Dr Jianbo Jiao

Dr Jianbo Jiao

School of Computer Science
Assistant Professor in Computer Science

Contact details

University of Birmingham
B15 2TT

Jianbo Jiao is an Assistant Professor in Computer Vision and Machine Learning in the School of Computer Science at University of Birmingham. Before joining Birmingham, he was a Postdoctoral Researcher in the Biomedical Image Analysis (BioMedIA) group and the Visual Geometry Group (VGG) at the University of Oxford, working on the projects SeeBiByte and VisualAI.

His research is mainly about Computer Vision and Machine Learning, especially on learning from limited supervision and multi-modal data.

For more information, please visit Dr Jianbo Jiao’s Homepage.


Jianbo’s research interests include Computer Vision and Machine Learning, with their applications to challenging and interesting vision and healthcare problems. He obtained his MSc from Peking University in 2015, and PhD in Computer Science from City University of Hong Kong in 2018, supported by the Hong Kong PhD Fellowship Scheme. He was a visiting scholar with the Beckman Institute at the University of Illinois at Urbana-Champaign from 2017 to 2018, working with Prof Thomas S. Huang. After that, He joined the BioMedIA group and VGG in Oxford as a postdoctoral researcher, where he worked on the EPSRC projects SeeBiByte and VisualAI with Prof Alison Noble and Prof Andrew Zisserman.

He has been consistently publishing at top venues in computer vision and machine learning (e.g. ICCV/CVPR/ECCV/BMVC/TPAMI/IJCV/NeurIPS/AAAI, etc.), and holds 3 patents. He has also been actively contributing to the community, and served as the Co-Chair of the 25th Annual Conference on Medical Image Understanding and Analysis (MIUA), Area Chair of the 30th ACM International Conference on Multimedia (ACM MM), Senior Program Committee member of the 30th International Joint Conference on Artificial Intelligence (IJCAI), and at the program committee of several international leading conferences and journals. He has also been recognised by several Outstanding Reviewer Awards from the leading conferences and journals (e.g. CVPR, ECCV, ICCV, NeurIPS and MICCAI, etc.). 


Jianbo’s research is around the general problems in Computer Vision, Machine Learning and Medical Imaging. His current research interests mainly focus on learning from limited supervision (e.g. self-supervised/weakly-supervised/semi-supervised learning, transfer learning) and from multi-modal sensory data (e.g. image/video, speech/audio, text/NLP, depth/stereo, gaze/saliency, motion, etc.). 

More information with an updated publication list please see his Google Scholar profile.


Recent publications


Ren, S, Liu, W, Jiao, J, Han, G & He, S 2023, 'Edge Distraction-aware Salient Object Detection', IEEE Multimedia. https://doi.org/10.1109/MMUL.2023.3235936

Zhang, B, Xiao, J, Jiao, J, Wei, Y & Zhao, Y 2022, 'Affinity attention graph neural network for weakly supervised semantic segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 11, 9440699, pp. 8082-8096. https://doi.org/10.1109/TPAMI.2021.3083269

Fu, Z, Jiao, J, Suttie, M & Noble, JA 2022, 'Facial anatomical landmark detection using regularized transfer learning with application to Fetal Alcohol Syndrome recognition', IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 4, 9531054, pp. 1591-1601. https://doi.org/10.1109/JBHI.2021.3110680

Mao, A, Liang, Y, Jiao, J, Liu, Y & He, S 2022, 'Mask-guided deformation adaptive network for human parsing', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 18, no. 1, 11. https://doi.org/10.1145/3467889

Wang, J, Jiao, J, Bao, L, He, S, Liu, W & Liu, YH 2022, 'Self-supervised video representation learning by uncovering spatio-temporal statistics', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3791-3806. https://doi.org/10.1109/TPAMI.2021.3057833

Conference contribution

Kwon, J, Jiao, J, Self, A, Alison Noble, J & Papageorghiou, A 2023, A Kernel Density Estimation Based Quality Metric for Quality Assessment of Obstetric Ultrasound Video. in H Chen & L Luo (eds), Trustworthy Machine Learning for Healthcare: First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings. 1 edn, Lecture Notes in Computer Science, vol. 13932, Springer, pp. 134-146, Trustworthy Machine Learning for Healthcare - First International Workshop, TML4H 2023, Proceedings, Virtual, Online, 4/05/23. https://doi.org/10.1007/978-3-031-39539-0_12

Fu, Z, Jiao, J, Yasrab, R, Drukker, L, Papageorghiou, AT & Noble, JA 2023, Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound. in L Karlinsky, T Michaeli & K Nishino (eds), ECCV 2022: Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III. 1 edn, vol. 2022, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13803 LNCS, Springer, Cham, pp. 422-436, 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 23/10/22. https://doi.org/10.1007/978-3-031-25066-8_23

Zhao, N, Jiao, J, Xie, W & Lin, D 2023, Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment. in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, IEEE, pp. 6317-6327, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, British Columbia, Canada, 18/06/23. https://doi.org/10.1109/CVPRW59228.2023.00672

Zhang, Z, Gao, G, Fang, Z, Jiao, J & Wei, Y 2022, Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation. in S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho & A Oh (eds), Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Advances in Neural Information Processing Systems, vol. 35, NIPS, 36th Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, United States, 28/11/22. <https://proceedings.neurips.cc/paper_files/paper/2022/hash/99b419554537c66bf27e5eb7a74c7de4-Abstract-Conference.html>

Siris, A, Jiao, J, Tam, GKL, Xie, X & Lau, RWH 2022, Scene context-aware salient object detection. in 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE International Conference on Computer Vision. Proceedings, Institute of Electrical and Electronics Engineers (IEEE), pp. 4136-4146, 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021, Virtual, Online, Canada, 11/10/21. https://doi.org/10.1109/ICCV48922.2021.00412


Li, Y, Huang, Z, Yu, G, Chen, L, Wei, Y & Jiao, J 2023 'Disentangled Pre-training for Image Matting' arXiv. https://doi.org/10.48550/arXiv.2304.00784

Wang, W, Wang, J, Chen, C, Jiao, J, Sun, L, Cai, Y, Song, S & Li, J 2023 'FreMAE: Fourier Transform Meets Masked Autoencoders for Medical Image Segmentation' arXiv. https://doi.org/10.48550/arXiv.2304.10864

Wang, W, Shen, J, Chen, C, Jiao, J, Zhang, Y, Song, S & Li, J 2023 'Med-Tuning: Exploring Parameter-Efficient Transfer Learning for Medical Volumetric Segmentation' arXiv. https://doi.org/10.48550/arXiv.2304.10880

Xu, W & Jiao, J 2023 'Revisiting Implicit Neural Representations in Low-Level Vision' arXiv. https://doi.org/10.48550/arXiv.2304.10250

Fu, Z, Jiao, J, Yasrab, R, Drukker, L, Papageorghiou, AT & Noble, JA 2022 'Anatomy-aware contrastive representation learning for fetal ultrasound'. https://doi.org/10.48550/arXiv.2208.10642

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