Institution: CERTH;
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
Universal Local Attractors on Graphs
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos
A Study on the Use of Attention for Explaining Video Summarization
An Open Dataset of Synthetic Speech
Explainable Video Summarization for Advancing Media Content Production
Selecting a Diverse Set of Aesthetically-pleasing and Representative Video Thumbnails using Reinforcement Learning
JGNN: Graph Neural Networks on Native Java
We introduce JGNN, an open source Java library to define, train, and run Graph Neural Networks (GNNs) under limited resources. The library is cross-platform and implements memory-efficient machine learning components without external dependencies. Model definition is simplified by parsing Python-like expressions, including interoperable dense and sparse matrix operations and inline parameter definitions. GNN models can be deployed on smart devices and trained on local data.
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.