Dr Yuanbo Wu

Uwch-ddarlithydd, Computer Science

l.y.wu@abertawe.ac.uk

223
Ail lawr
Y Ffowndri Gyfrifiadol
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

https://orcid.org/

Google Scholar

ResearcherID

Trosolwg

Derbyniodd Dr Lin Yuanbo Wu ei PhD o Brifysgol New South Wales, Kensington, Sydney, Awstralia. Mae ganddi ddiddordebau ymchwil eang o ran tasgau golwg cyfrifiadurol a dysgu peirianyddol gyda chymhelliad cryf i fynd i'r afael â heriau'r byd go iawn ac mae wedi cyhoeddi'n helaeth dros 70 o erthyglau ymchwil a adolygwyd gan gymheiriaid (gan gynnwys dwy bennod mewn llyfr) mewn cyfnodolion a thrafodion o bwys. Arbenigedd Dr Wu yw deall cynnwys fideo (megis segmentu enghreifftiau /panoptig/gwrthrychau, canfod/olrhain gwrthrychau mewn fideo), ail-nodi targedau, dysgu fesul cam, deallusrwydd artiffisial cynhyrchiol, a datblygiadau deallusrwydd artiffisial ar gyfer astudiaethau meddygol. Derbyniodd Wobr Twf 2021 yn 4edd Gystadleuaeth Arloesi ac Entrepreneuriaeth Eureka (Eureka IIEC 2021, Melbourne, Awstralia). Ar hyn o bryd mae Dr Wu yn Olygydd Cysylltiol IEEE Trans on Neural Networks and Learning Systems (IF: 14.255), IEEE Trans on Multimedia (IF: 8.182), IEEE Trans on Big Data (IF: 4.271), a Pattern Recognition Letters (IF: 4.757). Fe'i gwahoddwyd i fod yn Gadeirydd Ardal gyda ACM Multimedia 2023 (Ottawa), 2022 (Lisbon). Mae Dr Wu yn Uwch-aelod o’r IEEE.

Meysydd Arbenigedd

Gweld â chymorth cyfrifiadur
Canfod gwrthrych
Olrhain sawl gwrthrych
Segmentu enghreifftiau o delweddau a gwrthrych a segmentu panoptig
Dysgu peirianyddol
Deallusrwydd artiffisial ar gyfer biowyddoniaeth
Cynhyrchu fideos
Amcangyfrif gwrthrychau 6D

Uchafbwyntiau Gyrfa

Ymchwil

Mae Lin yn canolbwyntio ar heriau ymchwil sy'n torri tir newydd mewn amrywiaeth o feysydd gyda golwg cyfrifiadurol, dysgu peirianyddol, a disgyblaethau deallusrwydd artiffisial a’r biowyddorau sy'n dod i'r amlwg ac sy'n mynd y tu hwnt i ffiniau. Mae cyfleoedd presennol yn cael eu hamlinellu gyda chynhyrchu/golygu fideos, segmentu esiamplau ar ddelweddau a fideos, cynhyrchu/rhagfynegi strwythur proteinau, amcangyfrif gwrthrychau 6D ac ail-adnabod targedau mewn gwaith goruchwylio.

Dyma her deallusrwydd artiffisial cynhyrchiol cynllun ymchwil archwiliol Lin: A Tracking-Discriminating Deep Generative Model for Real-Time Anomaly Detection in Videos, wedi'i ariannu'n llawn gan Airbus Endeavr, Cymru (Mai 2023-Ebrill 2024, £150,000), gydag estyniad posib o fis Mai 2024-Ebrill 2026 (tua £300,000).

