4D Video Scene Understanding in the Wild Challenges of Deep Learning in Modern Computer Vision Contrastive Language-Image Pretraining (CLIP) for Video Analytics Deep Learning for Computer Vision and Robotics Egocentric Vision - Making Sense of the First-Person Perspective Human Vision Monocular Cues: Always-On Perception for 3D Computer Vision Multimodal Behaviour Understanding and Generation for Human-Robot Interaction Python (Pytorch) for Computer Vision Revolutionizing Medical Imaging Using Generative Models Self-supervised Learning Statistical Models of Shape and Appearance Stereo Vision & Feature Matching Structured Generative models for Computer Vision Uncertainty and Evaluation in Vision Video Understanding
Session Topic: Challenges of Deep Learning in Modern Computer Vision
Dr. Amir Atapour-Abarghouei is currently an Assistant Professor within the VIViD Research Group (Vision, Imaging and Visualisation in Durham) in the Department of Computer Science at Durham University in the UK. He has previously worked as a Lecturer in Computer Science at the School of Computing Science at Newcastle University in the UK. He received his Ph.D. degree from the Department of Computer Science at Durham University. His primary research is focused on machine learning, deep learning, computer vision, image processing, 3D scene analysis, semantic and geometric scene understanding, scene depth prediction, natural language processing and multimodal data analysis. His work includes the generalised high-impact GANomaly anomaly detection approach, which is now a part of Intel’s AI product line and used as the underlying method for anomaly detection in over 40 international patents. Amir has co-organised the CVPR-NAS workshop as well as workshops at IEEE Conf. BigData (BDA4CID and BDA4HM). He is currently the Chair of the BMVA Summer School in 2024 at Durham.
Session Topic: Egocentric Vision - Making Sense of the First-Person Perspective
Dr Michael Wray is a lecturer/Assistant Professor of Computer Vision at the School of Computer Science at the University of Bristol. Dr Wray's research interests are in multi-modal video understanding, particularly for egocentric videos — focusing on how both vision and language can be tied together towards tasks such as cross-modal retrieval, grounding and captioning. Dr Wray is part of MaVi and ViLab.
Session Topic: Statistical Models of Shape and Appearance
After completing a degree in Maths and Physics at Exeter University, and a PhD in Civil Engineering (studying a storm sewer overflow) at Sheffield City Polytechnic, Prof. Tim Cootes joined the University of Manchester in 1991.
Prof. Tim Cootes began as an RA, working with Prof Chris Taylor on modelling industrial components.
Prof. Tim Cootes was awarded an SERC Postgraduate fellowship in 1993, and an EPSRC Advanced Fellowship in 1995.
Prof. Tim Cootes became a Lecturer in ISBE in June 2001, and was promoted to Senior Lecturer in October 2002.
Prof. Tim Cootes became a Reader in Computer Vision in August 2005, and was appointed as a Professorial Research Fellow in August 2006.
Prof. Cootes research has concentrated on constructing statistical models of the shape and appearance of objects in images, and in developing algorithms to match such models to new images. We have applied these models to many problems in the industrial and medical domains, and to the interpretation of facial images.
Session Topic: Self-supervised Learning
Dr Oisin Mac Aodha is an Lecturer (aka Assistant Professor) in Machine Learning in the School of Informatics at the University of Edinburgh (UoE). Dr Oisin Mac Aodha is also a Turing Fellow and ELLIS Scholar. His current research interests are in the areas of computer vision and machine learning, with an specific emphasis on 3D understanding, human-in-the-loop methods, and computational challenges in biodiversity monitoring.
From 2016-2019 Dr Oisin Mac Aodha was fortunate to be a postdoc in Prof. Pietro Perona's Computational Vision Lab at Caltech working with the Visipedia team. Previous to Caltech, Dr Oisin Mac Aodha spent three great years (2013-2016) as a postdoc in the Department of Computer Science at University College London (UCL) with Prof. Gabriel Brostow and Prof. Kate Jones. There Dr Oisin Mac Aodha worked on interactive machine learning, where our goal was to design algorithms to enable non-programming scientists to semi-automatically explore events of interest in vast quantities of audio and visual data.
Dr Oisin Mac Aodha did both MSc (with Dr. Simon Prince) and PhD (with Prof. Gabriel Brostow) at UCL and have an undergraduate degree in electronic engineering from the University of Galway in Ireland. Before his PhD, Dr Oisin Mac Aodha was a research assistant for one year in Prof. Marc Pollefeys' group at ETH Zurich.
