Dr Paul Henderson

  • Lecturer in Machine Learning (School of Computing Science)

Biography

I hold the post of Lecturer in Machine Learning, in the School of Computing Science at the University of Glasgow, since January 2022. Previously, I completed a BA in Mathematics at the University of Cambridge in 2009, followed by an MSc in Informatics at the University of Edinburgh in 2010. I then worked at Blackford Analysis for four years, on research and development for medical imaging applications. I completed my PhD in 2018 at the University of Edinburgh in the CALVIN group supervised by Vittorio Ferrari (see here for my thesis). I spent three years as a postdoc in the Computer Vision and Machine Learning Group of Christoph Lampert at ISTA, the Institute of Science and Technology Austria.

Research interests

My research focuses on building machines that understand the visual world with minimal supervision, learning aspects of its structure such as 3D geometry and decomposition into objects. This work lies at the intersection of machine learningcomputer vision, and computer graphics. I also work on applications of machine learning and computer vision in the physical and life sciences.

 

Publications

List by: Type | Date

Jump to: 2022 | 2020 | 2018 | 2017 | 2016
Number of items: 7.

2022

Anciukevičius, T., Fox-Roberts, P., Rosten, E. and Henderson, P. (2022) Unsupervised Causal Generative Understanding of Images. Workshop on Spurious Correlations, Invariance and Stability at the International Conference on Machine Learning (ICML 2022), Baltimore, MD, USA, 22 Jul 2022.

Anciukevicius, T., Henderson, P. and Bilen, H. (2022) Learning to Predict Keypoints and Structure of Articulated Objects without Supervision. In: 26th International Conference on Pattern Recognition (ICPR 2022), Montréal, Québec, Canada, 21-25 August 2022, (Accepted for Publication)

2020

Henderson, P. and Lampert, C. H. (2020) Unsupervised Object-centric Video Generation and Decomposition in 3D. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 06-12 Dec 2020, pp. 3106-3117.

Henderson, P. and Ferrari, V. (2020) Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision, 128(4), pp. 835-854. (doi: 10.1007/s11263-019-01219-8)

2018

Henderson, P. and Ferrari, V. (2018) Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision. In: 29th British Machine Vision Conference (BMVC 2018), Newcastle upon Tyne, UK, 03-06 Sep 2018,

2017

Henderson, P. and Ferrari, V. (2017) End-to-end training of object class detectors for mean average precision. In: Lai, S.-H., Lepetit, V., Nishino, K. and Sato, Y. (eds.) Computer Vision – ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V. Series: Lecture notes in computer science (10115). Springer: Cham, pp. 198-213. ISBN 9783319541921 (doi: 10.1007/978-3-319-54193-8_13)

2016

Henderson, P. and Ferrari, V. (2016) Automatically selecting inference algorithms for discrete energy minimisation. In: Leibe, B., Matas, J., Sebe, N. and Welling, M. (eds.) Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V. Series: Lecture notes in computer science (9909). Springer: Cham, pp. 235-252. ISBN 9783319464534 (doi: 10.1007/978-3-319-46454-1_15)

This list was generated on Sun Sep 25 11:29:51 2022 BST.
Number of items: 7.

Articles

Henderson, P. and Ferrari, V. (2020) Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision, 128(4), pp. 835-854. (doi: 10.1007/s11263-019-01219-8)

Book Sections

Henderson, P. and Ferrari, V. (2017) End-to-end training of object class detectors for mean average precision. In: Lai, S.-H., Lepetit, V., Nishino, K. and Sato, Y. (eds.) Computer Vision – ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V. Series: Lecture notes in computer science (10115). Springer: Cham, pp. 198-213. ISBN 9783319541921 (doi: 10.1007/978-3-319-54193-8_13)

Henderson, P. and Ferrari, V. (2016) Automatically selecting inference algorithms for discrete energy minimisation. In: Leibe, B., Matas, J., Sebe, N. and Welling, M. (eds.) Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V. Series: Lecture notes in computer science (9909). Springer: Cham, pp. 235-252. ISBN 9783319464534 (doi: 10.1007/978-3-319-46454-1_15)

Conference or Workshop Item

Anciukevičius, T., Fox-Roberts, P., Rosten, E. and Henderson, P. (2022) Unsupervised Causal Generative Understanding of Images. Workshop on Spurious Correlations, Invariance and Stability at the International Conference on Machine Learning (ICML 2022), Baltimore, MD, USA, 22 Jul 2022.

Conference Proceedings

Anciukevicius, T., Henderson, P. and Bilen, H. (2022) Learning to Predict Keypoints and Structure of Articulated Objects without Supervision. In: 26th International Conference on Pattern Recognition (ICPR 2022), Montréal, Québec, Canada, 21-25 August 2022, (Accepted for Publication)

Henderson, P. and Lampert, C. H. (2020) Unsupervised Object-centric Video Generation and Decomposition in 3D. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 06-12 Dec 2020, pp. 3106-3117.

Henderson, P. and Ferrari, V. (2018) Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision. In: 29th British Machine Vision Conference (BMVC 2018), Newcastle upon Tyne, UK, 03-06 Sep 2018,

This list was generated on Sun Sep 25 11:29:51 2022 BST.

Supervision

I am currently accepting PhD students to supervise. Possible topics include deep generative models, applications of machine learning in the sciences and computer graphics, and other areas within machine learning and computer vision.

If you want to apply, please first check that your research interests align with mine (read some of my papers!). If so, send me an email with your CV and a short research proposal (2–3 pages) mentioning why you want to work in my group in particular, and what skills make you suited. Note that I expect students to produce at least two 'good' papers during their PhD (CVPR, NeurIPS, etc.). Applicants should have a strong background in coding and maths (e.g. probability, linear algebra).

Teaching

  • CS5002 Advanced Programming

Additional information