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Wu, Y., Liu, L., Wang, Y., Zhang, Z., Boussaid, F., Bennamoun, M., & Xie, X. (2023). Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification. IEEE Transactions on Image Processing, 32, 4800-4811.
https://doi.org/10.1109/tip.2023.3305817, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64124
http://dx.doi.org/10.1109/tip.2023.3305817
Liu, D., Wu, L., Li, B., Boussaid, F., Bennamoun, M., Xie, X., Liang, C., & Wu, Y. (2024). Jacobian norm with Selective Input Gradient Regularization for interpretable adversarial defense. Pattern Recognition, 145, 109902
https://doi.org/10.1016/j.patcog.2023.109902, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64108
http://dx.doi.org/10.1016/j.patcog.2023.109902
Wu, Y., Hong, R., Wang, Y., & Wang, M. (2019). Cross-Entropy Adversarial View Adaptation for Person Re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 1-1.
https://doi.org/10.1109/tcsvt.2019.2909549, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63560
http://dx.doi.org/10.1109/tcsvt.2019.2909549
Liu, D., Wu, Y., Hong, R., Ge, Z., Shen, J., Boussaid, F., & Bennamoun, M. (2023). Generative Metric Learning for Adversarially Robust Open-world Person Re-Identification. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(1), 1-19.
https://doi.org/10.1145/3522714, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63558
http://dx.doi.org/10.1145/3522714
Wu, Y., Liu, D., Guo, X., Hong, R., Liu, L., & Zhang, R. (2022). Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks. In Proceedings of the Thirty-First International Joint Conference on Artificial IntelligenceInternational Joint Conferences on Artificial Intelligence Organization.
https://doi.org/10.24963/ijcai.2022/204, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63557
http://dx.doi.org/10.24963/ijcai.2022/204
Wu, Y., Wang, Y., Yin, H., Wang, M., & Shao, L. (2019). Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification. IEEE Transactions on Image Processing, 29, 1233-1245.
https://doi.org/10.1109/tip.2019.2940684, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63201
http://dx.doi.org/10.1109/tip.2019.2940684
Wu, Y., Wang, Y., Gao, J., Wang, M., Zha, Z., & Tao, D. (2021). Deep Coattention-Based Comparator for Relative Representation Learning in Person Re-Identification. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 722-735.
https://doi.org/10.1109/tnnls.2020.2979190, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63200
http://dx.doi.org/10.1109/tnnls.2020.2979190
Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, Y., He, M., & Ge, Z. (2023). Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation. Journal of Biomedical Informatics, 138, 104281
https://doi.org/10.1016/j.jbi.2023.104281, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63181
http://dx.doi.org/10.1016/j.jbi.2023.104281
Liu, D., Wu, Y., Zheng, F., Liu, L., & Wang, M. (2022). Verbal-Person Nets: Pose-Guided Multi-Granularity Language-to-Person Generation. IEEE Transactions on Neural Networks and Learning Systems, 1-13.
https://doi.org/10.1109/tnnls.2022.3151631, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63180
http://dx.doi.org/10.1109/tnnls.2022.3151631
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

