Video understanding is a popular field in computer vision and AI where we aim to learn/assess the world around us from video footage and can benefit many real-world applications, such as training and education, patient monitoring, sports assessment, and security systems. By automating these applications through video analysing, not only we can save money and time for their users, but also, we can decrease human errors. Despite the recent advances in the other areas of computer vision, e.g. image analysis, video understanding is still an unsolved problem and is considered a very challenging task.
The proposed workshop on video understanding aims to address the challenges in this field by making the following contributions:
Potential topics include, but are not limited to:
Papers will be limited to 9 pages according to the BMVC format (c.f. main conference authors guidelines). Papers will be published in BMVC 2023 workshop proceedings.
All the papers should be submitted using CMT website https://cmt3.research.microsoft.com/VUABMVC2023.
Robert Gordon University, Sir Ian Wood Building, Garthdee Campus
João is a senior research scientist at Google DeepMind, and prior to that, he was a postdoctoral researcher at the University of California, Berkeley. He is the first author of the paper 'Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset,' a groundbreaking work in the field of video understanding.
Dima is a full professor in computer vision at the University of Bristol, she is also a senior research scientist at Google DeepMind. Dima is currently an EPSRC Fellow (2020-2025), focusing her research interests in the automatic understanding of object interactions, actions and activities using wearable visual (and depth) sensors. She is the project lead for EPIC-KITCHENS, the largest dataset in egocentric vision, with accompanying open challenges.
Fabian is a Senior Research Scientist at Adobe working at the intersection of video understanding and generation. His main interests center around on the development of ML models aligned with creative human intent. He co-organized the ActivityNet and CVEU workshops during multiple editions.
University of Surrey, United Kingdom
University of Surrey, United Kingdom
University of Surrey, United Kingdom
BBC R&D, United Kingdom
University of Surrey, United Kingdom
We gratefully acknowledge our reviewers:
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