programming in C++ and these concepts.