Session Topic: Stereo Vision & Feature Matching
Krystian Mikolajczyk did his undergraduate study at the University of Science and Technology (AGH) in Krakow, Poland. He completed his PhD degree at the Institute National Polytechnique de Grenoble, France. He then worked as a research assistant in INRIA, University of Oxford and Technical University of Darmstadt (Germany), before joining the University of Surrey as a Lecturer, and Imperial College London as a Reader in 2015. His main area of expertise is in image and video recognition, in particular in problems related to matching, representation and learning. He participated in a number of EU and UK projects in the area of image and video analysis. He publishes in computer vision, pattern recognition and machine learning forums. He has served in various roles at major international conferences co-chairing British Machine Vision Conference 2012, 2017 and IEEE International Conference on Advanced Video and Signal-Based Surveillance 2013. In 2014 he received Longuet-Higgins Prize awarded by the Technical Committee on Pattern Analysis and Machine Intelligence of the IEEE Computer Society.
Session Topic: Uncertainty and Evaluation in Vision
Before moving to Bath Prof. Neill Campbell was a Research Associate in the Virtual Environments and Computer Graphics Group at University College London working with Jan Kautz and Simon Prince on synthesizing and editing photorealistic visual objects funded by the EPSRC.
Previously Prof. Campbell was a Research Associate in the Computer Vision Group of the Machine Intelligence Laboratory, in the Department of Engineering at the University of Cambridge working on the EU Hydrosys Project led by Ed Rosten.
Prof. Neill Campbell completed his PhD, in the Computer Vision Group at the University of Cambridge, under the supervision of Roberto Cipolla and the guidance of George Vogiatzis and Carlos Hernández. Prof. Neill Campbell was funded by a Schiff Foundation Scholarship and Toshiba Research Europe.
Session Topic: Python (Pytorch) for Computer Vision
Before moving to Bath Prof. Neill Campbell was a Research Associate in the Virtual Environments and Computer Graphics Group at University College London working with Jan Kautz and Simon Prince on synthesizing and editing photorealistic visual objects funded by the EPSRC.
Previously Prof. Campbell was a Research Associate in the Computer Vision Group of the Machine Intelligence Laboratory, in the Department of Engineering at the University of Cambridge working on the EU Hydrosys Project led by Ed Rosten.
Prof. Neill Campbell completed his PhD, in the Computer Vision Group at the University of Cambridge, under the supervision of Roberto Cipolla and the guidance of George Vogiatzis and Carlos Hernández. Prof. Neill Campbell was funded by a Schiff Foundation Scholarship and Toshiba Research Europe.
Session Topic: 4D Video Scene Understanding in the Wild
Dr. Armin Mustafa is an Associate Professor in Computer Vision and AI, at the Centre for Vision, Speech and Signal Processing, University of Surrey. She also holds a Royal Academy of Engineering Fellowship in ‘4D Vision’. The emergence of machines that interact with their environment has led to an increasing demand for automatic visual understanding of real-world scenes. Her research focuses on AI to better understand complex scenes so that machines can efficiently model and interpret real-world data for a range of socially beneficial applications including autonomous systems, augmented reality and healthcare. She has previously finished her PhD in 'General dynamic scene reconstruction from multi-view videos' in 2016, under Prof. Adrian Hilton and she has previously worked at Samsung Research Institute in Computer Vision.
Session Topic: Human Vision
Dr Ulrik Beierholm is an Associate Professor in the Psychology Department at Durham University, working in Computational Neuroscience. His work crosses the border between neuroscience and computer science, by examining how computational models from machine learning can be used to build models of human perception, decision making, and action.
Session Topic: Structured Generative models for Computer Vision
Dr Paul Henderson is a Lecturer (aka Assistant Professor) in Machine Learning at the University of Glasgow. His 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 draws on techniques from machine learning (particularly deep generative models), computer vision, and computer graphics.
Session Topic: Multimodal Behaviour Understanding and Generation for Human-Robot Interaction
Dr Oya Celiktutan is a Senior Lecturer in Robotics (Associate Professor) at the Department of Engineering, King’s College London, UK, where she leads the Social AI and Robotics Laboratory. Her research interest lies in machine learning to develop socially aware systems capable of autonomously interacting with humans. This includes addressing challenges in multimodal perception, understanding and generating human behaviour, as well as advancing navigation, manipulation, and interaction within the context of human-centric robotics. Her work has been supported by EPSRC, The Royal Society, and the EU Horizon, as well as through industrial collaborations. She received the EPSRC New Investigator Award in 2021 and her team’s research has been recognized with several awards, including the Best Paper Award at IEEE Ro-Man 2022, NVIDIA CCS Best Student Paper Award Runner Up at IEEE FG 2021, First Place Award and Honourable Mention Award at ICCV UDIVA Challenge 2021.