Holl Ymchwil

Erthyglau Cylchgronau

Liu, D., Wu, L., Li, B., Boussaid, F., Bennamoun, M., Xie, X., Liang, C., & Wu, Y. (2024). Jacobian norm with Selective Input Gradient Regularization for interpretable adversarial defense. Pattern Recognition, 145, 109902
https://doi.org/10.1016/j.patcog.2023.109902, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64108
http://dx.doi.org/10.1016/j.patcog.2023.109902
Wu, Y., Liu, L., Wang, Y., Zhang, Z., Boussaid, F., Bennamoun, M., & Xie, X. (2023). Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification. IEEE Transactions on Image Processing, 32, 4800-4811.
https://doi.org/10.1109/tip.2023.3305817, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64124
http://dx.doi.org/10.1109/tip.2023.3305817
Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, Y., He, M., & Ge, Z. (2023). Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation. Journal of Biomedical Informatics, 138, 104281
https://doi.org/10.1016/j.jbi.2023.104281, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63181
http://dx.doi.org/10.1016/j.jbi.2023.104281
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
Liu, D., Wu, Y., Hong, R., Ge, Z., Shen, J., Boussaid, F., & Bennamoun, M. (2023). Generative Metric Learning for Adversarially Robust Open-world Person Re-Identification. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(1), 1-19.
https://doi.org/10.1145/3522714, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63558
http://dx.doi.org/10.1145/3522714
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
Liu, D., Wu, Y., Zheng, F., Liu, L., & Wang, M. (2022). Verbal-Person Nets: Pose-Guided Multi-Granularity Language-to-Person Generation. IEEE Transactions on Neural Networks and Learning Systems, 1-13.
https://doi.org/10.1109/tnnls.2022.3151631, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63180
http://dx.doi.org/10.1109/tnnls.2022.3151631
Wu, Y., Wang, Y., Gao, J., Wang, M., Zha, Z., & Tao, D. (2021). Deep Coattention-Based Comparator for Relative Representation Learning in Person Re-Identification. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 722-735.
https://doi.org/10.1109/tnnls.2020.2979190, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63200
http://dx.doi.org/10.1109/tnnls.2020.2979190
Wu, Y., Wang, Y., Yin, H., Wang, M., & Shao, L. (2019). Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification. IEEE Transactions on Image Processing, 29, 1233-1245.
https://doi.org/10.1109/tip.2019.2940684, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63201
http://dx.doi.org/10.1109/tip.2019.2940684
Wu, Y., Hong, R., Wang, Y., & Wang, M. (2019). Cross-Entropy Adversarial View Adaptation for Person Re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 1-1.
https://doi.org/10.1109/tcsvt.2019.2909549, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63560
http://dx.doi.org/10.1109/tcsvt.2019.2909549
Yuan, X., Zhang, Z., Wang, X., & Wu, Y. (n.d.) Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval. IEEE Transactions on Information Forensics and Security, 18, 4681-4694.
https://doi.org/10.1109/tifs.2023.3297791, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64127
http://dx.doi.org/10.1109/tifs.2023.3297791

Cyfraniadau Cynadleddau

Wu, Y., Liu, D., Guo, X., Hong, R., Liu, L., & Zhang, R. (2022). Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks. In Proceedings of the Thirty-First International Joint Conference on Artificial IntelligenceInternational Joint Conferences on Artificial Intelligence Organization.
https://doi.org/10.24963/ijcai.2022/204, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63557
http://dx.doi.org/10.24963/ijcai.2022/204

Goruchwyliaeth

The Assessment and Spotting of Generative Artificial Intelligence (cyfredol)

PhD

Goruchwyliwr arall: Prof Xianghua Xie
A multimodal agent for general tasks based on browser (cyfredol)

MSc

Goruchwyliwr arall: Dr Benjamin Mora
Antibody Design based on Diffusion Model using Vector Field Networks (cyfredol)

MSc

Goruchwyliwr arall: Dr Jonathan Mullins

Modiwlau a Addysgir

CSCM70 Mathematical Skills for Data Scientists

This course is an introductory course to the mathematical methods needed by a data scientist. It covers the basics of algebra, optimisation techniques, statistics, and Fourier analysis. The main goal of the class is for students to gain practical experience of the mathematical methods and tools that are essential in data science and that will be used in the other modules of this programme. The module is aimed at students with basic experience in mathematics.
CSCM77 Computer Vision and Deep Learning

This module introduces students to the important and modern topics and concepts of computer vision and deep learning, including image processing, feature extraction, camera calibration, stereo vision, motion and tracking, recognition, deep neural network and their application to vision problems. It teaches techniques that are used to understand and interpret the contents of images and videos and dissects state-of-the-art vision systems, such as Microsoft Kinect. Practical examples in Python are provided throughout the lectures.

Dr Yuanbo Wu

Cyfeiriad ebost

Dolenni Ymchwil

Trosolwg

Meysydd Arbenigedd

Uchafbwyntiau Gyrfa

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Holl Ymchwil

Erthyglau Cylchgronau

Cyfraniadau Cynadleddau

Goruchwyliaeth

The Assessment and Spotting of Generative Artificial Intelligence (cyfredol)

A multimodal agent for general tasks based on browser (cyfredol)

Antibody Design based on Diffusion Model using Vector Field Networks (cyfredol)

Modiwlau a Addysgir

CSCM70 Mathematical Skills for Data Scientists

CSCM77 Computer Vision and Deep Learning