Author: Vasileios Mezaris
A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods
Facilitating the Production of Well-tailored Video Summaries for Sharing on Social Media
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos
A Study on the Use of Attention for Explaining Video Summarization
Explainable Video Summarization for Advancing Media Content Production
Selecting a Diverse Set of Aesthetically-pleasing and Representative Video Thumbnails using Reinforcement Learning
Data-driven personalisation of Television Content: A Survey
This survey considers the vision of TV broadcasting where content is personalised and personalisation is data-driven, looks at the AI and data technologies making this possible and surveys the current uptake and usage of those technologies. We examine the current state-of-the-art in standards and best practices for data-driven technologies and identify remaining limitations and gaps for research and innovation. Our hope is that this survey provides an overview of the current state of AI and data-driven technologies for use within broadcasters and media organisations. It also provides a pathway to the needed research and innovation activities to fulfil the vision of data-driven personalisation of TV content.