Session Topic: Contrastive Language-Image Pretraining (CLIP) for Video Analytics
Prof. Victor Sanchez is the Head of the Signal and Information Processing (SIP) Lab of The University of Warwick. He received an M.Sc. degree from the University of Alberta, Canada, in 2003, and a Ph.D. degree from The University of British Columbia, Canada, in 2010. From 2011 to 2012, he was with the Video and Image Processing Laboratory, at the University of California at Berkeley, as a Postdoctoral Researcher. In 2012, he was a Visiting Lecturer with the Group on Interactive Coding of Images, Universitat Autònoma de Barcelona. From 2018 to 2019, he was a Visiting Scholar with the School of Electrical and Information Engineering, The University of Sydney, Australia. His research interests include computer vision with applications to multimedia analysis, biometrics, forensics, and security. He has authored several technical articles and book chapters in these areas. His research has been funded by the Newton Fund; the Natural Sciences and Engineering Research Council of Canada; the Canadian Institutes of Health Research; the FP7 and the H2020 Programs of the European Union; the Engineering and Physical Sciences Research Council, U.K; Ford Motor Company, USA, the Defence and Security Accelerator, U.K., and Research England. He is the Chair of the Technical Committee on Computational Forensics under the auspices of the International Association for Pattern Recognition (IAPR). He currently serves as an associate editor of IEEE Signal Processing Letters, IEEE Access, and ACM Computing Surveys.
Session Topic: Video Understanding
Dr Andrew Gilbert is an Associate Professor at the University of Surrey. His academic pursuits are primarily focused on video understanding and Generative Models. His research portfolio comprises over 65 articles published in the leading international vision conferences and journals, and he co-leads the C-CATS research group at Surrey. Dr Gilbert's extensive research work ranges from intelligent creative arts, such as fine-grained style search, movie trailer genre understanding, and 4D performance capture, to enabling computers to perceive and understand their complex and cluttered surroundings using multiple training techniques, including self-supervised and multiple data modes. Previously, his research work encompassed 3D human pose estimation and complex real-world activity recognition, with early work on tracking people on vast surveillance networks. Moreover, Dr Gilbert is an active British Machine Vision Association (BMVA) Executive Committee member and coordinates the national BMVA technical meetings. These meetings offer a forum for key experts from industry and academia to discuss and identify solutions to current problems in specialist areas of computer vision and machine learning.
Session Topic: Revolutionizing Medical Imaging Using Generative Models
Dr Daniele Raví is a reader in Artificial Intelligence (AI) for healthcare at the University of Hertfordshire, specializing in medical imaging, image-guided surgery, disease progression modeling, and smart sensing. He also holds an honorary contract as an associate professor at University College London. He obtained both a BSc and MSc in Computer Science, followed by a PhD in computer vision from the University of Catania. He enhanced his academic journey with a year as a PhD visiting student at the University of Surrey and gained postdoctoral experience at Imperial College London and University College London. Beyond academia, he also gained valuable industrial experience at ST Microelectronics and various startups. He actively contributed to numerous research projects funded by prestigious entities such as the EU, EPSRC/Wellcome Trust, and Innovate UK, and published his research work extensively in top-tier journals and conferences, including MICCAI, MIDL, Medical Image Analysis, IEEE Transactions on Medical Imaging, and the Journal of Biomedical Health Informatics. In some of his research projects, Dr. Raví has focused on developing and commercializing these AI pipelines for practical, real-world healthcare applications, bridging the gap between academic research and clinical implementation.
Session Topic: Monocular Cues: Always-On Perception for 3D Computer Vision
Gwangbin is a postdoctoral researcher at Dyson Robotics Lab (Imperial College London), supervised by Prof. Andrew Davison. He completed his PhD at the University of Cambridge under the supervision of Prof. Roberto Cipolla. His research focuses on learning and exploiting monocular cues in 3D computer vision.
Session Topic: Deep Learning for Computer Vision and Robotics
Dr. Oscar Mendez is an award-winning, internationally recognised researcher specializing in robotics, computer vision, and machine learning. Currently serving as the Machine Vision Lead at Locus Robotics, he has significantly contributed to the fields of autonomous agents, collaborative robotics, and high-level scene understanding. Dr. Mendez earned his Ph.D. in Robotics and Computer Vision from the University of Surrey, where his thesis was awarded the BMVA Sullivan Thesis Prize in 2018. His postdoctoral work includes leading several innovative projects such as Autonomous Valet Parking (AVP) and the SMILE Project for sign language recognition. Dr. Mendez's research has been widely published and recognized, including an Outstanding Paper Award at IEEE ICRA 2022. He has also been involved in various professional activities, including serving on the BMVA Executive Committee and as an associate editor for IEEE ICRA. Besides his work at Locus Robotics, Dr. Mendez also extends to maintaining and enhancing robotic platforms, securing research funding, and engaging in public outreach through media appearances and events.