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 learning, computer 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: 2024 | 2023 | 2022 | 2020 | 2018 | 2017 | 2016
Number of items: 14.

2024

Anciukevicius, T., Manhardt, F., Tombari, F. and Henderson, P. (2024) Denoising Diffusion via Image-Based Rendering. In: 12th International Conference on Learning Representations, Vienna, Austria, 7-11 May 2024, (Accepted for Publication)

2023

Henderson, P. , Ghazaryan, A., Zibrov, A. A., Young, A. F. and Serbyn, M. (2023) Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene. Physical Review B, 108(12), 125411. (doi: 10.1103/PhysRevB.108.125411)

Killick, G., Henderson, P. , Siebert, P. and Aragon Camarasa, G. (2023) Foveation in the Era of Deep Learning. In: 34th British Machine Vision Conference (BMVC 2023), Aberdeen, UK, 20-24 Nov 2023, (Accepted for Publication)

Liu, Q., Gu, X., Henderson, P. and Deligianni, F. (2023) Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation. In: 34th British Machine Vision Conference (BMVC 2023), Aberdeen, UK, 20-24 Nov 2023, (Accepted for Publication)

Anciukevicius, T., Xu, Z., Fisher, M., Henderson, P. , Bilen, H., Mitra, N. J. and Guerrero, P. (2023) RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation. In: 2023 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 18-22 Jun 2023, pp. 12608-12618. ISBN 9798350301298 (doi: 10.1109/CVPR52729.2023.01213)

Ivanova, D., Williamson, J. and Henderson, P. (2023) Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans. Computer Graphics Forum, 42(2), pp. 133-148. (doi: 10.1111/cgf.14749)

Anciukevicius, T., Fox-Roberts, P., Rosten, E. and Henderson, P. (2023) Unsupervised Causal Generative Understanding of Images. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 28th November - 9th December 2022, ISBN 9781713871088

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, pp. 3383-3390. ISBN 9781665490627 (doi: 10.1109/ICPR56361.2022.9956688)

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.

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 Thu Apr 18 13:37:46 2024 BST.
Jump to: Articles | Book Sections | Conference or Workshop Item | Conference Proceedings
Number of items: 14.

Articles

Henderson, P. , Ghazaryan, A., Zibrov, A. A., Young, A. F. and Serbyn, M. (2023) Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene. Physical Review B, 108(12), 125411. (doi: 10.1103/PhysRevB.108.125411)

Ivanova, D., Williamson, J. and Henderson, P. (2023) Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans. Computer Graphics Forum, 42(2), pp. 133-148. (doi: 10.1111/cgf.14749)

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., Manhardt, F., Tombari, F. and Henderson, P. (2024) Denoising Diffusion via Image-Based Rendering. In: 12th International Conference on Learning Representations, Vienna, Austria, 7-11 May 2024, (Accepted for Publication)

Killick, G., Henderson, P. , Siebert, P. and Aragon Camarasa, G. (2023) Foveation in the Era of Deep Learning. In: 34th British Machine Vision Conference (BMVC 2023), Aberdeen, UK, 20-24 Nov 2023, (Accepted for Publication)

Liu, Q., Gu, X., Henderson, P. and Deligianni, F. (2023) Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation. In: 34th British Machine Vision Conference (BMVC 2023), Aberdeen, UK, 20-24 Nov 2023, (Accepted for Publication)

Anciukevicius, T., Xu, Z., Fisher, M., Henderson, P. , Bilen, H., Mitra, N. J. and Guerrero, P. (2023) RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation. In: 2023 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 18-22 Jun 2023, pp. 12608-12618. ISBN 9798350301298 (doi: 10.1109/CVPR52729.2023.01213)

Anciukevicius, T., Fox-Roberts, P., Rosten, E. and Henderson, P. (2023) Unsupervised Causal Generative Understanding of Images. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 28th November - 9th December 2022, ISBN 9781713871088

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, pp. 3383-3390. ISBN 9781665490627 (doi: 10.1109/ICPR56361.2022.9956688)

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 Thu Apr 18 13:37:46 2024 BST.

Grants

  • Royal Society Research Grant, 2022-2023 (£20K; sole PI)

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).

  • Chaichakan, Tanatta
    Weakly-supervised learning for medical image understanding
  • De Almeida, Palliya Ralalage
    Music Video Generator: Audio-conditioned Video Generation with Latent Diffusion Models
  • Fan, Shiyu
    Reconstructing Physcially Feasible 3D Human Model in Close Human-Human and Human-Object Interactions from Monocular Images
  • He, Zhuo
    Generalized Machine Learning with limited Training Data
  • Liu, Qianying
    Deep Learning for Health Informatics
  • McHard, Paul
    Autonomous Robotic Detection and Correction of Surface Defects in Manufacturing
  • Ru, Yingdong
    Dexterous robotic grasping and manipulation for deformable objects
  • Shi, Tong
    Deep Learning based Audio-Visual Emotion Recognition to Enhance Human-Robot Interaction
  • Sivangi, Kaushik Bhargav
    Human Motion Analysis for Healthcare Applications
  • Sulaiman, Yiliyasi
    Visual Nowcasting for Robot Control
  • Xu, Wenning
    Modelling Two-person and Human-object Close Interactions
  • Zhuang, Lipeng
    Manipulation of deformable objects based on a behavior-based reinforcement learning appraoch

Teaching

  • CS5002 Advanced Programming
  • CS4061 Machine Learning

Additional information

Homepage: www.pmh47.net

Google Scholar: https://scholar.google.co.uk/citations?user=HN7fd4MAAAAJ


Portrait photo of Dr Paul Henderson